Recent Advances in Untargeted and Targeted Approaches Applied in Herbal-Extracts and Essential-Oils ﬁngerprinting-Areview

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Recent advances in untargeted and targeted approaches applied in herbal-extracts and essential-oils ﬁngerprinting-Areview

Mourad Kharbach, Ilias Marmouzi, Meryem El Jemli, Abdelaziz Bouklouze, Yvan Vander Heyden

PII: S0731-7085(19)31484-0 DOI: https://doi.org/10.1016/j.jpba.2019.112849 Article Number: 112849 Reference: PBA 112849

To appear in: Journal of Pharmaceutical and Biomedical Analysis

Received Date: 15 June 2019 Revised Date: 27 August 2019 Accepted Date: 29 August 2019

Please cite this article as: Kharbach M, Marmouzi I, El Jemli M, Bouklouze A, Heyden YV, Recent advances in untargeted and targeted approaches applied in herbal-extracts and essential-oils ﬁngerprinting-Areview,Journal of Pharmaceutical and Biomedical Analysis (2019), doi: https://doi.org/10.1016/j.jpba.2019.112849

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Mourad Kharbach1,2*, Ilias Marmouzi3, Meryem El Jemli4, Abdelaziz Bouklouze2, Yvan Vander Heyden1*

1 Department of Analytical Chemistry, Applied Chemometrics and Molecular Modelling, CePhaR, Vrije Universiteit Brussel (VUB), Laarbeeklaan 103, B-1090 Brussels, Belgium 2 Biopharmaceutical and Toxicological Analysis Research Team, Laboratory of Pharmacology and Toxicology, Faculty of Medicine and Pharmacy, University Mohammed V- Rabat- Morocco 3 Pharmacodynamics Research Team, Laboratory of Pharmacology and Toxicology, Faculty of Medicine and Pharmacy, University Mohammed V- Rabat- Morocco 4 Pharmacokinetics Research Team, Laboratory of Pharmacology and Toxicology, Faculty of Medicine and Pharmacy, University Mohammed V- Rabat- Morocco

*Corresponding authors. E-mail address: [email protected] (Yvan Vander Heyden); [email protected] (Mourad Kharbach).

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Graphical abstract

Highlights Journal Overview of the most useful fingerprintingPre-proof techniques used for taxonomic identification, geographical discrimination and quality assessment of herbal extracts and essential oils.  Screening a wide range of untargeted and targeted fingerprinting approaches.

 Proper application of fingerprinting techniques on herbal extracts and essential oils has been discussed.  Chemometric tools applied to the fingerprinting techniques are reviewed.

Abstract

Herbal extracts and essential oils have been used over the centuries for their dietary, cosmetic and therapeutic properties. Quality control is needed to guarantee the safety and quality of these consumables. In this regard, fingerprinting techniques are important for inspection of the authenticity and for quality control. Analytical fingerprinting techniques provide signals related to the composition of a matrix (oil, plant extract, food…). The resulting fingerprint (spectrum or chromatogram) obtained for an untargeted or targeted approach is coupled to chemometric data processing, which may allow, for instance, the desired identification or discrimination of the sample considered. In this context, recent advances in untargeted/targeted fingerprinting approaches (especially chromatographic and spectroscopic) were described and their application in the taxonomic identification, classification and authentication of plants (medicinal) and essential oils discussed. An overview of the applications of untargeted/targeted fingerprinting techniques on herbal- extracts and essential-oils analysis, using different chemometric tools, has been included.

List of abbreviations

 Instrumentation ATR-FTIR: attenuated total reflectance Fourier transform infrared; CP: cold pressing; DAD: diode array detector; DART-TOF-MS: direct analysis in real time time-of-flight mass spectrometry; DSC: differential scanning calorimetry; EI: electron ionization; E-nose: electronic nose; ESI: electrospray ionization; ELSD: evaporative light scattering detection; FID: flame ionization detection, FIMS: flow injection mass spectrometry; FIA-ESI-MS: flow injection analysis electrosprayJournal ionization mass spectrometry; GC : Pre-proofgas chromatography; HPLC: high performance liquid chromatography; HPLC-CAD: HPLC-charged aerosol detector; HPLC-ECD: HPLC- eletrochemical detector; HPSEC: high performance size-exclusion chromatography; HS-SPME: headspace solid-phase microextraction; 1H NMR: Hydrogen nuclear magnetic resonance ; ICP: inductively coupled plasma-mass spectrometry; iEESI-MS: internal extractive electrospray ionization mass spectrometry; LIBS: laser induced breakdown spectroscopy; MAE: microwave-

3 assisted extraction; MALDI: matrix-assisted laser desorption/ionization; MF: mean field; MID-IR: mid-infrared; pCEC: pressurized capillary electrochromatography; PCD-HPLC: pre-column derivatization HPLC; PDA: photo diode array; PS-MS: paper spray mass spectrometry; MS: mass spectroscopy; NIR: near infrared; PS: paper spray; QAMS: quantitative analysis multi-components by single marker; SAW: surface acoustics wave; SCFE: super-critical-ﬂuid extraction; SDE: simultaneous distillation and extraction; SFS: spectral fluorescence signature; SH: static headspace; TLC: thin-layer chromatography; U(H)PLC –QTOF MS/MS: ultra-(high)-performance liquid chromatography coupled with quadrupole time-of-flight mass spectrometry; UAE: ultrasound-assisted extraction; UV: ultraviolet; WD: water distillation; XPS: x-ray photoelectron spectroscopy.

 Chemometrics AHC: agglomerative hierarchical clustering; AMWFA: alternative moving window factor analysis; ANN(s): Artificial neural networks; ANOVA: analysis of variance; AR: alternating regression; ATLD: Alternating trilinear decomposition; BDA: Bayes discriminant analysis; BP-ANN: back propagation-ANN; CA: cluster analysis; CART: classification and regression trees; CCA: canonical correlation analysis; CDA: canonical discriminant analysis; COW: correlation optimized warping; CVA: canonical variates analysis; CP-ANN: counter propagation artificial neural network; DFA: discriminant function analysis; DS-MCR-ALS: distance-selection multivariate-curve-resolution alternating least squares; DTW: dynamic time warping; EFA: evolving factor analysis; EWOP: evolving window orthogonal projection; FA: factor analysis; FSMWEFA: fixed-size moving- window evolving factor analysis; FW: fuzzy warping; HCA: hierarchical cluster analysis; HELP: heuristic evolving latent projections; H-PLS: hierarchical-PLS; ICA: independent component analysis; ITTFA: iterative target transformation factor analysis; KDA: kernel discriminant analysis; KNN: k-nearest neighbors; LDA: linear discriminant analysis; LS-SVM: least-squares support vector machine; LWR: locally weighted regression; MB-PLS; multiBlock-PLS; MCR-ALS: multivariate-curve-resolution alternating least squares; MCR-FEQZ: MCR-objective function equal to zero; MDS: multidimensional scaling; MF-ICA: mean field independent component analysis; MLP: multilayer perceptron; MLR(A): multiple linear regression (analysis); MSC: multiplicative scatter correction; MW-PCA: Moving window-PCA; OPLS: orthogonal projections to latent Journal structures; OPLS-DA: OPLS-discriminant Pre-proof analysis; OPR : orthogonal projection resolution; OSC: orthogonal signal correction; PARAFAC: parallel factor analysis; PCA: principal component analysis; PCR: principal component regression; PLS(R): partial least squares (regression); PLS-DA: PLS-discriminant analysis; PP: projection pursuit; PTW: parametric time warping; SA: similarity analysis; SBM: simplified Borgen method; SFA: sub-window factor analysis; SIA: Selective ion analysis; SIMCA: soft independent modeling of class analogy;

SIMPLISMA: simple-to-use interactive self-modeling mixture analysis; SIMPLS: statistically inspired modification of PLS; SNV: standard normal variate; SOM: self-organising map; STW: semi-parametric time warping; SVM: support vector machine; SVMR: SVM-regression TPA: target peak alignment; RBF-ANN: radial basis function-ANN; VIP : variable importance in projection; VSMW-EFA: variable-size moving-window evolving factor analysis; UPCA: unfold-PCA; UVE-PLS: uninformative variable elimination PLS; WFA: window factor analysis.

Keywords: Herbal extracts, Essential oils, Chemometric tools, Untargeted fingerprinting, Targeted fingerprinting, Taxonomic identification, Geographical origin discrimination.

Content: 1. Introduction

2. Chemometric tools applied in fingerprinting analysis 2.1. Pretreatment techniques 2.2. Exploratory data analysis and classification tools 2.3. Regression tools 2.4. Mixture analysis

3. Herbal extracts and their untargeted/targeted fingerprintings 3.1. Spectroscopic fingerprinting of herbal extracts 3.1.1. Seasonal and geographic indications 3.1.2. Chemotaxonomic identification 3.1.3. Processing and extraction methods 3.1.4. Metabolomics, quality and authentication 3.2. Chromatographic fingerprinting of herbal extracts 3.2.1. Seasonal and geographic indications 3.2.2. Chemotaxonomic identification Journal3.2.3. Processing and extraction methodsPre-proof 3.2.4. Metabolomics, quality and authentication

4. Essential oils and their untargeted/targeted fingerprintings 4.1. Spectroscopic fingerprintiang of EOs

4.1.1. Seasonal and geographic indications 4.1.2. Chemotaxonomic identification 4.1.3. Metabolomics, quality and authentication 4.2. Chromatographic fingerprinting of EOs 4.2.1. Seasonal and geographic indications 4.2.2. Chemotaxonomic identification 4.2.3. Processing and extraction methods 4.2.4. Metabolomics, quality and authentication.

