Electronic Nose Chemical Sensor Feasibility Study for the Differentiation of Apple Cultivars

ELECTRONIC NOSE CHEMICAL SENSOR FEASIBILITY STUDY FOR THE DIFFERENTIATION OF APPLE CULTIVARS

W. N. Marrazzo, P. H. Heinemann, R. E. Crassweller, E. LeBlanc

ABSTRACT. The ability to analytically differentiate and match intact apple (Malus domestica, Borkh) fruit and fruit juice extracts from different apple cultivars is of interest to the food industry. This study tested the feasibility of detecting the difference among volatile gases evolved from intact ‘McIntosh (Buhr),’ ‘Delicious,’ and ‘Gala’ apples and their extracted juice using a prototype 32−array polymeric detector chemical sensor. All data were first processed to obtain principal components. PCA analysis clearly separated whole ‘McIntosh,’ ‘Gala,’ and ‘Delicious’ samples from juiced on day 1. PCA analysis of day 2 samples showed clustering of whole vs. juiced for all three cultivars, although there was some overlap between the clusters. A soft independent modeling of class analogy (SIMCA) class discrimination of the sensor principal component data sets was then performed to determine the degree of difference. SIMCA analysis of the same samples showed a pronounced difference (SIMCA value >3.00) for only the ‘McIntosh’ samples. SIMCA values between 2.00 and 3.00 were found for the other two cultivars on day 1. For day 2 samples, no SIMCA values greater than 2.00 were found for any cultivar whole vs. juiced. PCA analysis showed clear separation between cultivars for day 1 whole samples. SIMCA analysis showed that there was a difference between ‘Delicious’ and ‘McIntosh’ and between ‘Delicious’ and ‘Gala.’ Neither PCA nor SIMCA showed good separation between day 2 whole cultivars, nor between juiced cultivars on either day. As a reference, the same sample headspace volatile gases were analyzed with a mass spectrometer. A hierarchical cluster analysis (HCA) of the principal components from the mass spectrometer data sets revealed five clusters that discriminated differences among intact apple and apple juice samples but did not discriminate between samples from different apple cultivars. Keywords. Apple volatiles, Electronic nose, Nanotechnology, Sensory evaluation.

ualitative and quantitative differences have been Delwiche, 1998). The primary disadvantages of solid−state found in the volatiles emitted from intact apples sensors, however, are sensor poisoning, alcohol and water in- (Malus domestica, Borkh) of different cultivars terference, lack of volatile structural information, short and and from juice extracts of the same apples (Di- long term drift, and time−consuming calibrations (InfoMe- Qmick and Hoskin, 1983; MacGregor et al., 1964; trix, 1999). Morton and MacLeod, 1990). Refinement of analytic detec- Several studies have investigated the use of “electronic tion methods for rapid detection of these differences contin- nose” type sensors for apple quality evaluation. Royal Gala ues. The quest remains to replace the human nose as the apple aroma was characterized through the use of an sensory analysis tool most widely used for quantification of electronic nose in a study by Young et al. (1999). The goal food−associated volatile gases. Volatile gases emitted from was to determine the utility of the nose as a maturity foods are often measured using chromatography and/or spec- indicator. Apples harvested at four different maturity levels troscopy with either temporal or spectral separation. Recent were assessed and compared to gas chromatography and a studies have compared the advantages and disadvantages of sensory panel. The electronic nose and the GC techniques gas chromatography−mass spectroscopy (GC−MS; Heiden et were able to classify the apples by harvest date. al., 2002) to separate, identify, and quantify volatile gases. Brezmes et al. (2001) assessed ripeness of Pinklady apples More recently, there has been increased reliance on the use using an electronic nose and PCA, fuzzy logic, and neural of chemometric techniques. These detectors vary from rapid network classification. The fuzzy logic and neural network analysis mass spectrometers to solid−state sensors (Sarig and approaches were successful in classifying the apples based on shelf life. The PCA was unsuccessful in determining clusters based on ripeness. Article was submitted for review in November 2004; approved for Rye and Mercer (2003) used an electronic nose to publication by the Information & Electrical Technologies Division of determine differences in the headspace of processed vs. ASABE in September 2005. non−processed apple cider, as well as differences based on The authors are William N. Marrazzo, Former Graduate Fellow, and different thermal processing temperatures. The electronic Paul H. Heinemann, ASABE Member Engineer, Professor, Department of Agricultural and Biological Engineering, The Pennsylvania State nose was able to show significant differences between apple University, University Park, Pennsylvania; Robert E. Crassweller, cider processed at 90°C and unprocessed apple cider. Professor, Department of Horticulture, The Pennsylvania State University, However, there were no significant differences between University Park, Pennsylvania; and Eric LeBlanc, Statistician, USDA− unprocessed cider and cider processed at 60°C, 70°C, and ARS−BARC Statistics Group, Beltsville, Maryland. Corresponding 80°C. author: Paul Heinemann, 224 Agricultural Engineering Bldg., University Park, PA 16802; phone: 814−865−2633; fax: 814−863−1031; e−mail: An electronic nose and a mass spectrometer based [email protected]. electronic nose (MSE−nose) were used by Lammertyn et al.

