Beer Experts and Sensory Experiences

Home , Banana chip

BEER EXPERTS AND SENSORY EXPERIENCES: THE CASE OF REVIEW WRITING LEXICAL ANALYSIS OF AROMA AND FLAVOUR DESCRIPTIONS AND EVALUATION OF CONSISTENCY AND INFORMATION USABILITY OF EXPERT BEER REVIEWS

Aantal woorden: 14,037

Liesbeth Allein Studentennummer: 01304441

Promotor: Prof. dr. Els Lefever

Masterproef voorgelegd voor het behalen van de graad master in de richting meertalige communicatie: talencombinatie Nederlands, Engels, Duits

Academiejaar: 2016 – 2017

Verklaring i.v.m. auteursrecht

De auteur en de promotor(en) geven de toelating deze studie als geheel voor consultatie beschikbaar te stellen voor persoonlijk gebruik. Elk ander gebruik valt onder de beperkingen van het auteursrecht, in het bijzonder met betrekking tot de verplichting de bron uitdrukkelijk te vermelden bij het aanhalen van gegevens uit deze studie.

Het auteursrecht betreffende de gegevens vermeld in deze studie berust bij de promotor(en). Het auteursrecht beperkt zich tot de wijze waarop de auteur de problematiek van het onderwerp heeft benaderd en neergeschreven. De auteur respecteert daarbij het oorspronkelijke auteursrecht van de individueel geciteerde studies en eventueel bijhorende documentatie, zoals tabellen en figuren. De auteur en de promotor(en) zijn niet verantwoordelijk voor de behandelingen en eventuele doseringen die in deze studie geciteerd en beschreven zijn.

Acknowledgements

I would like to thank my promotor, Els Lefever, for advising and helping me with the construction of the automatic colour prediction system and for giving me the opportunity to co- write and submit an accepted paper on the subject of this thesis. Furthermore, I would like to thank my significant other, Stijn, for listening to me when I talked for hours about the interesting things I encountered in my search for related research and the results of the four experiments I conducted. It will be of no surprise if he already knows the entire content of this thesis. Two other people that deserve a spot in this preface are my parents. They have given me the opportunity to read at university whatever lies in my field of interest and have supported me both emotionally and financially during my four years at Ghent University.

2 Table of Content

1. INTRODUCTION ...... 7 2. RELATED RESEARCH ...... 9 2.1 SENSES AND LANGUAGE ...... 9 2.2 DESCRIPTION OF BEER PROPERTIES ...... 10 2.2.1 Colour and aroma description: correlation ...... 10 2.2.2 Flavour and aroma description: correlation ...... 11 2.2.3 Colour and flavour description: correlation ...... 12 2.3 EXPERTS VERSUS NOVICES: BIOLOGICAL DIFFERENCE AND DIFFERENCE IN BEER PROPERTY DESCRIPTION ...... 13 2.4 THE AUTOMATIC PREDICTION SYSTEM ...... 14 3. RESEARCH QUESTIONS AND HYPOTHESES ...... 17 4. CORPUS ...... 19 5. EXPERIMENTS ...... 21 5.1 EXPERIMENT 1: LEXICAL ANALYSIS FLAVOUR AND AROMA DESCRIPTIONS ...... 21 5.2 EXPERIMENT 2: AUTOMATIC COLOUR PREDICTION SYSTEM BASED ON FLAVOUR AND AROMA DESCRIPTIONS ...... 28 5.3 EXPERIMENT 3: COMPARATIVE ANALYSIS OF LEXICAL DESCRIPTORS IN BEER AND WINE REVIEWS AND AUTOMATIC COLOUR PREDICTION SYSTEMS ...... 34 5.4 EXPERIMENT 4: POSSIBLE FURTHER BEER PROPERTY PREDICTIONS BASED ON EXPERT REVIEWS ...... 37 5.4.1 Colour and bitterness ...... 38 5.4.2 Bitterness and category ...... 41 5.4.3 Country of Origin ...... 42 6. DISCUSSION ...... 43 7. CONCLUSION ...... 47 8. BIBLIOGRAPHY ...... 49 APPENDIX A. FULL LIST AROMA AND FLAVOUR TERMS RANKED BY FREQUENCY ...... 53 APPENDIX B. BEER CATEGORY AND BITTERNESS LABEL LIST ...... 130

word count: 14,037

4 Abstract

Over the years, beverages experts have been giving more attention to beer review writing. This thesis examines the lexical descriptors of sensory experiences, especially aroma and flavour perception, in expert beer reviews. For the evaluation of lexical consistency and information usability of those reviews, an automatic colour prediction system based on flavour and aroma descriptions is constructed. A corpus of written beer reviews is compiled from data collected from a North American beverages expert website. Consequently, it is analysed on lexical descriptors and serves as training and testing data for the prediction system. The prediction system is similarly constructed as the system for wine colour prediction of Hendrickx et al. (2016). The results of this thesis indicate that beer experts primarily use source-based terms and metaphors and that these experts are subject to odour-taste synaesthesia. Moreover, the roughly 60% accuracy of the prediction system implies a certain level of consistency, structure and information usability of beer reviews. Lastly, beer experts bear similarities to wine experts in wording their sensory experiences.

6 1. Introduction

In Belgium, one of the world’s top beer brewing countries, beer is gaining more and more attention from food and beverages experts. This is not only the case in this country, but also in several other countries all over the world. In addition, restaurants worldwide are providing their customers with both a wine and beer list on a more regular basis, which provides beer with more prestige. This gained prestige is reflected in newspapers, journals and on the internet, whereas beer experts regularly describe their perceptions and opinions about beer in multiple reviews.

Firstly, this master’s thesis examines the content and wording of these beer reviews. In other words, it explores the different phrases and vocabulary that is used for the description and evaluation of sensory experiences during beer tasting. Therefore, a lexical analysis of these descriptions is conducted. Previous research has mainly focused on the content and wording of wine reviews (Croijmans & Majid, 2016; Hendrickx et al., 2016; Paradis & Eeg-Olofsson, 2013; Hopfer & Heymann, 2014), but less attention has been given to beer reviews. Given the rising popularity of beer among experts and restaurant customers, it is highly interesting to bring beer reviews into the spotlight and compare beer reviews to wine reviews.

Secondly, this master’s thesis examines whether expert beer reviews are consistently phrased and informative. For the evaluation of the usability of beer reviews for information retrieval, an automatic prediction system is built to predict a missing beer property based on beer properties described in expert beer reviews. While this thesis focuses on descriptions of sensory experiences, the automatic prediction system is built to predict sight perceptions based on taste and smell perceptions. In other words, the prediction system is built to automatically predict colour properties based on flavour and aroma properties in the expert beer reviews. If the accuracy of this prediction system is sufficient, the experts’ reviews will probably contain enough useful information and these experts will assumedly describe beer properties in a consistent manner. This automatic prediction system is trained on a large corpus of beer reviews, for which data is automatically extracted from the beverages expert website www.tastings.com. For the construction of the prediction system, this thesis builds further upon previous research by Hendrickx et al. (2016). They applied natural language processing (NLP) to a corpus of wine reviews to build a system that can automatically predict several wine

7 properties based on multiple descriptions in expert wine reviews. This thesis, however, examines whether an automatic prediction system can be built for beer properties instead of wine properties and whether that system can base its colour predictions on both aroma and flavour descriptions from experts’ reviews. As a result, the outcomes of this thesis will be compared to the results of the study of Hendrickx et al. (2016).

In general, this thesis provides more insight in the lexical properties of reviews in the beverages industry, the vocabulary of sensory experience description, the possibilities of automating beer property prediction based on sensory descriptions, the similarities and differences in sensory descriptions between beer and wine experts and the lexical variance in both beer and wine expert reviews.

The remainder of this thesis has the following structure: chapter two explores related research and chapter three contains the research questions and hypotheses. Chapter four elaborates on the data set and on the construction of the corpus. Chapter five describes the four experiments that are conducted. The first experiment analyses the lexical properties of flavour and aroma descriptions. For the second experiment, the automatic colour prediction system is constructed, which bases its colour predictions on flavour and aroma descriptions in the reviews. Furthermore, experiment three contains a comparative analysis on lexical descriptors and automatic colour prediction systems between beer and wine reviews. Lastly, beer properties bitterness, beer category and country of origin are analysed in experiment four. Chapter six contains the discussion and is followed by the conclusion in chapter seven.

8 2. Related research

2.1 Senses and language

Beer experts write about their sensory experiences. Therefore, the correlation between language and senses is explored first. Olofsson and Gottfried (2015) listed two neurocognitive limitations of language on aroma and flavour description: naming failure and configural perception. Naming failure implies the inability of naming every single flavour and aroma correctly, which is the result of the limited vocabulary for olfactory description. Configural perception is the ability of identifying odours in a mixture. Both experts and novices, however, are incapable of identifying all odours in a mixture. Furthermore, people tend to blend perceptual qualities so that even pure odours can be perceived as a mixture of odours due to almost constant exposure to binary odour mixtures (Olofsson & Gottfried, 2015). These two neurocognitive limitations of language suggest that the vocabulary for flavour and aroma description in the beer reviews will be limited and that some aroma and flavour descriptions will be present and others will be absent in reviews of the same beer due to different configural perceptions. In other words, one expert will describe the aroma of a beer as ‘hoppy’ and ‘rich’, whereas another expert uses ‘rich’ and ‘fruity’. The same applies to flavour description. Each expert perceives a mixture differently and consequently describe it differently. In this thesis, however, only one review of each beer will be adopted in the training corpus. Therefore, the number of words describing the aroma and flavour of a single beer will probably be more limited.

Regarding the vocabulary for aroma and flavour description, the study of Croijmans and Majid (2016) about the vocabulary used by novices, wine experts and coffee experts shows that, when analysing the words and terms these three groups use in their sensory experience descriptions, both wine and coffee experts tend to use more specific source-based terms (metaphors or ‘it smells like’+ source) and novices more evaluative terms (e.g. ‘nice’, ‘bad’, ‘good’). Consequently, the reviews in the corpus of this thesis are expected to contain more source- based descriptions and fewer evaluative terms for the description of aroma and flavour properties. This is because the data for the corpus is extracted from a beverages expert website. This thesis examines whether that claim is applicable to beer reviews written by beer experts as well.

9 Majid and Levinson (2011) suggest that there are “intrinsic limits of language” (p. 7): senses cannot be fully coded by language, but senses can be more richly coded in one language than another. This suggests that the limits of language for describing sensory experiences are not universal. In addition, sensory experiences of individuals of the same language and culture can differ, but they are associated with the same terms that are learned in the culture. For example, two individuals can experience the colour ‘blue’ differently, but they both name the colour with the same term, namely ‘blue’. Taste and smell are perceptual categories that are generally neglected in western cultures and that are described by using a more concise vocabulary. Sight, on the other hand, is a perceptual category that is more valued by western cultures and is, therefore, described by using a more elaborate vocabulary (Majid & Levinson, 2011). Following that study, the corpus of this thesis is expected to contain more words describing colour properties than words describing aroma and flavour properties. Consequently, this thesis examines whether this is the case for beer reviews written by beer experts.

The following subsection discusses the correlation between colour, aroma and flavour descriptions. While the automatic colour prediction system for beer will base its colour predictions on both flavour and aroma descriptions, the three descriptions need to be related in a way.

2.2 Description of beer properties

2.2.1 Colour and aroma description: correlation

Research has shown that perceptions of food and beverages depend on both visual and orthonasal sensory input, especially before the tasting (Spence & Piqueras-Fiszman, 2014). The colour and look of a drink influence both the perception and description of aroma (Morrot, Brochet & Dubourdieu, 2001). Furthermore, Dematte, Sanabria and Spence (2006) state that people link certain aromas with certain colours. It is therefore assumed that sight and colour perceptions are closely related to aroma perceptions. Consequently, certain colour descriptions in the reviews are possibly accompanied by the same aroma descriptions and certain aroma descriptions by the same colour descriptions. Therefore, an automatic prediction system could predict missing colour properties by looking at the terms in the reviews for aroma descriptions it is often accompanied by, and vice versa. For example, ‘gold’ refers to the colour of a beer and in the corpus, reviews about golden coloured beer contain often the words ‘hoppy’, ‘rich’

10 and ‘herbs’ to describe the aroma of that golden beer. If a new review lacks a colour description, but the review contains the words ‘hoppy’, ‘rich’ and ‘herbs’, the automatic prediction system would be able to define the colour of the beer as ‘gold’, even though the reviewer does not clearly mention any colour or sight property. This thesis examines whether beer colour can be predicted based on aroma and flavour descriptions. Therefore, an automatic colour prediction system is built. However, there are studies that oppose claims of linked sight and aroma perceptions. Calvert, Spence and Stein (2004), for example, state that aromas only display flavour properties, but not auditory or visual sensations. Due to opposite views, this thesis examines whether colour and aroma descriptions in beer reviews are closely linked. Furthermore, an automatic prediction system is built to test whether colour can be predicted based on aroma descriptions in a new beer review.

2.2.2 Flavour and aroma description: correlation

Not only colour and aroma descriptions, but also flavour and aroma descriptions are claimed to be closely connected. According to Spence (2015), food flavour is described by both gustatory and olfactory stimuli. There are two olfactory stimuli: orthonasal smell and retronasal smell, which are respectively the smell we sniff before the food or drink is tasted and the smell that is pulsed out after the food or drink had been swallowed. It is the combination of retronasal aromas and gustatory cues that defines a flavour and lead to descriptions such as ‘fruity’ and ‘malty’ (Spence, 2015). Calvert, Spence and Stein (2004) consider odour-taste synaesthesia (smelling tastes and tasting smells) a factor of the link between smell and taste. When people smell an aroma, they recognise the aroma as a flavour and describe it as such, e.g. something smells ‘sweet’. In addition, people recognise a taste-like quality more easily than a more specific quality and describe it metaphorically. This is also due to the co-occurrence of retronasal odour stimulation and oral stimulation and the result of a unitary perception. (Calvert, Spence & Stein, 2004). Odour-taste synaesthesia, however, is not a universal given. Howes (2006) has evidence of audio-odour synaesthesia in many Melanesian languages and Young (2005) has evidence of colour-odour synaesthesia among the Anangu of Australia’s Western Desert (Howes, 2006). According to Stevenson and Boakes (2004), not everyone describes aromas the same way. For example, aromas of vanilla, strawberry and caramel are described as smelling ‘sweet’ in western cultures, whereas it can be perceived and described differently elsewhere (Stevenson & Boakes, 2004). The website on which the corpus is based, is North American and therefore, the reviewers working for this website will assumedly be subject to odour-taste synaesthesia

11 and describe flavours and aromas similarly. Similar to colour and aroma descriptions, it can be suggested that the flavour and aroma descriptions in the reviews of the corpus are possibly closely connected. Consequently, certain aroma descriptions in the reviews are claimed to be accompanied by the same flavour descriptions and certain flavour descriptions will assumedly be accompanied by the same aroma descriptions. Therefore, an automatic prediction system could possibly predict missing aroma properties by looking at the flavour descriptions in the reviews it is often accompanied by, and vice versa. This thesis, however, focuses on automatically predicting beer colour. As a consequence, the automatic prediction of aroma or flavour properties is not tested in this thesis. Unlike the correlation between aroma and colour descriptions, flavour and aroma descriptions are probably more likely to be phrased similarly and to consist of the same vocabulary due to the assumed odour-taste synaesthesia, which claims that people of western cultures perceive flavour and aroma unitarily and describe these properties by using similar terms. This thesis, therefore, examines whether beer experts use unique or identical words for flavour and aroma description and whether odour-taste synaesthesia can be found in beer reviews.

2.2.3 Colour and flavour description: correlation

Considering the abovementioned hypotheses of linked colour and aroma descriptions and linked flavour and aroma descriptions, it can be suggested that colour and flavour descriptions are closely linked as well and that colour properties can be predicted based on flavour descriptions, and vice versa. This may lead to a triangular relationship between colour, aroma and flavour descriptions. If one of the descriptions lacks in a new beer review, that property could possibly be predicted based on the other two descriptions that are present in the review. Consequently, this thesis examines not only the assumed correlation colour/aroma and aroma/flavour, but also the correlation colour/flavour. Therefore, this thesis confines itself to building an automatic prediction system that predicts lacking colour properties based on both flavour and aroma properties. If the automatic colour prediction system shows a high accuracy level, it can be suggested that colour and flavour might be closely connected as well.

12 Figure 1. Triangular relation between colour, aroma and flavour descriptions

COLOUR DESCRIPTIONS

AROMA DESCRIPTIONS FLAVOUR DESCRIPTIONS

2.3 Experts versus novices: biological difference and difference in beer property description

For this master’s thesis, a corpus of online beer reviews written by experts is composed. Experts are widely considered to be more accurate and detailed in their aroma, flavour and colour descriptions, which is important for the construction of an automatic prediction system. This is because too general descriptions lead to incorrect, less accurate and general predictions of beer properties. For an automatic prediction system, it is important to have unique descriptions for each beer property so that the system can classify the descriptions in specific categories and produce more precise and accurate predictions.

The following two studies suggest that experts are biologically different from novices. Bartoshuk (2000) claims that experts or supertasters have 16 times more buds on their tongues than non-tasters. Garneau et al. (2014), however, claim that people’s sensitivity to bitter-tasting foods and drinks influences the tasting ability and depends more on their sensitivity to PROP1 and on the variation in the TAS2R38-gene2 than on the density of taste buds. In sum, both researches claim that experts are biologically superior to novices when it comes to distinguishing flavours.

Croijmans and Majid (2016) conducted research on the capability of novices, wine experts and coffee experts to describe and name aromas and flavours of wine and coffee. Despite the fact that the wine experts’ aroma and flavour descriptions for wine proved to be more consistent

1 PROP or 6-n-propylthiouracil is a bitter tastant chemical (Garneau et al., 2014; Hayes, Barthoshuk, Kidd & Duffy, 2008) 2 TAS2R38 or taste receptor 2 member 38 is a bitter taste receptor gene (Tepper et al., 2008)

13 than the wine descriptions of the coffee experts and the novices, the coffee experts in the study were not more consistent in describing aromas and flavours of coffee, their own field of expertise, than the wine experts and the novices were. This suggests that not all experts are always more consistent in describing aromas and flavours in their own field of expertise than experts of a different field of expertise. Furthermore, no expert group identified everyday aromas or flavours more accurately than the other. Hence, experts appear to have an advantage when describing aromas and flavours in their own field of expertise, but not necessarily in every field of expertise. This thesis, however, does not examine the difference between novices and experts because the corpus only contains expert reviews. Therefore, a comparison between novice and expert descriptions cannot be made with the data collection in the corpus.

2.4 The automatic prediction system

For the examination and evaluation of description consistency and information reliability in the experts’ reviews, an automatic prediction system for beer properties is built. If the reviews are well-structured and consistently phrased, an automated system will be able to automatically extract, label and predict useful information.

This thesis examines whether an automatic prediction system can be built to predict beer colour based on flavour and aroma descriptions in expert beer reviews. The approach that is used for the automatic prediction system is machine learning (Witten, Frank, Hall & Pal, 2016): the system detects patterns in data, in this case in flavour and aroma descriptions in the reviews of the annotated corpus. These patterns enable the automatic prediction system to make fast and accurate predictions of beer properties. The system trains it predictions on the annotated data of the corpus. It is important to have a large corpus, because the higher the number of annotated data, the higher the accuracy of the system will probably be.

The machine learning technique used in this thesis is supervised machine learning, which means that every instance in the beer review corpus is labelled (Kotsiantis, Zaharakis & Pintelas, 2007). There are several supervised machine learning techniques such as logic based algorithms, perceptron-based techniques, and statistical learning algorithms. This thesis uses Support Vector Machines (SVM). SVMs learn a linear hyperplane that separates two data classes, in this case colour classes, where the margin is maximal. The margin is the distance

14 from the hyperplane to the nearest data point. The data is mapped onto a higher-dimensional space, which is called the feature space. Kernel functions such as linear or RBF kernels are used to map new data points into the feature space. Limitations of SVMs are the low training speed and the impracticality for multi-class problems (Kotsiantis, Zaharakis & Pintelas, 2007).

Figure 2. A simple linear support vector machines (Tong & Koller, 2001)

The punctuation of the data retrieved from a specialist website, in this case the website www.tastings.com, is stripped off in the Python script. Subsequently, the reviews are considered as bag-of-words (BoW) representations. The automatic prediction system is then trained and tested with a ten-fold cross validation.

The automatic prediction system is based on the system of Hendrickx et al. (2016). They have built an automatic colour prediction system based on various sensory experience descriptions in expert wine reviews. They pre-processed the data with the Stanford toolkit (Manning et al., 2014) to perform tokenisation, POS-tagging and lemmatisation. Subsequently, they randomised and split the randomised data set into an 80% training and 20% test set. The information sources consist of both lexical and semantic features. Each wine review is converted into a bag-of- words (BoW) feature vector for the first experimental setup. Only content words such as nouns, verbs and adjectives that occurred more than once in the training set are selected for the construction of these BoW features. BoW, however, ignores word order (Wallach, 2006). If the feature vector, both unigrams and bigrams, of a new review shows sufficiently similarities with a feature vector in the training corpus, the automatic prediction system considers the wine colour in the new review and the one in the trained review identical. The BoW features are

15 combined with 100 LDA topics and 100 Word2Vec clusters for the second experimental setup. The colour classification consists of three classes: ‘red’, ‘white’ and ‘rose’. For the experiments, 14,213 reviews are used as test instances. The f-scores for these colour classes are respectively 98.4, 97.4 and 77.9. They have also built prediction systems for country of origin, grape variety and price (Hendrickx et al., 2016).

After the construction of the automatic colour prediction system for beer in the second experiment of this thesis, both automatic prediction systems are compared in the third experiment.

16 3. Research questions and hypotheses

This thesis is based on previous research conducted by Hendrickx et al. (2016) and therefore, adopts similar research questions and hypotheses. In addition, new research questions are added to this thesis in order to guarantee a more elaborate account. The following four research questions will be addressed:

1. Are expert reviews meaningful providers of information considering the limited vocabulary and ways of describing sensory perceptions such as aroma, flavour and colour?

Therefore, sensory experiences should be worded in a consistent manner. Considering the results of the research of Hendrickx et al. (2016), which states that wine experts are capable of describing wine properties in such a consistent manner, beer experts are probably capable of producing beer descriptions in a consistent manner and provide useful information as well. It can therefore be hypothesised that expert beer reviews are meaningful providers of information considering the limited vocabulary and ways of describing sensory perceptions such as aroma, flavour and colour.

2. Can lacking colour properties be automatically predicted based on expert reviews considering a link between aroma, flavour and colour descriptions?

The automatic prediction system is expected to be able to predict colour properties on the sole basis of aroma and flavour properties. This means that the system can assign colour properties to the beer in the review even though colour descriptions are lacking. The automatic prediction system bases its colour predictions on the aroma and flavour descriptions present in the review. Automatic predictions should also be possible for lacking aroma and flavour properties. The automatic prediction system then bases its aroma property predictions on colour and flavour descriptions and its flavour property predictions on aroma and colour descriptions. These possibilities, however, are not tested in this thesis. For the construction of an accurate automatic colour prediction system, flavour and aroma descriptions need to be closely related to colour descriptions. Based on abovementioned studies, this claim is assumed to be true.

3. How are the descriptions worded? What are the stylistic and lexical patterns?

Based on the research mentioned in the second chapter of this thesis, beer experts will probably use more specific source-based terms than evaluative terms for describing flavour and aroma. Furthermore, aroma and flavour properties are possibly described by the following constructions and phrases: ‘it tastes like + source’ construction, ‘it smells like + source’ construction, metaphors and adjectives such as ‘sweet’, ‘malty’ and ‘fruity’. The experts of the collected beer reviews are assumed to be subject to odour-taste synaesthesia. Consequently, flavour and aroma descriptions will probably contain similar terms and metaphors. The difference between aroma and flavour descriptions will therefore be less clear. Lastly, the vocabulary for flavour and aroma descriptions is claimed to be less varied than that for colour descriptions.

A fourth research question is added, while this thesis can also examine the differences and similarities between the automatic prediction of beer properties and the automatic prediction of wine properties.

4. In what way do the automatic prediction of beer properties and lexical descriptors of beer reviews differ from the automatic prediction of wine properties and lexical descriptors of wine reviews? And what are the similarities between the two tasks?

The general hypothesis is that beer experts, just like wine experts, are capable of describing beer properties in a sufficiently consistent manner, which allows beer properties to be automatically predicted on the basis of experts’ reviews. This thesis has a shared hypothesis with the study of Hendrickx et al. (2016), while the beer and wine industry can be considered similar: beer and wine are both alcoholic drinks, with different subgroups and differentiated characteristics. There are beer and wine experts that are specialised in distinguishing different brands and subgroups and in describing taste, smell and sight properties. They are respectively named zythologists and oenophiles. Furthermore, the lexical descriptors in both beer and wine reviews and the automatic colour prediction systems will be compared to each other as well. It is hypothesised that the lexical descriptors and the results of the automatic prediction systems will be similar.

18 4. Corpus

The data for the corpus is retrieved from expert website www.tastings.com. This website has been powered by the Beverage Testing Institute since 1981. It hosts over 3,723 spirit reviews, 15,876 wine reviews and 3,065 beer reviews. In total, 2,205 beer reviews can be downloaded and contain information on beer properties that can be automatically extracted. Due to the two columns in which the beer properties are clearly detailed and described in a structured manner, the beer properties in the beer reviews can also be automatically labelled. The first column, of which an example of Clatham Brewing Imperial Porter can be found below, displays the following beer properties: name, category, date of tasting, country of origin, alcohol percentage, points awarded by the reviewer (80-100) and the medal and rating adjective that accompany this score. For the corpus, the beer properties name, category, country of origin and alcohol percentage are automatically extracted3 and labelled from this column.