5. Conclusions & perspectives 1. Introduction The use of herbal parts (raw materials) for medicinal formulations, cosmetics, fragrances, nutrition and food additives (spices) implies also the need for sophisticated analytical techniques and methods to assess their quality [1]. The World Health Organization (WHO) has accepted fingerprint analysis as a methodology for assessing herbal quality and in safety evaluation of herbal products [2, 3]. Traditional formulations can be presented as either a single or a combination of medicinal herbs. Generally, plant extracts are complex mixures of hundreds of compounds exhibiting synergetic properties. Then, metabolites with even low concentrations may be important for the quality, safety and efficacy of the herbal formulation [4]. The biological activities in a plant are correlated to the complex interactions between the molecules. The challenge is to establish the relevant fingerprint. Nowadays, fingerprint development, i.e. the determination of a characteristic profile, of active (marker) constituents and of molecular composition, has become important in the quality control and in safety evaluation of herbal products. The ﬁngerprint represents a comprehensive profile, applied for the purpose of taxonomic specification, quality evaluation, identiﬁcation and authentication of herbal products [5]. Due to geographic and seasonal variations, and to environmental conditions, the chemical composition in active compounds of plants may change and affect the fingerprints. Many other factorsJournal may also affect the metabolit Pre-proofes contents, including maturity (harvest period), drying processes, and storage. In this regard, plant quality, toxicity and efficacy may be affected both by natural and processing conditions [1]. Taxonomic, physicochemical and sensorial similarities are being used to counterfeit original products. Authentic plants can usually be distinguished from counterfeits by a careful analysis of

6 their chemical composition [6]. Further on, adulteration is also a common practice on herb markets. The proper choice of a ﬁngerprinting technique depends on the characteristics of the constituents of the plant material. Several targeted techniques, e.g. HPLC, GC, CE, TLC coupled to many detectors, e.g. UV, DAD, FID, MS are applied for developing chemical- composition fingerprints [3, 4, 7]. Spectroscopic techniques, e.g. FTIR, NIR, NMR, and UV, are also used to construct herbal fingerprints [8]. Fingerprinting data combined with chemometric tools have the potential to assess the complex composition of herbal extracts and essential oils. Recent advances in chemometric tools, including sampling, designs of experiments, exploratory data analysis, data pretreatment tools, variable selection, regression tools and pattern-recognition techniques are dedicated to handle the plant fingerprints [9-11]. This paper provides a comprehensive overview of the analysis of herbal extracts and essential oils using untargeted and targeted fingerprinting techniques in combination with multivariate data analysis, in order to assess their: i) seasonal and geographic indications; ii) chemotaxonomic identification; iii) processing and extraction; and iiii) metabolomics, quality and authentication. The general analytical workflow and the chemometric tools used in the fingerprinting objectives are summarized in Figure1.

2. Chemometric tools applied in fingerprinting analysis The relationships between molecular composition in plants and their biological behavior, taxonomic identification; extraction-, harvest- and storage optimisation, , , environmental conditions and/or quality-control objectives require frequently the use of chemometric tools [8, 11-13]. Chemometric methods are crucial tools for sampling, optimization (designs of experiments), and to extract relevant information from the raw data (fingerprints), applying data pretreatment, variable selection, exploratory data analysis, multivariate calibration, and pattern recognition techniques [9-11]. Figure 2, shows the main steps and chemometric tools applied on the data obtained in untargeted and targetedJournal fingerprinting. It should be noticed Pre-proof that usually not all steps are required for the fingerprints of a given case study. Moreover, the sequence of some steps can be different than what is represented in Figure 2.

2.1. Pretreatment techniques

Chemometric pretreatments, i.e. transformations of fingerprints are important for eliminating and reducing variation in the data that is not of interest [10, 12]. Further, in herbal products the number of chemical markers and their proportion is dependent on experimental variations and sample treatment [4]. Therefore, pretreatment of the fingerprint data is necessary to achieve the most accurate results [5, 12]. Chemometric signal preprocessings, such as baseline correction, mean centering, derivatization, normalization, standard normal variate and multiplicative scatter correction are the most applied in vibrational spectroscopy [12]. Pretreatment tools as column centering, SNV, MSC, first and second derivatives are also widely applied in chromatographic fingerprinting [4]. Numerous approaches for peak alignment and retention-time correction, including correlation optimized warping, parametric time warping, target peak alignment, dynamic time warping, fuzzy warping, and semi-parametric time warping, have been applied [4, 5]. However, COW is carried out in many studies [4, 14]. Curve resolution algorithms on chromatographic data provide tools to preprocess overlapping and embedded peaks of a complex mixture [5, 15, 16]. Analytical spectral detectors coupled in hyphenated chromatographic systems analyze components qualitatively and quantitatively in one run, both in the chromatographic (time) and spectral directions [17]. Thus, chemometric data pretreatment approaches, both iterative and non-iterative, may be required to improve resolution, for quantitative estimation, determination of peak purity and detection of interferences [16-18].

2.2. Exploratory data analysis and classification tools Nowadays, the analytical techniques generate massive amounts of data that challenges the chemometric treatments to explore the hidden information. Exploratory data analysis is applied to localize possible relationships between samples and/or variables. Exploratory tools include, for instance, principal component analysis, factor analysis and projection pursuit methods which try to reduce and remove the redundancy of the data, and provide visualization means [4, 5]. However, PCA is the most applied exploratoryJournal technique to visualize and reduce Pre-proof the original data dimensionality, retaining maximal data variability. PCA defines new variables, i.e. latent variables, in the original data space. The projections of the samples on these variables are called scores and are linear transformation of the data variables. The weights in these transformations are

8 called loadings. PCA is used to localize similarities or dissimilarities between samples and to detect outliers [19]. Other unsupervised methods include cluster analysis and similarity analysis. They represent also basic techniques for variables information [4]. The CA purpose is to establish patterns or clusters in the data on the basis of similarities, e.g. distance-, correlation-based or some combination of both [20]. CA groups or clusters objects based on similarities applying diverse clustering approaches, like single- or complete-linkage clustering, Ward's method, and centroid method (k-means algorithm) [4, 20, 21]. Hierarchical cluster analysis is the most used technique to study similarity among herbal fingerprints and several papers applying HCA are cited in our present review. On the other hand, SA is an appropriate technique for the evaluation of similarity or dissimilarity between individual fingerprints [21]. Pattern recognition methods, such as linear and non-linear classification techniques, have been intensively integrated for the classification and identification of herbal products [10, 22]. A wide range of techniques,, including partial least squares–discriminant analysis, soft independent modeling of class analogy, support vector machine, linear discriminant analysis, canonical correlation analysis, k- neighbors, orthogonal projections to latent structures–discriminant analysis, and artificial neural networks, is applied for the classification and identification of herbal products [10, 11, 19, 20]. LDA is a linear supervised classification method, which focuses on finding optimal boundaries between classes [23]. The LDA method is closely related to the PCA method, which maximizes and minimizes the between-classes variance and the within-classes variance, respectively [10, 19, 23]. PLS-DA is also a parametric and linear multivariate projection technique applied when the Y responses are categorical. This technique identifies latent variables, which discriminate between the different objects (X block) in multivariate space, maximizing their covariance [20]. OPLS-DA is an extension of PLS-DA and is another multivariate classification technique applied in fingerprinting analysis [24]. In contrast, SIMCA is a supervised classification method based on the PCA discriminationJournal power, for each class only Pre-proof the significant principal components are retained [20, 25]. SVM is a computational learning technique, based on optimal separating in a hyperplane between the positive and negative objects. SVM is also a powerful tool for resolving both classification and regression problems, and which is applied in wide range of fields [20]. Further, ANNs are supervised pattern recognition and non-parametric

9 classification techniques applied for modeling non-linear systems [20, 23]. Each classification technique has its own advantages and drawbacks or limits and their selection depends on the case study. The quality of herbal products is influenced by several factors and depends on the number of chemical constituents occuring and their concentration [26, 27]. Therefore, linking fingerprints to bio-markers might represent a reliable characteristic to assess the quality of herbal medicines [10].

2.3. Regression tools In herbal quality analysis, multivariate calibration techniques have been intensively applied to indicate some markers and to model pharmacological properties as a function of the recorded fingerprints. Multivariate calibration methods were applied for solving various problems with herbal medicines data and especially in relation to quantitative information [28]. The multivariate modeling involves establishing a relationship between multivariate analytical data (fingerprints) and a quantitative response [28, 29]. Regularly applied multivariate calibration tools, are partial least squares regression [28], principal component regression [28], multiple linear regression [30], support vector machine regression [31], and orthogonal projections to latent structures [32]. PLS regression, also known as projection to latent structures, is a supervised technique that combines latent variables from the X data matrix (fingerprint) and based on the covariance with the Y vector or matrix (response) [28]. O-PLS is a supervised extension of PLS removing in the X matrix the non-correlated or orthogonal systematic variation to Y, prior to model building [32]. In PCR modeling, PCA is used to decompose the independent variables matrix X into principal components (orthogonal basis) and the new PC-scores matrix is used to model and predict the Y matrix [28]. On the other hand, an MLR-based method is a linear combination modeling between the single dependent variable Y and a number of independent variables from X. The regression coefficients are estimatedJournal with the least squares criterion [4,Pre-proof 30]. MLR is limited to situations where the number of variables is smaller than the number of samples (else variable selection is needed) and may suffer from multicollinearity between the variables [4]. SVMR is an extension of SVM, a nonlinear calibration technique, used for regression problems [31]. Variable selection algorithms are frequently applied to improve the analytical

10 performance of the regression techniques in order to construct robust calibration models [33].