Transactions of the ASAE Vol. 48(5): 1995−2002 2005 American Society of Agricultural Engineers ISSN 0001−2351 1995 (2003) to investigate changes in volatile emissions from MULTIPLE ARRAY CHEMICAL SENSOR − DATA LOGGER apples after a storage time of eight months. The apples were PROTOTYPE stored under three different conditions, and then the head- The multiple array chemical sensor − data logger used in this spaces were measured for 15 days after removing the apples study was provided by Cyrano Sciences, Inc., Pasadena, from storage. Canonical variate (CV) analysis was able to California (prototype data logger serial number distinguish differences in storage and changes over the DL−0027−012999, sensor set number CN−7100A−608). It has 15−day shelf−life period. However, the electronic nose could a portable polymeric resistance−change sensor unit that con- only detect differences over the shelf−life period and could nects to a laptop computer. The instrument is also referred to as not show differences between storage conditions. a data logger, multiple array chemical sensor, chemical sensor, To alleviate the disadvantages previously described by “e−nose,” or “electronic nose.” The chemical sensors (32 total) InfoMetrix (1999), a prototype chemical sensor was devel- are carbon elements impregnated with distinct non−conducting oped by Cyrano Sciences (Doleman et al., 1998). This sensor polymers whose resistance changes when exposed to different is made up of an array of carbon black organic polymer volatile gases. Since each sensor is unique, each will respond to composites that swell differentially and reversibly upon different chemical compounds after exposure to a headspace exposure to various volatile gases or classes of volatile sample. The sensors are enclosed in an electronically heated chemical compounds. The chemical sensor array’s quick chamber with inlet and outlet ports. Two intake lines (sample response time, instrument portability, and ease of use would and purge, with an electronic valve to switch between them) facilitate the industrial and laboratory need for a practical feed the sampling pump connected to the inlet of the sensor tool to identify and characterize agricultural commodities chamber (fig. 1). When the sample gas stream passes over the and other foods through analyses of volatile gases. The sensors, they swell depending on each sensor’s susceptibility to objective of this study was to evaluate the feasibility of the compounds in the sample. This swelling causes a change in detecting differences between volatile gases evolved from sensor resistance, which is measured by the device and given as intact apples and apple juice extracts from different cultivars output (fig. 2). Hence, each sample provides up to 32 different using this prototype array chemical sensor. resistance responses, which can be used to characterize the sample. Only 31 of the 32 sensors were used in this study since the MATERIALS AND METHODS manufacturer indicated that the output from one of the sensors was unreliable and should not be used. The prototype SAMPLES chemical sensors used in the data logger were not expected ‘McIntosh,’ ‘Delicious,’ and ‘Gala’ apples were pur- to be as stable, accurate, or precise as those manufactured chased from a retail market in June 1999. For each variety, using refined mass−production techniques. In this regard, the apples less than 8.5 cm diameter were randomly selected, results obtained in this study with the prototype sensors were weighed, and transferred to a two−gallon glass jar, which was considered a worst−case scenario. sealed and fitted with ports for flow−through gas sampling. Each sample jar contained five fruit with a total weight CHEMICAL SENSOR PROCEDURE between 850 and 900 g. Samples were prepared and The steady−state headspace atmosphere of samples headspace volatile gases measured on two consecutive days. The ambient temperature during measurements was 23.3°C prepared as described above was measured using a nitrogen ±0.5°C and 25.3°C ± 0.7°C on day 1 and day 2, respectively. After measuring evolved volatile gases from each five−fruit sample, juice was extracted from each five−fruit sample using a potato ricer lined with cheesecloth. A potato ricer is a kitchen utensil used to pass cooked potatoes through a grating, which produces a rice−like stream of material. The aliquot of all juice was transferred to a 1.0 L Erlenmeyer flask and sealed for flow−through measurement of headspace gases.