Image 1. First column of a beer review on www.tastings.com

The first column is followed by a written review. The review itself contains an average number of 43 words and can therefore be called short and concise. The reviews are written by fifteen different reviewers and are similarly structured: the first sentence contains a colour description, the second sentence an aroma description followed by a flavour description and the third sentence a bottom line. Due to this reoccuring structure, an accurate automatic prediction system can be built more easily. Not only the information in the first column, but also in the

3 All automatic data processing was performed by means of a Python script.

19 written review can be automatically extracted and labelled. Three examples of written reviews are provided below.

1. Dark brown color. Bright, attractive aromas and flavors of chocolate cherry bon bons, chestnut brittle, brown sugar, and sarsaparilla candy with a supple, tangy, finely carbonated, dry-yet-fruity medium-to-full body and a tingling, complex, medium-long finish that shows impressions of salty roasted nuts, vanilla salt, grassy earth, and peppery radish. An exceptionally zesty, well balanced and attractive imperial porter. (Chatham Brewing Imperial Porter, USA)

2. Brilliant old gold color. Rich green apple, golden raisin pound cake and nougat aromas and flavors with a fruity, tangy medium-full body and a long, mouthwatering Meyer lemon and tangerine accented finish. Absolutely delicious. (Brouwerij Lindemans – Pomme Lambic, Belgium)

3. Pale light brilliant light gold color. Delicate, yeasty, grainy herbal curious aromas and flavors of egg sandwich, salty roasted corn, and pear skin with a supple, crisp, fizzy, fruity light-to-medium body and a smooth, interesting, medium-length melon, apple, sweet cream, and grass finish. A very tasty, nicely fruity lager that is super sessionable. (Innis & Gunn Brewing Company – Lager Beer, Scotland)

In the second column, the reviewer provides a structured overview of style, aroma, flavour, bitterness, pairing possibilities and repeats the bottom line of the written review. The use of these columns makes it more accessible to automatically extract and label the different beer properties. The second column for the Clatham Brewing Imperial Porter can be found below.

Image 2. Second column of a beer review on www.tastings.com

20 In total, nine beer properties are automatically extracted, labelled and put in a tab-separated text file that will be used for the examination of lexical properties and the construction of the automatic prediction system: name of the beer, country of origin, aroma, flavour, bitterness, category, style, colour and written review. Style is the only beer property that will not be discussed in this thesis. Unlike the other beer properties, colour descriptions could not be automatically extracted from the two columns, because they are only present in the written review itself. Therefore, the word that is prior to the word ‘colour’ in the review was labelled as the colour description. This master’s thesis elaborates on the following six properties: country of origin, aroma, flavour, bitterness, category and colour.

5. Experiments

This chapter contains four experiments. In the first experiment, the lexical properties of both flavour and aroma descriptions are analysed. In addition, the lexical differences and similarities between both descriptions are examined. For the second experiment, an automatic colour prediction system based on flavour and aroma descriptions is built for the evaluation of the informativeness and lexical consistency of expert beer reviews. The third experiment explores the differences and similarities between the lexical analysis of beer reviews and the lexical analysis of wine reviews (Hendrickx et al., 2016). The automatic colour predicition systems for beer reviews and wine reviews are compared as well. Lastly, the fourth experiment analyses possible further beer property predictions based on the expert beer reviews in the corpus.

5.1 Experiment 1: lexical analysis flavour and aroma descriptions

As mentioned earlier, this thesis aims at developing an automatic colour prediction system based on aroma and flavour descriptions to evaluate the structure, consistency and usability of expert beer reviews. Before constructing that system, the lexical properties of both flavour and aroma descriptions are examined. In chapter two, several studies claim that the perception and description of both flavour and aroma are closely connected in western cultures. This experiment analyses the lexical components of aroma and flavour descriptions and examines whether the experts use unique terms or mainly identical terms for the description of either aroma or flavour. This leads to the examination of odour-taste synaesthesia (Calvert, Spence

21 and Stein, 2004). Furthermore, the differences and similarities between aroma descriptions and flavour descriptions are explored.

Firstly, the presence of the ‘it tastes like + source’ construction for flavour description and the ‘it smells like + source’ construction for aroma description is examined. According to Croijmans and Majid (2016), these constructions, as well as metaphors, are used for aroma and flavour description by wine and coffee experts. All the beer reviews in the corpus are similarly structured: the first sentence contains the colour description, the second sentence aroma description followed by flavour description and the third sentence the bottom line. Therefore, the three beer review examples that were given in the previous chapter are repeated to illustrate the analysis of the present or absent ‘it tastes/smells like + source’ constructions.

The ‘it smells/taste like + source’ constructions are absent in the written reviews of the corpus. The beer experts, however, adopt the following constructions: - metaphors - adjective/noun + ‘aromas’ - adjective/noun + ‘flavours’ - ‘aromas of’ + adjective/noun - ‘flavours of’ + adjective/noun

22 These constructions reoccur in the bulk of the written beer reviews in the corpus. Therefore, it can be suggested that beer experts consistently structure and phrase aroma and flavour descriptions. For completeness sake, the constructions for colour descriptions in the written reviews are analysed as well. As a result, it can be stated that these experts use the ‘adjective/noun + colour’ construction for colour description. Concerning the adjectives and nouns that are used for aroma and flavour description, the top 20 most frequently used terms in the corpus for aroma and flavour description are ranked in table 1. These terms are automatically extracted from the second columns of the reviews, but not from the written reviews. For aroma description, 19 out of the 20 most frequently used terms are nouns and source-based and one term is an adjective (‘dark’). The latter is rather surprising, because that adjective is considered to be mostly used for sight/colour description. This could perhaps contradict the hypothesis of odour-taste synaesthesia. In the corpus, however, ‘dark’ always precedes a noun and forms terms such as ‘dark chocolate’, ‘dark toast’ and ‘dark roasted nuts’. It does not occur on itself for the description of aromas and therefore, only describes an aroma describing noun. Consequently, it does not imply that the experts are subject to colour-odour synaesthesia, which is mentioned by Howes (2006). For flavour description, all 20 most frequently used terms are nouns and source-based as well.

Table 1. Top 20 most frequently used terms for aroma and flavour description Aroma Freq Aroma Freq Flavour Freq Flavour Freq nuts 360 banana 143 pepper 518 bread 150 chocolate 360 fruits 136 nuts 279 radish 138 bread 324 peach 133 citrus 254 peppery 137 toast 255 pepper 133 lemon 234 arugula 132 apple 231 honey 131 apple 223 root 122 lemon 211 muffin 129 greens 216 melon 117 citrus 176 dark 127 grass 158 toast 116 cake 158 orange 123 nut 154 fruit 115 fruit 157 herb 115 chocolate 153 orange 111 nut 146 corn 114 tangy 151 earth 99

Concerning the uniqueness of the most frequently used terms for aroma and flavour description, it can be deduced from table 1 that many terms are used for both descriptions. Approximately half of the top 20 most frequently terms are unique when compared to the top 20 most frequently used terms of the other property. In order to investigate the uniqueness of the vocabulary for aroma and flavour description in the entire corpus, the terms used for aroma description and the

23 terms used for flavour description are ranked by frequency and compared with each other. Both full rankings can be found in appendix A (p 53). Table 2 shows the number of unique terms for respectively aroma and flavour description in the entire corpus. When a term is uniquely used for the description of either aroma or flavour, it is only present in one ranking. The full aroma ranking consists of 3,121 terms and the full flavour ranking of 2,466 terms. In total, there are 1,993 unique terms for aroma description and 1,456 unique terms for flavour description. In other words, respectively 63.86% and 59.04% of the rankings are unique.

Table 2. Unique terms compared with the entire corpus Terms for aroma description Terms for flavour description Number of unique Percentage in entire Number of unique Percentage in entire terms corpus (%) terms corpus (%) Top 10 Top 10 0 0 1 0.0405515 Top 25 Top 25 1 0.03204101 1 0.0405515 Top 50 Top 50 1 0.03204101 2 0.081103 Top 100 Top 100 2 0.06408202 5 0.202757502 Top 250 Top 250 12 0.38449215 35 1.419302514 Top 500 Top 500 60 1.92246075 98 3.97404704 Top 1,000 Top 1,000 284 9.099647549 318 12.89537713 Top 1,500 Top 1,500 644 20.63441205 697 28.26439578 Top 2,000 Top 2,000 1,057 33.86735021 1,087 44.07948094 Entire corpus Entire corpus 1,993 63.8577379 1,456 59.04298459

The analysis of table 2 shows that the ten most frequently used terms for aroma description are also used for flavour description, which means that none of these terms are unique for aroma description. Only one term (‘roasted nuts’) is unique in the top 25 and even in the top 50 most frequently used terms in the aroma ranking. In its top 100 most frequently used terms, only two terms (‘roasted nuts’ and ‘danish’) are unique for aroma description. For the description of flavour properties i.e. flavour, only one term (‘tangy’) in the top 10 and top 25 most frequently used terms is unique. Two terms (‘tangy’ and ‘grassy’) are unique in the top 50 of the flavour

24 ranking and in its top 100, five terms (‘tangy’, ‘grassy’, ‘driven’, ‘radish sprouts’ and ‘bitter greens’) are solely used for flavour description.

Graph 1. Unique terms for aroma and flavour description in the entire corpus

2500

2000

1500

1000

500

0 Entire Top 2,000 Top 1,500 Top 1,000 Top 500 Top 250 Top 100 Top 50 Top 25 Top 10 Unique terms aroma description Unique terms flavour description

Graph 1 displays the similarly, rapidly stagnating number of unique terms for aroma and flavour description used in the entire corpus and in the top 500. This can be explained by the frequency of the terms in the corpus: in the aroma ranking, 1,832 terms are only used once, 389 twice and 189 three times in the entire training corpus. In the flavour ranking, 1,416 terms are used once, 310 twice and 160 three times. These are probably rather uncommon terms used for both aroma and flavour description. The probability of a less frequent unique aroma term being used in a new review is lower than the probability of a more frequent unique aroma term being used in a new review. The same applies for less and more frequent unique flavour terms. Therefore, the top 500, 250, 100, 50, 25 and 10 most frequently used terms in the corpus contain more reliable descriptions of aroma and flavour than their top 1,000, 1,500, 2,000 and the entire corpus.

As mentioned above, more than half of the terms used for the description of aroma and flavour are only used once or twice in the training corpus. When, for example, a term in the top 50 for aroma description is only used once for flavour description, it is labelled as ‘not unique’. However, the probability of that term being used for the description of an aroma property is higher than the probability of that term being used for describing flavour. For a more accurate labelling of unique terms, the top 10, 25, 50 and 100 of both rankings are this time not compared to the entire corpus, but to the top 100 most frequently used terms for aroma and flavour

25 description. Therefore, the labelling of frequently used aroma terms as not unique due to the occurrence of those terms in the bottom of the flavour ranking is prevented, and vice versa. Table 3 shows the unique terms for aroma and flavour description compared with the top 100 of each ranking.

Table 3. Unique terms compared with the top 100 most frequently used terms for aroma description and the top 100 most frequently used terms for flavour description

Terms for aroma description Terms for flavour description Number of unique Percentage in top Number of unique Percentage in top terms 100 (%) terms 100 (%) Top 10 Top 10 0 0 2 2 Top 25 Top 25 3 3 8 8 Top 50 Top 50 13 13 19 19 Top 100 Top 100 47 47 47 47

In comparison to the figures in table 2, the results in table 3 show that both rankings have a significant higher number of unique terms when compared to the top 100 ranking than to the entire corpus. This was expected while table 3 excludes less frequent terms of both rankings. The number of unique terms in the top 100 most frequently used terms for aroma description and flavour description has respectively increased from 2 and 5 to 47 terms. This means that more than half of the terms in the top 100 are still used for both aroma and flavour description. Regarding the top 10 most frequently used terms for aroma and flavour description, the number of unique terms has not increased substantially. There are still no unique terms for aroma description, but the number of unique terms for flavour description has increased from one to two terms.

The introduction of this thesis mentions the hypothesis of Calvert, Spence and Stein (2004) about odour-taste synaesthesia, which means that people recognise and describe aromas and flavours, respectively smell and taste properties, similarly. The low number of unique terms for both aroma description and flavour description and the high number of identical terms confirm this hypothesis. Table 4 and table 5 display the 47 uniquely used terms for both categories.

Table 4. 47 unique terms for aroma description

Unique terms for aroma description Term Freq. Term Freq. Term Freq. butter 113 soufflé 48 ginger 33 roasted nuts 111 brittle 48 raisin bread 32 apples 93 scone 46 nut bread 32 toffee 88 raisin toast 46 fig 32 pastry 83 nougat 45 peaches 31 mango 75 praline 44 coconut 31 yogurt 72 dark chocolate 42 tea 31 sourdough 70 honeycomb 39 sugar 30 pineapple 69 cheese 39 jerky 29 herbs 69 nut brittle 38 soy 29 pie 61 beans 37 berry 29 baguette 56 herb muffin 35 danish 29 egg 52 lavender 34 cookie 29 spicy 50 buttery 33 color 29 chutney 49 aromas 33 omelet 28 clay 49 Chocolate nuts 33

Table 5. 47 unique terms for flavour description

Unique terms for flavour description Term Freq. Term Freq. Term Freq. grass 158 jicama 55 sorbet 41 tangy 151 root vegetable 54 nutskin 39 radish 138 minerals 52 water 39 peppery 137 mocha 51 radish sprouts 37 arugula 132 peppered greens 51 starfruit 37 earth 99 crisp 50 peel 36 sprouts 98 lemon pepper 50 sweet potato 36 hop 88 potato 49 bitter greens 35 lettuce 85 mineral 46 pepper bread 35 watercress 76 rapini 46 rind 35 grassy 75 wafer 46 salty 35 kiwi 67 radicchio 45 creamy 34 skin 63 driven 44 bark 33 turnip 63 kale 44 carrot 33 peppercorn 62 chestnut 42 yams 33 vegetable 59 green apple 42

When looking at the lexical and stylistic patterns of the most frequently uniquely used terms displayed in table 4 and 5, the claim of Croijmans and Majid (2016) that experts’ reviews contain more source-based descriptions and fewer evaluative terms, can be confirmed. In the corpus of this thesis, almost all descriptions are exclusively source-based and terms are used as methaphors or in the constructions ‘aromas/flavours of + term’ and ‘term + aromas/flavours’.

27 Most terms are noun phrases or adjectives derived from nouns. Therefore, not only wine experts’ reviews and coffee experts’ reviews, but also beer experts’ reviews contain more source-based terms and fewer evaluative terms. Regarding the constructions ‘it tastes/smells like + source’, they are almost absent from all reviews in the corpus. Not only the hypothesis of Croijmans and Majid (2016), but also the hypothesis of Calvert, Spence and Stein (2004) about odour-taste synaesthesia can be confirmed. Many terms in the corpus are used for both aroma and flavour description, especially the top 10 most frequently used terms for the description of both beer properties.

5.2 Experiment 2: automatic colour prediction system based on flavour and aroma descriptions

For the evaluation of the lexical consistency and usability of the information in the expert reviews, an automatic prediction system is built. If the accuracy and precision of this prediction system is fairly high, the reviews probably contain similar descriptors for aroma and flavour properties and the provided information is useful. For this thesis, the focus lies on an automatic prediction system for colour properties based on flavour and aroma descriptions in the review, while this thesis is based on the study of Hendrickx et al. (2016). They constructed an automatic colour prediction system based on flavour and aroma descriptions in wine reviews. This thesis, therefore, examines whether this is possible for beer colour prediction.

Unlike the other beer properties, colour descriptions could not be automatically extracted from the website, because they were only present in the written review itself and not in a seperate column. Therefore, the word that is prior to the word ‘colour’ in the review was labelled as ‘colour description’. Altogether, 49 different colour terms were extracted. This number, however, is too high and makes the automatic colour prediction for new reviews more difficult and less accurate. Therefore, the terms referring to the same colour class are taken together and eleven new colour classes are formed: ‘very light/white’, ‘yellow’, ‘amber’, ‘brown’, ‘black’, ‘red/rose’, ‘green’, ‘cloudy’, ‘hazy’, ‘oak’ and ‘deep’. This classification groups adjectives and nouns that refer to the same colour and assembles them under one class. Terms that have no link to colour in any way, such as prepositions and articles, and words that only occur once in the corpus (indigo, brilliant, violet, platinum, gray, nickel, wood) are not considered for the colour classification. In the classification, however, four classes do not directly refer to a

28 specific beer colour, but they are artefacts introduced by the automatic extraction of the label: ‘cloudy’, ‘hazy’, ‘oak’ and ‘deep’. ‘Cloudy’, ‘hazy’ and ‘deep’ add an extra dimension to a specific colour and describe the turbidity of a beer. ‘Oak’, on the other hand, can refer to multiple colours. Its definition depends on the adjective that preceeds the word, for example ‘light’ or ‘dark’. Despite this, these four terms are considered as four colour classes, because they occur a sufficient number of times in the corpus. The colour class and the colour labels that are assembled under each class can be found below. Two colour labels, however, can be subsumed in two colour classes: ‘orange’ and ‘sienna’. The word ‘sienna’ can refer to either ‘amber’ or ‘brown’ and the word ‘orange’ to either ‘gold’ or ‘amber’. For the colour mapping, they are each subsumed in one categorie that reflects their meaning best: ‘sienna’ in ‘brown’ and ‘orange’ in ‘amber’. There are sufficiently enough unique terms in these categories for a feasible accurate and less biased automatic colour prediction system.

Colour class Colour labels Very light/white silver Gold gold, yellow, golden, sunburst, straw, light, bright, brassy, sunset, white, sunrise Amber amber, copper, bronze, orange, maroon, penny Brown brown, mahogany, medium-brown, walnut, dark-brown, sienna Black black, ebony, cola Red/rose ruby, garnet, pink, red, salmon Green green, emerald Cloudy cloudy Hazy hazy Oak oak Deep deep

The classification algorithm that is used for this experiment, is Support Vector Machines as implemented in the LIBSVM toolkit (Chang and Lin, 2011). The experiment is evaluated with a 10-fold cross validation: 90% of the corpus function as training data and 10% of the corpus as testing data. This process is repeated until every fold (representing 10% of the corpus) is used as testing data and the remaining nine folds (90% of the corpus) as training data. Beer reviews that do not contain any specific colour description are excluded for this experiment, which brings the total number of tested and trained reviews from 2,205 to 2,121 instances.

The task of predicting the colour of the beers was conceived as a supervised classification task. Two sets of bag-of-words features were extracted as information sources from the expert review texts: unigrams (single words) and bigrams (sequences of two words). Sentences containing a

29 colour description were automatically removed from the review for the examination of the viability of automatically predicting the colour of the beer based on the sole review text. The reviews were further preprocessed by removing all punctuation marks and by lower-casing all words contained in the review.

After programming and testing the automatic prediction system, the results of this system are validated with precision, recall and !"-score. Firstly, recall is the number of times that a predicted label corresponds with the correct class in relation to the times that label occurs in the corpus. To put it differently, recall is the number of correctly predicted labels divided by the number of predicted labels for that class. Secondly, precision is the number of times that a predicted label corresponds with the correct class in relation to the times that label is predicted. In other words, precision is the number of correctly predicted labels divided by the total number of gold standard labels. Thirdly, f-score reflects the weighted average of both precision and recall scores. Finally, the accuracy is calculated by dividing the total number of correctly predicted labels by the overall number of test instances, without differentiating between the different class labels.

,-./%$ )1 &)$$%&234 5$%6'&2%6 37/%3( #$%&'(')* = 8)273 *-./%$ )1 5$%6'&2%6 37/%3(

,-./%$ )1 &)$$%&234 5$%6'&2%6 37/%3( 9%&733 = 8)273 *-./%$ )1 :)36 (27*67$6 37/%3(

#$%&'(')* ∗ 9%&733 ! − (&)$% = 2 ∗ #$%&'(')* + 9%&733

# &)$$%&234 5$%6'&2%6 2%$.( ?&&-$7&4 = # 2)273 *-./%$ )1 2%$.(

A first experiment is run with the linear kernel of LIBSVM (Hsu & Lin, 2002). These are the results of this first experiment:

30 Classes Frequency Recall Precision F-score in corpus Very 9 0.0 0.0 0.0 Light/white Deep 2 0.0 0.0 0.0 Hazy 3 0.0 0.0 0.0 Cloudy 2 0.0 0.0 0.0 Gold 683 59.8 62.7 61.2 Amber 819 55.7 62.8 59.0 Brown 366 52.4 49.7 51.1 Black 148 41.2 31.8 35.9 Red/rose 41 0.0 0.0 0.0 Green 18 0.0 0.0 0.0 Oak 18 0.0 0.0 0.0

The average cross-validation accuracy is 56.05%. Classes ‘very light’, ‘deep’, ‘hazy’, ‘cloudy’, ‘red/rose’, ‘green’ and ‘oak’ have no recall, precision and F-score results. This is due to their low frequency in the corpus. It is nearly impossible for an automatic prediction system to make unbiased and accurate predictions when there is an unsufficient amount of training data. This low frequency has various reasons. Firstly, the number of gold, amber, brown and black coloured beers are significantly higher than the number of silver coloured, red/rose coloured and green coloured beers in reality. This is also reflected in the corpus: there are more golden, amber, brown and black beers than red/rose and green beers. Secondly, ‘oak’ has the same frequency as ‘red/rose’, which is too low to build an accurate automatic prediction system. According to the results above, ‘deep’, ‘hazy’ and ‘cloudy’ only occur twice or three times in this corpus. These terms, however, are much more frequently used in the corpus than is stated in the results above. This is due to automatic extraction of the colour label. On the website www.tastings.com, the colour property is not explicitely mentioned in one of the two columns listing the other beer properties such as flavour, aroma and bitterness. In order to automatically extract the colour description, the term that comes before the word ‘colour’ in the written review is labelled as the colour class of that particular beer. The terms ‘hazy’, ‘cloudy’ and ‘deep’, however, generally add a hue to the colour description that follows. For example: ‘hazy brown’, ‘deep red’ and ‘cloudy amber’. These three terms scarcely precede the word ‘colour’ directly and are therefore rarely labelled as colour description.

The frequency of the colour terms used for ‘gold’, ‘amber’, ‘brown’ and ‘black’ in the corpus is high enough to train an automatic prediction system on. The results show that not only the recall scores, but also the precision scores and the f-scores for ‘gold’, ‘amber’ and ‘brown’ are

31 significantly higher than for ‘black’: the recall, precision and f-score for ‘black’ are respectively 41.2%, 31.8% and 35.9% compared to ‘gold’ (59.8/62.7/61.2%), ‘amber’ (55.7/62.8/59.0%) and ‘brown’ (52.4/49.7/51.1%). Furthermore, the results for ‘gold’ and ‘amber’ are in general better than for ‘brown’, especially the precision scores. This can be easily explained by the frequency of ‘black’, ‘gold’, ‘amber’ and ‘brown’ terms in the corpus. ‘Gold’ and ‘amber’ are more represented in the corpus than ‘brown’ and ‘black’. As a consequence, the automatic prediction system can be trained on more ‘gold’ and ‘amber’ data than ‘brown’ and ‘black’ data. The automatic colour prediction system is, therefore, more biased towards predicting ‘gold’ and ‘amber’ over ‘brown’ or ‘black’. Regarding the precision scores for these four classes, the precision score for ‘gold’ (62.7%) and ‘amber’ (62.8%) are significantly better than for ‘brown’ (49.7%) and ‘black’ (31.8%). Regarding the recall scores for these four classes, the recall score for ‘gold’ (59.8%), ‘amber’ (55.7%) and ‘brown’ (52.4%) are significantly better than the recall score for ‘black’ (41.2%).

Table 6. Results for ‘gold’, ‘amber’, ‘brown’ and ‘black’

Predicted class Labelled Gold Amber Brown Black class Gold 428 237 11 1 Amber 221 514 75 9 Brown 16 111 182 56 Black 4 27 70 47

Table 6 displays the results of the automatic colour prediction system. After analysing the results, it appears that the automatic prediction system often predicts a labelled class as a neighbouring class. The labelled class ‘gold’ is 237 times predicted as ‘amber’, whereas ‘amber’ is 221 times predicted as ‘gold’ and 75 times as ‘brown’, and ‘brown’ is 111 times predicted as ‘amber’ and 56 times as ‘black’. It is, however, remarkable that the automatic prediction system predicts the labelled ‘black’ more as ‘brown’ (70) than as ‘black’ (47). This could mean that for the prediction of ‘black’, the automatic prediction system is biased towards ‘brown’. It is, however, rather predictable that the automatic prediction system predicts a class that is closely related to the labelled class. In reality, the difference between the beer colour classes ‘gold’, ‘amber’, ‘brown’ and ‘black’ is not as clear as the difference between wine colours white, rosé and red, for example. On the following page, Image 3 shows that there is a gradual transition from ‘gold’ to ‘amber’, ‘amber’ to ‘brown’ and ‘brown’ to ‘black’: deep gold is very similar to pale amber, amber brown to brown and deep brown to black. Consequently,

32 experts might be unable to accurately define the right colour and probably use the same terms to describe these closely related colours. This leads to an automatic colour prediction system that predicts a labelled class as a neighbouring class.

Image 3. Gradual transition of beer colour (Mosher, 2015)

A second experiment is run with the other settings of the classifier. A grid search was performed on one training fold in order to optimise the parameter settings for the default (RBF) kernel. This resulted in an optimised c parameter value of 2.0 and a g parameter value of 0.0078125. These are the results for the second experiment:

Category Frequency Recall Precision F-score in corpus Very 9 0.0 0.0 0.0 Light/white Deep 2 0.0 0.0 0.0 Hazy 3 0.0 0.0 0.0 Cloudy 2 0.0 0.0 0.0 Gold 683 64.5 63.8 64.2 Amber 819 58.6 72.8 64.9 Brown 366 55.5 57.9 56.7 Black 148 55.9 12.8 20.9 Red/rose 41 0.0 0.0 0.0 Green 18 0.0 0.0 0.0 Oak 18 0.0 0.0 0.0

The average accuracy is 59.88%, which is higher than the accuracy of the first experiment (56.05%). The recall scores for ‘gold’, ‘amber’, ‘brown’ and ‘black’ are raised with respectively 4.7%, 2.9%, 3,1% and even 14.7%. The change in settings had an especially postive effect on the recall score of ‘black’: it has risen from 41.2% to 55.9%. Opposed to this, the precision and f-score of ‘black’ are negatively affected: the precision score considerably dropped with 19% from 31.8% to 12.8% and the f-score with 15% from 35.9% to 20.9%. Considering the precision and f-scores of ‘gold’, ‘amber’ and ‘brown’, the performance of the automatic colour prediction

33 system for these three labels has increased, with the precision score of ‘amber’ even reaching 72.8%.