2.4. Mixture analysis In the literature many chemometric approaches are used to get concentration profiles and spectral information of individual compounds in mixtures. For this purpose, curve resolution techniques result in chromatographic and spectral profiles of pure components. Examples of such techniques are heuristic evolving latent projections, orthogonal projection resolution, evolving factor analysis, window factor analysis, sub- window factor analysis, simple interactive self-modeling mixture analysis, evolving window orthogonal projections, alternating regression, iterative target transformation factor analysis and multivariate curve resolution-alternating least squares [15, 17, 18, 34, 35].

3. Herbal extracts and their untargeted/targeted fingerprints Herbal medicines have been widely used for hundreds of years in the prevention and treatment of human diseases [12]. A characteristic chemical profile of a plant or a fingerprint can be obtained by spectroscopic, chromatographic or electrophoretic techniques. This plant fingerprint may be crucial for the identification and classification of herbal species. The application of multivariate data analysis techniques for exploratory analyses, modeling and pattern recognition is useful to extract characteristic chemical information [4]. The fingerprint approach becomes a powerful standard for these purposes [7]. The current section of this review will disscuss relevant publications applying untargeted and targeted fingerprinting techniques on herbal-extracts, associated with chemometric tools, for their application in geographical-origin determination, taxonomic variation, processing, and quality assessment. For this section, relevant papers in this field using chemometric approaches in this field published between 2003 and 2019 will be discussedJournal. Pre-proof

3.1. Spectroscopic fingerprinting of herbal extracts Spectroscopic techniques operate at different ranges of electromagnetic radiation. Among the various techniques, in the field of plant analysis, spectroscopic application

11 includes near infrared spectroscopy (NIR), mid-IR analysis, Fourier transform infrared spectroscopy (FTIR), FTIR-ATR (Attenuated total reflection), Raman spectroscopy, nuclear magnetic resonance (NMR) and mass spectroscopy (MS). Spectroscopic techniques can be divided into targeted and non-targeted. Spectral fingerprinting has acquired on increasing attention in the field of metabolomics and chemotaxonomy and has been used for a wide variety of plants and plant products. Fingerprints are acquired from solid samples or herbal-extracts without prior separation. The analysis may provide useful information about the biochemical composition, structure, and interactions within a sample, especially in combination with chemometric techniques. The execution of an experimental design and the data processing of the spectral fingerprints can be a useful tool for characterizing the sources of variation in plant metabolomics [36]. Relevant studies carried out using spectral fingerprinting techniques and chemometric methods to assess and evaluate the herbal extract are listed in the Table 1. Some are discussed in the section below.

3.1.1. Seasonal and geographic indications Plant metabolomics, plant quality and the resulting fingerprints are significantly influenced by seasonal variations, maturity and/or geographic location. In this regard, the international community has adopted the Protected Geographic Indications (PGI) for food and herbal traceability. Hence, the determination of the provenance of any herbal extract or formulation is an important procedure to establish its value, identity and authenticity. As an example, grape berries biochemistry depends on the genotype, environmental conditions and growing practices in a given territory. To distinguish between samples from different territories, the skin and pulp of mature berry extracts from different locations in the sout west of France were analyzed using both conventional physicochemical analyses and 1H NMR spectrometry [37]. PCA was applied to both data sets to investigate the variability of the grape composition and to characterize groups of samples. AJournal significant clustering of the samples Pre-proof according to the metabolic profile of pulps or skins, in relation to their geographic origins, was observed. The physicochemical analyses were found to be more discriminant than the 1H NMR data, but NMR spectroscopy allowed identifying in the fingerprintings metabolites.

An interesting and widely used medicinal plant is Ganoderma lucidum[38]. The correct identification of its cultivation origin is an important step for proper quality assurance. However, till now this is mainly dependent on subjective morphological examinations. To develop an efficient way of discriminating its cultivation sources, an NMR-based metabolomic differentiation was examined. Conventional PCA showed some overlaps between the samples from China and Korea, while orthogonal projections to latent structure discriminant analysis provided a clean distinction between samples from both locations. The model was validated by means of a test set. The contributing variables to cultivation origin discimination were identified using an s-plot and their significance determined with a t-test. Both NMR and PCA analysis were applied to differenciate between Arabidopsis thaliana ecotypes samples [39]. Zhu et al [40], collected 11 samples of Portulaca oleracea from different sources to establish GC-MS and FTIR fingerprints in order to identify the plants geographic location. A similarity evaluation and hierarchical cluster analysis were performed to study the chemical variability between the samples. The results showed that both GC–MS and IR spectral fingerprints could be used for plant similarity evaluation. However, it should be noticed that the size of the data set seems to be too small to draw relevant conclusions.

3.1.2. Chemotaxonomic identification Taxonomic identification is crucial for plant authentication, quality and safety. The extracts of the Amazonian plant Arrabidaea chica are used as red pigments, antimicrobial agents and astringents [41]. Based on 3-deoxyanthocyanidins ions in ESI(+)-MS fingerprinting, three varieties cultivated in this region were discriminated using PLS-DA data. The exploratory descriminative techniques PCA and LDA were used in kiwi and pomelo discrimination according to the fruit species and subspecies (4 subsp for kiwi and 3 for pomelo) using mass-spectrometry data [42]. MS data was also used with analysis of variance-PCA to discriminate two varieties of broccoli (Brasscia oleracea) in different growing treatments.Journal The PCA loadings allowed Pre-proof identifying the molecular and fragment ions providing the most significant chemical differences [43]. Species identification based on seeds is still rather unexplored, despite the fact that the seeds contain all genetic information [44]. Chemical fingerprints of Datura species were acquired by direct analysis in real-time mass spectrometry and in-source collision-

13 induced dissociation fragmentation. While intraspecies chemical signatures were similar, Datura interspecies fingerprints were distinct enough to be discriminative using LDA. Lesiak et al, [44] suggested that the method can be applied to a very large number of species even if little information is available on the plant’s chemical makeup or the identities of any of its biomarkers. Moreover, DART-TOF-MS is a powerful technique for rapid classification of morphologically similar plants [45]. This technique was applied on powdered raw materials (Angelica tenuissima, Angelica gigas, Angelica dahurica and Cnidium officinale) to devellop a chemical fingerprinting and the OPLS-DA method was used for discrimination purposes. Applying DART-MS fingerprints for rapid quality control of 6 Salvia species (S. apiana, S. dominica, S. elegans, S. officinalis, S. farinacea and S. patens) was studied in [46]. The acquired fingerprints were submitted to kernel discriminant analysis and SIMCA for Salvia species discrimination and classification. Four different kinds of Gynostemma pentaphyllum samples were classified by using a rapid FIMS technique [47]. Chemometrics, such as PCA and PLS-DA, exhibited the discrimination and classification of each sample in 2 min. A taxonomic classification based on plant metabolic fingerprints from MALDI-TOF-MS using PCA and HCA, was reached [48]. Geographically discriminated marginal populations of Arabidopsis lyrata subsp. petraea may have different metabolics within the plant-foliage as was discussed in [49]. PCA on metabolite fingerprints from LCT-MS suggested that glucosinolates were responsible for separating different populations [49]. A study by Laursen et al. [50], has demonstrated that semi-quantitative Inductively Coupled Plasma-Mass Spectrometry in combination with PCA provides a fast and powerful alternative to traditional full- quantitative ICP-MS on grains of different rice (Oryza sativa) genotypes. This method constitutes a promising analytical tool for detecting the authentication of food products. The metabolic fingerprints of Dutch wild Carrot and of western orange Carrot cultivar shoots using 1H NMR combined with PCA and PLS-DA tools exhibited only quantitative differences in chemical content, representing a relative low separation after domesticationJournal [51]. NMR-based metabonomics Pre-proof was applied efficiently to distinguish wild and transgenic Arabidopsis by PLS-DA by applying orthogonal signal correction [52]. PCA of the 1H NMR spectra of tobacco plants displayed a clear separation between samples and allows an efficient differentiation between wild type and transgenic plants (CSA tobacco, Nicotiana tabacum ‘Samsun' NN) without any pre-purification step [53]. NMR

14 spectroscopy with PCA, was applied in [54], and exhibited also a clear dissimilarity between 5 grape cultivars from Portugal. The study showed that the chemical composition is highly affected by the extraction method [54]. This latter observation is in fact generally valid, what is measured during profiling depends on the extraction applied. Thus, it is important to optimize the extraction for a given situation. The genus Cistanche generally has four species in China, i.e. C. Deserticola, C. tubulosa, C. salsa and C. sinensis [55]. To clarify their sources, clinical efficacy and safety, a multi-step IR macrofingerprint method was developed and SIMCA was applied fro classification. PCA applied to FTIR spectroscopic data were performed to investigate the fingerprints of Coffee cultivars, i.e. traditional red bourbon and three genetically modified cultivars [56]. Another study used FTIR combined with multivariate analysis to identify the nature of polyphenolic extracts [57]. Through both PCA and HCA, dos Santos Grasel et al. [57], observed a clear discrimination between condensed (quebracho and black wattle) and hydrolysable (valonea, chestnut, myrobalan, and tara) tannins. Spectral and chromatographic analysis, NIR, GC-MS, and ICP-AES, combined with chemometrics approches (PCA and PLS-DA) were used in [58] to follow unintended compositional changes in transgenic rice seeds.