MASS SPECTROMETER PROCEDURE Apple volatile gases from the intact fruit and juice samples were quantified using a quadrupole MS procedure. Five replicate 10 mL gas samples were transferred from the headspace of each fruit and juice sample to sealed 10 mL evacuated vials using a gas−tight syringe. The samples were heated to 40°C and held for 5 min before being automatically injected into a quick headspace−sampling MS (HP−4440A, Agilent Technologies, Rockville, Md.) via a 3 mL loop. The MS source temperature and quadrupole temperatures were 230°C and 150°C, respectively. Mass spectra were collected over the range of 40 to 225 mHz.

Figure 1. Purge (a) and sample (b) cycles for the chemical sensor (Cyrano Sciences, 2000).

1996 TRANSACTIONS OF THE ASAE A hierarchical cluster analysis (HCA with average linkage) (SAS, 1997) was performed using the first four principal components and the 67 selected volatile ion components. Analysis of the cluster history using the pseudo F statistics revealed that the number of clusters formed was five. The contents of the five clusters are shown in table 1. The clusters separated intact apple samples from juice samples with the exception of ‘Delicious’ juice. A single cluster contained all three whole apple cultivars from day 1.

Resistance (R) A single cluster contained all three whole apple cultivars from day 2 coupled with ‘Delicious’ day 1 juice. All day 2 juice samples and day 1 ‘Gala’ juice samples formed another cluster. ‘McIntosh’ juice and the air blanks each formed a cluster. These data served as a reference for the study. The results show that the MS was able to differentiate two classes: Time apples and apple juice (with the exception of ‘Delicious’ Figure 2. Resistance response during sampling. The DR value is depen- apple juice from day 1). dent on each individual sensor’s susceptibility to a particular compound or group of compounds. POLYMERIC CHEMICAL SENSOR The data sets for each sample were normalized for each of gas flow−through system connected to the input of the chemical the sensors to compensate for total volatile concentration sensor. The developer of the prototype chemical sensor differences. The data (change in resistance) from all the designated the sampling protocol cycle as a 10 s baseline room sensors were initially entered into a principal component air flush, followed by a 25 s headspace sampling, and then a 60 analysis (PCA) using Pirouette software (Infometrix, Inc., s room air flush. Sensor chamber temperature was maintained Woodinville, Wash.). at 28°C, and flow rate through the sensor chamber was a The sample pair separation was tested using SIMCA constant 150 mL min−1 using the 3V default pump setting. A (soft independent modeling of class analogy analysis) single data set consisted of the average of five consecutive software, analyzing the principal component data of the 31 readings of the same sample. Since immediate data analysis was chemical sensors. The analysis was performed on separa- not possible, two data sets were consecutively collected and tion by cultivars, sampling date, and sample type. The then statistically analyzed. Only data sets in which the number of principal components required for this analysis headspace atmosphere maintained an apparent steady state varied from case to case to optimize the SIMCA model. throughout sampling were used for analysis. The determination The number ranged from one to four principal component of steady state was made after readings were taken by observing reduced data sets. The principal component loadings the change from reading to reading. Each sample was measured indicated that data from five sensors (sensors 11 through 10 times in sequence. 15) were redundant. Detection of Volatile Gas Differences Between Intact Apples and Apple Juice Samples of the Same Cultivar RESULTS AND DISCUSSION In figure 4, the difference in headspace volatile gases MASS SPECTROSCOPY among fruit samples measured on day 1 is shown with a plot The reference mass spectra data consisted of the total ion of principal component 1 versus 2 for ‘Gala’ and ‘Delicious’ abundance of all volatile ion components over the range of apple samples and principal component 1 versus 3 for 40 to 225 mHz in the headspace atmosphere of samples. An ‘McIntosh’ apple samples. Clustering of the data when example of the mass spectrum (relative abundance vs. plotted by these principal components is clear, particularly frequency) for an apple sample is shown in figure 3. The with the ‘Gala’ and ‘Delicious’ cultivars, indicating that the peaks in the graph correspond to specific compounds that are sensors were able to detect differences in volatiles released present within the sample headspace. Mass spectra data for from the intact and juiced apples. each treatment were normalized and analyzed using a A similar set for day 2 is shown in figure 5. Principal principal component procedure (SAS, 1997). The principal components for these data were 4 versus 5 for ‘McIntosh,’ component analysis uses linear combinations of the original 1 versus 3 for ‘Gala,’ and 2 versus 4 for ‘Delicious.’ variables to explain the variance−covariance structure of the Clustering of the sample data when plotted by principal data, and provides an output of those principal components components is still evident in the day 2 set, although not as that account for the greatest percentage of variance. Most of strong as found in the day 1 set. There is some overlap in the the variability can often be explained by a small number of ‘McIntosh’ values. principal components (Johnson and Wichern, 1992). The The principal component data were modeled with SIM- data are then examined in the eigenvector principal compo- CA, and class differences were calculated (table 2). The nent space for separation. The first four principal components greater the difference between compared samples, the higher accounted for 40.6%, 16.4%, 12.5%, and 10.2% of the the SIMCA difference determination value. A SIMCA variance, respectively (i.e., 79.7% of the total variance). The difference of 3.00 and above indicates a pronounced volatile ion components over the range of 40 to 225 mHz that difference. Day 1 ‘McIntosh’ intact apple versus apple juice best define these four principal components were determined had a pronounced SIMCA difference of 3.13. All other intact by the loading values. apple versus apple juice SIMCA difference tests were not

Vol. 48(5): 1995−2002 1997 100

50 Abundance 40

Relative 30

0 40 60 80 100 120 140 160 180 200 220

mHz Figure 3. Mass spectra of an apple sample. Peaks correspond to specific compounds found within the headspace of the sample.