In summary, the colour descriptions in the corpus are subsumed into eleven classes: ‘very light’, ‘deep’, ‘hazy’, ‘cloudy’, ‘gold’, ‘amber’, ‘brown’, ‘black’, ‘red/rose’, ‘green’ and ‘oak’. Due to frequency numbers, the automatic colour prediction system is only able to predict the following four colour classes: ‘gold’, ‘amber’, ‘brown’ and ‘black’. The first and second experiment of the prediction system show that the system is biased towards predicting ‘gold’, ‘amber’ and ‘brown’ over ‘black’. Regarding the rather promising accuracy results of both experiments, respectively 56.05% and 59.88%, it can be claimed that expert beer reviews contain useful information and the descriptors are consistent enough to train a classification system. This is in line with the results of the study of Hendrickx et al. (2016), in which is suggested that wine experts name wine properties in a consistent manner and include useful information in their reviews. Furthermore, the ability to construct an automatic colour prediction system based on both flavour and aroma descriptions proves that flavour and aroma descriptions are linked to certain colour descriptions. It is therefore probable that flavour, aroma and colour perceptions influence each other. It can, therefore, be claimed that the hypothesised triangular relation between flavour, aroma and colour descriptions is indeed true.

5.3 Experiment 3: Comparative analysis of lexical descriptors in beer and wine reviews and automatic colour prediction systems

Following the lexical analysis of aroma and flavour descriptions in experiment one and the evaluation of the lexical consistency and information usability by constructing an automatic colour prediction system based on those flavour and aroma descriptions in experiment two, the results of these two experiments can be compared to the results of the study of Hendrickx et al. (2016). In this experiment, not only the differences and similarities between the lexical descriptors for wine and beer aromas and flavours, but also the results of both automatic colour prediction systems for wine and beer are explored.

This thesis distuingished eleven colours classes, of which four (‘gold’, ‘amber’, ‘brown’ and ‘black’) are predicted by the classifier (recall, precision and f-scores > 0%). Hendrickx et al. (2016), on the other hand, distuingished three classes (‘red’, ‘white’ and ‘rosé’) for wine colour classification. Mutual Information (MI) feature scoring is performed in order to gain insight in

34 which n-grams are most characterising of the colour-labelled dataset (Lefever, Allein & Jacobs, 2017). MI is a feature selection filter metric and can be used for the characterisation of both the relevance and redundancy of variables (Guyon & Elisseeff, 2003). The MI between two random variables is a non-negative value, which measures the dependency between the variables. The higher the MI, the more dependent these variables are. If and only if these two variables are independent, the MI equals zero. This MI implementation is performed according to the Scikit- learn toolkit (Pedregosa et al., 2011). Table 7 represents the most characteristic n-grams for the automatic colour prediction system for beer and wine reviews. Approximately all n-gram with the highest MI scores are related to odour and flavour descriptions. It can therefore be concluded that odour and flavour descriptions are pivotal for the automatic colour predictions (Lefever, Allein & Jacobs, 2017). This is the case for both beer and wine colour predictions.

Table 7. Mutual Information results of automatic colour prediction system for beer and wine reviews MI beer reviews (top 20) MI wine reviews (top 20) MI score n-gram MI score n-gram 1.89E-01 chocolate 1.05E-01 cherry 6.80E-02 stout 9.56E-02 tannin 6.54E-02 of chocolate 7.43E-02 peach 5.98E-02 coffee 7.17E-02 apple 4.97E-02 cider 6.72E-02 citrus 4.69E-02 dark 6.26E-02 black 3.68E-02 lemon 5.80E-02 pear 3.58E-02 light-to-medium body 5.67E-02 blackberry 3.56E-02 light-to-medium 4.46E-02 pineapple 3.43E-02 cherry 4.44E-02 berry 3.28E-02 porter 4.20E-02 chardonnay 3.21E-02 dark chocolate 3.73E-02 lime 3.11E-02 nuts 3.68E-02 lemon 3.03E-02 mocha 3.61E-02 melon 2.70E-02 apple 3.30E-02 tropical 2.47E-02 chocolate and 3.10E-02 chocolate 2.36E-02 medium-to-full body 2.97E-02 raspberry 2.35E-02 roasted 2.94E-02 crisp 2.33E-02 cocoa 2.79E-02 apricot 2.21E-02 toffee 2.69E-02 blanc

Regarding lexical differences and similarities between aroma and flavour descriptors in beer and wine reviews, it seems that wine experts and beer experts use similar words to describe aroma and flavour properties. Table 7 shows that ‘chocolate’, ‘lemon’, ‘cherry’ and ‘apple’ are top features for the prediction of both wine and beer colour. Moreover, eleven other top features for wine colour prediction are also present in the aroma and flavour descriptions in the beer corpus: ‘peach’, ‘citrus’, ‘pear’, ‘black’, ‘pineapple’, ‘berry’, ‘melon’, ‘tropical’, ‘raspberry’, ‘crisp’ and ‘apricot’. The terms ‘black’ and ‘tropical’, however, are only used in the

35 combination ‘adjective + noun’ in the beer review corpus. It cannot be deducted from the study of Hendrickx et al. (2016) whether this term is used on its own or only in combination with another noun. Therefore, the frequency and ranking of ‘black’ and ‘tropical’ are excluded from the next analysis. Table 8 displays the frequency of thirteen highly characteristic terms for wine colour prediction in this thesis’ beer corpus and what place they hold in the frequency ranking of respectively aroma and flavour description in this corpus.

Table 8. Frequency and ranking of high MI wine terms in the beer corpus Term Freq. Aroma Place Ranking Freq. Flavour Place Ranking apple 231 5 223 5 apricot 43 67 35 88 berry 29 96 15 187 cherry 65 40 56 44 chocolate 360 2 153 9 citrus 176 7 254 3 crisp 5 571 50 56 lemon 211 6 234 4 melon 31 89 117 16 peach 133 14 54 46 pear 50 52 57 43 pineapple 69 37 29 110 raspberry 21 150 9 293

Four terms (‘apple’, ‘chocolate’, ‘citrus’ and ‘lemon’) are present in the top 10 and two (‘cherry’ and ‘peach’) in the top 50 of both aroma and flavour rankings. Due to occurance of many highly characteristic MI wine terms in the flavour and aroma descriptions in the beer corpus, it can be suggested that both beer reviewers and wine reviewers use similar terms for flavour and aroma description. Moreover, it appears that both mainly use source-based terms instead of evaluative terms. This, however, is merely a hypothesis and can not be completely confirmed, because the study on wine reviews does not elaborate on the lexical descriptors for flavour and aroma properties in the reviews. Regarding the references of the top features, it can be remarked that thirteen out of the twenty top features for wine colour prediction have a reference towards fruit, whereas chocolate- and coffee-related terms are the main MI features

36 for beer colour prediction and only three out of the twenty top features for beer colour prediction are fruit-related terms. However, all the fruit-related, highly characteristic MI terms for wine colour prediction are frequently used for aroma and flavour descriptions of beer. In addition, the wine colour prediction features ‘tannin’, ‘blackberry’, ‘chardonnay’ and ‘blanc’ are absent in this thesis’ corpus. This could mean that certain terms are solely used for the description of wine properties. Consequently, certain terms could be solely used for the description of beer properties. Therefore, it is highly interesting to conduct further research on the uniqueness of vocabulary in beer and wine reviews.

The automatic colour prediction system is based on the system for wine colour prediction of Hendrickx et al. (2016). Therefore, the two automatic colour prediction systems are compared with each other. Regarding the recall, precision and f-score results for both the automatic prediction system for wine colour of Hendrickx et al. (2016) and the automatic prediction system for beer colour in this thesis, the system for wine operates more accurately and precisely than the system for beer. The f-score for wine colour classes ‘red’, ‘white’ and ‘rosé’ are respectively 98.4%, 97.4% and 77.9%, whereas the f-score for beer colour classes ‘gold’, ‘amber’, ‘brown’ and ‘black’ are respectively 64.2%, 64.9%, 56.7% and 20.9% in the second experiment. The better functioning automatic colour prediction system for wine is due to the substantially larger training corpus (76,585 wine reviews opposed to 2205 beer reviews), the lower number of colour classes (three instead of eleven) and the clear-cut distinction between the colours (opposed to the gradual distinction between beer colours) (Hendrick et al, 2016).

5.4 Experiment 4: possible further beer property predictions based on expert reviews

For the previous experiments, only three of the eight beer properties that were automatically extracted from the expert website, namely flavour, aroma and colour, and the written review are used and analysed. Consequently, the other five beer properties - name of the beer, country of origin, bitterness, category and style – were left aside. This means that a great part of the corpus is still available for analysis. This fourth experiment analyses three of these beer properties – bitterness, category and country of origin – and conducts introductory research on possible links between these three beer properties or with the beer properties that already have

37 been examined. The following three analyses can serve as a preliminary study for further research.

5.4.1 Colour and bitterness

On the expert website, all beers are assigned a level of bitterness: ‘none’, ‘faint’, ‘low’, ‘low/medium’, ‘medium’, ‘medium/high’, ‘high’ and ‘extreme’. In this subsection, it is examined whether there is a possible link between colour and bitterness. Furthermore, the possibility of predicting the level of bitterness based on colour description and the possibility of predicting colour based on bitterness description is explored. To corroborate colour prediction, an automatic colour prediction system may base its predictions on bitterness descriptions, and to corroborate bitterness prediction, an automatic bitterness prediction system may base its predictions on colour descriptions. For the examination of possible links between colour and bitterness, the percentage of respectively gold, amber, brown and black coloured beer in the corpus that corresponds with each level of bitterness is calculated. On the following page, figure 4 displays the results of this analysis. The blue line in the figures reflects the percentages of respectively ‘gold’, ‘amber’, ‘brown’ and ‘black’ with each level of bitterness, whereas the orange line in reflects the percentages of all beers in the corpus (2201 reviews with a reference to bitterness) that correspond with each level of bitterness.

38 Figure 4. Link colour-bitterness per colour

Gold Amber

30 30 20 20 10 10 0 0

Gold General Amber General

Brown Black

40 50 30 40 30 20 20 10 10 0 0

Brown General Black General

Figure 5. Combination four graphs Bitterness - colour

45 40 35 30 25 20 15 10 5 0 None faint low low/medium medium medium/high high extreme

Gold Amber Brown Black It can be deduced from figure 5 that reviewers link colours with certain levels of bitterness. When looking at the levels of bitterness, several observations can be made. The label ‘none’ dominates 21.52% of the entire ‘gold’ collection in the corpus, whereas it only represents 10.62% of ‘amber’, 6.69% of ‘brown’ and merely 1.35% of ‘black’. The results for the label

39 ‘faint’ resemble the label ‘none’; 21.52% of all golden beers, 9.16% of amber beers, 7.24% of brown beers and 1.35% of black beers in the corpus have this label. Therefore, it can be assumed that the probability of a beer with bitterness label ‘none’ or ‘faint’ to be ‘gold’ is higher than the probability of it to be ‘amber’, ‘brown’ or ‘black’. This works in the opposite direction as well. For example, a bitterness label for a beer is missing in the review and it is known that that beer is black. The bitterness label will less likely be ‘none’ or ‘faint’, whereas golden beer tends to be less bitter than black beer. Hence, the probability of a beer with bitterness label ‘low’ or ‘faint’ to be gold is higher. The results for bitterness labels ‘low’ and ‘low/medium’ are the opposite; the probability of a beer with these bitterness labels to be ‘gold’ or even to be ‘amber’ is significantly lower than the probability of it to be ‘brown’ or ‘black’. 11.13% of gold beers have bitterness label ‘low’ and 5.86% have bitterness label ‘low/medium’. Respective results for ‘amber’, ‘brown’ and ‘black’ are 11.11% and 6.47%, 31.20% and 16.16%, and, 23.65% and 12.84%. Consequently, beers with low or low/medium bitterness are more likely to be brown or black. Bitterness label ‘medium’ shows a preference for ‘black’, while 42.57% of all black beers in the corpus have this label. Hence, the probability of a beer with bitterness label ‘medium’ to be ‘black’ is higher than the probability of it to be ‘gold’, ‘amber’ or ‘brown’. Bitterness label ‘medium’ is the most popular bitterness label in the list: for ‘black’, ‘amber’ and ‘gold’, it is the label with the highest percentage in the corpus. As a consequence, this label will probably be predicted when bitterness is enquired and only colour descriptions for ‘black’, ‘amber’ or ‘gold’ are provided in the review. Bitterness labels ‘medium/high’, ‘high’ and ‘extreme’ are most likely to be given to amber coloured beers, whereas respectively 16.48%, 16.85% and 3.54% of amber beers have these labels, as opposed to ‘gold’ (4.39/7.32/0.88%), ‘brown’ (14.21/6.96/0.28%) and ‘black’ (12.16/6.08/0%). According to the data in the corpus, a beer with bitterness label ‘extreme’ cannot be ‘black’, and vice versa. To exclude this entirely, more data should be collected, labelled and analysed.

In summary, ‘none’ and ‘faint’ labelled beers are most probable to be ‘gold’, ‘low’ and ‘low/medium’ labelled beers ‘brown’, ‘medium’ labelled beers ‘black’, ‘medium/high’, ‘high’ and ‘extreme’ labelled beers ‘amber’. In the reversed way, ‘gold’, ‘amber’ and ‘black’ are most probable to be predicted as ‘medium’ and ‘brown’ as ‘low’. On the following page, table 9 displays the results in a clear and structured way. The probabilities are ranked as followed: the first class is the most probable predicted class and the class at the bottom of the ranking is the least probable predicted class. It needs to be emphasised that these probabilities are solely based

40 on frequency figures and that they are hypothetical. Further research is necessary to examine these hypotheses by means of statistical correlation analyses.

Table 9. Probable predictions of an automatic prediction system for bitterness and colour ranked by probability Colour prediction based on bitterness description None Faint Low Low/medium Medium Medium/high High Extreme Gold Gold Brown Brown Black Amber Amber Amber Amber Amber Black Black Gold Brown Gold Gold Brown Brown Gold Amber Amber Black Brown Brown Black Black Amber Gold Brown Gold Black Black Bitterness prediction based on colour description Gold Amber Brown Black Medium Medium Low Medium None – faint High Medium Low Low Medium/high Low/medium Low/medium High Low Medium/high Medium/high Low/medium None Faint High Medium/high Faint High None – faint Extreme Low/medium None (Extreme) Extreme Extreme

5.4.2 Bitterness and category

In appendix B (p 130), a list can be found with the eight bitterness labels – ‘none’, ‘faint’, ‘low’, ‘low/medium’, ‘medium’, ‘medium/high’, ‘high’ and ‘extreme’ - and the beer categories that are consistently accompanied with that particular bitterness label. Consequently, the bitterness level of a beer category could be automatically and accurately predicted when an automatic bitterness prediction system bases its predictions solely on a provided beer category. This, however, is only feasible when that beer category is consistently labelled with the same bitterness level. Therefore, beer categories that are labelled with two or more bitterness levels in the beer review corpus are excluded from the bitterness-category list. The prediction of bitterness level for these excluded beer categories would probably be less accurate than for included beer categories. When this is examined the other way around, in other words automatically predicting beer category solely based on bitterness descriptions in beer reviews,

41 the task would be extremely difficult due to the high number of beer categories that are subsumed under one or more bitterness labels. For the accurate prediction of beer category, an automatic prediction system would have to base its prediction on multiple descriptions, for example flavour, aroma and colour. The automatic colour prediction system in this thesis bases its predictions on the vocabulary present in the written beer reviews. However, a combined machine learning system that bases its predictions on both written reviews and structured labels will probably obtain better results for the prediction of certain beer properties. In this case, further research is necessary to explore the possibilities of automatically and accurately predicting both the bitterness level of beer categories that are linked with more than one bitterness label and beer categories.

5.4.3 Country of Origin

This subsection examines whether country of origin could be automatically predicted based on the beer reviews in the corpus. There are in total 45 countries of origin in the corpus. The number of beers originating from those countries range from one (Colombia, Croatia, Denmark, Estonia, Ethopia, Jamaica, Kenya, Russia, South Africa, Thailand) to 1,710 (USA). The high number of beer reviews about beers from the USA is not a surprise: the website from which the reviews are extracted, is North American and the number of foreign beers in the USA is much more limited than domestically brewed beers and therefore less available and accessible in the USA. A total of 45 countries of origin would be too many for the automatic prediction system, which makes an accurate prediction of the country of origin for new beer reviews highly difficult. Therefore, the different countries are grouped together and the number of categories for country of origin is reduced to six: Oceania (Australia and New Zealand, 27 reviews), South America (Barbados, Brazil, Chile, Colombia, Dominican Republic, El Salvador, Guatemala, Honduras, Jamaica, Mexico, Peru, 35 reviews), North America (Canada and USA, 1,854 reviews), Europe (Germany, Austria, Belarus, Belgium, Croatia, Czech Republic, Denmark, England, Estonia, France, Greece, Ireland, Italy, the Netherlands, Poland, Scotland, Slovakia, Spain, Switzerland, United Kingdom, 260 reviews), Asia (China, Japan, Philippines, Russia, Singapore, Thailand, Vietnam, 26 reviews) and Africa (Ethopia, Kenya, South Africa, 3 reviews). The higher the number of training data, the more accurately an automatic prediction system is assumed to predict the country of origin for unknown beers. In this case, the automatic prediction system would probably predict the country of origin of beers originating from North America more easily and accurately than of beers originating from Africa, for example.

42 Moreover, the automatic prediction system would be heavily biased towards predicting North America to the disadvantage of the other five categories. The data retrieved from www.tastings.com is therefore insufficient for the construction of an unbiased and accurate prediction system for country of origin. Further research could gather a corpus that has a similar quantity of data for the six country categories, in order to construct a less biased or unbiased automatic country prediction system.

6. Discussion

This thesis aims at examining the informativeness and consistency in lexical choices in expert beer reviews and analysing the lexical descriptors of sensory experiences in these reviews. For the examination and evaluation of the usability of information in expert beer reviews, an automatic colour prediction system based on flavour and aroma descriptions in the beer reviews is constructed.

Firstly, a lexical analysis of flavour and aroma description is conducted. In chapter two of this thesis, several studies claim that flavour and aroma perceptions are closely related. This thesis, therefore, hypotises that the flavour and aroma descriptions in beer reviews are likely to be similar and to consist of the same vocabulary due to the assumed odour-taste synaesthesia in the Western world. In the first experiment, the unique terms in aroma descriptions and flavour descriptions are examined. It appears that 63.86% of all terms used for aroma description and 59.04% of all terms used for flavour description are unique. These figures, however, are not representative, while more than half of the terms used for flavour and aroma description occur only once in either the aroma frequency ranking or the flavour frequency ranking. Therefore, the top 100 most frequently used terms for aroma is compared with the top 100 most frequently used terms for flavour, and vice versa. As a result, 47% of both top 100 rankings appear to be unique. This means that more than half of the flavour and aroma terms are frequently used to describe both perceptions. Furthermore, only two of the top 10 most frequently used terms for flavour description and even none of the top 10 for aroma description are unique. Therefore, it can be concluded that the claim of Calvert, Spence and Stein (2004), which states that people recognise and describe aromas and flavours similarly, appears to be correct. People tend to use the same terms for both descriptions, but some terms are preferably used for one of the two

43 categories. Moreover, it is hypothesised in chapter two that there is a triangular relation between colour, flavour and aroma descriptions. The results of the lexical analysis confirm that indeed flavour and aroma descriptions are linked. The other two links, namely flavour-colour and aroma-colour is examined in experiment two, which is discussed in the following paragraph. Regarding the lexical properties of the flavour and aroma descriptions in beer reviews, the experts almost exclusively used source-based terms and metaphors. Evaluative terms for aroma and flavour description are almost absent in the corpus. The construction mentioned in the study of Croijmans and Majid (2016), ‘it smells/tastes like +source’, is absent in the beer reviews of the corpus. There are other similar constructions for the description of flavour and aroma, however, that consistently appear in the corpus: ‘aromas of + source’, ‘source + aromas’, ‘flavours of + source’ and ‘source + flavours’. Instead of using verbal constructions, these reviewers worded their perceptions more nominally. In chapter two, the study of Majid and Levinson (2011) is mentioned. They claim that flavour and aroma descriptions contain fewer unique words than sight descriptions. Consequently, a corpus is expected to contain fewer terms for flavour and aroma description than for sight description. In this corpus, sight descriptions are limited to colour descriptions. Moreover, it is claimed that the vocabulary used for the description of sensory perceptions such as flavour and aroma is limited. In the entire beer review corpus, 3,121 terms and 2,466 terms are respectively used for aroma and flavour description in the structured labels opposed to 94 terms for colour description that are automatically extracted from the written reviews. This contradicts the claim of a higher number of terms describing sight properties. The significantly lower number of colour describing terms, however, can be explained by the limitation on sight description in the corpus, whereas the beer experts mainly elaborate on colour and less on other sight properties.

Secondly, an automatic colour prediction system based on flavour and aroma descriptions is built for the evaluation of the lexical consistency and usability of expert beer reviews. The construction of this system is based on the automatic colour prediction system for wine of Hendrickx et al. (2016). In total, 49 colour classes are extracted from the expert website. The number of colour classes for beer is reduced to eleven, but only four classes – ‘gold’, ‘amber’, ‘brown’ and ‘black’ - can be predicted by the automatic prediction system. The average cross- validation accuracy of the first experiment is 56.05%, whereas the accuracy of the second (optimised) system is 59.88%. Due to the gradual difference between these four classes, similar terms are used for flavour and aroma description of two different colour classes. This explains the high number of incorrectly predicted neighbouring colour classes. For the prediction of

44 ‘black’, however, the system shows a bias towards ‘brown’, which is probably a result of the higher number of ‘brown’ reviews than ‘black’ reviews in the corpus on which the automatic prediction system was trained. In order to build a more accurate automatic prediction system, the training corpus should contain considerably more reviews. Even though this prediction system can be optimised, it confirms the general hypothesis of this thesis. Beer experts are evidently capable of describing beer properties and their sensory perceptions in a sufficiently consistent manner, which allows beer properties, in this case colour, to be automatically predicted on the basis of experts’ reviews. This system bases its predictions on both aroma and flavour descriptions, because both descriptions are lexically closely related. In addition, the results of this experiment do not only confirm the link between flavour and aroma, which is proven in the first experiment of this thesis, but also the link between aroma and colour, which is hypothesised in preliminary research, and between flavour and colour, which is hypothesised in this thesis. Consequently, it confirms the hypothesis of a triangular relation between colour, aroma and flavour descriptions.

After analysing the lexical descriptors and constructing the automatic colour prediction system, the differences and similarities between both lexical descriptors in beer and wine reviews, and between the automatic prediction systems for beer and wine colour are examined. Regarding the lexical properties of the wine and beer reviews, both experts use similar words and constructions to describe aroma and flavour properties. Moreover, the bulk of the top features for the automatic prediction of wine colour are present in the flavour and aroma term lists of this thesis’ beer review corpus. Regarding the automatic colour prediction systems, it can be concluded that the system for wine achieves better classification performance than the system for beer. The better performing automatic colour prediction system for wine is probably due to the considerably larger training corpus, the lower number of colour classes and the clear-cut distinction between wine colours. Firstly, the automatic colour prediction for beer bases its predictions on a corpus of which the size is only 2.88% of the size of the corpus on which the automatic prediction system for wine is based on. In other words, the corpus of wine, which contains 76,585 wine reviews, is more than 34 times the size of the corpus of beer reviews, which contains 2205 beer reviews. This enables training the automatic colour prediction system for wine on much more data. Secondly, the system for wine has to predict three classes, whereas the system for beer has to predict eleven different classes. Thirdly, the distinction between ‘gold’, ‘amber’, ‘brown’ and ‘black’ is in reality gradual. Therefore, flavour and aroma descriptions can refer to one or more colour classes. The distinction between the wine colour

45 classes ‘red’, ‘white’ and ‘rosé’ is more clear-cut. However, the f-score for the automatic prediction of ‘rosé’ is lower than for the other two wine colours: ‘rosé’ is often misclassified as ‘red’ due to the lower frequency of ‘rosé’ in the corpus and the use of similar grapes for processing red and rosé wine (Hendrickx et al., 2016). In this thesis, the f-score for ‘black’ prediction is considerably lower than the f-score for the other three colour classes due to the lower frequency of reviews for black beer in the corpus. It is highly probable that low frequency in the corpus and similarities with other colour classes lead to a lower accuracy of the automatic colour prediction system for both wine and beer.

For completeness sake, relations between other beer properties and possibilities for automatically predicting other beer properties are explored. The results of experiment four show that there seems to be a correlation between bitterness and colour. In addition, an automatic bitterness prediction system based on colour descriptions or an automatic colour prediction based on bitterness prediction could be built and analysed. It would be highly interesting to analyse this correlation in further research and extend the information used for the automatic colour prediction system that is now solely based on flavour and aroma descriptions in the review text. The system may be able to base its colour predictions not only on flavour and aroma descriptions in the review text, but also on structured bitterness information in beer reviews. The other results of the fourth experiment indicate that predicting beer properties on beer category or country of origin based on the data in this corpus seems nearly impossible. There are too many beer categories (112) and 1710 out of 2205 reviews were about beers from the USA, which would bias the automatic prediction system.