3.1.3. Processing and extraction methods Chemometric analysis is an interesting tool to help choosing the proper food processing conditions or an advantageous extraction procedure. Different extracts of Chelidonii herba, obtained with different solvents, were characterized metabolomically by 1H NMR fingerprinting analysis and PCA for phytochemical and functional toxicity against HepG2 liver cells [59]. NMR was also used as a fingerprinting tool to assess the overall composition of the Saw palmetto berries using three extraction processes (supercritical

CO2, hexane and ethanol) [60]. The PCA revealed a similar composition of the Saw palmetto extracts obtained with CO2 and hexane, with slight but in significant differences for the ethanol extract. ToJournal better understand the factors vintage Pre-proof and soil, influencing the grape berry skins composition, the cultivars harvested in 2002, 2003 and 2004 from five geographical locations in Bordeaux, were analysed using 1H-NMR spectroscopy [61]. The PCA and PLS methods gave a good separation of samples according to vintages, but not to soils. Plant cell extracts were analysed by 1H-NMR spectroscopy combined with multivariate data

15 analysis to investigate the metabolomic consequences of Cd2+ exposure in Silene cucubalus cell cultures [62]. PCA of the proton-NMR spectra exibited clear separation between control and Cd2+ dosed groups, indicating biochemical effects of pollutants in this plant. A 1H-NMR-based metabolomics approach was carried out to study four Curcuma species (C. zedoaria, C. xanthorrhiza, C. aeruginosa and C. mangga) dried by three drying methods (air, freeze and oven), and two extraction processes (50% and 100% v/v ethanol) [63]. The four species and their extracts were compared via their total phenolics profile and pharmaco-activities, including antioxidant activity, α-glucosidase and nitric-oxide potentials. The results combined with PCA allowed a clear separation between the four species and the three drying techniques for both C. mangga and C. xanthorrhiza, while PLS modelling indicated the potential biomarkers responsible for the biological activities [63].

Cluster analysis of FTIR fingerprints was used as a tool to study chemical responses in inter-species competition between a monocotyledon Brachypodium distachyion and a dicotyledon Arabidopsis thaliana [64]. This study reveals the global metabolic profiles of plants in response to biotic interactions. Chemometric tools, such as PCA and CA, applied on ATR FT-MIR spectra showed a noticeable potential for determining the nature of tannin extracts based [65]. FTIR spectroscopy and chemometrics were used to study the effect of salinity on tomato fruit varieties (Edkawy and Simge F1) [66]. PCA displayed no distinguishion between the control and salt-treated fruit for either variety, while, discriminant function analysis (DFA) classified control and salt-treated fruit in both varieties [66]. FTIR, i.e. second derivative infrared spectroscopy, and two-dimensional correlation infrared spectroscopy were also used for the fingerprinting of the traditional Chinese medicine Angelica and its different extracts (by petroleum ether, ethanol and water) [67]. This approach was able to discriminate the extracts from different extraction processes. FTIR was also applied to study the degree of roasting of Arabica coffee (medium or dark) using extracts with six organic solvents (dichloromethane, ethyl acetate, hexane, acetone, ethanol, and acetic acid) [68]. The infrared spectra of these extracts, in combinationJournal with PCA and SIMCA, were applied Pre-proof to discriminate four origins (Colombia, Costa Rica, Ethiopia, and Kenya) that had been roasted to two roast degrees (medium or dark) [68]. X-ray photoelectron spectroscopy and FTIR analyses were also applied to study the effect of Boron deficiency on the cell walls of navel orange (Citrus sinensis) resulting in growth defects in the plant leaves [69]. The FTIR spectra coupled to the PCA exploratory analysis showed discrimination between the cell walls from the upper or

16 lower leaves with different Boron treatments and from the leaves of the control plants. The polyphenolic profiles of Hippophae rhamnoides berry, extracted by different separation techniques, i.e. ultrasound-assisted extraction, rapid extraction under pressure and subcritical fluid extraction, and with different proportions of used solvents, were compared by their antioxidant potential [70]. The Raman and FTIR spectra, coupled with the antioxidant activity, were applied in combination with PCA and PLS-DA to differentiate between the samples according to their preparation method. To understand the stress-induced effects produced by wounding, a metabolomic strategy based on ESI-TOF-MS has been investigated for the model plant Arabidopsis thaliana. In this study, PCA and HCA on principal coordinates were applied for data analysis. The latter approach confirmed a clear grouping of plant specimens, choosing the most discriminating ions, leading to detect the specific interesting ions (m/z values).

3.1.4. Metabolomics, quality and authentication Metabolomic fingerprinting of a herbal medecine is important to address the plant metabolites variability and quality. A specific chemical fingerprint of the plant may help authenticating the product and detecting adulteration. In this regard FTIR spectroscopy, combined with PCA and SIMCA, was selected as a quality control tool for the rapid confirmation of a wide diversity of herbal samples [72]. A total of 36 extracts of different ethanol concentrations from Phaleria macrocarpa were subjected to FTIR fingerprinting and test for their α-glucosidase inhibitory potential [73]. OPLS regression was able to model and predict successfully the inhibitory activity based on the FTIR spectra. Another research study for quality control used FTIR spectra with PCA analysis to discriminate flours of 26 maize landraces produced in southern Brazil [74]. PLS-DA and PCA were used to identify specific peaks in the FTIR spectra of the extract of poppy heads, for the characterisation and classification of cultivars, offering a potential forensic application in opiate harvest inspection [75]. FTIR fingerprints of seven Iranian Thymus species, combined with regression tools (i.e. PCR, iPLS, and PLS), were used to predict the antioxidantJournal activity, while PLS-DA and PCA -DAPre-proof served to classify the samples according to their chemical profiles [76]. The FT-NIR fingerprints of Hydrastis canadensis (goldenseal), combined with MW-PCA and SIMCA, allowed discriminating pure goldenseal from 4 common adulterants (yellow dock, yellow root, coptis, oregon grape)

[77]. Further, PLS regression was able to quantify and predict the adulteration in goldenseal plant. NIR spectroscopy was used to trace authentic fingerprint from Eleutherococcus senticosus and eight other herbs (Panax ginseng, P. quinquefolium, P. notoginseng, Lepidiummeyenii, Withania somnifera, Angelica sinensis, Pfaffia paniculata and ginger) [78]. The spectra, processed by SIMCA, DA and PLS-DA tools were able to detect adulteration or counterfeit mixture of E. senticosusmaterial with the eight herbs, both related and not to the Araliaceae family [78]. Saffron (Crocus sativus) samples were analysed using 1H NMR with the application of OPLS-DA and bidirectional OPLS-DA (O2PLS-DA) for authentication and prediction of authentic or adulterated plants (four adulterants considered: Crocus sativus stamens, safflower, turmeric, and gardenia) [79]. NMR metabolite fingerprinting associated with PCA analysis for rigorous quality control of Ginseng roots was discussed in [80]. PCA allowed identifying potential biomarkers, capable of differentiating Ginseng species, varieties, and commercial products. A metabolomic approach using 1H NMR spectra was applied to investigate the mode of action of a pyrenophorol from Drechslera avenae [81]. Spectra of crude leaf extracts from untreated Avena sterilis seedlings and from seedlings treated with pyrenophorol were compared with those resulted from treatments with the herbicides diuron, glyphosate, mesotrione, norflurazon, oxadiazon, and paraquat [81]. PLS-DA and SIMCA classification methods allowed good discrimination among those various treatments [81]. Both 1H NMR spectroscopic and LC–MS metabolic fingerprinting associated to PCA and PLS-DA allowed the discrimination and the classification of 11 different Leontopodium species in [82]. The metabolic changes caused in Lemna minor by various phytotoxic substances were descriminated and classified using 1H NMR fingerprinting, PLS-DA and HCA analyses [83]. A NMR based metabolomics approach and a PLS regression model (fingerprint vs activity) indicated that some phenolic compounds, terpenoids, and sulfur-containing glucosides in oven- and air dried leaf extracts are the main components responsible for the antioxidant and α-glucosidase-inhibitory activities of ClinacanthusJournal nutans [84]. An untargeted NMRPre-proof metabolomics approach was applied for the quality control and the authentication assessment of true Cinnamon (Cinnamomum verum J. Presl) from the closely similar Cassia (C. cassia (L.) J. Presl) [85]. PCA allowed a clear separation between the two species, while OPLS-DA confirmed this discrimination and was used to find potential biomarkers. Recently, 1H NMR metabolomic fingerprints of