Table 1. HCA clusters derived from mass spectra data but the damage inflicted by juicing evidently produces of ‘McIntosh,’ ‘Delicious,’ and ‘Gala’ apples and volatiles that are similar between cultivars. their juice extracts collected over two days. The principal component data were modeled with SIM- Cluster 1: ‘Gala’ apples Day 1 CA, and the class differences were calculated (table 3). Day 1 ‘McIntosh’ apples Day 1 intact apples ‘Delicious’ versus ‘McIntosh’ and ‘Delicious’ ‘Delicious’ apples Day 1 versus ‘Gala’ had pronounced SIMCA differences of 3.36 Cluster 2: ‘McIntosh’ apples Day 2 and 4.91, respectively. The day 1 juice sample SIMCA ‘Delicious’ apples Day 2 differences for these were 2.51 and 2.90, respectively. All ‘Delicious’ apples Day 2 other differences showed SIMCA differences less than 3.00. ‘Delicious’ apple juice Day 1 Cluster 3: ‘Gala’ apple juice Day 1 ‘Gala’ apple juice Day 2 ‘McIntosh’ apple juice Day 2 CONCLUSIONS ‘Delicious’ apple juice Day 2 The prototype chemical sensor measurements of head- Cluster 4: ‘McIntosh’ apple juice Day 1 space atmospheres of ‘McIntosh,’ ‘Delicious,’ and ‘Gala’ Cluster 5: Air blank Day 1 apples and their juice extracts indicated differences in Air blank Day 2 volatile composition. This difference between apples and apple juice was also noted in the reference MS analyses. pronounced (less than 3.00). The highest of these was 2.67, Detection of these differences in the fresh or processed fruit while the lowest was 1.22. The ranking of highest to lowest and vegetable industries is a potential application for the SIMCA intact apple versus apple juice differences is: day 1 chemical sensor. Analyzed measurements for intact and ‘Delicious’ (2.67), day 1 ‘Gala’ (2.13), day 2 ‘Gala’ (1.70), juiced apple samples were compared. With the sensor, PCA day 2 ‘Delicious’ (1.60), and day 2 ‘McIntosh’ (1.22). analysis clearly separated whole ‘McIntosh,’ ‘Gala,’ and ‘Delicious’ samples from juiced on day 1. PCA analysis of Detection of Volatile Gas Differences of Intact Apples and day 2 samples showed clustering of whole vs. juiced for all Apple Juice Samples of Different Cultivars three cultivars, although there was some overlap between the PCA analysis of different cultivars for intact apple and the clusters. A SIMCA analysis of the same samples showed a juice from those apples for day 1 headspace samples is shown pronounced difference (SIMCA value >3.00) for only the in figure 6. The same comparisons for day 2 apples are shown ‘McIntosh’ samples. SIMCA values between 2.00 and 3.00 in figure 7. Clear separation between samples of the three were found for the other two cultivars on day 1. For day 2 intact apple cultivars can be seen in figure 6. After juicing, samples, no SIMCA values greater than 2.00 were found for however, the separation is not as evident, and there is some any cultivar whole vs. juiced. overlap between several of the samples. Intact apples may PCA analysis showed clear separation between cultivars emit volatiles that are distinctly different between cultivars, for day 1 whole samples. SIMCA analysis showed that there