46 7. Conclusion

It appears that beer experts are not that different from wine experts in their way of phrasing and describing sensory experiences. They both use mainly metaphors and noun phrases composed of source-based terms. Furthermore, their reviews are well-structured, consistent and the vocabulary used for the flavour and aroma descriptions is rather extensive and sufficiently unique for training an automatic colour prediction. Due to the extensive, unique vocabulary and the structured information in the reviews, the automatic colour prediction system shows promising results, with an average accuracy score of about 60%. Consequently, expert beer reviews are meaningful providers of information. In addition, odour-synaesthesia could be found in the expert beer reviews. Together with the proven links colour/aroma and colour/flavour, the link aroma/flavour confirms the hypothesised triangular relation between colour, flavour and aroma descriptions.

Further research could probably improve the precision, recall, f-score and accuracy of the automatic prediction system by extracting and labelling a higher number of reviews and by adding more information from the other structured beer properties to train the classifier. If better accuracy scores for the colour prediction systems are attained, further research can look into the possibility of combining both automatic colour prediction systems into one system for both beer and wine. This thesis can also serve as a pioneering study for a multilingual automatic colour prediction system for beer. Moreover, the possibilities of building automatic prediction systems for other beer properties such as bitterness, country of origin or beer category are definitely worth investigating. These additional systems could be combined with the existing or improved colour prediction system. Regarding the lexical properties in beer reviews, the lexical descriptors in the English beer reviews can be compared to the lexical descriptors in beer reviews in other languages or in reviews of other beverages. This thesis confirmed the hypothesis of odour-taste synaesthesia in the western world (Calvert, Spence & Stein, 2004). The training data was extracted from an North American website and flavour and aroma descriptions were partially similar. Further research could examine whether odour-taste synaesthesia is also present in European beer reviews. In sum, there are ample possibilities for further research that further builds upon this thesis.

48 8. Bibliography

Bartoshuk, L.M. (2000). Comparing sensory experiences across individuals: recent psychophysical advances illuminate genetic variation in taste perception. Chem. Senses, 25, 447-460.

Calvert, G., Spence, C., & Stein, B.E. (2004). The Handbook of Multisensory Processes. Cambridge: MIT Press.

Carvalho, F.R., Velasco, C., van Ee, R., Leboeuf, Y., & Spence, C. (2016). Music Influences Hedonic and Taste Ratings in Beer. Frontiers in Psychology, 7, 636-648.

Chang, C.-C., & Lin, C.-J. (2011). LIBSVM: A Library for Support Vector Machines. ACM Transactions on Intelligent Systems and Technology (TIST), 2(3), 1-27.

Croijmans, I., & Majid, A. (2016). Not All Flavor Expertise Is Equal: The Language of Wine and Coffee Experts. PLoS ONE, 11(6): e0155845. doi:10.1371/journal.pone.0155845.

Dematte, M.L., Sanabria, D., & Spence, C. (2006). Cross-Modal Associations Between Odors and Colors. Chemical Senses, 31(6), 531-538.

Garneau, N.L., Nuessle, T.M., Sloan, M.M., Santorico, S.A., Coughlin, B.C., & Hayes, J.E. (2014). Crowdsourcing taste research: Genetic and phenotypic predictors of bitter taste perception as a model. Frontiers in integrative neuroscience, 8, 33.

Guyon, I., & Elisseeff, A. (2003). An introduction to variable and feature selection. Journal of machine learning research, 3, 1157-1182.

Hayes, J.E., Bartoshuk, L.M., Kidd, J.R., & Duffy, V.B. (2008). Supertasting and PROP Bitterness Depends on More Than the TAS2R38 Gene. Chemical Senses, 33(8), 255-265.

Hendrickx, I., Lefever, E., Croijmans, I., Majid, A., & van den Bosch, A. (2016). Very quaffable and great fun: Applying NLP to wine reviews. In Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (pp. 306-312). Berlin: Association for Computational Linguistics.

Hopfer, H., & Heymann, H. (2014). Judging wine quality: Do we need experts, consumers or trained panelists? Food Quality and Preference, 32, 221-233.

Howes, D. (2006). Cross-talk between the Senses. The Senses and Society, 1(3), 381-391.

49 Majid, A., & Levinson, S.C. (2011). The Senses in Language and Culture. The Senses and Society, 6(1), 5-18.

Hsu, C. W., & Lin, C. J. (2002). A comparison of methods for multiclass support vector machines. IEEE transactions on Neural Networks, 13(2), 415-425.

Kotsiantis, S. B., Zaharakis, I., & Pintelas, P. (2007). Supervised machine learning: a review of classification techniques. Informatica, 31(3), 249-269.

Lefever, E., Allein, L. & Jacobs, G. (2017). In the Eye of the Beer-Holder. Lexical Descriptors of Aroma and Taste Sensations in Beer Reviews. Proceedings of The Third International Conference on Human and Social Analytics (HUSO 2017), Nice, France. (to appear)

Manning, C.D., Surdeanu, M., Bauer, J., Finkel, J. Bethard, S.J., & McClosky, D. (2014). The Standford CoreNLP natural language processing toolkit. Association for Computational Linguistics (ACL) System Demonstrations, 55-60.

Morrot, G., Brochet, F. & Dubourdieu, D. (2001). The Colors of Odors. Brain and Language, 79(2), 309-320.

Mosher, R. (2015). Mastering Homebrew: The Complete Guide to Brewing Delicious Beer. Chronicle Books.

Olofsson, J.K., & Gottfried, J.A. (2015). The muted sense: neurocognitive limitations of olfactory language. Trends in Cognitive Sciences, 19(6), 314-321.

Paradis, C., & Eeg-Olofsson, M. (2013). Describing sensory experience: The genre of wine reviews. Metaphor and Symbol, 28(1), 22-40.

Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., Vander- plas, J., Passos, A., Cournapeau, D., Brucher, M., Perrot, M., & Duch- esnay, E. (2011). Scikit-learn: Machine learning in Python. Journal of Machine Learning Research, 12, 2825–2830.

Spence, C. (2015). Multisensory Flavor Perception. Cell, 161(1), 24-35.

Spence, C., & Piqueras-Fiszman, B. (2014). The Perfect Meal: The Multisensory Science of Food and Dining. Oxford, UK: John Wiley and Sons.

Stevenson, R.J., & Boakes, R.A. (2004). Sweet and sour smells: Learned synaesthesia between the senses of taste and smell. In G.A. Calvert, C. Spence, B.E. Stein (Eds.) The Handbook of Multisensory Processing. 69-83. Cambridge: MIT Press.

50 Tepper, B. J., Koelliker, Y., Zhao, L., Ullrich, N. V., Lanzara, C., d'Adamo, P., Ferrara, A., Ulivi, S., Esposito, L., & Gasparini, P. (2008). Variation in the Bitter-taste Receptor Gene TAS2R38, and Adiposity in a Genetically Isolated Population in Southern Italy. Obesity, 16(10), 2289-2295.

Tong, S., & Koller, D. (2001). Support vector machine active learning with applications to text classification. Journal of machine learning research, 2(Nov), 45-66.

Wallach, H. M. (2006,). Topic modeling: beyond bag-of-words. In Proceedings of the 23rd international conference on Machine learning (pp. 977-984). ACM.

Witten, I.H., Frank E., Hall, M.A., & Pal, C.J. (2016). Data Mining: Practical Machine Learning Tools and Techniques. Burlington, MA: Morgan Kaufmann.

Young, D. (2005). The Smell of Greenness: Cultural Synaesthesia in the Western Desert. Etnofoor, 18(1), 61-77

52 APPENDIX A. full list aroma and flavour terms ranked by frequency

Aroma Freq Flavour Freq nuts 360 pepper 518 chocolate 360 nuts 279 bread 324 citrus 254 toast 255 lemon 234 apple 231 apple 223 lemon 211 greens 216 citrus 176 grass 158 cake 158 nut 154 fruit 157 chocolate 153 nut 146 tangy 151 banana 143 bread 150 fruits 136 radish 138 peach 133 peppery 137 pepper 133 arugula 132 honey 131 root 122 muffin 129 melon 117 dark 127 toast 116 orange 123 fruit 115 herb 115 orange 111 corn 114 earth 99 butter 113 sprouts 98 roasted nuts 111 spice 93 custard 110 pine 90 raisin 106 hop 88 apples 93 sweet 87 toffee 88 lettuce 85 marmalade 85 corn 80 caramel 85 custard 80 pastry 83 watercress 76 grapefruit 82 grain 75 pine 79 grassy 75 mango 75 coffee 74 spice 73 grapefruit 73 root 73 banana 72 yogurt 72 honey 72 sourdough 70 green 68 pineapple 69 fruits 67

53 Aroma Freq Flavour Freq herbs 69 kiwi 67 vinaigrette 65 skin 63 cherry 65 turnip 63 coffee 63 peppercorn 62 pie 61 vegetable 59 baguette 56 pear 57 hint 56 cherry 56 sweet 55 jicama 55 tart 52 peach 54 vegetables 52 root vegetable 54 egg 52 cream 53 rye 51 delicate 53 pudding 50 minerals 52 spicy 50 spices 52 pear 50 mocha 51 chutney 49 peppered greens 51 clay 49 vegetables 51 soufflÃ© 48 cake 50 brittle 48 crisp 50 greens 47 lemon pepper 50 cream 47 potato 49 scone 46 root vegetables 49 raisin toast 46 cocoa 48 spices 46 dark 46 cocoa 46 mineral 46 root vegetables 46 rapini 46 nougat 45 tart 46 praline 44 wafer 46 vanilla 44 caramel 45 apricot 43 radicchio 45 brulee 42 driven 44 creme 42 kale 44 dark chocolate 42 marmalade 44 creme brulee 41 muffin 43 honeycomb 39 vanilla 43 delicate 39 chestnut 42 cheese 39 green apple 42 nut brittle 38 hint 42 beans 37 rye 42 grain 36 vinaigrette 42 herb muffin 35 sorbet 41

54 Aroma Freq Flavour Freq lavender 34 nutskin 39 buttery 33 water 39 aromas 33 creme 37 chocolate nuts 33 pudding 37 ginger 33 radish sprouts 37 green 33 starfruit 37 raisin bread 32 peel 36 nut bread 32 raisin 36 fig 32 sweet potato 36 molasses 31 apricot 35 melon 31 bitter greens 35 peaches 31 brulee 35 coconut 31 creme brulee 35 tea 31 herb 35 sugar 30 molasses 35 jerky 29 pepper bread 35 soy 29 rind 35 berry 29 salty 35 danish 29 creamy 34 cookie 29 bark 33 color 29 carrot 33 omelet 28 yams 33 mocha 28 lime 32 sandwich 28 apples 31 peppercorn 27 latte 31 blossoms 27 peppercorns 31 peanut 27 toffee 31 mint 27 nougat 30 overripe 26 spicy 30 pepper bread 26 butter 29 cherries 26 nutshell 29 spice cake 26 pineapple 29 dough 26 yogurt 29 cola 26 peppery greens 28 jam 26 apple skin 27 grass 26 mango 27 rind 25 mint 27 peels 25 astringent 26 cider 25 grassy hop 26 fruit cake 25 herbs 26 potato 25 melon rind 26

55 Aroma Freq Flavour Freq candy 25 mustard 26 fruitcake 24 salad 26 coriander 24 soufflÃ© 26 fruity 24 tea 26 salty 24 pie 25 crust 24 praline 25 tropical fruit 24 grassy earth 24 sauce 24 hops 24 focaccia 23 mesclun 24 yams 23 mustard greens 24 rye toast 23 peppery spices 24 herb bread 23 bamboo 23 lemon pepper 23 chewy 23 cracker 23 cider 23 cinnamon 23 roasted grain 23 pumpkin 23 beet 22 waxy 22 lettuces 22 sorbet 22 peels 22 chestnuts 22 peppery spice 22 oregano 22 refreshing 22 bread pudding 22 savory 22 straw 22 wheat 22 lime 22 zesty 22 frittata 21 baking 21 compote 21 bean 21 chocolate toffee 21 coconut 21 taffy 21 peppery hop 21 banana nut 21 rye toast 21 orange marmalade 21 scone 21 figs 21 sugar 21 raspberry 21 chutney 20 brown sugar 21 grilled citrus 20 toasty 20 honeyed nuts 20 peel 20 brittle 19 egg sandwich 20 nectarine 19 baking 20 radishes 19 salad 20 sauce 19 candle 20 snap 19 succotash 19 straw 19 sourdough toast 19 toasty 19 grapefruit marmalade 19 ash 18

56 Aroma Freq Flavour Freq lemongrass 19 brown sugar 18 pretzel 19 carrots 18 gingerbread 19 cookie 18 burnt 19 espresso 18 hay 19 kumquat 18 wet 19 pith 18 pumpernickel 18 stone 18 grilled citrus 18 vinegar 18 carrot 18 apple cider 17 fudge 18 baguette 17 chestnut 18 delicate spice 17 strawberry 18 earthy 17 ham 18 fig 17 beer 18 gelato 17 rice 18 grains 17 marmalades 17 juice 17 sourdough bread 17 orange pepper 17 mango chutney 17 paper 17 gelato 17 water chestnut 17 fruit custard 17 chalk 16 veggies 17 herbal 16 pears 17 mousse 16 tropical fruits 17 pastry 16 apple butter 17 salt 16 apple pie 17 warming 16 sweet potato 17 wax 16 stone 17 berry 15 skin 17 body 15 bar 17 grainy 15 banana nut muffin 16 leafy 15 mousse 16 peaches 15 corn bread 16 peppered nuts 15 blossom 16 raisin toast 15 grape 16 bamboo shoot 14 wax 16 bread pudding 14 leather 16 cedar 14 peppery 15 cracker 14 croissant 15 ginger 14 lemon scone 15 golden beet 14 citrus peels 15 grilled root vegetable 14 turnip 15 nut bread 14

57 Aroma Freq Flavour Freq cashew 15 nutskins 14 banana bread 15 overripe 14 chocolate mousse 15 peppered lettuce 14 banana cake 15 pretzel 14 apple cider 15 shoot 14 vinegar 15 blood 13 bark 15 blood orange 13 soda 15 brandy 13 soap 15 bubblegum 13 graham 15 candy 13 hop 15 chip 13 okra 14 chocolate mousse 13 pretzel bread 14 curd 13 relish 14 meyer 13 hops 14 meyer lemon 13 berries 14 peppered kale 13 cereal 14 plastic 13 cannabis 14 pretzel bread 13 grains 14 pumpernickel 13 bbq 14 romaine 13 potatoes 14 sap 13 sap 14 tropical fruit 13 bean 14 beans 12 root beer 14 bibb 12 meat 14 bibb lettuce 12 rubber 14 buttery 12 chicken 14 chestnuts 12 leaves 14 cigar 12 buttercream 13 citrus peel 12 waxy honeycomb 13 citrus peels 12 flan 13 clay 12 nut toast 13 cucumber 12 bubblegum 13 fruit cake 12 egg croissant 13 grilled grapefruit 12 zucchini 13 grilled romaine 12 honey butter 13 nut brittle 12 cheese rind 13 okra 12 peanut butter 13 orange marmalade 12 bananas 13 parchment 12 cranberry 13 parsnip 12 floral honey 13 pomegranate 12

58 Aroma Freq Flavour Freq circus 13 sour cherry 12 float 13 tomato 12 maple 13 turnips 12 pizza 13 zest 12 milk 13 apple sauce 11 turkey 13 Asian 11 edamame 12 cheese 11 cider vinaigrette 12 chili 11 cobbler 12 citrus marmalade 11 barnyard 12 fruity 11 granola 12 green apple skin 11 fruit pastry 12 ice 11 latte 12 jam 11 delicate spice 12 peppercorn muffin 11 marmalade on toast 12 peppered jicama 11 milk chocolate 12 pine sap 11 tomato 12 rubber 11 chips 12 soda 11 Rich 12 spice cake 11 wood 12 stone fruit 11 tree 12 balsamic 10 earth 12 cider vinaigrette 10 honeyed nuts 11 citrus custard 10 peppercorn muffin 11 clementine 10 sarsaparilla 11 core 10 peach custard 11 corn pudding 10 peppercorns 11 dark roasted nuts 10 lemon marmalade 11 figs 10 roasted nut 11 fruit salad 10 pepper muffin 11 medium 10 curd 11 milk 10 lemon herb muffin 11 nut toast 10 chocolate yogurt 11 oak 10 papaya 11 sassafras 10 bread dough 11 touch 10 carrot cake 11 wheat wafer 10 baking chocolate 11 baking chocolate 9 sour cherry 11 beets 9 preserves 11 blossom 9 peanuts 11 cereal 9 kiwi 11 chocolate mint 9

59 Aroma Freq Flavour Freq orange blossom 11 cinnamon 9 peppers 11 coffee bean 9 stone fruits 11 granita 9 pork 11 husk 9 chip 11 lemon sorbet 9 mincemeat 10 lingering 9 mangos 10 mouthwatering 9 nectarine 10 nutshells 9 stewed veggies 10 quince 9 grilled grapefruit 10 raspberry 9 lemon curd 10 skins 9 beets 10 tangerine 9 dark roasted nuts 10 tobacco 9 zucchini bread 10 tropical fruits 9 raspberries 10 veggies 9 frosting 10 wheatgrass 9 caramel apple 10 apple tart 8 corn pudding 10 balsa 8 putty 10 beer 8 pickle 10 cherries 8 oranges 10 cider vinegar 8 fig bread 10 cigar ash 8 orange blossom honey 10 citrusy 8 circus peanut 10 dark chocolate 8 fruit salad 10 dust 8 vinaigrette on greens 10 grape 8 pine sap 10 grilled root vegetables 8 apple sauce 10 honeycomb 8 coffee beans 10 honeyed pear 8 espresso 10 lemon curd 8 pound cake 10 limestone 8 glue 10 peanut 8 gum 10 pit 8 moss 10 relish 8 vegetable 10 seltzer 8 powder 10 soy 8 pound 10 strawberry 8 baker 10 supple 8 baker's chocolate 10 turnip greens 8 Playdoh 9 watermelon 8 mesclun 9 alfalfa 7

60 Aroma Freq Flavour Freq panettone 9 apple cider vinaigrette 7 citrus marmalade 9 artichoke 7 fruity caramel 9 baking spices 7 banana taffy 9 bar 7 peach pastry 9 carob 7 wintergreen 9 cashew 7 peach cobbler 9 celery 7 crusty 9 chard 7 kale 9 chili pepper 7 licorice 9 compote 7 dark toast 9 cone 7 zest 9 corn bread 7 thyme 9 dryish medium body 7 tangerine 9 flan 7 asparagus 9 gravel 7 pumpkin pie 9 leafy greens 7 carrots 9 lemon juice 7 orange peel 9 light 7 Creamy 9 mocha latte 7 Almond 9 mole 7 graham cracker 9 orange blossom 7 squash 9 peppery arugula 7 sweet potatoes 9 pine bark 7 bacon 9 pine cone 7 wheat 9 pumpkin 7 salt 9 root beer 7 brazil 9 seed 7 horse 9 sourdough 7 honeyed toast 8 sweet citrus 7 pumpernickel toast 8 sweet corn 7 schmaltz 8 swiss chard 7 parsnip 8 taffy 7 banana custard 8 tamari 7 pinecone 8 tangy citrus 7 baklava 8 tropical citrus 7 pineapples 8 vanilla custard 7 herb baguette 8 white chocolate 7 herb toast 8 wood 7 fruit custard tart 8 alfalfa sprouts 6 meringue 8 apple butter 6 plantain 8 apple cider vinegar 6

61 Aroma Freq Flavour Freq butterscotch 8 apple core 6 molasses cake 8 aromas 6 apple cider vinaigrette 8 asian spices 6 pineapple cake 8 banana chip 6 bread crust 8 banana custard 6 lemon cake 8 biscuit 6 chamomile 8 brazil 6 crayon 8 caramel apple 6 pine blossoms 8 chocolate citrus peels 6 raisins 8 citrus pith 6 lemons 8 cola 6 cider vinegar 8 coriander 6 delicate herbs 8 crayon 6 chestnut honey 8 creamy vanilla 6 chocolate orange 8 crust 6 white toffee 8 delicate spices 6 orange cake 8 egg 6 biscuit 8 fig bread 6 puff 8 finish 6 sour cream 8 flourish 6 rice cake 8 fruitcake 6 mustard 8 granola 6 pine needle 8 grapefruit peel 6 syrup 8 hearts 6 mushroom 8 herb bread 6 cigar 8 honeyed nut 6 needle 8 interplay 6 ash 8 lavender 6 Bold 8 lemon marmalade 6 ice 8 lemon scone 6 paper 8 lemonade 6 water 7 lemongrass 6 rinds 7 lettuce greens 6 brioche 7 licorice 6 peach vinaigrette 7 mango chutney 6 lemon vinaigrette 7 mocha cream 6 buttery nut brittle 7 notes 6 peppery spices 7 omelet 6 cinnamon raisin toast 7 pepper dust 6 chocolate muffin 7 peppery radish 6 peppery spice 7 plantain 6

62 Aroma Freq Flavour Freq mincemeat pie 7 powder 6 nettles 7 punch 6 sassafras 7 salty nuts 6 apple custard 7 sarsaparilla 6 cherry vinaigrette 7 sea 6 caramel corn 7 sea salt 6 berry vinaigrette 7 seamless 6 cashew butter 7 spouts 6 talc 7 sweet lettuces 6 peach butter 7 zucchini 6 orange peels 7 apple pie 5 tropical citrus 7 apricot marmalade 5 walnuts 7 artichoke hearts 5 artichoke 7 baking spice 5 dark chocolate mousse 7 banana bread 5 tortilla 7 banana nut muffin 5 sprouts 7 banana pudding 5 baked beans 7 birch 5 pink peppercorn 7 blossom water 5 orange pepper 7 brown grass 5 mustard greens 7 cabbage 5 artisan 7 cappuccino 5 crackers 7 caramelized fruits 5 lettuce 7 cayenne 5 spinach 7 chicory 5 brazil nuts 7 chocolate cream 5 meats 7 coffee beans 5 cocoa powder 7 cranberry 5 cabbage 7 dark rye 5 pizza crust 7 dry-yet-fruity 5 basil 7 espresso bean 5 rub 7 float 5 cone 7 focaccia 5 chili 7 fruit punch 5 root beer float 7 grapefruit custard 5 green apple 7 grilled turnip 5 lit 7 herb muffin 5 Brussels 7 honeyed toast 5 amber 7 jerky 5 dust 7 kiwi skin 5 roll 7 leaves 5

63 Aroma Freq Flavour Freq forest 7 lemon custard 5 fat 7 lemon pith 5 flowers 7 mangos 5 light 6 molten plastic 5 honeyed raisin toast 6 needle 5 raisin scone 6 nettles 5 jalapeÃ±o 6 orange peel 5 mango custard 6 parsley 5 spic 6 parsnips 5 hominy 6 peach skin 5 lychee 6 pears 5 hazelnuts 6 pepper muffin 5 grilled pineapple 6 peppered arugula 5 custard pie 6 peppery hop snap 5 banana pudding 6 pine needle 5 chocolate nut 6 piney 5 vanilla yogurt 6 pink pepper 5 tapioca 6 potatoes 5 herb butter 6 potato chip 5 tropical fruit custard 6 puff 5 apple yogurt 6 pulp 5 apple tart 6 radish spouts 5 apple pastry 6 raisin scone 5 pork rinds 6 salty baguette 5 delicate spices 6 sherbet 5 balsamic 6 spinach 5 chocolate cake 6 squash 5 geranium 6 stewed veggies 5 spicy pumpkin 6 succotash 5 chocolate cherry 6 sweet lettuce 5 grilled root vegetables 6 tree 5 orange spice cake 6 tropical fruit custard 5 cherry cola 6 wet grass 5 hawaiian bread 6 wheat cracker 5 Denver omelet 6 zucchini bread 5 pellets 6 ale 4 raspberry jam 6 apple brandy 4 cheesecake 6 apple sorbet 4 muffins 6 banana cake 4 turkey jerky 6 banana muffin 4 wafer 6 banana nut bread 4

64 Aroma Freq Flavour Freq breads 6 barrel 4 juniper 6 bbq 4 pine cone 6 berries 4 brussels sprouts 6 birch bark 4 green apples 6 bittering 4 brown butter 6 Brazil nut 4 graham crackers 6 bread crust 4 incense 6 buttercream 4 flavors 6 buttery caramel 4 hop pellets 6 cacao 4 root vegetable 6 candle 4 cigar ash 6 caramel corn 4 onion 6 carrot cake 4 ice cream 6 cherry pit 4 eggs 6 chips 4 juice 6 chocolate espresso beans 4 beef 6 chocolate nuts 4 seed 6 circus 4 Denver 6 circus peanut 4 la mode 5 cobbler 4 box 5 cocoa powder 4 la 5 coconut custard 4 poppyseed 5 color 4 raisin baguette 5 corn husk 4 macaroon 5 creamy chocolate 4 buttery caramel 5 delicate peppery spices 4 lemon muffin 5 dusty root vegetables 4 apple soufflÃ© 5 fleur 4 melon rind 5 gingerbread 4 mango yogurt 5 gooseberry 4 arugula 5 grain husk 4 coconut custard 5 grape skin 4 lemon pepper muffin 5 grapefruit marmalade 4 chocolate caramel 5 grapefruit pith 4 carob 5 grapefruit zest 4 dark raisin toast 5 green apples 4 lemon yogurt 5 hard candy 4 caramel cake 5 hay 4 mocha latte 5 herbal honey 4 lemon zest 5 herbal tea 4 nut butter 5 hint of pepper 4