Curcuma longa and C. heyneana were investigated in [86], for authenticity determination. PCA and OPLS-DA exhibited clearly the distinction between pure C. longa and the one adulterated with C. heyneana. A quality control profiling of commercial Curcuma longa samples was done in [87], using UV, FT-IR, and 1H NMR fingerprints, and HPLC-DAD as reference method. The results, combined with PCA and HCA, allowed the discrimination between high-quality samples and low-quality ones according to their curcuminoids content. PCA and CCA were applied to establish the correlation between the atipyretic activity and the UV fingerprint data of Chinese herbal medicines (e. g. Radix bupleuri, R. isatidis Flos lonicerae, and Fructus forsythiae) [88]. In a pilot study, [89], the classification of tomatoes (Solanum lycopersicum) and sweet bell peppers (Capsicum annuum) from organic versus conventional farming was studied using DART-TOFMS data. PCA and LDA models were developed for sample classification. A first application of paper spray mass spectrometry as a chemical fingerprinting methodology with PCA data analysis for tracing the origins, establishing the authenticity, and assessing the overall quality of Bansha herbal tea, is described in [90]. This technique was recorded as simple, rapid, and robust for pharmaceutical analysis and quality assessment. ESI(+)-MS profiles were applied for the characterization of green Arabica and Robusta coffees, and to verify the feasibility of this technique for discriminating defective from non-defective coffee beans [91]. Mass-spectra associated with PCA and CA analysis allowed good separation between defective and non-defective (low-quality) coffees. ESI- MS and 1H NMR techniques combined with PCA and CA analyses were employed for the characterization of plant extracts [92]. The results showed that the negative ion mode ESI- MS was particularly effective for the characterization of the extracts [92].

3.2. Chromatographic fingerprinting of herbal extracts Chromatographic herbal fingerprints are being developed for identification, quality control and authentication of herbal extracts. In order to visualize the pattern of separatedJournal compounds, chromatography is combinedPre-proof with a suitable detection technique offering a tool for developing a characteristic profile of the sample [4]. Chemometric analysis of the chromatographic data is done to extract relevant information in relation to seasonal, geographic and taxonomic properties, from plant profile. Additionally, it can also be used for quality control, authentication and process optimisation. Relevant studies

19 using chromatographical separation applied to herbal extracts fingerprinting are listed in Table 1. Some studies are discussed below.

3.2.1. Seasonal and geographic indications Chemometric analysis, using PCA and PLS-DA, was employed to classify GC data obtained from trimethylsilyl derivatisation of Artemisia annua samples at five developmental stages [93]. Both direct two-way methods (PCA and HCA) and HCA after data minimization employing a three-way method (PARAFAC) were used to handle HPLC fingerprints of crude extracts of Colombian Piper species in the establishment of chemical composition relationships similarities within species [94]. PCA on the concentrations of iridoid glycosides, using UPLC-TOF-MS fingerprinting, and on the β-glucosidase activity in leaves of Plantago lanceolata and Plantago major, revealed plant identity and age- related differences in their metabolomes [95]. The discrimination between both Plantago species can be clearly related to the occurrence of different chemotaxonomic markers (i.e. iridoid glycosides). The potential of GC-MS metabolic fingerprinting for discriminating Myriophyllum spicatum individuals growing in different lakes under varying environmental conditions was explored in [96] to determine its applicability in an ecological context. In this regard, PCA based on GC–MS-metabolic fingerprints was able to distinguish the metabolite compositions of the plants in space and time. The anthocyanin profile from LC-DAD-MS was investigated in [97] for 11 types of grapes of different varieties and geographical origins, including 10 wine grape varieties (Vitis vinifera) and one hybrid variety. The anthocyanin profiles combined to PCA exploratory analysis, allowed discriminating the different wine grapes varieties, but not by geographical origin. Untargeted HPTLC and targeted LC-MS/MS approaches coupled to PCA data handling allowed the discrimination and classification of Rosmarinus officinalis samples from different locations in Sardinia [98]. Applying PCA and HCA on UPLC/DAD/ESI–qTOF-MS and GC-MS metabolic fingerprints of date palmJournal fruits of 21 Phoenix dactylifera varieties Pre-proof from Egypt, allowed distinguishing the similarities and differences among those varieties [99]. Hakimzadeh et al [100], proposed a HPLC-DAD fingerprinting method. MCR-ALS was used as data pretreatment and provides decomposition of the data for the geographic classification of Salvia reuterana belonging to five origins. Applying PCA and kNN methods allowed the differentiation and

20 discrimination between four clusters. Seven genotypes of Lippia gracilis Schauer were differentiated in [101] and their relationship with the place of origin was established (Sergipe and Bahia states, Brazil). The samples were discrimanted by PCA on LC-DAD fingerprints of both leaves and stems. Spectrophotometric and HPLC-DAD fingerprints of the flavonoid fraction of twenty-six Sage species, belonging to the Salvia genus and harvested in three vegetation seasons were studied [102]. A PCA decomposition revealed a differentiation between the harvesting years and the investigated Sage species. The Evodia rutaecarpa HPLC-DAD-MS profile of the alkaloids extract was investigated to distinguish different collecting locations in China [103]. HCA and PCA allowed the classification and differentiation between the samples according to their origins. UPLC-TOF-MS fingerprinting of Angelica sinensis samples associated to PCA data analysis discriminate samples at different growth stages [104].

3.2.2. Chemotaxonomic identification HPLC-DAD fingerprinting on root contex was used for to the chemotaxonomic classification of eight species: Paeonia suffruticosa, P. qiui, P. ostii, P. rockii, P. jishanensis, P. decomposita, P. delavayi, and P. ludlowii [105]. PCA and HCA, based on 47 major chemical compounds, discriminate between the peony species and four groups were identified. In order to discriminate between Arctostaphylos pungens, and Arctostaphylos uva-ursi, a morphologically similar plant with lower arbutin content, a UHPLC-PDA-ESI-TOF/MS fingerprinting method was applied on the methanolic leaf extracts [106]. PCA analysis on the chromatographic data allowed distinguishing clearly between the two species and the most distinctive compounds were indicated. HPLC-fingerprints combined with PCA and DA were used to discriminate between red and white rice bran [107]. The phytochemical composition of Amazonian medicinal plants from several genera was fingerprinted by LC-MS using samples obtained with in vivo solid-phase microextraction [108]. PCA on the fingerprints revealed significant differences between species, especially for PsychotriaJournal stenostachya, P. subsp., Tabernaemontana Pre-proof siphilitica, T. maxima, and T. sananho. Viaene et al. [109], applied HPLC-UV fingerprinting to distinguish two genera (Mallotus and Phyllanthus) on the one hand, and the six species (Mallotus apelta, M. paniculatus, Phyllanthus emblica, P. reticulatus, P. urinaria and P. amarus) on the other. After fingerprint data pretreatment, PCA was conducted to exclude outlying samples from

21 the calibration set used to develop the LDA, QDA, SIMCA and CART classification models. The models built allowed good classification and discrimination between the species, with the SIMCA model indicated as the most adequate. A multi-wavelength selection was applied on HPLC-DAD fingerprinting from for discrimination of Valeriana officinalis from other herbs (V. wallichii, V. edulis and Passiflora incarnata) [110]. The enhanced fingerprints used for SIMCA and PLS-DA models allowed good classification and discrimination between V. officinalis and the other herbs [110]. Chromatographic fingerprints were developed to study the similarities and differences between rhizoma Chuanxiong and rhizoma Ligustici samples [111]. The correlation coefficients between the entire fingerprints, combined with PCA, PP and HCA visualisation techniques allowed discrimination between both rhizomas. Taxonomic species differentiation and similarity evaluation of Paeonia lactiflora Pallas and P. veitchii Lynch was carried out in [112] using HPLC-DAD chromatographic fingerprints and PCA analysis. Metabolic fingerprinting for taxonomic-relationship, -similarities and -differences purposes between 11 Leontopodium species was achieved using LC-MS and NMR spectroscopy [82]. PCA on the LC–MS fingerprints allowed distinguishing three groups and identifiying several discriminating compounds, i.e. two bisabolane derivatives and one ent-kaurenoic acid derivative. The application of LC–MS on ethanolic root extract followed by PCA has also been successfully applied to discriminate six Rauwolfia species [113]. Chemical differences discrimination in organic and conventional sweet basil (Ocimum basilicum) leaf samples was achieved using a PCA analysis on HPLC and FIMS fingerprints [114]. The discrimination of three common types of Rhizoma Curcumae, based on separate and combined GC–MS, GCxGC–MS and HPLC–DAD fingerprints was studied [115]. PCA analysis on the combined data matrix showed improved discrimination of the three groups compared to PCA analysis on the fingerprints of the individual techniques [115] . Then, LDA, BP-ANN and LS-SVM classification models on the combined data showed a good discrimination. Seven Gaultheria species were distinguished based on their overall chemical Journal profil from UPLC-PDA fingerprint ingPre-proof combined with SA, HCA and PCA data handling [116]. A taxonomic discrimination between two commonly confused traditional Chinese medicines, Epimedium wushanense and E. koreanum, was carried out using HPLC- DAD fingerprints [117]. PCA and HCA exhibited two clear clusters, then SIMCA and BP- ANN allowed good discrimination. The leaves of eight mulberry clones from Morus alba

22 and Morus nigra, cultivated in Spain, were discriminated, based on phenolic fingerprinting from UHPLC-MS profiles and chemometric analysis (CA and PCA) [118].