1998 TRANSACTIONS OF THE ASAE Figure 4. Chemical sensor principal component analysis of whole and juice apple headspace gas, day 1 samples. Each graph shows the difference between whole apple and apple juice for the specific cultivar.

Figure 5. Chemical sensor principal component analysis of whole and juice apple headspace gas, day 2 samples. Each graph shows the difference between whole apple and apple juice for the specific cultivar.

Vol. 48(5): 1995−2002 1999 Table 2. Chemical sensor SIMCA class difference determination values of whole apple and apple juice, day 1 and day 2, ‘McIntosh,’ ‘Gala,’ and ‘Delicious’ cultivars, for comparison between whole apple and apple juice (Pirouette software analysis; Roy, 1999). Values of 3.00 or greater indicate a pronounced difference. Whole Apple Apple Juice Whole Apple Apple Juice ‘McIntosh’ − Day 1 ‘McIntosh’ − Day 2 Principal components 1 3 Principal components 3 3 Whole apple 0.88 1.46 Whole apple 0.59 0.77 Apple juice 3.13 0.68 Apple juice 1.22 0.62 ‘Gala’ − Day 1 ‘Gala’ − Day 2 Principal components 4 4 Principal components 4 4 Whole apple 0.49 2.13 Whole apple 0.54 0.95 Apple juice 1.67 0.51 Apple juice 1.70 0.42 ‘Delicious’ − Day 1 ‘Delicious’ − Day 2 Principal components 3 2 Principal components 2 1 Whole apple 0.52 2.67 Whole apple 0.57 1.60 Apple juice 1.28 0.55 Apple juice 1.25 0.86

Figure 6. Chemical sensor principal component analysis of whole and juiced apple headspace gas, day 1 samples. was a difference between ‘Delicious’ and ‘McIntosh;’ and confidence for the detection of damage or other similar ‘Delicious’ and ‘Gala.’ Neither PCA nor SIMCA showed applications. good separation between day 2 whole cultivars, nor between One of the biggest problems found with the prototype unit juiced cultivars on either day. was that the sensors were manufactured by hand and may not The PCA and SIMCA results indicate that the polymeric have had consistent surfaces. This led to variability in the chemical sensor has the potential to discriminate between the baseline of the sensor response, which may have caused volatile gases in the headspace atmosphere of intact apples inconsistency in the actual sample responses. Improvements and apple juice extracts of different cultivars under certain in the manufacturing of a production unit, with precise sensor circumstances. A primary potential use includes detection of coatings, would most likely improve the success of the sensor damage in fresh or processed apples, although further testing in differentiating between volatiles emitted by different will be required to determine if the unit can be used with cultivars and juiced vs. whole apples.