65 Aroma Freq Flavour Freq radish 5 honeyed radish 4 teriyaki 5 hot sauce 4 apricots 5 leaf 4 romaine 5 leather 4 turmeric 5 lemons 4 nut fudge 5 lemon cake 4 embers 5 lemon granita 4 sour dough 5 lemon peel 4 golden raisin 5 macaroon 4 baking spices 5 malt 4 sour cream frosting 5 meringue 4 crispy 5 mesclun greens 4 orange marmalade on toast 5 mossy earth 4 chocolate mint 5 newspaper 4 watermelon 5 nibs 4 pumpkin cheesecake 5 oranges 4 root veggies 5 orange blossom water 4 brownie 5 orange peels 4 almonds 5 orange sorbet 4 banana chips 5 overripe apple 4 eraser 5 panettone 4 pancakes 5 parsley root 4 vanilla candle 5 passionfruit 4 celery 5 pear salad 4 espresso beans 5 peat 4 malt 5 peppers 4 brown spices 5 peppered citrus 4 burnt sugar 5 phosphate 4 smoky 5 pickle 4 cucumber 5 plum 4 bbq rub 5 potato skin 4 reduced lemon 5 pumpkin pie 4 mint cookie 5 raisin bread 4 sherry 5 root beer float 4 pink pepper 5 rye cracker 4 faint 5 sack 4 wet grain 5 sour apple 4 stone fruit 5 sprout 4 roast 5 sweet potatoes 4 ale 5 tapioca 4 cinnamon roll 5 tart apple 4

66 Aroma Freq Flavour Freq crisp 5 tree bark 4 turnover 5 vanilla bean 4 garlic 5 vanilla cream 4 green tea 5 vegetal 4 date bread 5 watermelon sorbet 4 prairie 5 wet grain 4 span 5 white ash 4 filling 5 white pepper 4 cedar 5 yam 4 hint of glue 5 yellow apple 4 latex 5 apple juice 3 hearts 5 apple vinaigrette 3 copper 5 baby 3 foster 5 banana bread pudding 3 chicken fat 5 banana cream pie 3 flower 5 banana taffy 3 apples on the tree 5 Band-Aid 3 Asian 5 berry vinaigrette 3 mode 5 bitter bark 3 oil 5 bitter chocolate 3 garden 4 black licorice 3 nutshells 4 blossoms 3 overripe citrus 4 blueberry 3 multigrain toast 4 bon 3 saltine 4 boozy 3 grapefruit custard 4 Bright 3 eggy 4 brown paper 3 peach marmalade 4 burnt coffee 3 pineapple marmalade 4 burnt sugar 3 raisin rye toast 4 cacao nib 3 raisin bread toast 4 candle wax 3 yeasty dough 4 carbonation 3 tamari 4 cashew brittle 3 lemon custard 4 catsup 3 peach custard pastry 4 cheese rind 3 lemon poppyseed 4 cherry cola 3 banana muffin 4 chestnut brittle 3 cashew brittle 4 chestnut honey 3 chocolate creme brulee 4 chicory coffee 3 asparagus frittata 4 chive muffin 3 citrus pastry 4 chocolate macaroon 3

67 Aroma Freq Flavour Freq parsnips 4 chocolate mint cookie 3 vanilla toffee 4 chocolate orange peel 3 strudel 4 chocolate pudding 3 peach yogurt 4 chocolate yogurt 3 vanilla custard 4 citrus creme brulee 3 apricot pastry 4 cocoa nibs 3 fruit custard pastry 4 coconut cream 3 chocolate citrus peels 4 coffee gelato 3 apple custard tart 4 coffee grounds 3 fruit compote 4 corn bread pudding 3 peach bread 4 corn puff 3 fruit chutney 4 craisin 3 lozenge 4 cream soda 3 chocolate pastry 4 creamy cappuccino 3 caramel fudge 4 dark coffee 3 mangoes 4 dark rye toast 3 tomato focaccia 4 date bread 3 peppered greens 4 diet 3 chocolate pudding 4 dry-yet-fruity medium-to-full body 3 lilacs 4 earthy hop 3 sauerkraut 4 espresso gelato 3 waxy apple 4 exotic peppercorn 3 minty 4 flavors 3 sherbet 4 flour 3 fruit tart 4 flower 3 whiff 4 frittata 3 schmaltz cookie 4 fruit compote 3 chives 4 fruit pastry 3 slaw 4 fruit pith 3 chocolate figs 4 fruity caramel 3 grainy 4 fudge 3 sour cherries 4 ginger snap 3 lemon peel 4 golden beets 3 grassy hops 4 graham 3 macadamia 4 graham cracker 3 clementine 4 grain sack 3 apple bread 4 grapes 3 piney 4 grapefruit sorbet 3 barnyard hay 4 grassy hops 3 buckwheat 4 green coffee 3 pretzels 4 grilled carrot 3

68 Aroma Freq Flavour Freq soda bread 4 grilled fruit 3 grassy herbs 4 grilled pineapple 3 beeswax 4 grounds 3 rhubarb 4 herb toast 3 dark bread 4 honey butter 3 lemon basil 4 honeydew 3 lemon poppy seed muffin 4 husky 3 apple cider vinegar 4 juicy 3 chocolate orange peel 4 kumquats 3 herbal honey 4 lemon grass 3 potpourri 4 lemon ice 3 pine blossom 4 lemon soufflÃ© 3 pastries 4 lemon vinaigrette 3 pine bark 4 lemon yogurt 3 candy corn 4 lemon zest 3 chocolate graham crackers 4 lime curd 3 dill 4 lime leaves 3 coffee cake 4 lit 3 soy rice cake 4 lush 3 soy sauce 4 lychee 3 fresh herbs 4 mango custard 3 wheat cracker 4 mango yogurt 3 peat 4 marmalades 3 snaps 4 marmalade on rye toast 3 broccoli 4 medley 3 wet straw 4 melon gelato 3 cream cheese 4 melon sorbet 3 dark coffee 4 metal 3 bean salad 4 milk chocolate 3 chocolate bar 4 mint leaf 3 herbal tea 4 mixed peppercorns 3 jasmine 4 moss 3 slices 4 nib 3 grapes 4 nut skin 3 Bananas Foster 4 orange bread 3 potato chip 4 orange marmalade on rye 3 wrapper 4 orange spice cake 3 white grape 4 orange zest 3 black licorice 4 overripe pear 3 salt water taffy 4 papaya 3 resin 4 paprika 3

69 Aroma Freq Flavour Freq yellow apple 4 pasty 3 blanket 4 peanut butter 3 pencil 4 pepper bread toast 3 cakes 4 pepper spice 3 yeast 4 peppercorn bread 3 sage 4 peppered lettuces 3 chicken skin 4 peppered mustard greens 3 bubble 4 peppered rapini 3 paste 4 pinecone 3 foam 4 pink peppercorn 3 soup 4 popsicle 3 pot 4 potato peel 3 leaf 4 potpourri 3 oak 4 pound 3 plastic 4 puree 3 breakfast 4 raisins 3 touch 4 rapini-like 3 fish 4 reduced lime 3 core 3 resin 3 match 3 rhubarb 3 floor 3 rice 3 Indian 3 roll 3 ground 3 rum 3 nutskin 3 salad greens 3 sulfury 3 seeds 3 gelatos 3 shells 3 rapini 3 slaw 3 yeasty fruitcake 3 soap 3 traw 3 sour lemon 3 grapefruit marmalades 3 sourdough toast 3 sourdough baguette 3 spiced fruits 3 sourdough raisin toast 3 stone fruits 3 apricot marmalades 3 sweet cream 3 banana soufflÃ© 3 sweet potato chip 3 lemon soufflÃ© 3 sweet spice 3 elderflower 3 syrup 3 chive muffin 3 tamarind 3 chocolate soufflÃ© 3 tart apple skin 3 overripe pear 3 tart citrus 3 chocolate nougat 3 thyme 3 grapefruits 3 tinny 3

70 Aroma Freq Flavour Freq citrus compote 3 toasty grain 3 apricot marmalade 3 turnover 3 pineapple custard 3 twang 3 peach compote 3 wave 3 jicama 3 wax paper 3 fruity custard 3 white grape 3 overripe apple 3 accent 2 raisin bread pudding 3 acetone 2 rye raisin bread 3 alcohol 2 honey vinaigrette 3 almond cookie 2 bonbon 3 amber 2 dark pumpernickel toast 3 anise 2 lemon sorbet 3 apple bread 2 peach tart 3 apple cider vinegar on greens 2 coconut creme brulee 3 apple granita 2 borscht 3 apple peel 2 kumquat 3 apple seltzer 2 salty pretzel bread 3 apple skins 2 pepper bread toast 3 arugula like hop 2 citrus blossoms 3 Asian pear 2 caramels 3 asparagus 2 banana bread pudding 3 baby carrot 2 cherry compote 3 baker 2 banana nut bread 3 bakers 2 vanilla yogurt nuts 3 bakers chocolate 2 turnip greens 3 balsamic vinegar 2 tropical fruit custard tart 3 bananas 2 herb pepper bread 3 barky hop 2 apple sorbet 3 batter 2 hoppy 3 bit 2 rye cracker 3 bitter mustard greens 2 lemon verbena 3 bitter turnip 2 cashews 3 bittersweet 2 peach pie 3 blood orange sorbet 2 polenta 3 branch 2 fresh herb baguette 3 branch water 2 chocolate orange peels 3 brazil nuts 2 apple strudel 3 brown spices 2 fruit bread 3 butterscotch 2 lavender talc 3 buttery baguette 2 fresh baguette 3 buttery caramel corn 2

71 Aroma Freq Flavour Freq cranberry bread 3 cake batter 2 bouillon 3 caramel cream 2 spicy carrot cake 3 cardboard 2 tamarind 3 cashew butter 2 verbena 3 cashew nougat 2 cardamom 3 cauliflower 2 apple cider vinaigrette on mesclun 3 chamomile tea 2 coffee gelato 3 char-roasted nut 2 haystack 3 cherry brandy 2 menthol 3 cherry chutney 2 pistachio 3 cherry vinaigrette 2 spicy pumpkin pie 3 chestnut toffee 2 apricot jam 3 chili powder 2 orange blossoms 3 chocolate cherries 2 cherry butter 3 chocolate cherry mousse 2 traw color 3 chocolate creme brulee 2 cheese bread 3 chocolate espresso bean 2 fresh oregano 3 chocolate granola bar 2 honeysuckle 3 chocolate nut 2 apple pie crust 3 chocolate orange peels 2 corn puff 3 chocolate toffee 2 pomegranate 3 chocolate wafer 2 hazelnut 3 cilantro 2 paprika 3 cinnamon bark 2 fresh gingerbread 3 cinnamon raisin toast 2 pecans 3 cinnamon roll 2 chocolate graham cracker 3 citrus fruits 2 rye crisp 3 citrus soufflÃ© 2 spicy apple sauce 3 citrus vinaigrette 2 pez 3 cloth 2 cider vinaigrette on greens 3 cocoa-dusted nuts 2 cherry preserves 3 corn puff cereal 2 fresh raspberries 3 cornbread 2 sweet spices 3 cornucopia 2 peach vinaigrette on lettuce 3 crabapple 2 acetone 3 crackers 2 orange marmalade on rye 3 cranberries 2 chili pepper 3 cranberry relish 2 menthol mint 3 cranberry toast 2 chocolate mint cookie 3 creamy vanilla tapioca 2 ginger snaps 3 crystallized ginger 2

72 Aroma Freq Flavour Freq vanilla cream 3 cucumber skin 2 banana chip 3 currants 2 nutmeg 3 custard pastry 2 circus peanuts 3 dark toast 2 apple butter on toast 3 dates 2 vanilla bean 3 delicate pepper 2 maple fudge 3 delicate peppery spice 2 chocolate coffee bean 3 delicate sweet 2 pancake 3 dirt 2 golden beets 3 dolmades 2 bran 3 dough 2 fried pickle 3 dreamsicle 2 turnovers 3 dry-yet-fruity medium body 2 savory 3 dryish medium-to-full body 2 strawberry jam 3 du 2 raspberry jam on toast 3 dulce 2 cola float 3 dulce du leche 2 herbal pizza crust 3 dusty earth 2 alfalfa 3 dusty grain 2 cream soda 3 dusty nut 2 oatmeal 3 dusty potato 2 cleanser 3 dusty radish 2 ginger beer 3 dusty root vegetable 2 chai 3 dusty sweet potato 2 candies 3 earthy hops 2 melon balls 3 earthy nut 2 candle wax 3 edible flowers 2 French toast 3 exotic citrus 2 apple turnover 3 exotic peppercorns 2 pine resin 3 exotic spices 2 fig cookie 3 field greens 2 artichoke hearts 3 fig jam 2 silly putty 3 filling 2 cole slaw 3 fleur de sal 2 white blossoms 3 fleur de sel 2 fruit loops 3 floral honey 2 maple syrup 3 flowers 2 whipped cream 3 foamy 2 lime leaves 3 forest 2 sweet potato chip 3 frisÃ©e 2 pies 3 fruit brandy 2

73 Aroma Freq Flavour Freq pollen 3 fruit caramel 2 jasmine tea 3 fruit chutney 2 rum 3 fruit tart 2 mole 3 gherkin 2 straw mat 3 ginger ale 2 chalk 3 glue 2 cardboard 3 gooseberry jam 2 German chocolate 3 grainy bread 2 italian herbs 3 grainy pasty 2 bubble gum 3 grainy pumpernickel 2 mushrooms 3 grapefruit creme brulee 2 fig paste 3 grapefruit marmalade on toast 2 herb garden 3 grapefruit rind 2 green tomato 3 grassy hop flourish 2 charcoal 3 grassy hop snap 2 suede 3 Greek yogurt 2 coffee grounds 3 green coffee bean 2 flour 3 green grape skin 2 loops 3 green pear 2 apple core 3 greens-like hop 2 barn 3 grilled apples 2 bacon bits 3 grilled apple salad 2 horse blanket 3 grilled nut toast 2 mat 3 grilled peach 2 brush 3 grilled pear 2 copper color 3 grilled tropical fruits 2 cole 3 grilled yams 2 seeds 3 gum 2 grounds 3 habanero 2 tobacco 3 hazelnut 2 balls 3 heart 2 woods 3 herb butter 2 grey 3 herbal hop 2 bits 3 hibiscus 2 soil 3 hibiscus tea 2 flower patch 3 hint of apple 2 bars 3 hint of mint 2 bath 3 hint of rubber 2 forest floor 3 hint of spice 2 drop 3 hominy 2 dates 3 honey cream 2

74 Aroma Freq Flavour Freq patch 3 honey yogurt 2 cup 3 hot pickle 2 instant 2 jalapeno 2 tape 2 Jell-O 2 hearts of palm 2 kumquat marmalade 2 palm 2 latex 2 lots 2 leche 2 basket 2 lemon bread 2 bay 2 lemon herb muffin 2 gas 2 lemon pepper muffin 2 band 2 lemon pepper on greens 2 station 2 lemon pulp 2 hair 2 lilac 2 dry-yet-fruity 2 limes 2 mango-orange 2 lit cigar 2 lemon-orange custard 2 lozenge 2 orange-peach marmalade 2 lumber 2 fig-date bread 2 mandarin 2 herb-spice bread 2 mandarin oranges 2 zucchini-fig bread 2 mango sorbet 2 apple-pear pie 2 Manuka 2 Bit-o-Honey candy 2 Manuka honey 2 nibblets 2 meats 2 microgreens 2 medium-length 2 honeycrisp 2 mellow 2 honeyed fruits 2 mesh 2 nectarine soufflÃ© 2 Meyer lemon sorbet 2 buttery praline 2 microgreens 2 sourdough muffin 2 mincemeat 2 raisin compote 2 mincemeat pie 2 peach soufflÃ© 2 mineral water 2 peach marmalades 2 mint tea 2 overripe peaches 2 mixed peppercorn 2 grilled pumpernickel 2 mulch 2 yeasty bread 2 nettle 2 corn nibblets 2 nut skins 2 blini 2 oat 2 starfruit 2 onion 2 pumpernickel rye 2 orange blossom honey 2 oregano focaccia 2 orange marmalade on rye toast 2 lemon peppercorn muffin 2 orange soda 2

75 Aroma Freq Flavour Freq lemon poppyseed muffin 2 palm 2 vanilla soufflÃ© 2 paste 2 herb focaccia 2 peach butter 2 citrus custard tart 2 peach butter on rye 2 peach chutney 2 peach cobbler 2 grilled mangos 2 peach pit 2 apricot chutney 2 peach tart 2 grapefruit peels 2 peach tea 2 pepper focaccia 2 peach yogurt 2 citrus rind 2 pear skin 2 caramel yogurt 2 pepper cheese bread 2 grapefruit yogurt 2 pepper omelet 2 papaya custard 2 pepper snap 2 spicy custard 2 pepper-herb bread 2 sourdough nut bread 2 peppered chocolate 2 honeyed tea 2 peppery hops 2 chocolate custard 2 peppery mole 2 mango gelato 2 peppery radish sprouts 2 peach sorbet 2 peppery radish-like hop 2 herb sourdough bread 2 peppery rapini-like 2 salty baguette 2 persimmon 2 dark caramelized nuts 2 pillowy 2 buttercream frosting 2 pine resin 2 mocha gelato 2 pine sol 2 smores 2 pineapple cake 2 waxy apples 2 pineapple custard 2 chocolate macaroon 2 pineapple marmalade 2 banana flan 2 piney hops 2 gherkin 2 pizza 2 burnt raisin toast 2 Playdoh 2 creme brulee crust 2 plush 2 orange marmalades 2 polenta 2 melon sorbet 2 pomelo 2 lemon rind 2 popcorn 2 aminos 2 prairie 2 apricot yogurt 2 prairie grass 2 chocolate nut muffin 2 pumpernickel toast 2 fruity taffy 2 rainbow 2 dark raisin baguette 2 rainbow sherbet 2 pineapple yogurt 2 raisin nut toast 2 multigrain bread dough 2 raisin rye toast 2

76 Aroma Freq Flavour Freq lemon herb toast 2 raspberries 2 radicchio 2 red apple skin 2 kefir 2 root veggie 2 herb nut toast 2 root veggies 2 pineapple pastry 2 rubber sap 2 fritter 2 rutabaga 2 pepper herb muffin 2 rye bread 2 grilled beets 2 sal 2 nut pastry 2 salsa 2 fig soufflÃ© 2 salty popcorn 2 maraschino 2 satiny 2 asparagus omelet 2 sauerkraut 2 lime custard 2 sel 2 apple vinaigrette 2 seltzer water 2 egg custard 2 sherry 2 peach custard danish 2 soil 2 gingerbread pudding 2 sol 2 berry custard 2 sorbets 2 strawberry muffin 2 sorghum 2 sour dough raisin bread 2 soup 2 horsehair 2 sour green apple 2 blueberry muffin 2 spicy apple tart 2 citrus pith 2 spicy chutney 2 berry compote 2 spicy rye 2 chocolate caramel fudge 2 sprout-like 2 raspberry pastry 2 stalk 2 dark raisin bread 2 star fruit 2 peanut brittle 2 stones 2 Brach 2 strawberries 2 nectarines 2 suede 2 chocolate banana 2 sweet potato bread 2 tangerines 2 sweet potato chips 2 chestnut brittle 2 sweet potato pie 2 baked peaches 2 sweet potato skin 2 jalapenos 2 sweet spices 2 dark rye toast 2 tangy banana 2 fruit yogurt 2 tangy citrus custard tart 2 dark nut toast 2 tangy molasses 2 creamy flan 2 tangy nectarine 2 chocolate fruits 2 tangy sour cherry 2 apple custard pie 2 taro 2

77 Aroma Freq Flavour Freq dark baguette 2 taro root 2 chocolate cherries 2 toasted fruitcake 2 spicy banana cake 2 tobacco ash 2 berry sorbet 2 toffeed 2 citrus peel 2 toffeed nut 2 cranberry nut bread 2 tomatoes 2 pretzel breads 2 touch of pepper 2 herb cracker 2 tropical fruit punch 2 mincemeat pies 2 twig 2 vanilla pudding 2 vanilla milk 2 marzipan 2 vanilla nougat 2 tropical fruit chutney 2 vanilla pudding 2 grapefruit marmalade on toast 2 vegemite 2 lemon herb biscuit 2 veggie 2 caramel cookie 2 violet 2 dark caramel 2 wafers 2 nibs 2 walnut 2 minty herbs 2 watercress-like hop 2 berry yogurt 2 wet limestone 2 vanilla frosting 2 wheat grass 2 maraschino cherry 2 wheat toast 2 milk chocolate toffee 2 whisky 2 habanero pepper 2 white balsamic 2 golden raisin bread 2 white strawberry 2 chocolate nougat bar 2 whole nut 2 blueberry cobbler 2 young coconut 2 buckwheat honey 2 zesty peppery 2 matchstick 2 zucchini bread toast 2 chocolate coconut 2 zucchini-fig bread 2 fresh baked banana bread 2 acacia 1 chicory 2 acacia honey 1 chocolate berries 2 acetone with a supple 1 pith 2 acrid 1 habanero 2 aggressive grassy 1 powdered lemon 2 Alfalfa sprout 1 pickling 2 algae 1 scallions 2 alpine 1 potato bread 2 alpine herbs 1 Hawaiian toast 2 ambrosia 1 cranberry relish 2 Anjou 1 dark chocolate nut 2 apple bread pudding 1

78 Aroma Freq Flavour Freq apple butter on cinnamon raisin tropical fruit tart 2 toast 1 tropical fruit pastry 2 apple butter on lemon scone 1 apple blossoms 2 apple butter on toast 1 herb muffins 2 apple cereal 1 piney herbs 2 apple cider vinaigrette on arugula 1 crushed nuts 2 apple cider vinaigrette on frisÃ©e 1 orange bread 2 apple custard 1 pumpkin pie spices 2 apple gelato 1 taffy wrapper 2 apple nut muffin 1 cacao nibs 2 apple peels 1 pasty 2 apple pie filling 1 cassis 2 apple slices 1 corn husk 2 apple soda 1 orange marmalade on rye toast 2 apple strudel 1 caulk 2 apple vinaigrette on lettuces 1 menthol mint lozenge 2 apple vinaigrette on lettuce 1 fig pastry 2 apple vinegar 1 bons 2 apple water 1 orange sherbet 2 apple wood 1 apple butter on raisin toast 2 apple-banana 1 lime honey 2 apple-banana yogurt 1 quince 2 apple-ginger 1 milk chocolate nuts 2 apple-ginger relish 1 green peppercorn 2 apple-pear salad 1 sweet pineapple 2 apple-pear-nut tart 1 tangerine peel 2 apple-pie 1 cherry fudge 2 apple-plum custard 1 husk 2 apricot chutney 1 prunes 2 apricot compotes 1 chocolate fondue 2 apricot creme brulee 1 pineapple slices 2 apricot jam 1 maple nut fudge 2 apricot marmalade on toast 1 rilliant amber color 2 apricot skin 1 lemon cream 2 apricot soufflÃ© 1 sassafras float 2 apricot-quince jam 1 apple pie dough 2 Arnold 1 fruit preserves 2 Arnold Palmer 1 cornbread 2 Aromas of succotash 1 cacao 2 artichoke heart 1 corn puff cereal 2 artisan chili chocolate bar 1

79 Aroma Freq Flavour Freq mache 2 artisanal mocha 1 dank 2 arugula like hops 1 Starburst 2 arugula slap 1 artisan dark chocolate 2 arugula-like hop 1 plantain chips 2 ash tray 1 herbal hops 2 ashy cheese rind 1 floral hops 2 Asian spice fade 1 bakers chocolate 2 Asian tea 1 raisin toast with apple butter 2 asparagus frittata 1 apple cider vinaigrette on greens 2 aspirin 1 floral spice 2 astringent bittering 1 campfire embers 2 astringent coffee 1 pear sauce 2 astringent herbal pizza crust 1 grape Pez 2 astringent Key lime custard 1 peony 2 astringent nutshell 1 onion bread 2 astringent peppery hop flourish 1 milkshake 2 astringent tangerine juice 1 puree 2 Attractive aromas 1 fruit danish 2 baby corn 1 egg white omelet 2 bags 1 banana cakes 2 bag 1 beeswax candle 2 bagel 1 chocolate peanut butter bar 2 baked beans 1 lentils 2 baker chocolate 1 ginger cookie 2 baklava 1 cherry vinaigrette on greens 2 balance 1 grape soda 2 balance between roasted grain 1 chamomile tea 2 ball 1 dusty grain 2 balsa board 1 fruity hop 2 balsamic glazed beets 1 balsamic vinaigrette on salty apple turnovers 2 artichoke hearts 1 bubblegum dust 2 bamboo shoots 1 exotic tropical fruits 2 bamboo spouts 1 tropical fruit salad 2 banana bread crust 1 mane 2 banana chips 1 burrito 2 banana cookie 1 creamy milk chocolate 2 banana creme brulee 1 berry danish 2 banana creme fraiche 1 charcoal embers 2 banana custard pie 1 mixed peppercorns 2 banana custard tart 1

80 Aroma Freq Flavour Freq clover honey 2 banana flan 1 kelp 2 banana orange soufflÃ© 1 soy bean 2 banana peel 1 Bright spicy 2 banana pie 1 chocolate peanut butter cup 2 banana rye cracker 1 grape jam 2 banana scone 1 almond cookie 2 banana yogurt 1 Meyer lemon 2 Band-Aid plastic 1 seaweed 2 Band-Aid tin 1 fondue 2 barky earth 1 warm tapioca 2 barky hops 1 cream puff 2 barn 1 brazil nut 2 barn wood 1 herb salt 2 barnyard 1 cranberries 2 basil 1 oat 2 bath 1 horsehair brush 2 bbq rub 1 pepper dust 2 beeswax 1 apple pie filling 2 beet juice 1 bagel 2 beet root 1 Greek yogurt 2 bell 1 chiffon 2 berry candy 1 sorghum 2 berry gastrique 1 supple 2 berry lipstick 1 beet 2 berry phosphate 1 German chocolate cake 2 berry skins 1 burnt rubber 2 berry vinaigrette on lettuce 1 golden raisins 2 berry-lime vinaigrette 1 cling 2 bib 1 pellet 2 bib lettuce 1 milled soap 2 bibb lettuce with Anjou pear 1 wheat wafer 2 birch beer 1 pickle juice 2 biscuits 1 gelatin 2 bit of tree bark 1 pear skin 2 bitter char-roasted nut 1 flakes 2 bitter espresso 1 fig jam 2 bitter grapefruit pith 1 lemonade 2 bitter green coffee bean 1 campfire 2 bitter grilled nectarine 1 lime powder 2 bitter orange 1 bakers 2 bitter papaya-banana custard 1