3.2.3. Processing and extraction methods HPLC-DAD chemical fingerprints were used for harvesting time optimization of tangerine peels by screening three species (Citrus reticulata ‘Chachi’, C. reticulata ‘Dahongpao’ and C. erythrosa Tanaka) during their growth period [119]. The HELP pretreatment was applied to resolve overlapping peaks. Then, a PCA-scores plot allowed identifying the changes in the footprint of tangerine peel’s secondary metabolites during growth. Other the hand, chemical markers that have the largest contribution to those footprint changes were showed by PCA-loading plots Pankoke et al. [120], investigated the influence of different treatments (e.g. mineral nitrogen availability (fertilisation), interspecific competition and the association with arbuscular mycorrhizal fungi) and their interactions on biomass production in Plantago lanceolata (Plantaginaceae) using HPLC-TOF-MS. A targeted analysis using PCA revealed that only limited supply of mineral nitrogen caused the most pronounced changes with respect to plant biomass and biomass allocation patterns, and altered the concentrations of more than one third of the polar plant metabolome. The evaluation of thermal- and high-pressure processing of carrots (Daucus carota cv. Nerac) at different processing intensities was investigated in [121] by an untargeted approach. Important differences between processing treatments were addressed using headspace GC-MS fingerprinting. PCA and PLS-DA discrimination shwed to be suitable tools to compare the impact of different processing methods. A new role for chlorogenic acids as active role players in the priming of plants (Nicotiana tabacum cells) was proposed and followed by UHPLC-qTOF-MS/MS metabolic fingerprinting in combination with PCA analysis [122]. PCA revealed separation between treated samples and control samples, indicating diverse metabolite distribution patterns. Potential biomarkers responsible for the discrimination, were recognized by the PCA loading plots. A mixtureJournal design was used in [123] to study Pre-proofsolvent interaction effects and the variation of solvent proportions on the extracted substances from Erythrina speciosa leaves. HPLC- DAD and UV-Vis fingerprints associated with PCA, Tucker3 and PARAFAC analyses were applied to study those different extracts. The PCA analyses showed the existence of five different chromatographic fingerprints. Tucker3 and PARAFAC analyses were identified

23 simultaneously correlations between chromatographic peaks, spectral band absorbances and solvent compositions. The effect of the fermentation process on the chemical contents in extracts from Cyclopia maculata was studied using HPLC-DAD fingerprints, while similarity was assessed by PCA analysis [124]. Recently, different extraction techniques, such as maceration, heat reflux extraction, ultrasound-assisted extraction and microwave-assisted extraction, were investigated in order to obtain a high recovery of the target analytes (polyphenols) from pomegranate juice and peel (Punica granatum) of commercial and experimental samples [125]. The polyphenols in the pomegranate extracts were analysed by HPLC-UV/DAD, HPLC-ESI-MS and MS/MS. PCA indicated samples with an interesting content of bioactive components in the peels, while the commercial samples represented the best source of healthy juice. The effect of different extraction methods (soxhlet, maceration and supercritical fluid

(CO2)) and sample pretreatments (enzyme treatment and/or sonication) on the roots of Asparagus racemosus were investigated by HPLC-DAD fingerprints , while HCA, PCA and PLS-DA were used to discriminate between the extracts [126]. Response surface methodology, studying the influence of three factors (solid-liquid ratio, ethanol fraction, and extraction time) on the antioxidant activity, was used to optimize the extraction conditions of Thymus quinquecostatus Celak [127]. The extracts at optimal conditions were used to trace HPLC-DAD fingerprints of twelve T. quinquecostatus samples. The results, combined with HCA and PCA, were used to discriminate according to their geographic origin or harvest times.

3.2.4. Metabolomics, quality and authentication Similarity analysis, PCA and HCA techniques were applied to analyze the information in chromatographic fingerprints of dried ripe fruits of Gardenia Jasminoides Ellis in order to identify interesting chemical markers for their quality control [128]. PCA and HCA allocated 23 characteristic peaks for the effective quality control of G. jasminoides Ellis. These compounds were analyzed and identified by HPLC–DAD–ESI-MS. HPLC-DAD fingerprintJournal profiles of Flos Sophorae Immaturus Pre-proof (Sophora japonica) were applied in [129] for comprehensive quality evaluation as well as for the simultaneous quantification of three major flavonoids (rutin, narcissin and quercetin). Additionally, HPLC-DAD-ESI-MS was used to identify the characteristic peaks. Furthermore, SA, HCA and PCA classified the variability between the fingerprints of 14 batches of Flos Sophorae Immaturus. HPLC-DAD

24 fingerprinting (phenolics profile) was successfully applied for the discrimination and quality control of Sarcandra glabra [130]. SA, HCA and PCA were integrated in the methodology to identify fingerprints key herb-marker peaks contributing to the discrimination. The herbal-markers were further identified by HPLC–ESI-MS/MS and HPLC–ESI-TOF-MS targeted analyses [130].

Metabolic fingerprinting represents a very advantageous examining part of biomarker recognition. In this context, machine learning algorithms (i.e. kNN, SVM, multilayer perceptron, RBF, and Naïve Bayes) were applied to UPLC-TOF/MS metabolic fingerprints of the Arabidopsis thaliana plant for the discovery of wound biomarkers [131]. A quality control strategy using HPLC-DAD–ESI-MS/MS coupled with SA, PCA, and HCA tools demonstrated both the similarities and the differences in the chromatographic patterns of A. barbadensis samples [132]. A HPLC-DAD fingerprinting method for identifying the origin and assessing the quality of Pyrrosiae folium has been investigated, and was combined with chemometric methods, such as SA, HCA, PCA and the quantitative analysis multi-components by single marker method [133]. Quality control assessment of thirteen varieties Zanthoxylum bungeanum leaves was carried out by HPLC-DAD fingerprinting of their flavonoids composition [134]. The chromatographic fingerprints, subjected to HCA, PCA and DA tools, distinguished the thirteen varieties into four groups according to their chemical and bioactive variabilities. Chromatographic fingerprinting, using the potential of the HPLC-PDA and HPLC-MS techniques, was applied to identify regulated plants present in illegal herbal preparations intended for the treatment of erectile dysfunction [135]. The UPLC-Qqq-MS/MS fingerprints of Chinese Salvia miltiorrhiza were applied for quality control purposes [136]. PCA allowed discriminating between the samples regarding their metabolite’s variability and indicated biomarkers. The sensory metabolic profiling of Myristica fragrans (nutmeg), obtained by GC/MS and coupled to PCA and HCA, was used to discriminate between fruit, arillus and seed of M. fragrans [137]. OPLS-DA was applied to classify unroasted and roasted M. fragrans seeds. MetabolomicJournal study by GC-MS fingerprin ts,Pre-proof conducted on several west Bengaline Nymphaea pubescens flower extracts, was performed for the identification of acetylcholinesterase inhibition biomarkers [138]. Chemometric analysis, using PCA, PLS- DA and OPLS-DA, distinguished the flower extracts according to their bioactive chemical variation and acetylcholinesterase inhibitory, potential while gallic acid and kaempferol were found to inhibit the enzyme. The global metabolite profiles from pyrolysis GC-MS

(PY-GC-MS), coupled to PCA and PLS-DA, were successfully applied in the quality control and discrimination of various commercial Angelica acutiloba root (Yamato-toki) samples [139]. An HPTLC assay of apigenin 7-O-glucoside, an active marker in chamomile flowers, was developed, validated and applied for the fingerprint discrimination of chamomile like materials, such as Anthemis spp., Bellis spp., Chrysanthemum sp. and Tanacetum sp. collected by a local population assuming these materials were chamomile [140]. PCA analysis on the fingerprints was used to discriminate chamomile from chamomile-like species. Moreover, HPTLC fingerprint profiles associated with PCA and HCA methods were used to distinguish wild from cultivar chamomiles, as well as from chamomile-like flowers, for authentication investigation [141]. It was found that crude flowers sold in spice shops were adulterated [141]. The combination of HPTLC fingerprints, image analysis, and chemometric tools (i.e. PCA, CA and PCA-LDA) allowed characterizing and classifying 42 medicinal plants according to their antioxidant potential [142]. Authentication of various ginseng species (Panax ginseng, P. quinquefolium, P. notoginseng) and evaluation of the stability of ginseng preparations using HPTLC chromatographic fingerprint analysis and a pattern recognition software (computer- aided-similarity-evaluation (CASE)) is described in [3]. Chromatographic fingerprints were also applied in the modelling and prediction of pharmacological activities and the indication of potential markers responsible for the activity. For Mallotus species, the HPLC peaks potentially antioxidant compounds were indicated using an O-PLS regression model [143]. PLS and O-PLS models linking plant chromatograms with the antioxidant activity of Pistacia atlantica samples were built in [144] and several peaks were recognized as bioactive markers. Moreover, PCA and OPLS- DA analyses were applied to identify metabolite markers for different plant organs in Brachychiton acerifolius [145]. Application of metabolomic profiles along with chemometrics provide an understanding in the relationship between the metabolome of green tea and its quality [146]. The quality of Green tea samples was discriminated through PCA.Journal Subsequently, projection to Pre-proof latent structures by means of PLS was performed to construct a predictive model using the metabolic fingerprints. PCA analysis was applied to the combination of of two chemical fingerprints, i.e., HPLC and 1H NMR, as well as to a pharmacological fingerprint i.e. ABTS•+ antioxidant activity for the quality control of Salix alba samples preparations formulated as capsules of Salix