2000 TRANSACTIONS OF THE ASAE Figure 7. Chemical sensor principal component analysis of whole and juice apple headspace gas, day 2 samples.

Table 3. Chemical sensor SIMCA class difference determination values ian Fruit Research Center (Kearneysville, West Virginia), the of whole apple and apple juice, day 1 and day 2, for a comparison USDA−ARS−BARC Produce Quality and Safety Laboratory between ‘McIntosh,’ ‘Gala,’ and ‘Delicious’ cultivars (Pirouette software analysis; Roy, 1999). Values of (Beltsville, Maryland), and Agilent Technologies. 3.00 or greater indicate a pronounced difference. ‘McIntosh’ ‘Gala’ ‘Delicious’ Intact Apples, Day 1 REFERENCES Principal components 1 2 2 Brezmes, J., E. Llobet, X. Vilanova, J. Orts, G. Saiz, and X. Correig. ‘McIntosh’ 0.87 1.25 3.36 2001. Correlation between electronic nose signals and fruit ‘Gala’ 2.05 0.77 4.91 quality indicators on shelf−life measurements with pinklady ‘Delicious’ 1.88 2.03 0.85 apples. Sensors and Actuators B: Chemical 80(1): 41−50. Intact Apples, Day 2 Cyrano Sciences. 2000. The Cyrano 320 Electronic Nose: User’s Principal components 4 3 3 Manual. Part No. 11−60001, revision 270600. Pasadena, Cal.: ‘McIntosh’ 0.49 0.83 1.33 Cyrano Sciences. Dimick, P. S., and J. C. Hoskin. 1983. Review of apple flavor − ‘Gala’ 0.82 0.54 1.54 State of the art. CRC Critical Reviews in Food Science and ‘Delicious’ 0.76 0.63 0.54 Nutrition 18(4): 387−409. Apple Juice, Day 1 Doleman, B. J., R. D. Sanner, E. Severin, R. H. Grubbs, and N. S. Principal components 2 3 4 Lewis. 1998. Use of compatible polymer blends to fabricate ‘McIntosh’ 0.75 1.38 2.51 arrays of carbon black−polymer composite vapor detectors. Anal ‘Gala’ 0.95 0.66 2.90 Chem. 70: 2560−2564. ‘Delicious’ 0.97 1.70 0.79 Heiden, A. C., C. Gil, and L. S. Ramos. 2002. Rapid screening of Apple Juice, Day 2 headspace samples: Pros and cons of using MS−based electronic Principal components 4 4 3 noses versus fast chromatography. AppNote. Mulheim an der ‘McIntosh’ 0.72 0.54 0.85 Ruhr, Germany: Gerstel GmbH. InfoMetrix. 1999. Electronic nose instrumentation: Comparing odor ‘Gala’ 0.54 1.03 0.86 evaluation technologies. Application Note. Woodinville, Wash.: ‘Delicious’ 0.84 1.12 0.58 InfoMetrix, Inc. Johnson, R. A., and D. W. Wichern. 1992. Applied Multivariate ACKNOWLEDGMENT Statistical Analysis. 3rd ed. Englewood Cliffs, N.J.: A grant from the USDA and a grant from Cyrano Sciences Prentice−Hall. supported this research. Additional support was provided by Lammertyn, J. J., S. Saevels, E. A. Veraverbeke, A. Z. Berna, C. di The Pennsylvania State University, the USDA−ARS Appalach- Natale, and B. M. Nicolaï. 2003. Mass spectrometry based

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2002 TRANSACTIONS OF THE ASAE