81 Aroma Freq Flavour Freq whole nuts 2 bitter peppery hop 1 sweet potato chips 2 bitter Pine Sol 1 pencil eraser 2 bitter turnip greens 1 Indian spice cake 2 bittersweet chocolate 1 floral soap 2 black cherries 1 waffle 2 black cherry 1 grain sack 2 black currants 1 white pepper 2 black forest cake 1 hard cheese rind 2 black pepper 1 chai tea 2 black strap molasses 1 fruit leather 2 black walnut pie 1 veggie 2 blackberries 1 green herbs 2 blackberry sorbet 1 fresh coffee 2 blackstrap 1 flax 2 blackstrap molasses 1 nutshell 2 blood orange chiffon 1 fruit punch 2 blood orange mimosa 1 root beer candy 2 blood orange zest 1 ground nuts 2 blue cheese puff 1 lit candle 2 blueberry fruit salad 1 lemon drop 2 blueberry pancakes 1 pumpkin seeds 2 bold habanero hot sauce 1 ginger snap 2 bold pineapple-mango tart 1 barn hay 2 bons 1 mossy earth 2 bonbon 1 husky 2 boozy alcohol 1 cough syrup 2 boozy wintergreen-apple 1 ramps 2 bouillon 1 jams 2 bourbon 1 batter 2 bourbon barrel 1 tree fruit 2 brandied figs 1 hop pellet 2 bread crumbs 1 olden amber color 2 bread dough 1 lilac 2 breezy pinecone 1 stuffing 2 breezy wafer 1 crispy chicken skin 2 Bright black licorice candy 1 Asian spices 2 bright citrus soufflÃ© 1 biscuits 2 brioche 1 wicker 2 brioche with lemon curd 1 spun 2 brown paper bag 1 prairie grass 2 Brussels 1

82 Aroma Freq Flavour Freq aroma 2 Brussels sprouts 1 pine forest 2 buds 1 pits 2 Buddha 1 lipstick 2 buffalo 1 tree bark 2 buffalo jerky with a supple 1 funky cheese 2 bun 1 roast beef 2 burnt bread crust 1 deep fried pickle 2 burnt chocolate chip walnut cookies 1 brandy 2 burnt cigar tobacco 1 hint of matchstick 2 burnt corn 1 black pepper 2 burnt marshmallow 1 rag 2 burnt nuts 1 whiff of acetone 2 burnt plastic 1 clover 2 burnt raisin toast 1 bbq woods 2 butterscotch pudding 1 sack 2 buttery apple turnover 1 green apple skin 2 buttery fruit cake 1 brewer 2 buttery praline 1 hint of lavender 2 cabbagy 1 horse mane 2 cabbagy cole slaw 1 hot pepper 2 cakes 1 pot roast 2 campfire 1 popcorn 2 campfire logs 1 sausage 2 cantaloupe 1 breakfast burrito 2 caramels 1 attic 2 caramel custard pie 1 paper mache 2 caramel fudge 1 bee pollen 2 caramel green apple 1 cough 2 caramel nougat bar 1 curry leaves 2 caramel nut apple 1 yellow squash 2 caramel nut fudge 1 white grapes 2 caramel on sourdough toast points 1 curry 2 caramel taffy 1 salsa 2 caramel yogurt 1 water chestnut 2 carob rice bar 1 saddle 2 carrot juice 1 poultry 2 carrot soufflÃ© 1 Meyer 2 cashews 1 Dense 2 cashew butter on burnt toast 1 wax paper 2 cassis 1 hint of rubber 2 catsup on a bun 1

83 Aroma Freq Flavour Freq peas 2 catsup on a napkin 1 barrel 2 cauliflower puree 1 bon 2 cayenne on mango 1 punch 2 celery juice 1 cigarette 2 celery root 1 earl 2 celery stalk 1 Robust 2 chai 1 snap 2 chai tea 1 minerals 2 chalk eraser 1 lit match 2 chalky earth 1 bee 2 charcoal 1 ripe 2 chardonnay 1 tire 2 chardonnay grapes 1 earl grey 2 cheery 1 nose 2 cheese notes 1 rubber band 2 cherry balsamic on greens 1 roses 2 cherry barrel 1 brewer's yeast 2 cherry bonbon 1 polish 2 cherry bubblegum 1 pipe 2 cherry butter 1 shoe 2 cherry cider vinaigrette on mesclun 1 gas station coffee 2 cherry cola float 1 cotton 2 cherry cream 1 horse hair 2 cherry honey lozenge 1 branches 1 cherry popsicle 1 lawn 1 cherry pulp 1 cherry shortcake with whipped tin 1 cream 1 canvas 1 cherry skin 1 ruby 1 cherry soufflÃ© 1 mineral 1 cherry syrup 1 black forest 1 cherry turnover 1 cherry vinaigrette on a grilled apple tangerines on the tree 1 salad 1 stack 1 cherry vinaigrette on greens 1 bay leaf 1 cherry-berry pastry 1 pears in a wicker basket 1 chestnut honey on greens 1 plastic tape 1 chewy bakers chocolate 1 old school cannabis 1 chewy bread pudding 1 sunset 1 chewy pear 1 raw cabbage in dirt 1 chewy pine resin 1 roots 1 chewy plum 1

84 Aroma Freq Flavour Freq stones 1 chewy toffee 1 bath oil 1 chia 1 silk 1 chiffon 1 box of instant potatoes 1 chili pepper hint 1 vitamin 1 chili pepper on jicama 1 passion 1 chili pepper seed 1 berry danish in a box 1 chive butter on rye toast 1 Thai 1 chocolate berries 1 brass 1 chocolate biscuit 1 touch of latex 1 chocolate buttercream 1 fish oil 1 chocolate caramel apple 1 butt 1 chocolate caramel corn 1 emphasis 1 chocolate cereal 1 bit of clay 1 chocolate cherry bon bons 1 chicken stock 1 chocolate cherry bon bon 1 Joy 1 chocolate chestnuts 1 smoke 1 chocolate citrus peel 1 leg 1 chocolate cream pie 1 cookies 1 chocolate fig muffin 1 buffalo 1 chocolate grapefruit peel 1 newspapers 1 chocolate lemon peels 1 hip vitamin 1 chocolate mint toffee 1 treat 1 chocolate muffin 1 split 1 chocolate nougat 1 gold color 1 chocolate on carrots 1 shell 1 chocolate peanut butter bar 1 flower bed 1 chocolate phosphate 1 hint of new shoe 1 chocolate raisin 1 dish 1 chocolate scone 1 potter 1 chocolate soufflÃ© 1 melon in a bag 1 chocolate syrup 1 mesh 1 chocolate tangerine peels 1 car tire 1 chowder 1 Compact 1 cider vinaigrette on greens 1 rain 1 cider vinegar with a creamy 1 raisins in a box 1 cigar wrapper 1 hip 1 cinnamon apple jack cereal 1 toy 1 cinnamon apple pastry 1 newspaper 1 cinnamon candy 1 cinnamon raisin toast with apple American hop pellets with the emphasis 1 butter 1

85 Aroma Freq Flavour Freq hot dogs 1 citrus compote 1 dogs 1 citrus cream 1 bush 1 citrus curd 1 bed 1 citrus ice milk 1 cut 1 citrus Jell-O 1 Classic 1 citrus marmalade on biscuit 1 wine 1 citrus marmalade on dark rye 1 medium 1 citrus marmalade on scone 1 fee 1 citrus peel on scone 1 fish food 1 citrus rice candy 1 jack 1 citrus rind 1 diet 1 citrus vinaigrette on greens 1 Buddha's hand 1 citrus yogurt 1 village 1 citrus zest 1 chair 1 citrusy herbs 1 bag 1 citrusy hops 1 rain on the prairie 1 citrusy hop 1 haystack in a field 1 cleanser 1 cork board 1 clipping 1 dream 1 clove 1 iron 1 cob 1 flat sprite in a plastic cup 1 cocoa latte 1 finish 1 cocoa mousse 1 hot iron 1 cocoa nuts 1 drink 1 cocoa puff 1 tip 1 cocoa-dusted grapefruit 1 andoffee 1 coconut cream pie 1 Band-Aid 1 coconut gelato 1 cherry-rhubarb 1 coconut husk 1 Grain-driven 1 coconut macaroon 1 Jell-O 1 coconut mango custard 1 nutskins 1 coconut milk 1 andanana 1 coconut water 1 apple-carrot 1 coconut yogurt 1 banana-orange 1 coffee cakes 1 banana-plum 1 coffee ice cream 1 banana-raisin 1 coffee substitute 1 citrus-peach 1 coffee yogurt 1 fig-banana 1 cole 1 fig-grape 1 collard 1 peach-banana 1 collard greens 1

86 Aroma Freq Flavour Freq peach-raisin 1 combination 1 pepper-scallion-oregano 1 combination of resinous hops 1 pineapple-grapefruit 1 Comet 1 pineapple-orange 1 Comet cleanser 1 sandwich-like 1 complex cornucopia of spicy 1 span-like 1 complex interplay of savory herbs 1 spic n span-like 1 complex tangy 1 apple-pear soufflÃ© 1 complex zesty spice 1 banana-pear soufflÃ© 1 compost 1 orange-pear soufflÃ© 1 compotes 1 lemon-orange marmalade 1 concentrate 1 herb-pepper focaccia 1 confectionary 1 pepper-herb focaccia 1 cookies 1 peach-mango chutney 1 corn chowder 1 pineapple-mango chutney 1 corn cob 1 pineapple-mango buttercream 1 corn in wax paper 1 mango-orange custard 1 corn muffin 1 pineapple-banana custard 1 corn pops cereal 1 apple-lemon vinaigrette 1 corn salsa 1 apricot-pineapple compote 1 corn tortilla 1 mango-pineapple compote 1 cornichon 1 honey-buttered scone 1 cotton 1 pepper-oregano focaccia 1 cotton ball 1 dry-yet-fruity medium-to-full body 1 cream white toffee 1 herb-pepper baguette 1 creamy apricot flan 1 onion-quince marmalade 1 creamy banana custard 1 grapefruit-apricot custard 1 creamy carbonation 1 grapefruit-mango custard 1 creamy chocolate cherry mousse 1 pineapple-grapefruit custard 1 creamy chocolate mousse 1 pineapple-orange custard 1 creamy citrus custard 1 pineapple-banana sorbet 1 creamy cupcake 1 peach-mango yogurt 1 creamy latte 1 andanana muffin 1 creamy mocha 1 banana-raisin muffin 1 creamy sour Greek lemon yogurt 1 blueberry-orange muffin 1 creamy vanilla nougat 1 cranberry-orange muffin 1 creme brulee crust 1 char-grilled apples 1 creme caramel 1 apple-mango chutney 1 crisp apple 1 cherry-raisin chutney 1 crisp citrusy 1 lemon-garlic chutney 1 crisp pear 1 lemon-tangerine chutney 1 crisp pig skin 1

87 Aroma Freq Flavour Freq lemon-thyme chutney 1 croissant 1 mango-mint chutney 1 crumbs 1 peach-raisin chutney 1 crushed green apple 1 tomato-pepper chutney 1 crushed nuts 1 chocolate-coconut macaroon 1 crushed pineapple 1 char-roasted chestnuts 1 crusty apple pie 1 apple-peach yogurt 1 crusty bread 1 apple-lemon tart 1 cucumber peel 1 pineapple-peach tart 1 cucumber salad 1 lemon-peach pastry 1 cucumber sandwich 1 pineapple-peach pastry 1 cupcake 1 peach-mango bread 1 currant 1 charr-grilled grapefruit 1 dainty 1 lemon-mango gelato 1 dainty starfruit 1 sesame-nut brittle 1 damp cloth 1 char-grilled corn 1 damp wood 1 pineapple-mango relish 1 dandelions 1 pineapple-apple yogurt 1 dank pine cone with a supple 1 apple-berry tart 1 dark berries 1 apple-pumpkin tart 1 dark bread 1 peach-apple tart 1 dark cacao nibs 1 apple-banana pastry 1 dark caramel 1 peach-banana pastry 1 dark chocolate cocoa nibs 1 mango-orange relish 1 dark chocolate creme brulee 1 mango-pineapple relish 1 dark chocolate ginger snap 1 peach-pineapple cobbler 1 dark chocolate mousse 1 banana-orange pudding 1 dark herb bread 1 chive-garlic-herb bread 1 dark honeyed nuts 1 garlic-chive bread 1 dark honeyed raisin toast 1 herb-mint bread 1 dark orange bread toast 1 orange-pepper bread 1 dark rye raisin toast 1 peach-date bread 1 date-cherry bread 1 pepper-scallion-oregano bread 1 day old sourdough 1 zucchini-herb bread 1 dead yeast 1 zucchini-raspberry bread 1 decadent chocolate liqueur 1 char-roasted walnuts 1 delicate baking spices 1 lemon-pear granita 1 delicate baking spice 1 mango-guava relish 1 delicate citrus 1 mango-papaya-mint relish 1 delicate clementine custard 1 mango-peach relish 1 delicate greens 1 papaya-mango relish 1 delicate green bark 1

88 Aroma Freq Flavour Freq Dr. Pepper 1 delicate herbs 1 cruciform-like hops 1 delicate hint of spiced fruits 1 nori-like hops 1 delicate honeyed toast 1 pineapple-peach-mango cake 1 delicate lemon pepper 1 vanilla-maple fudge 1 delicate soufflÃ© 1 char-grilled vegetable 1 delicate spice cookie 1 apricot-pineapple jam 1 delicate spice notes 1 egg sandwich-like 1 delicate spices nuts 1 cherry-date jam 1 delicate spice-sage bread 1 fig-grape jam 1 delicate spicy nougat 1 lemon-lavender soap 1 delicate sweet spices 1 pineapple-peach-pepper salsa 1 Denver 1 yeasty pepper-herb bread 1 Denver omelet 1 ark-brown color 1 dew 1 apple-peach custard strudel 1 diet ginger ale 1 apricot-mango creme brulee 1 diet melon soda 1 orange-mango creme brulee 1 diet red cream soda 1 Toasty citrus-peach pastry 1 dimensional tropical citrus 1 lemon-orange custard pie 1 Dirty root veggie 1 apple-orange nut muffin 1 drinking 1 char-grilled tropical citrus 1 drinking vinegar 1 spicy orange-date bread 1 dry toast 1 spicy apple-pear pie 1 dusty carrot 1 char-grilled tropical fruits 1 dusty carrots 1 spicy apple-carrot relish 1 dusty greens 1 Spic n Span 1 dusty kale 1 fig-banana dark bread 1 dusty mineral 1 leather-bound book 1 dusty rutabaga 1 strawberry-blueberry cream pie 1 dusty turnip 1 powdery raspberry-almond cookie 1 dusty wicker 1 Bit-o-Honey candy wrapper 1 Earl 1 Vibrant hop-driven aromas grapefruit 1 Earl Grey tea sorbet 1 sage-oregano turkey stuffing 1 earthy grains 1 peach-orange marmalade on toast 1 earthy green notes 1 spicy apricot-orange marmalade on honeyed raisin toast 1 earthy hop snap 1 portabellos 1 earthy lettuce greens 1 tartin 1 earthy mineral hop 1 shittake 1 earthy radish green-like hop snap 1 craisin 1 earthy vegetable 1 aroma of thyme-chive focaccia 1 eggs 1

89 Aroma Freq Flavour Freq profiterole 1 egg croissant 1 shmaltz 1 egg soufflÃ© 1 Cherrios 1 elegant interplay of chamomile tea 1 banana-plum compote on pound cake 1 eraser 1 grapefruit-apricot-coconut chutney on pound cake 1 espresso creme brulee 1 gastrique 1 espresso ice milk 1 Sulfury eggy aromas 1 eucalyptus 1 dry-yet-fruity medium body 1 exotic Manuka honey 1 cajeta 1 fabric 1 Sourish citrus 1 fabric of herbs 1 mango gelatos 1 fade 1 peach gelatos 1 faint spice cake 1 citrus soufflÃ© 1 fall 1 overripe mango 1 fall fruit 1 cornichon 1 fancy lemon-mint bath soap 1 tart tartin 1 fig cookie 1 cashew nougat 1 fig Newton 1 buttery toffee 1 fig paste 1 raisin chutney 1 fig pastry 1 overripe banana 1 fine 1 apricot soufflÃ© 1 flat sassafras 1 Biscuity 1 flatbread 1 overripe lemon 1 flavor 1 overripe apples 1 flavors of chocolate citrus peels 1 overripe melon 1 fleur de sal driven 1 praline aromas 1 floor 1 pineapple soufflÃ© 1 floral hop 1 grapefruit scone 1 floral pear 1 eggy custard 1 floral spices 1 melon soufflÃ© 1 flour sack 1 niblets 1 floury 1 lemon shmaltz scone 1 floury nut bread 1 nopales 1 flower patch 1 lemon marmalades 1 foam 1 overripe cherries 1 foamy rapini 1 lingonberries 1 forest floor 1 buttery baguette 1 fraiche 1 Hallertau 1 fresh apple cider 1 macadamia gelatos 1 fresh apple juice 1 radicchio frittata 1 fresh cashew butter 1

90 Aroma Freq Flavour Freq spicy soufflÃ© 1 fresh fruit salad 1 buttery caramel muffin 1 fresh herbs 1 lychee custard 1 fresh herb baguette 1 lemongrass buttercream 1 fresh marjoram 1 grapefruit compote 1 fresh raspberry 1 buttery brioche 1 fresh tropical fruit punch 1 peach scone 1 frisee 1 cashew toffee 1 frisee-like hop 1 pumpernickel rye toast 1 frothy creamy nougat 1 caramel buttercream 1 frothy pine 1 chocolate craisin 1 frothy yellow apple 1 peach gastrique 1 fruit bread 1 grapefruit buttercream 1 fruit compote on toast 1 cocoa soufflÃ© 1 fruit custard tart 1 epazote 1 fruit gum 1 citrus vinaigrette 1 fruit loops 1 pear soufflÃ© 1 fruit punch soda 1 overripe pears citrus 1 fruit sauce 1 Honeycrisp apples 1 fruit wax 1 toasty custard pastry 1 fruit with a crisp 1 sourdough herb baguette 1 fruity bubblegum 1 mocha toffee 1 fruity caramel flan 1 lemon sourdough toast 1 fruity granola 1 sourdough nut toast 1 fruity medium body 1 Toasty grapefruit custard pastry 1 fruity spice hop 1 grapefruit creme brulee 1 fries 1 peppercorn herb muffin 1 garlic greens 1 delicate soufflÃ© 1 gastrique 1 coriander muffin 1 gauze 1 sour overripe grapefruit 1 gelatos 1 kumquats chutney 1 gentle water chestnut 1 herb frittata 1 ghost 1 tangerine soufflÃ© 1 ghost pepper hot sauce 1 overripe fruit 1 ginger soda 1 mocha custard 1 ginger vinaigrette on greens 1 soufflÃ©s 1 gingersnaps 1 overripe oranges 1 golden raisin 1 vinaigrettes 1 good carbonation 1 rye raisin toast 1 gooseberries 1 vinegary 1 gooseberry jam on spicy rye toast 1 spicy peach soufflÃ© 1 graceful roasted root vegetable 1

91 Aroma Freq Flavour Freq yeasty fruit custard pastry 1 grainy custard pastry 1 chocolate raisin muffin 1 grainy dough 1 caramel herb scone 1 grand marnier fruit cake 1 citrus creme brulee 1 grape popsicle 1 nut toffee 1 grape-coconut 1 grapefruit chutney on graham danishes 1 cracker 1 oregano muffin 1 grapefruit curd on toast 1 grapefruit flan 1 grapefruit marmalades 1 spicy poppyseed muffin 1 grapefruit pepper 1 butter sourdough baguette 1 grapefruit pulp 1 pistachio nougat 1 grapefruit seed 1 butter praline 1 grapefruit-orange 1 pineapple vinaigrette 1 grapefruit-orange custard 1 peppercorn bread 1 grasses 1 grapefruit rind 1 grass clipping 1 yeasty raisin bread dough 1 grass earthy 1 peach buttercream 1 grassy driven finish 1 vanilla nougat 1 grassy earthy hops 1 Oloroso 1 grassy herb 1 mango creme brulee 1 grassy hop kick 1 toast aromas 1 gravel road 1 peach creme brulee 1 great interplay of grain 1 mango custard tart 1 green apple skins 1 pepper frittata 1 green apple vinaigrette on melon 1 mango custard pastry 1 green apricot 1 skunky 1 green bamboo 1 apricot creme brulee 1 green herbs 1 pineapple chutney 1 green hop 1 toasty grapefruit pastry 1 green peach 1 grilled grapefruits 1 green peppercorn 1 sourdough peach bread 1 green peppercorns 1 pineapple creme brulee 1 green tea 1 pistachio praline 1 green tomato 1 banana creme brulee 1 green tomatoes 1 floury 1 green-apple-kiwi relish 1 spicy toffee 1 Grey 1 consommÃ© 1 grilled apricot 1 artichoke frittata 1 grilled baguette 1 lemon chutney 1 grilled citrus peels 1 crispbread 1 grilled corn 1

92 Aroma Freq Flavour Freq herb vinaigrette 1 grilled grapefruit peels 1 banana baguette 1 grilled grapefruit peel 1 mesclun greens 1 grilled kumquats 1 banana sourdough bread 1 grilled lemon 1 raspberries vinaigrette 1 grilled mango 1 sourdough bread pudding 1 grilled pumpernickel bread 1 schmaltz bread 1 grilled vegetables 1 Buttery peach pastry 1 habanero pepper 1 raspberry scone 1 ham 1 caramel pastry 1 Hawaiian bread 1 grapefruit curd 1 hazelnut mocha latte 1 lemon sourdough bread 1 hearts of palm 1 lemon nut scone 1 heart of palm salad 1 compotes 1 heat 1 Saaz 1 heather 1 toast banana nut muffin 1 hemp 1 chive bread 1 hemp seeds 1 mango sorbet 1 herb butter on rye toast 1 peach danishes 1 herbs de Provence 1 overripe orange 1 herb flatbread 1 yeasty lemon cake 1 herb focaccia 1 anisette 1 herb pepper bread 1 coriander herb muffin 1 herb pepper spice mix 1 melon rinds 1 herb pretzel bread 1 yeasty bread dough 1 herb salt 1 herbs with a frothy dryish medium- lychees 1 to-full body 1 saltines 1 herb-mint muffin 1 zucchini muffin 1 herb-pepper bread snap 1 citrus bread toast 1 herb-pepper muffin 1 mango papaya custard 1 herbal anchovy pizza 1 papaya chutney 1 herbal mint 1 toasty peach cobbler 1 hint of apple cider vinegar 1 toasty bread 1 hint of Asian spices 1 creamy nougat 1 hint of bark 1 yeasty apple pastry 1 hint of bubblegum dust 1 blackstrap molasses 1 hint of buffalo jerky 1 kumquats 1 hint of candle wax 1 banana soufflÃ©s 1 hint of cedar 1 banana lemon muffin 1 hint of cigar ash 1 focaccia pepper bread 1 hint of dank pine cone 1

93 Aroma Freq Flavour Freq pepper bread focaccia 1 hint of fancy french milled soap 1 orange peppercorn vinaigrette 1 hint of floral spice 1 orange soufflÃ© 1 hint of grass 1 pear creme brulee 1 hint of lemon water 1 butterscotch taffy aromas 1 hint of mint leaf 1 buttery caramel corn 1 hint of new shoe 1 peach bread toast 1 hint of peach brandy 1 apricot compotes 1 hint of peat 1 raspberry vinaigrette 1 hint of peppered greens 1 banana herb muffin 1 hint of sassafras 1 pepper omelet 1 hint of sherry 1 orange raisin muffin 1 hint of soy 1 yuzu gelato 1 hint of stewed veggies 1 fudges 1 hint of tea 1 lemon gelato 1 hint of vanilla bean 1 raisin bran muffin 1 hint of wafer 1 nut brittle aromas 1 hint of water chestnut 1 coriander-turmeric spice rub on poultry meat 1 hint of wax 1 lemon sough dough scone 1 hips 1 dark caramelized apples 1 honey on poppy seed cake 1 grilled mango 1 honey on pumpernickel 1 banana bread toast 1 honey soufflÃ© 1 honeycrisp apple flavor with a kiwi mango tart 1 accent 1 toasty caramel cake 1 honeydew melon 1 apple caramel custard tart 1 honeyed date 1 yeasty spice cake 1 honeysuckle 1 rye crispbread 1 horchata 1 buttery honey 1 hot cocoa 1 nut bread toast 1 husky grainy grass 1 toasty nut bread 1 incense 1 tangerine vinaigrette 1 interplay of sarsaparilla 1 mocha nuts 1 jack 1 cheese soufflÃ© 1 jalapeÃ±o 1 fig pumpernickel toast 1 jams 1 taffy apples 1 jasmine 1 mocha yogurt 1 jasmine tea 1 buttery raspberry pastry 1 jazzy 1 saltine cracker 1 jicama with cayenne 1 mulberries 1 juicy fruit cake 1 krispie 1 juicy marriage of fruity malt 1