26 alba bark were separated of S. alba cortex samples on the HPLC + NMR score-plots [147]. Loading plots allowed indicating potential markers for it. PCA on the ABTS•+ profiles also revealed a clear separation between the preparations [147]. HPTLC fingerprinting associated with different multivariate image analyses were compared for their ability to study phenolic profiles in plant resins as a tool for samples quality [148]. Images of HPTLC chromatograms were used to derive different variables such as gray intensities of pixels, peak area and mean peak values, which were applied as input data for the multivariate data analysis [148]. The chromatographic fingerprint was also found useful in [149] to better understand the nutraceutical traits of the goji berry. Furthermore, chromatographic fingerprints were applied in foodomics to describe mulberry fruits chemical composition, the level of given compounds responsibe for the bioactivity, and for a comprehensive authentication and quality control of the fruits [150]. TLC fingerprints obtained in an intra- and inter-day precision study for Piper betle folium methanolic extract were studied for their peak- marker recognition and identification possibilities to come to a better authentication analysis [151]. To authenticate the Piper betle extract from the Piper species extracts, TLC fingerprint-data were used to provide sample information, for instance, numerical chromatographic parameters, and were submitted to HCA and PCA. The PC1-loading values were correlated linearly to the antifungal activity as well as to the biomarker concentration. A prediction of the antioxidant activity of Turnera diffusa using PLSR based on multi- wavelength chromatographic fingerprints (HPLC-DAD) was discussed in [152]. Studing regression variables in the scores projection and loadings of LV1 made it possible to find which chromatographic peaks (compounds) are related to the antioxidant activity. HPLC- UV polyphenolic profiles were exploited in [153] for the classification and characterization of cranberry-fruit extracts. The chromatographic fingerprinting data was associated to PLS regression to quantify the percentages of adulteration cranberry- fruit extracts by grape, blue-berry, or raspberry-fruit extracts. Authenticity screening to distinguishJournal Asian palm civet coffee from regular Pre-proof coffee and coffee blended with 50 wt % civet coffee was achieved by OPLS-DA classification models and GC/FID fingerprinting [154]. A PLS modelling was fitted, using metabolite compounds data obtained by HPLC- DAD and GC-MS analysis, for predicting the antioxidant activity of nine Mentha species,

27 while PCA and HCA were used to discriminate between samples according to their metabolite composition [155].

4. Essential oils and their untargeted/targeted fingerprints Essential oils (EOs) are complex mixtures of volatile aroma compounds including alcohols, aldehydes, and ketones [27]. EOs are produced from different plants matrices (seeds, roots, stems, bark, fruits, ﬂowers and leaves) by several extraction processes, such as (hydro distillation, steam distillation, supercritical ﬂuid extraction or mechanical pressing) [156]. Gas chromatographic and spectroscopic methods have been applied in profiling the essential oils [11]. Gas chromatography was traditionally used for the qualitative and quantitative analysis of EOs, but it is expensive and time consuming. Vibrational spectroscopy is reported a useful and fast technique for the chemical classification and quality control of essential oils from different species and fragrances [157]. Because of their numerous applications (medicinal, dermatology, perfumery, food preservation…), EOs are strongly demanded wolrdwide. The current part of this review will report on the untargeted and targeted fingerprinting techniques of EOs associated with chemometric tools, and their application in geographical-origin determination, taxonomic variation and quality assessment. Relevant papers published between 1994 and 2019 are discussed.

4.1. Spectroscopic fingerprinting of essential oils

Spectroscopy provides powerful analytical methods for both the qualitative and quantitative quality control of plants [158, 159]. Currently NMR, NIR, MIR, UV-Visible and Raman spectroscopy have been applied on many complex matrices. Rapid, low-cost, sensitive and reliable are properties that have motivated the use of (vibrational) spectroscopy, associated with chemometric tools, to monitor plants and their metabolites [159, 160]. Many studiesJournal were carried out using either Pre-proof targeted or untargeted fingerprinting and chemometric methods to assess and evaluate the essential oils. Table 2 lists more than 200 studies.

4.1.1. Seasonal and geographic indications

FTIR fingerprinting has been used for the identification and geographical differentiation of volatile oils of Portulaca oleracea from different sources from the South of China [40]. Both similarity analysis and HCA were performed to evaluate the similarity and difference of these samples. The data handling of the FTIR fingerprints was in agreement with that from the GC-MS fingerprints. The potential of FT‐IR spectroscopy combined with canonical discriminant analysis was established to discriminate Greek Mentha pulegium (pennyroyal) essential oil samples according to their geographical origin [161]. MID-IR and chemometric analysis (PCA, CA) were applied to classify (authenticate) Nutmeg essential oils according to their geographical origin [162]. Chemometric treatment (HCA, PCA) of FTIR fingerprints was assessed to differentiate the geographic origin of Cinnamon essential oils from different locations [163]. The FTIR results subjected to PCA and CA classify the samples as accurately as the GC-MS analysis did, which is a more time-consuming technique. A platform of fingerprinting techniques, including GC-FID/MS, FT-MIR, H-NMR and UHPLC-TOF-MS, was applied to classify Citrus oil residues according to their geographical origin [164]. PCA and OPLS-DA were carried out to assess the classification and to identify significant markers. The same fingerprinting techniques were applied to differentiate of lemon essential oils according to their geographical origin or their extraction process based on volatile and non-volatile fractions [165]. In addition, the potential of spectral approaches (FT-MIR and H-NMR) combined with PCA, HCA and OPLS-DA classification tools allowed distinguishing the geographical origin of those samples equally well as the separation techniques. NIR spectroscopy was found a very sensitive technique to distinguish sandalwood oils from different geographical origins [166]. The NIR spectra combined with PCA, HCA and self organising map clearly differentiate the oils according to their geographical origin. In addition, SVMR was successfully applied to predict the purity of the oils as quality criterion. The fluorometric fingerprints of Indonesian patchouli oils (Pogostemon cablin) were studied for their geographical origin classification [167]. PCA allowed the identification of key variables which were used to fit the decision‐tree model classification.Journal Pre-proof

4.1.2. Chemotaxonomic identification The potential of NIR and PLS prediction of the oil content in dried Peppermint leaves was discussed in [168]. PCA on the NIR spectra allowed a good discrimination

29 between different Mentha oils. Raman spectroscopy and HCA were carried out for the chemotaxonomic classification of different essential oils of the genus Mentha [169]. ATR- IR and NIR-FT-Raman spectroscopy in combination with PCA were applied to classify the essential oils of various commercial Citrus samples [170]. PLS regression was able to predict accurately the content of chemical compounds of the Citrus oils. Various chemotypes essential oils from Thyme, Origano and Chamomile species were classified by both ATR/FT-IR and NIR spectra coupled to PCA and PLS regression [171]. ATR-IR, NIR and Raman spectroscopy, with HCA and PLS for data analysis were applied in the chemotaxonomic classification of essential oils isolated from Basil, Chamomile, Thyme and Oregano plants [26]; of essential oils from Marjoram and Oregano [172]; and of essential oils isolated from various plant species (Origanum, Satureja, Salvia, Sideritis, Thymus, Calamintha, Lavandula, Ziziphora and Thymbra) [173]. Recently, the ATR-IR spectra for 14 EOs from different Myrtaceae genera (Eucalyptus, Corymbia, Melaleuca, Syzygium, and Eugenia) were recorded, then combined with HCA and PCA, and applied for chemotaxonomic identification and classification purposes [174]. The volatile oils from Arabica and Robusta coffees were discriminated using FT-Raman spectra and PCA classification [175]. NIR-FT-Raman, ATR-IR and NIR spectroscopy in combination with PLS and HCA was used for the classifications of peppercorn, pepper oil, and pepper oleoresin according to their different monoterpene and sesquiterpene compositions [176]. The essential oils isolated from several Eucalyptus species were analysed by two complementary techniques, ATR/FT-IR and NIR-FT-Raman spectroscopy, followed by chemometric analysis (HCA, PLS) [177]. Quality control of several commercially available essential oils was performed using Raman spectroscopy and cluster analysis [178]. Raman spectroscopy with PCA and PLS-DA multivariate analysis was carried out to classify Rosewood essential oils extracted from different parts (wood, leaves and branches) of the Amazon tree Aniba rosaeodora [179]. Both MIR and NIR spectroscopy combined with PCA and OPLS-DA chemometric tools were applied successfully in the taxonomic classification of the essential oils of four Lippia species [180Journal]. ATR-FTIR, FT-Raman and NIRPre-proof spectroscopy combine with HCA were carried out to classify different wild Fennel chemotypes according to their essential oils [181]. FT-IR and dispersive-Raman were used for the chemotaxonomic classification of essential oils from Lamiaceae plants, while GC–MS fingerprints were applied as reference technique [182]. Laser-induced fluorescence and Raman spectroscopy combined with CA

30 were successfully used in the chemotaxonomic differentiation of different genotypes of Thymus daenensis essential oils [183]. Various spectroscopic techniques Raman [184]; NIR [185] and MIR spectra [186], associated with PCA and PLS-DA classification analysis, were used to assess the chemotaxonomic characterization of Lavender and Lavandin essential oils. The phytotaxonomic classification of seven Ocimum species was established based on 1H- NMR and GC–MS fingerprints of essential oils [187]. HCA, PCA, and PLS-DA methods discriminated the EOs into three chemotype groups, while the 1H-NMR fingerprints was more informative for chemotaxonomic identification.