94 Aroma Freq Flavour Freq waxy chocolate 1 juicy nectarine 1 kumquat pastry 1 juniper 1 waxy pears 1 juniper candy 1 corn niblets 1 kale like hop 1 dark sourdough rye baguette 1 ketchup 1 mocha pastry 1 ketchup on pumpernickel 1 schmaltz cracker 1 khaki 1 scallion omelet 1 khaki head 1 dark caramelized fruits 1 kick 1 chestnut toffee 1 kids cereal 1 citrus pudding 1 kiss 1 gruyere omelet 1 kiss of earthy hops 1 popover 1 kiwis 1 blackstrap 1 kiwi custard 1 coconut vinaigrette 1 kiwi fruit tart 1 spicy flan 1 kiwi marmalade 1 fresh sourdough baguette 1 kiwi soufflÃ© 1 banana yogurt 1 kiwi-apple vinaigrette on arugula 1 peach custard pie 1 kiwi-coconut custard 1 apple scone 1 kumquat chutney 1 zucchini bread toast 1 kumquat juice 1 lemon meringue tart 1 kumquat peel 1 sourdough cranberry bread 1 lapsang tea 1 Fruity aromas 1 latex with a chewy 1 grapefruit relish 1 lattÃ© 1 pineapple mango 1 latte gelato 1 grainy toast 1 lavender hibiscus tea 1 buckwheat blini 1 lavender thyme bread 1 chocolate gelato 1 layers 1 caramel mocha latte 1 leafy green hops 1 jalapeÃ±o corn fritter 1 leafy greens like hops 1 butter toast 1 leafy greens like hop 1 apple creme brulee 1 leafy mustard greens 1 carob granola 1 lemon basil 1 grilled lemon 1 lemon candy 1 herb pretzel bread 1 lemon cream tart 1 lemon pastry 1 lemon custard pie 1 coconut macaroon 1 lemon custard tart 1 grapefruit zest 1 lemon grapefruit granita 1 lemon pepper herb muffin 1 lemon grass pastry 1 salty nut brittle 1 lemon Italian ice 1

95 Aroma Freq Flavour Freq Rich honeyed pastry 1 lemon mango pastry 1 buttery fruit pastry 1 lemon marmalade on scone 1 Banana custard pie 1 lemon marmalade on toast 1 peach pudding 1 lemon muffin 1 berry scone 1 lemon pepper bread 1 limeade 1 lemon pepper like hops 1 apple sourdough bread 1 lemon pepper snap 1 kiwi marmalade 1 lemon peppercorn muffin 1 eggy fruit pastry 1 lemon poppy seed muffin 1 sour cherry soufflÃ© 1 lemon raisin scone 1 peppered kale 1 lemon rind 1 zucchini herb bread toast 1 lemon seltzer 1 citrus fruits 1 lemon spritz 1 cherry custard 1 lemon tart 1 pomegranate chutney 1 lemon thyme 1 caramel pear 1 lemon verbena 1 toasty fruit pastry 1 lemon vinaigrette on arugula 1 Resinous hoppy 1 lemon-banana nut muffin 1 pear sorbet 1 lemon-basil vinaigrette 1 herbal marinated shittake 1 lemon-fig tart 1 orange creme brulee 1 lemon-herb lozenge 1 apple marmalade 1 lemon-lime custard 1 spicy vanilla custard 1 lemon-pineapple tart 1 raspberry sorbet 1 lemony snap 1 orange custard pastry 1 lentil-barley soup 1 citrus relish 1 light baking spices 1 hazelnut buttercream 1 light citrus 1 grilled kale 1 light jicama 1 tangelo 1 light molasses 1 grilled jalapenos 1 light pepper 1 corn pepper muffin 1 light pine 1 caramel fruit tart 1 lilacs 1 macadamia gelato 1 lime juice concentrate 1 herbs toast 1 lime mineral water 1 grape custard 1 lime popsicle 1 sweet grapefruit marmalade 1 lime rind 1 matzoh 1 lime sherbet 1 spicy gherkin 1 lime vinaigrette on mesclun 1 peach yogurts 1 limeade 1 papaya yogurt 1 linen 1 fruity caramels 1 lipstick 1

96 Aroma Freq Flavour Freq clementine rind 1 liqueur 1 chocolate bonbon 1 logs 1 citrus zest 1 long chocolate nut 1 teriyaki nuts 1 long cornucopia of fruits 1 overripe tropical fruit 1 long fruity 1 long medley of honeyed honeyed buttery peach honey pasty 1 orange 1 mocha mousse 1 long refreshing 1 grilled pears 1 long salty radicchio 1 spicy mincemeat pie 1 long spicy 1 kiwi chutney 1 loops 1 yeasty orange cake 1 lush apricot custard 1 lemon poppyseed muffins 1 lychee tart 1 doughy apple custard pie 1 macadamia 1 delicate peppery spice 1 malt vinegar 1 cinnamon toast 1 mango chutney on nut toast 1 doughy apple tart 1 mango custard on the long chewy 1 yuzu 1 mango-citrus chutney 1 orange peach compote 1 mango-orange sorbet 1 caramel nut fudge 1 maple 1 toasty apple pastry 1 maple fudge 1 lemon bread 1 marjoram 1 Sour vinegary 1 marnier 1 pistachio gelato 1 marriage 1 apricot marmalades on raisin toast 1 marshmallow 1 lemon apple muffin 1 marzipan cookie 1 mango relish 1 mat 1 chive dumpling 1 matcha 1 mossy pinecone 1 meadow 1 nutty toffee 1 meadow dew 1 honey yogurt 1 meat 1 waxy fruit 1 medicinal bark 1 cocoa nuts 1 medicinal herb 1 crusty rye bread 1 medley of grilled tropical fruits 1 fresh praline 1 mellow clementine 1 lemon herb bread 1 melons 1 peach cider 1 melon custard 1 bubblegum spices 1 melon in cream 1 egg muffin 1 melon taffy 1 unripe mango 1 menthol 1 tropical fruit marmalades 1 menthol mint 1

97 Aroma Freq Flavour Freq cranberry toast 1 mesclun-greens 1 vanilla nut brittle 1 mesquite 1 burnt toast 1 metallic twang 1 dark rye raisin bread 1 Meyer lemon curd 1 cherry cider vinaigrette 1 Meyer lemon granita 1 pear yogurt 1 Meyer lemon like citrus twang 1 spicy banana bread 1 Meyer lemon marmalade 1 oregano honey 1 Meyer lemon relish on melon 1 lemon blossoms 1 mild earth 1 spice aromas 1 mild pine 1 fruity custard danish 1 milk chocolate shake 1 raspberry yogurt 1 mimosa 1 berry chutney 1 mineral notes 1 pear tart 1 minerals on the refreshing 1 chocolate caramels 1 minerality 1 gouda omelet 1 mint soufflÃ© 1 zucchini nut bread 1 minty 1 coffee creme brulee 1 minty herbs 1 Biscuity malt 1 mix 1 strawberry cider vinaigrette 1 mixed lettuces 1 sweet peach vinaigrette 1 mixed nuts 1 orange muffin 1 mocha creme brulee 1 granita 1 mocha custard 1 lemon pepper bread 1 mocha gelato 1 fresh sourdough bread 1 mocha ice milk 1 Chanterelle 1 mocha phosphate 1 Spicy gingerbread 1 mocha pudding 1 chocolate gingerbread 1 mocha yogurt 1 chocolate bread 1 mode 1 salad lemon vinaigrette 1 molasses cake 1 yogurts 1 molasses on rye 1 apricot marmalades on scone 1 molasses spice cake 1 pomegranate molasses 1 mole sauce 1 mango chutney on raisin rye toast 1 Montmorency 1 orange buttercream 1 Montmorency cherry 1 chevre 1 morbier cheese 1 ciabatta 1 mossy earthy 1 banana bread crust 1 mossy stones 1 vanillin 1 mouth-drying 1 grapefruit marmalades on rye toast 1 multigrain toast 1 golden raisin baguette 1 mushrooms 1

98 Aroma Freq Flavour Freq pepper herb bread 1 mushroom 1 grilled tangerine 1 mushroom tart 1 corn bread pudding 1 mustard green 1 rum raisin bread pudding 1 mustard greens with vinegar 1 spicy zucchini bread 1 mustard greens-like hop 1 caramel brownie 1 musty cloth 1 chocolate fruit peels 1 napkin 1 toasty vanilla cake 1 nectarines 1 fruit nut pastry 1 nectarine pit 1 caramel pastries 1 nectarine skins 1 pineapple custard tarts 1 nesquick 1 cassis tart 1 nettle-like bittering hop 1 kiwi sorbet 1 new shoe with a supple 1 sour cherry vinaigrette 1 Newton 1 dark chocolate nut muffin 1 nice bitter wave 1 cherry sorbet 1 nice grainy pumpernickel 1 rhubarb yogurt 1 nice interplay of toasty grain 1 banana nut cake 1 nice layers 1 macaroons 1 nice peppery 1 stalky vegetables 1 nice savory 1 dark chocolate citrus peels 1 nice toasty grain 1 schmaltz doughnut 1 nice wave 1 coconut yogurt 1 nopales 1 rhubarb tart 1 nori 1 chive pancake 1 nuances 1 sweet toffee 1 nut brittle aromas 1 mber 1 nut butter 1 sour cherry compote 1 nuts pepper 1 apple vinaigrettes 1 nut shells 1 Bright fruity custard aromas 1 Nutella 1 raspberry lozenge 1 nutmeg pod 1 grapefruit marmalade on scone 1 nutshell-like oak 1 toasty pie crust 1 nutty 1 ham omelet 1 oak chips 1 bonbons 1 oak spice 1 grapefruit marmalade on soda raisin bread toast 1 oak stave 1 fondant bonbon 1 oat bread 1 vanilla caramel cake 1 oat granola 1 grilled berries 1 oatmeal 1 lemon balsamic 1 Odd vinegar 1

99 Aroma Freq Flavour Freq veggies fruits 1 old carob bar 1 spicy sour cherry chutney 1 old celery 1 grilled asparagus 1 old coffee 1 cashew fruit 1 old fruit 1 papayas 1 old gum 1 coconut aminos 1 old okra 1 pear marmalade on sourdough toast 1 onion jam 1 rutabaga 1 onion sprout 1 lime sorbet 1 onion-pepper tart 1 banana strawberry yogurt 1 orange aspirin 1 singed herbs 1 orange blossoms 1 jalapeÃ±o peppers 1 orange cake 1 pumpkin tart 1 orange candy 1 melba toast 1 orange creme brulee 1 dark pepper bread toast 1 orange custard 1 crusty cranberry nut bread 1 orange gelato 1 rye fruit bread 1 orange granita 1 citrus preserves 1 orange ice 1 resinous pine 1 orange marmalades 1 rown 1 orange marmalade on toast 1 floury biscuit 1 orange pepper bread 1 spicing 1 orange slice 1 spice bread 1 orange vinaigrette on watercress 1 pineapple marmalade on lemon scone 1 orange vinegar 1 hazelnut pastry 1 orange-apricot vinaigrette 1 grapefruit soda 1 orange-mango chutney 1 chocolate citrus peel 1 orange-mint jam on toast 1 burnt nuts 1 orange-peach cobbler 1 apple taffy 1 orange-pineapple salsa 1 dark berry custard tart 1 oregano 1 salty butter 1 over-roasted nuts 1 ginger apple chutney 1 overripe apple core 1 pepper corn bread 1 overripe grapefruit 1 raisin roast 1 overripe lemon 1 sweet vinaigrette 1 overripe limes 1 mango marmalades on toast 1 overripe melon 1 Zesty hoppy aromas 1 overripe nectarine skin 1 crusty apple tart 1 overripe peaches 1 grapefruit marmalade on rye toast 1 overripe strawberries 1 peach marmalades on toast 1 Palmer 1 dark pretzel bread 1 pancakes 1

100 Aroma Freq Flavour Freq pear blossoms 1 pancake 1 peach spice cake 1 papaya yogurt 1 syrupy peaches 1 papaya-banana 1 peach marmalades on croissant 1 paprika on jicama 1 chocolate cherry bonbon 1 parchment paper 1 fig bread toast 1 passionfruit vinaigrette 1 pear sherbet 1 passionfruit-kiwi gelato 1 fresh crusty baguette 1 patch 1 Delicate citrus marmalade on scone 1 peach butter on rye toast 1 citrus fruit cake 1 peach creme brulee 1 pineapple marmalades on toast 1 peach custard 1 sarsaparilla candy 1 peach gelatos 1 spicy apple yogurt 1 peaches in cream 1 onion focaccia 1 peach jam 1 kiwi tart 1 peach lemon tea 1 dusty turnip 1 peach nut tart 1 Fruity banana taffy circus peanut 1 peach phosphate 1 tropical fruit compote 1 peach soufflÃ© 1 apple bread pudding 1 peach-apple custard 1 cherry pastry 1 peanut brittle 1 passionfruit 1 pear cider 1 peach preserves 1 pear in syrup 1 overripe whole pineapples 1 pear seltzer 1 floral honeycomb 1 pear skins 1 oily nuts 1 pear sorbets 1 lemon vinaigrette on mesclun 1 pear soufflÃ© 1 pple 1 pear vinaigrette on frisÃ©e 1 herbal Spic 1 pear-banana soufflÃ© 1 Vegetal honey 1 peat moss 1 veggie omelet 1 pecans 1 peach butter on pumpernickel toast 1 pecan pie 1 fresh baked baguette 1 penetrating apple cider vinegar 1 lemon spice cake 1 pepper accent 1 Aroma nutshells 1 pepper bitter melon 1 peach pastries 1 pepper bread crust 1 chocolate walnuts 1 pepper cracker 1 chocolate cracker 1 pepper egg bread 1 grape tart 1 pepper mesh 1 lime yogurt 1 pepper mole 1 pear cider 1 pepper nut bread 1 orange marmalade on sourdough toast 1 pepper pastry 1

101 Aroma Freq Flavour Freq chocolate coconut macaroons 1 pepper spices 1 sarsaparilla float 1 pepper toast 1 blueberry yogurt 1 pepper-like hop 1 Robust multigrain nut bread 1 pepper-onion 1 banana pie 1 pepper-onion bread 1 fresh pretzel bread 1 peppercorn chive muffin 1 ginger bread 1 peppercorn-like hops 1 chocolate raisins 1 peppered kiwi 1 peppered lettuce-like hops on the caramel orange cake 1 balanced nutty 1 citrus marmalade on cherry scone 1 peppered wafer 1 lemon marmalade on pepper bread toast 1 peppery citrus 1 pple butter 1 peppery herbs 1 peppery herbal hop snap on the doughy apple pie crust 1 crisp 1 toasty yeast pastry 1 peppery hop flavors 1 tahini 1 peppery hop snap on the finish 1 pineapple marmalade on herb toast 1 peppery noble hop snap 1 bready malt 1 peppery pineapple relish 1 pepper cracker 1 peppery radishes 1 savory egg custard 1 peppery radish-like hop flourish 1 butterscotch brownie 1 peppery rapini-like hop 1 berry tart 1 peppery snap 1 mango chutney on herb toast 1 peppery sprout-like 1 exotic spiced chocolate 1 peppery turnip greens 1 orange marmalade on raisin toast 1 Pepsi 1 grilled grains 1 Pepsi cola 1 coconut custard pies 1 phenolic wax paper note 1 Mild yeasty dough 1 pickle fruits 1 creamy meringue 1 pie pilling 1 caramel sauce 1 pig 1 lavender thyme bread 1 pilling 1 mocha milkshake 1 pine blossom 1 lemon cream pastry 1 pine blossoms 1 delicate pickling spices 1 pine buds 1 tangelo peel 1 pine plank 1 banana peel 1 pine sapling 1 apple corn fritter 1 pineapples 1 dark banana bread 1 pineapple buttercream 1 Werther 1 pineapple chutney 1 caramel sour cream frosting 1 pineapple compote 1 peach marmalade on toast 1 pineapple hard candy 1

102 Aroma Freq Flavour Freq vanilla tapioca 1 pineapple limeade 1 crusty pumpkin 1 pineapple soufflÃ© 1 cola nut 1 pineapple-mango custard 1 pine nut 1 pineapple-peach-mango cake 1 mushroom consommÃ© 1 pink peppercorns 1 peach soda 1 pipe 1 honey nut cereal 1 pipe tobacco 1 orange cream brulee 1 piquant 1 dark rye bread 1 piquant pepper 1 orange tart 1 pistachio 1 herbal tomato pepper focaccia 1 pistachio praline 1 stone fruit soufflÃ© 1 pits 1 vanilla latte 1 plank 1 Oloroso sherry 1 plantain custard 1 lime marmalades on grilled rye bread 1 plantain frittata 1 chocolate cherry bonbons 1 plantain pudding 1 cherry bread 1 plastic tape 1 honey butter on pumpernickel toast 1 pleasant creamy 1 egg croissant sandwich 1 pleasant honeyed oatmeal 1 fresh toast 1 pleasant toast 1 cinnamon apple pastry 1 pleasant toasty 1 anisette cookie 1 plum skin 1 bbq jerky 1 plush brown sugar 1 orange bran muffin 1 pod 1 Bright spicy tangerine custard 1 pomegranate ice 1 spicy pickle 1 pomegranate molasses 1 caraway rye 1 pomegranate seed 1 maple nut fudges 1 pomegranate sorbet 1 dark berry compote 1 pomegranate soufflÃ© 1 lemon marmalade on toast 1 pomelo peel 1 apricot chutney on toast 1 ponzu 1 grilled root veggies 1 ponzu sauce 1 toasty warm granola 1 pops 1 catsup 1 pot 1 honey vanilla bean gelato 1 pot roast with carrots 1 scallion 1 potato peels 1 Twinkies 1 powdered lemon 1 eggy batter 1 powdered milk 1 crabapple 1 preserves 1 dark chocolate banana 1 pretzels 1 honey taffy wrapper 1 pretzel chip 1

103 Aroma Freq Flavour Freq banana breads 1 Provence 1 Bright banana gelato 1 prune pastry 1 delicate spice cake 1 pumpernickel bread pudding 1 crusty fresh baked peach pie 1 pumpkin pie filling 1 lemon marmalade on banana bread 1 pumpkin soufflÃ© 1 rye fig bread 1 putty 1 corn tortilla 1 quick molted plastic 1 apple bread dough 1 quick vegetable 1 orange granola 1 radish driven 1 chestnut honey butter 1 radish finish 1 coffee sorbet 1 radish greens notes 1 mint aromas 1 radish like hop 1 citrus jam 1 radish skin 1 tropical fruit yogurt 1 radish sprout 1 ginger pepper 1 radish sprouts like hop 1 mild sourdough 1 rain 1 sweet orange marmalade 1 rain on grass 1 Bright peppery spice 1 raisin bread pudding 1 apple butter on herb sourdough toast 1 raisin paste 1 crushed coriander 1 raisin sourdough bread 1 spicy apple butter 1 raspberry hard candy 1 honey butter on raisin toast 1 raspberry jam on wheat wafer 1 fresh nut bread 1 raspberry scone 1 potato bread dough 1 raspberry skin 1 dark chocolate orange peels 1 raspberry skins vinegar 1 cinnamon raisin oatmeal cookie 1 raspberry vinaigrette on lettuce 1 pepper cheese bread 1 raspberry-apple salad 1 chocolate fudge brownie 1 raspberry-cranberry sorbet 1 apple puff pastry 1 raspberry-lime seltzer 1 cinnamon spice cake 1 raw potato 1 fresh cashew butter 1 red bell pepper 1 chocolate cherry fudge 1 red grape skin 1 salty crackers 1 red licorice 1 red wine vinaigrette on artichoke crusty fresh bread 1 hearts 1 woodsy herbs 1 refined grain 1 stone fruit marmalades on raisin rye bread 1 resinous citrusy bark like hop 1 apricot marmalades on Hawaiian toast 1 resinous pepper 1 honey on raisin toast 1 rhubarb jams 1 fresh herb bread 1 rhubarb stalks 1 fresh grainy pepper bread 1 rice cereal 1

104 Aroma Freq Flavour Freq burnt crayon 1 rich baking spices 1 banana peppers 1 rich coffee 1 spice fruit cake 1 rich dark coffee 1 cheese popover 1 Rich honeyed pastry 1 ginger spice cake 1 rich roasted nut 1 mango sauce 1 rilliant amber color 1 Fresh raspberry tart 1 ripe grapefruit 1 chocolate cereal 1 ripe kiwi 1 wintergreen gum 1 ripe peach skin 1 lemon poppy seed scone 1 river stones 1 peppered bacon 1 roast 1 balsamic vinegar 1 Robust 1 unsweetened chocolate 1 Robust multigrain nut bread 1 yeasty green apple 1 roses 1 chocolate toffee bar 1 rose-hip 1 raspberry pie 1 round grapefruit sorbet 1 creamy vanilla frosting 1 rub 1 bruschetta 1 rubbery 1 pork jerky 1 rubbery bubblegum 1 garlicky dill pickle 1 ruby 1 salty plantain chips 1 rum barrel 1 chocolate berry cake 1 rum cake 1 crusty apple pie 1 rum raisin cake 1 grapefruit marmalade on cheese pepper bread 1 rye crisp 1 herbal lozenge 1 rye date bread 1 apple butter on sourdough toast 1 rye fig bread 1 cranberry soda bread 1 rye grain 1 Bold nut brittle 1 rye pepper bread 1 Tangy apple cider vinaigrette on mesclun 1 saddle leather 1 powdered citrus cleanser 1 sage 1 peach fruit salad 1 salt water taffy 1 leafy hops 1 saltine 1 dusty grassy hops 1 salty beet 1 mild coconut custard 1 salty dark baguette 1 creamy chocolate 1 salty nut 1 raspberry preserves 1 salty parsnip 1 tropical blossoms 1 salty pasty 1 artisan chocolate 1 salty potato pancake 1 exotic peppercorns 1 salty pretzel bread 1 apricot jam on raisin toast 1 salty Sicilian lemon vinaigrette 1

105 Aroma Freq Flavour Freq fresh pepper bread 1 salty sweet potato chips 1 cherry spice cake 1 sandwich 1 Aromas of buttery nut brittle 1 sangria 1 cappuccino gelato 1 sap smack 1 pineapple sauce 1 sapling 1 powdered honey 1 Saturn 1 artisanal cola 1 Saturn peach 1 lime zest 1 sausage 1 dill pickle relish 1 savory baguette 1 chocolate soda 1 savory cocoa 1 sweet corn pudding 1 savory cranberry relish 1 Bright buttery 1 savory grainy bread 1 dandelions 1 savory mole 1 lemon chiffon 1 savory parsnip 1 cherry cider vinegar 1 savory pepper focaccia 1 rich toffee 1 savory pumpkin puree 1 leafy greens 1 savory salty baguette 1 citrus pez candy 1 savory sweet potato chip 1 apple butter on cinnamon raisin toast 1 savory zucchini bread toast 1 corsage 1 sea salt bagel 1 dank greens 1 seamless apple 1 spice pumpkin pie 1 seamless citrus 1 dusty barnyard hay 1 seamless mesh of honeyed nut 1 Rich dark chocolate profiterole 1 seaweed 1 Pungent burnt nut 1 seaweed salad 1 mocha cream 1 seltzer-like minerality 1 butter apple pie dough 1 semi-dark chocolate 1 fresh dark baguette 1 shake 1 spicy strawberry preserves 1 shandy 1 delicate fruit preserves 1 sharp lemongrass 1 white macadamia toffee 1 shell 1 fresh banana cake 1 sherry vinegar 1 corn cereal 1 shittake 1 dark chocolate cake 1 shoe 1 sweet berry cider vinaigrette on mesclun 1 shoots 1 terrarium 1 shortcake 1 cheese corn bread sandwich 1 Sicilian lemon juice 1 banana bran muffins 1 silky foam 1 tarragon honey 1 singed herbs 1 lemon basil vinegar 1 singed praline 1 salty egg sandwich 1 slap 1

106 Aroma Freq Flavour Freq orange marmalade on muffin 1 slice 1 delicate baking spice 1 slices 1 pomegranates 1 slick sorghum 1 herb butter on nut toast 1 slight vegetal twang 1 dark oatmeal raisin cookie 1 smack 1 crushed apples 1 smoke 1 white balsamic vinaigrette 1 smoky mesquite 1 banana rum cake 1 snappy cayenne 1 leafy herbs 1 snappy lemon pepper 1 coconut vinegar 1 snappy pepper frittata 1 cherry ginger cake 1 soapy cilantro 1 coffee nut cake 1 sodas 1 lemon grass 1 sorghum with a crisp 1 lingonberries on toast 1 sour cherries 1 fresh baked spicy pumpkin pie 1 sour cherry chutney on toast 1 vanilla cola 1 sour cherry compote 1 fruity coffee cake 1 sour cherry pits 1 lanolin 1 sour cherry yogurt 1 cardamom orange cake 1 sour citrus 1 sage basil pepper muffin 1 sour cream 1 yellow apple soufflÃ© 1 sour lemon yogurt 1 vermouth 1 Sour vinegary 1 mixed berry soufflÃ© 1 soy bean 1 collard greens 1 soy beans 1 burnt coconut husks 1 Span 1 orange zest 1 Spic 1 husks 1 Spic n Span 1 brownie dough 1 Spic-N-Span 1 cling peaches 1 spice candy 1 banana cream pie 1 spice flavors 1 warm cherry compote 1 spice hops 1 apple cider vinaigrette on arugula 1 spice-sage 1 peach vinaigrette on greens 1 spiced fruits on the crisp 1 fruit pie 1 spicy apple pie 1 delicate sweet spices 1 spicy apple sauce 1 sunflower rye bread 1 spicy apple-pie 1 apple butter on sourdough bread 1 spicy banana cake 1 teriyaki turkey jerky 1 spicy carrot cake 1 floral spices 1 spicy creamy 1 exotic citrus 1 spicy fruit cake 1 buckwheat pancakes 1 spicy hot pickle 1

107 Aroma Freq Flavour Freq salty potato pancake 1 spicy orange soufflÃ© 1 raffia 1 spicy pineapple custard 1 fresh strawberry tart 1 spicy soufflÃ© 1 white nut brittle 1 spinach-like hop 1 brown sugar praline 1 spritz 1 peach tea 1 sprout-like hop 1 herbal polenta 1 sprouts-like 1 Attractive aromas 1 spruce 1 stinky cheese rind 1 stale raisin bread 1 chocolate coffee cake 1 stalks 1 lemon vinaigrette on greens 1 stalky greens 1 strawberry rhubarb pie 1 stave 1 dark chocolate goji berries 1 stevia 1 peach butter on toast 1 stewed veggies with a supple 1 dusty herbs 1 stone fruit compote 1 cinnamon apple pie 1 stone fruit custard 1 chai latte 1 stone fruit marmalades 1 currants 1 strap 1 marzipan cookie 1 straw mat 1 apricot butter on toast 1 strawberry ice 1 grapefruit juice 1 strawberry jam on biscuit 1 pumpkin cheesecake pie 1 strawberry preserves 1 chocolate sauce 1 strawberry vinaigrette 1 raspberry jam on lemon pepper muffin 1 strudel 1 milk chocolate caramel candy 1 styrofoam 1 faint spice cake 1 substitute 1 milk chocolate mousse 1 subtle grain 1 chestnut honey on rye toast 1 summer 1 rub spices 1 sun-dried tomato vinaigrette 1 watercress 1 super fine 1 sweet berry cider vinaigrette on stale butter 1 mesclun 1 Bold honeycomb 1 sweet cabbage 1 dark cocoa 1 sweet corn pudding 1 warm chocolate pudding 1 sweet mint tea 1 sour dough bagel 1 sweet pickle 1 dark sour dough 1 sweet potato fries 1 bbq crust 1 sweet summer salad with berries 1 cherry pie 1 sweet wheat cracker 1 peach jam on rye toast 1 symphonic 1 vanilla wafer 1 syrupy peaches 1

108 Aroma Freq Flavour Freq fresh dark bread crust 1 talc 1 cinnamon bark 1 tall grass 1 cherry vinegar 1 tamarind soda 1 dumpling 1 tamarind vinaigrette 1 soy beans 1 tamari on nori 1 barnyard hay bandage 1 tangerines 1 lavender herb tea 1 tangerine custard 1 apple vinaigrette on arugula 1 tangerine vinaigrette 1 green apple chutney 1 tangy apple 1 floral pear 1 tangy Asian pear 1 butter cookie 1 tangy cherry 1 peach nectar 1 tangy citrus gelato 1 burnt blueberry pie 1 tangy citrus marmalade 1 mossy riverbed 1 tangy clementine 1 fig soda bread 1 tangy cocoa 1 Aromas of succotash 1 tangy creme brulee 1 Greek lemon yogurt 1 tangy date nut bread 1 cigar embers 1 tangy fruit punch sorbet 1 honey vinaigrette on greens 1 tangy fruit salad 1 herb butter on toast 1 tangy grapefruit 1 Delicate smoky 1 tangy green apple 1 sangria 1 tangy herb citrus chutney 1 apple cream cheese pastry 1 tangy honeyed melon 1 fresh peanut butter 1 tangy kiwi 1 vanilla pancake 1 tangy lemon cake 1 ghee 1 tangy lemon sorbet 1 floral herbal talc 1 tangy lemon-lime sorbet 1 turnips 1 tangy mango yogurt 1 faint crayon 1 tangy mixed citrus sorbet 1 chocolate fig 1 tangy omelet 1 vinaigrette on grilled fruit salad 1 tangy orange bread 1 Vibrant buttery oatmeal 1 tangy orange sherbet 1 apple Starburst 1 tangy overripe apple 1 apple blossom 1 tangy overripe pear 1 Sweet cling peach 1 tangy papaya coconut custard 1 banana cake with buttercream frosting 1 tangy peach-date chutney 1 apple peel 1 tangy pepper 1 mouthwash 1 tangy peppered citrus 1 fresh baked wheat bread 1 tangy plantain 1 cayenne pork rind 1 tangy raspberry pastry 1 delicate charcoal embers 1 tangy sassafras 1

109 Aroma Freq Flavour Freq dark chocolate espresso beans 1 tangy sweet citrus 1 corn chowder 1 tape 1 beet chevre salad 1 tart berries 1 tropical citrus on brioche 1 tart cherry 1 spicy buttercream on orange cake 1 tart lemon 1 nut butter on dark toast 1 tart nectarine 1 pineapple slices on mesclun 1 tart star fruit 1 pink peppercorns 1 tea bags 1 caraway 1 tea on the delicate 1 golden raisin pound cake 1 teriyaki 1 exotic apples 1 teriyaki jerky 1 brioche with lemon curd 1 terse 1 pizza bread 1 Thai 1 fresh marzipan 1 tidy 1 raspberry butter on brioche 1 tin 1 goji 1 tinny banana 1 vanilla cream pie 1 toasty apple tart 1 hibiscus lime chutney on toast 1 toasty coconut custard 1 caramel chocolates 1 toasty meringue 1 collard 1 toasty omelet 1 coconut cream pie 1 toasty orange scone 1 malt vinegar 1 toasty pineapple cake 1 beef jerky 1 toasty pound cake with apple 1 peach compote on cheese 1 toasty raisin muffin 1 green caramel apple 1 toffeed nut with a bit molten plastic 1 strawberry milkshake 1 tomato core 1 green resinous hop aromas 1 tomato focaccia 1 hoppy wort 1 tomato molasses 1 burnt meats 1 tomato relish 1 nectarine skin 1 tonka 1 Chanterelle mushrooms 1 tonka bean 1 plantain chip 1 tootsie 1 dark berries 1 tootsie roll 1 caramel on fig toast 1 tortilla 1 vanilla hazelnut coffee 1 touch of cider vinegar 1 margherita 1 touch of cream 1 spicy French toast 1 touch of fruit 1 orange jams on herb nut toast 1 touch of greens 1 hint of waxy honeycomb 1 touch of jicama 1 cassis jam 1 touch of latex 1 honeydew 1 touch of peppered lettuce-like hops 1

110 Aroma Freq Flavour Freq Slight soapy aromas 1 touch of toasted fruitcake 1 stale soy 1 tray 1 minty wheat wafer 1 treacle 1 apple vanilla candle 1 tree fruit 1 vanilla mint bark 1 tropical citrus fruit 1 whole roasted peanut 1 tropical citrus gelato 1 fruit cookie 1 tropical fruit chutney 1 apple soda 1 tropical fruit salad 1 cherry butter on blueberry muffin 1 tropical fruit sorbet 1 peanut butter on toast 1 tropical punch Jell-O 1 sticky pineapples 1 turnip peels 1 twang 1 twigs 1 orange blossom honey on muffin 1 ucid golden amber color 1 crepes 1 Unattractive glue 1 Meyer lemon curd on toast 1 underripe orange 1 turmeric tea 1 vanilla apricot yogurt 1 exotic tropical fruity 1 vanilla flan 1 Bright orange marmalade on toast 1 vanilla nuts 1 gouda 1 vanilla nut brittle 1 pear vinaigrette on lettuce 1 vanilla salt 1 artichokes 1 vanilla soufflÃ© 1 banana leaves 1 vanilla taffy 1 Rich spicy chocolate graham cracker 1 vanilla toffee 1 shavings 1 vanilla yogurt nuts 1 artisanal coffee 1 vanilla-coconut yogurt 1 cream pasty 1 vanilla-orange custard 1 fruit gum 1 vegetable puree 1 coconut candle 1 vegetable soup 1 fresh crushed raspberries 1 vegimite 1 ancho chili 1 verbena 1 tarragon 1 vibrant citrus 1 persimmon 1 vinaigrette on grilled fruit salad 1 guava 1 vinegary 1 exotic spices 1 violet notes on the finish 1 berry vinaigrette on greens 1 vitamin 1 acorns 1 wafer cookie 1 apple sage bread 1 waffles 1 coconut flakes 1 warming peach compote 1 raspberry jam on scone 1 wasabi 1 old roasted corn 1 wasabi nuts 1 Bright lemon 1 wash 1

111 Aroma Freq Flavour Freq dusty hay 1 wash of grilled root vegetables 1 orange blossom honey on toast 1 water chestnuts 1 spun honey 1 water water 1 turnip green 1 watercress like hops 1 fruity bon bons 1 watermelon rind 1 bread flour 1 watermelon sherbet 1 rum raisins 1 watermelon sorbets 1 marinated meat 1 watery buttery caramel corn 1 white peppercorns 1 wave of caramelized fruits 1 stale beans 1 waxy 1 chocolate cherry bon bons 1 waxy honeycomb 1 funky cheese rind 1 wet grain sack 1 mango chutney on biscuits 1 wet stone 1 Bright chocolate brownie 1 wet straw 1 rye chip 1 wheat bread 1 dark chocolate graham crackers 1 wheat crackers 1 cranberry sauce 1 whiff 1 fresh pumpkin cheesecake 1 whiff of acetone 1 salty cheese turnover 1 whisky barrel 1 nut flour 1 white balsamic on radicchio 1 white balsamic vinegar on green garlic cheese bread 1 peaches 1 nachos 1 white chocolate graham cracker 1 eucalyptus honey 1 white chocolate granola bar 1 pretzel roll 1 white chocolate macadamia cookie 1 burnt banana leaf 1 white chocolate mint cookie 1 dark pastries 1 white chocolate nuts 1 egg yolks 1 white chocolate strawberries 1 oat cereal 1 white cigar ash 1 dark grain pastries 1 white cranberry juice 1 egg bagel sandwich 1 white grape-coconut salad 1 acacia honey 1 white peach tea 1 fresh apple pie 1 white peppercorn 1 Dense aromas pine cone 1 white raisin 1 hint of stewed veggies 1 white tea 1 chocolate powder 1 white toast 1 powdered cherry candy 1 whole chestnuts 1 sweet potato pie 1 whole chestnut 1 candelabra 1 wicker 1 white nut fudge 1 wild strawberry turnover 1 caramel on sourdough toast points 1 wine 1

112 Aroma Freq Flavour Freq creamy cupcake 1 wintergreen 1 ham sandwich 1 wintergreen-apple 1 mango jams on toast 1 woods 1 corn bread with jalapeÃ±o 1 wood smoke 1 milk chocolate caramel bar 1 woody 1 riverbed 1 woody incense 1 cherry sauce 1 Worcestershire 1 ham crepes 1 Worcestershire sauce 1 ginger mint cookie 1 wormwood 1 toasty rice krispie treat 1 wormwood tea 1 melba 1 wrapper 1 plantain skin 1 yeast 1 butters 1 yeasty fruitcake 1 exotic hops 1 yeasty lemon scone 1 chicken teriyaki 1 yellow apple core 1 pear brandy 1 yellow cake batter 1 cheese sandwich 1 yellow pepper 1 fresh ginger 1 zesty berry sorbet 1 color nuts 1 zesty chili pepper chocolate mousse 1 dark espresso beans 1 zesty citrus 1 tropical Starburst candy 1 zesty interplay of toasty malt 1 leche 1 zesty lime tart 1 fruit compote on artisan cheese 1 zesty peach vinaigrette on mesclun 1 strawberry candies 1 zesty peppery radicchio 1 gooseberry jam 1 zesty ruby grapefruit 1 hint of molasses spice cake 1 zippy lemon scone 1 powdery 1 tincture 1 cheese puff 1 cinnamon sugar 1 sweet potato puree 1 apple mint 1 yolks 1 crushed apple 1 chocolate milk 1 anise 1 butternut 1 Bold apple cider 1 Pungent 1 egg salad 1 doughnut 1

113 Aroma Freq Flavour Freq white chocolate macadamia cookie 1 lavender soap 1 dimensional habanero pepper 1 Fino 1 Smoky peat 1 farina 1 Aromas of egg croissant 1 pineapple skin 1 Aromas of mocha fudge 1 hint of nougat 1 lavender tea 1 patchouli 1 chipotle 1 apple candy 1 quiche 1 cupcake 1 pumpkin pie filling 1 black raspberry tart 1 wet greens 1 banana skin 1 subtle baking spices 1 green banana 1 lime candy 1 tortilla chips 1 papaya salsa 1 hint of Playdoh 1 ginger tea 1 Swedish rye cracker 1 pizza dough 1 artisan Mexican pepper chocolate 1 honey herb drop 1 berry cream danish 1 wet campfire embers 1 pizza herbs 1 Bold wet grain aromas 1 cheese turnovers 1 singed wood 1 fancy lemon soap 1 garlic onion bread 1 apple slices 1 grainy flour 1

114 Aroma Freq Flavour Freq ramen 1 fancy floral lemon soap 1 musty rag 1 celery root 1 fruit jam on toast 1 carob bar 1 opper color 1 sweet lettuce 1 carrot cakes 1 piney hop 1 sweet bibb lettuce 1 bergamot 1 corn kernels 1 mushroom ramen 1 orange gelatin 1 bibb 1 seaweed salad 1 fresh pine sap 1 subtle spices 1 Fino sherry 1 white chocolate graham cracker 1 turnip skins 1 almond butters on toast 1 fresh apple sauce 1 onion marmalades on brie 1 porridge 1 crab apples 1 gardenia 1 chocolate turtles 1 slight vegetal twang 1 sour chicken 1 brown spice 1 ruby grapefruit 1 wax candies 1 Unattractive glue 1 pecan pie 1 peanut butter ice cream 1 stemmy lawn clippings 1 fig preserves on toast 1 grape jam on toast 1 light skunky cannabis 1

115 Aroma Freq Flavour Freq warm lemon tea 1 cream farina 1 rye turkey stuffing 1 subtlety 1 seltzer 1 crush raspberries 1 sweet peppers 1 chalk eraser 1 orange slices 1 lemon bar 1 tropical scented candle 1 corned beef sandwich 1 tortilla chip 1 rich chocolate 1 musty attic 1 mint candies 1 nutty fig 1 twigs 1 cherry pits 1 white bread 1 chard 1 mint gum 1 bitter lemon 1 cucumbers 1 chili peppers 1 brown butter on toast 1 meatballs 1 kettle corn 1 orange blossom honey on muffins 1 wet pine 1 mber color 1 crusty beef pot pie 1 chowder 1 apple pits 1 brie 1 dill pickle barrel 1 hint of caramelized turkey bacon 1 balsa 1 espresso bean 1 green pepper 1 egg turnover 1

116 Aroma Freq Flavour Freq herbal hop pellets 1 burnt popcorn 1 apple brandy 1 plums 1 smoky mole sauce 1 Bartlett pear 1 apple jacks cereal 1 peach pit 1 puffs 1 choke cherry jam 1 margherita pizza 1 orange tea 1 dusty straw mat 1 pine cones 1 violets 1 pumpkin filling 1 cream puffs 1 puff rice 1 gelatin candy 1 sweet cole slaw 1 medicinal bark 1 dark berry shrub 1 fennel 1 dandelion 1 sausage pizza crust 1 ham on rye sandwich 1 pork loin 1 loin 1 rubber eraser 1 lime powder candy 1 Bold soy sauce 1 gauze 1 berry cough syrup 1 shortcake 1 tropical citrus like hops 1 toasty pound cake with apple 1 pumpkin cheesecake with graham cracker crust 1 Creamy milk chocolate bar 1 chocolate bar wrapper 1 pencil shavings 1

117 Aroma Freq Flavour Freq blueberries 1 Rich vanilla cream 1 cherry juice 1 bandage 1 grape leaves 1 coffee candy 1 stalks 1 dark German bread 1 molten wax 1 fresh whipped cream 1 cumin 1 wet hay 1 fuzz 1 medicinal tincture 1 ginger snap cookie 1 carnation 1 sprout 1 kumquat vinaigrette on a pear salad 1 brown apple 1 apple chip 1 herbal honey on apple 1 hint on spice 1 dulce 1 fruit stripe gum 1 soy rice cakes 1 maple sap 1 maple candy 1 smoky mesquite 1 candy sugar 1 olden color 1 mint tea 1 Bright ginger beer 1 Brach's caramels 1 lemon bars 1 homemade catsup 1 lime juice 1 husky wet grain 1 cauliflower 1 raspberry syrup on pancakes 1 ucid golden amber color 1 hint of cider vinegar 1

118 Aroma Freq Flavour Freq glazed donut 1 cedar bark 1 dusty old spices 1 vanilla bean ice cream 1 prune 1 light molasses on nuts 1 eautiful sunset orange color 1 warm eraser 1 grassy earth 1 nog 1 rich brown spices 1 fancy milled soap 1 honey on poppy seed cake 1 paraffin 1 bath talc 1 tarts 1 lit cigar 1 ashtray 1 brown grain 1 prom corsage 1 ferns 1 honey butter on cinnamon rolls 1 sandalwood 1 hint of turnip 1 oregano scream hop kiln 1 hint of citrus 1 herbal tonic 1 white maple fudge 1 orange juice 1 muddy hay 1 broccoli crowns 1 fruit loops cereals 1 maple sugar 1 sweaty 1 hint of peach fuzz 1 pumpkin pulp 1 formaldehyde 1 peach granola in cream 1 green peanuts 1 hint of jerky 1 apple salt water taffy 1

119 Aroma Freq Flavour Freq fancy floral soap 1 Werther's butterscotch 1 cheese popcorn 1 wet grass 1 rice crispy treaties 1 chaps 1 mixed greens 1 azy yellow color 1 lanolin lotion 1 floral herbal shampoo 1 tutti fruit ice cream 1 hint of dank pine cone 1 parchment 1 green beans 1 orange teas 1 spun sugar 1 mulch 1 rubber sap 1 orange aspirin 1 donut 1 Brussels sprout 1 celery salt 1 pear drops 1 Span cleanser 1 jasmine rice 1 veggie chips 1 grape skin 1 fruit cocktail 1 stones fruits 1 white vermouth 1 saltine with cheese 1 pure chocolate 1 hibiscus 1 milk chocolate rice bar 1 damp rag 1 yellow cake batter 1 strawberry jam on biscuit 1 mint leaves 1 wort 1 lime leaf 1 acidic coffee 1

120 Aroma Freq Flavour Freq mocha cheesecake with a pretzel crust 1 warm garlic pickle juice 1 Greek lemon soup 1 root vegetable medley 1 white cranberry 1 chicken pot pie 1 brownies 1 fresh black cherries 1 cloves 1 hint of melon 1 alpine herbs 1 butternut candy bar 1 white potpourri 1 watermelon ice 1 apple wood 1 flower terrarium 1 golden beet 1 old lemon 1 cedar incense 1 piney herbs notes 1 cayenne 1 cappuccino 1 black cherry soda 1 ripe apple 1 dulce de leche gelato 1 eep copper cherry wood color 1 egg shells 1 musk 1 cocoa powder tin 1 chardonnay grapes 1 Bright root beer 1 yam 1 eucalyptus 1 tomato paste 1 sprite 1 Bright black licorice candy 1 whiff of putty 1 apple tree 1 acacia 1 whole walnuts 1 olive bruschetta 1

121 Aroma Freq Flavour Freq hint of licorice 1 hint of bubblegum dust 1 apple ice 1 Asian pear 1 white wine vinaigrette of mesclun 1 white strawberry 1 stubs 1 hint of pepper 1 maple syrup on cereal 1 pepper oil 1 fancy soap 1 hint of cocoa 1 mint green tea 1 corn silk 1 soybeans 1 deep fried bananas 1 mesquite 1 maple syrup on pancakes 1 hint of bouillon 1 hint of apple sauce 1 cheeses 1 heather peat moss 1 nectar 1 red licorice 1 white clay 1 cheddar 1 lit bbq woods 1 eep amber copper color 1 orange slice 1 raspberries with cream cheese 1 hint of spice 1 English muffin 1 ginseng 1 whiff of potpourri 1 burnt plastic 1 wheat germ 1 coffee ice cream 1 mint leaf 1 PX sherry 1 jerk chicken rub 1 Asian herbs 1

122 Aroma Freq Flavour Freq wild strawberry puree 1 lear 1 hint of herbs 1 bubble gum dust 1 ham with pineapple 1 bean soup 1 Asian BBQ rub 1 old cocoa powder 1 mace 1 hint of bbq rub 1 Rich green apple 1 black walnuts 1 root cellar 1 pepper mesh 1 oat bar 1 margarita pizza 1 iodine 1 crowns 1 parsley 1 hint of wax candle 1 passion fruit 1 tonic 1 silky foam 1 Odd vinegar 1 herbal European hops 1 hint of soy 1 hot cocoa 1 hint of artichoke hearts 1 strawberries 1 raw cranberry 1 hamster 1 choke 1 ark chestnut brown color 1 leather chaps 1 shrub 1 Rich pure raspberry 1 hard watermelon candy 1 whole peanuts 1 micro greens 1 kiln 1 fresh raspberries in whipped cream 1

123 Aroma Freq Flavour Freq crispy pig skin 1 wet mop 1 cones 1 clippings 1 hint of cannabis 1 chardonnay 1 margarita 1 germ 1 cereals 1 Intricate 1 white white vinegar 1 Elmer 1 sweet tobacco 1 juniper flowers 1 kernels 1 red berries 1 floral bubble bath soap 1 rown black color 1 green tea leaves 1 diet ginger ale 1 rustic cheeses 1 cherry pipe tobacco ash 1 hint of buffalo jerky 1 spice garden 1 medley 1 Key lime pie 1 garlic shoots 1 light fruit 1 yellow color delicious apples 1 crisp turkey skin 1 compost 1 touch of honey butter 1 damp earth 1 old fruit 1 hint of dark turkey meat 1 date bread with raspberry jam 1 spice box 1 hay stack 1 breakfast potatoes 1 birch beer 1 sticky rice 1

124 Aroma Freq Flavour Freq mop 1 whole grain 1 Bold coffee grounds 1 berries in Greek yogurt 1 light pine 1 cigarette stubs 1 light potpourri 1 pork fat 1 chew 1 tres 1 ground almonds 1 brass candelabra 1 touch of cider vinegar 1 hint of grass 1 pea 1 white tea 1 wet meadow 1 mead 1 red grape 1 sunflower 1 Interesting piney 1 whisper 1 bucket of spic 1 hint of bacon 1 hemp 1 apple basket 1 compost heap 1 sweaty flesh 1 teas 1 chicken fat chips 1 red berry 1 aspirin 1 wood charcoal 1 bbq turkey leg 1 Thai basil 1 kettle 1 red clay 1 warm leather 1 whisky barrel 1 Belgian yeast 1 white ash 1

125 Aroma Freq Flavour Freq hint of saddle soap 1 bologna 1 rubber tire 1 barley 1 lear copper color 1 fish sauce 1 pancakes with syrup 1 barley soup 1 shoots 1 random fruit 1 cotton candy 1 raisin bread with a touch 1 turtles 1 turkey melt 1 oyster 1 Lush 1 earl grey tea 1 white strawberries 1 banana's foster 1 pine forest floor 1 coconut dream bar 1 fancy French soap 1 fresh cut grass 1 treaties 1 jerk 1 Almond Joy bar 1 toast with a hint 1 dark chocolate ice cream in a warm waffle cone 1 sweet pipe tobacco 1 heap 1 hint of meat 1 jacks 1 artichoke heart 1 hint of iodine 1 Bartlett 1 rubber balloon 1 lemon in baking cloth 1 light broccoli 1 sesame 1 birch 1

126 Aroma Freq Flavour Freq plum 1 hot peppers 1 date breakfast bread 1 orange drink 1 onions 1 cellar 1 hamster cage 1 instant coffee 1 rawhide chew toy 1 Swiss chard 1 ore 1 chocolates 1 melt 1 apple jack 1 PX 1 Italian parsley 1 gravel 1 hint of latex 1 red hot pepper 1 shampoo 1 baby oil aromas 1 black coffee 1 meadow 1 seamless 1 crush 1 old pot roast 1 seltzer water 1 litter 1 horse saddle 1 prairie flowers 1 pulp 1 prom 1 ginseng roots 1 ark 1 lumber 1 stripe 1 Robust noble hop 1 Buddha 1 shells 1 edible flowers 1 Nice subtlety of spicing 1

127 Aroma Freq Flavour Freq date breads 1 emphasis on cannabis 1 scream 1 homemade 1 lotion 1 bucket 1 old tea 1 cream of wheat 1 warm rubber band 1 slice 1 strawberry shortcake with lots 1 split pea soup 1 Elmer's glue 1 egg nog with lots 1 linen 1 hint of fat 1 hint of polish 1 Earthy apples on the tree 1 cocktail 1 cologne 1 floral bush 1 sesame bars 1 cork 1 hint of oyster shell 1 wet newspaper 1 Italian roll 1 tomatoes 1 lots of whipped cream 1 touch of fruit 1 apples on a tree 1 Palmer 1 cage 1 fish skin 1 balloon 1 lady grey tea with cream 1 flesh 1 deep dish pizza 1 everything in a barnyard barnyard 1 pig 1 alpine 1 cigarette butt 1

128 Aroma Freq Flavour Freq single village chocolate 1 beach grass 1 car wax 1 Arnold Palmer 1 skins 1 day old bread 1 shoe polish 1 flower patch earth 1 drops 1 dirt 1 green apples on the tree 1 hints 1 wood smoke 1 pit 1 Arnold 1 horse hair brush 1 rolls 1 kitty litter box 1 hints of coffee 1 cloth 1 potter's clay 1 wet dog 1 lots of nutmeg 1 fine French soap 1 oven 1 cod 1 leather chair 1

129 APPENDIX B. Beer category and bitterness label list

None: Low/Medium Common Cider Baltic Porter Lager English Cider Gluten Free Ale Flavored Ale Gluten Free Lager Flavored Lager Kolsch French Cider Trappist or Abbey Quadrupel Fruit Cider Trappist or Abbey Tripel Lambic Wood Aged Ale Lambic Faro Lambic Fruit Medium Lambic Gueuze Alt Perry Belgian Style Pale Ale Rauchbier Biere de Garde Sour Ale English Style IPA Spanish Cider ESB Flavored Wheat Beer Faint: Wheat Ale Berliner Weisse Cream Ale Medium/High Dunkelweizen American Style Brown Ale Eisbock Barrel Aged Imperial Stout English Style Brown Ale Barrel Aged Lager Flanders Style Brown Ale Belgian Style Specialty Ale Flanders Style Red Ale Specialty Single Hop Gose Hefeweizen High Irish Style Ale Specialty Lagers Kristallweizen Malt Liquor Extreme Munich Helles / Old Ale Reduced Calorie Lager Weizen Bock White Beer

Low Belgian Style Blonde Ale Doppelbock Keller/Zwickl Maibock Mild Non Alcoholic Lager Trappist or Abbey Dubbel Trappist or Abbey Singel

130