4.2.3. Metabolomics, quality and authentication Both MIR and NIR spectroscopic data with PLS regression were used for quantifying the main marker components in Honghua oil for the rapid quality assessment of this traditional Chinese medicinal oil preparation, as alternatives to the hyphenated chromatographic techniques [188]. NIR spectra and PLS calibrations were used to predict the content and composition of the essential oil from Fennel fruits (Foeniculum vulgare) [189]. NIR spectroscopy, combined with PLSR and LWR calibration analysis, was used to assess the authenticity of and to estimate the amount of adulteration in Sandalwood oil [190]. A comparative study on the quantitative prediction of five main compounds of Lavandula angustifolia essential oil was made, using NIR and MIR spectra [191]. The prediction abilities of PLS and three multiblock methods (concatenated, H-PLS and MB- PLS) were determined using the NIR and MIR data simultaneously. The potential of FTIR fingerprinting and PCA, CA for the assessment of some popular essential oils, in comparison to different classical approaches like HPLC, TLC, GC and DSC, was studied in [192]. FTIR spectroscopy combined with PLSR and DA was successfully applied for the quantification, classification and authentication of Pandanus conoideus oil (red fruit oil), when adulterated with Canola oil or Rice bran oil [193]. The quantitative analysis ofJournal pulegone in hydrodistilled pennyroyal Pre-proof oils (Mentha pulegium) by FTIR, combined with PLS regression, was investigated in [194]. MIR and NIR spectroscopy, in combination with PLS calibration models, were successfully used to predict the contents of seven major compounds of the essential oils of Melaleuca alternifolia [195]. Qualitative

31 and quantitative assessment of lavender oil (Lavandula angustifolia) biomarkers were made in [196], using MIR and NIR spectroscopy combined with PLS. ATR−FTIR spectra directly obtained from dried intact leaves of sage (Salvia officinalis) provided a reliable PLS calibration model for the direct qualitative and quantitative analysis of essential-oil components [197]. NIR, MIR and Raman spectroscopy, combined with OPLS-DA and PLSR, were used for quality assessment of Buchu oil prepared from Agathosma betulina or A. crenulata species [198]. Raman spectroscopy and PLS multivariate calibration modeling were combined for the identification and quantification of Rosemary essential oils in virgin olive oil, considering comprehensive two-dimensional gas chromatography (GC x GC) as reference technique [199]. Raman spectroscopy coupled to chemometric tools (LDA, PCA) used for the quality inspection of vegetable and essential oils (used in the cosmetic industry) [200, 201]. Various spectroscopic techniques such as: Raman [184], NIR [185] and MIR [186], associated with PLS calibration models, were used to predict the biomarkers of lavender and lavandin essential oils. NIR vibrational spectroscopy and PLS calibration were applied to predict the essential oil yield of rosewood powder [202]. Excitation-emission matrix fluorescence coupled to unfold-PCA and PARAFAC were carried out to explore essential oils in [203], while the association of the fluorescence with artificial neural networks to study clustering and classification of the essential oils, was discussed in [204]. On the other hand, the adulteration of several EOs was assessed through excitation-emission matrix fluorescence fingerprints handled with ANN and MLP modelling methods [205].

4.2. Chromatography fingerprinting of essential oils Chromatographic separation is a popular and powerful analytical approach for the qualitative and quantitative analysis of plants and their metabolites. Because of the various advantage, such as the efficient and sensitive detection, chromatographic separation coupled the appropriate detectors has become the banckmark in the research on plants Journal[1]. Chromatographic profiling associated Pre-proof to chemometric methods was applied for many purposes in the study of plants and their metabolites [10]. Several such studies carried out to assess and evaluate essential oils are listed in Table 2, and shortly discussed in the following sections.

4.2.1. Seasonal and geographic indications Chromatographic techniques have been widely applied in the geographical identification and seasonal assessment of the EOs. Gas chromatographic techniques (GC- FID and GC-MS), for instance, were used to study the variation and morphometric characters in Chamelaucium uncinatum leaves EOs [206]. CA and PCA were able to reveal the relationships between the essential oil profiles and the geographical region. The GC– MS fingerprinting was performed on Cortex Cinnamomi essential oils from four different producing areas [207]. On the one hand, the embedded peaks were enhanced successfully with the IOP resolution algorithm. On the other, the HELP and IPREP methods were applied for the resolution of overlapping peaks from the volatile components [208]. The volatile chemical constituents of Artemisia capillaris, in relation to the geographical locations, were assessed by GC-MS and the overlapping chromatographic peaks resolved using chemometric resolution methods (SFA, OPA, FSMWFA and EWOP) [209, 210]. The GC-FID and GC-MS profiles of the volatile leaf oils of Piper lanceaefolium from three regions were discriminated by PCA and confirmed with CA classification techniques [211]. The relationship between the oil composition and the geographical distribution of Cunila galioides was investigated in [212], three chemotype groups were discriminated by both CA and PCA. The essential oils variation of the Sardinian Rosmarinus officinalis were related to four harvesting areas (North, South, East, and West); PCA was carried out on chemical molecular markers and random amplified polymorphic DNA (RAPD) data, and four groups were distinguished [213]. Sim et al. [214], studied the potential of gas chromatography and the surface acoustics wave technique (GC-SAW) to analyze Orthosiphon stamineus volatile oils according to their geographical origins. The geographical classification of the O. stamineus samples was considered by PCA, CA and LDA pattern recognition methods [214]. The Coridothymus capitatus and Satureja thymbra EOs composition was related to their natural habitat types; PCA and DA analysis revealed the relationship between the composition and the geographical origin [215]. ClassificationJournal of 12 populations of Cunila incisePre-proof EOs was accomplished by 19 volatile compounds, determined in GC-MS fingerprints [216]. The relation between the geographic origin and chemical composition was studied and three groups were discriminated using HCA, CDA and PCA [216]. The chromatographic fingerprints of Fructus xanthii were evaluated by PCA and similarity analysis in order to study quality

33 changes between different producing areas and to evaluate their toasting process [217]. GC/MS chromatographic fingerprints classification of 21 Schizonepeta tenuifolia samples collected from different regions was performed using PCA and HCA [218]. The chemical composition of the EOs of Xanthium italicum from 25 Corsican locations was investigated; additionally, PCA and CA were performed showing that samples were well grouped according to location on the basis of the chemical composition [219]. The influence of seasonal variations (before flowering, full blooming and fructification) in the composition of X. italicum EOs was also studied. The geographical effect on the chromatographic fingerprints (GC-FID and GC-MS) of EOs extract from fresh and dried Mentha × villosa Hudson leaves was evaluated in [220]. PCA distinguished the EOs from fresh and dried leaves from three Brazilian regions. The geographical origin of six commercial thyme cultivars (Thymus vulgaris) was checked based on chromatographic results of EOs composition and genetic variability by randomly amplified polymorphic DNA (RAPD) coupled to cluster analysis [221]. The EOs composition and genetic variability was also studied [222], for Common sage (Salvia officinalis) cultivars and Judean Sage (S. judaica). GC–MS chemical fingerprinting coupled with HCA, PCA and PLS-DA was investigated in [223], for discriminating Curcumae longae rhizome EOs from different origins. The oil yield and the variation in EOs composition in the flowering aerial parts of Teucrium polium were evaluated by GC and GC-MS in [224]. The PCA analysis distinguished four chemotypes according to their geographical distribution [224]. The geographical-origin discrimination of Hyssopus officinalis subsp. aristatus, collected from Albania and Kosovo, was done by GC-MS profiling of their EOs, while HCA and PCA allowed distinghuishing [225]. On the other hand, PCA indicated the same discrimination power using only the e-nose volatile organic compounds data. The chemical variability of Perilla frutescens EOs in relation to the geographical distribution in 11 chinese regions was evaluated using CA and PCA [226]. In addition, these results revealed a relationship between environmental factors and biological activities Journal[226]. The chemical variability in EOsPre-proof from different wild Moroccan Populations of Mentha suaveolens (subsp. timija) was correlated in [227] to the altitude of the collection sites. Application of CA based on the main chemical components was highlighted four main EO groups [227]. The geographic variations in the EOs composition of Calendula arvensis from Algeria were investigated in [228] using CA. GC-MS-FID and

GC-GC-TOF-MS methods in combination with HCA, PCA and OPLS-DA were applied in [229] to assess the geographic variation of Leonotis leonurus EOs extract. GC-MS coupled to HCA, PCA and CCA was used to study the impact of geographic variation and environmental conditions on the chemical variability of EOs of Trachyspermum ammi [230]. Similarity analysis and PCA have been applied to the chromatographic fingerprints of Scutellaria barbata in the characterisation of samples from different producing areas [231]. A qualitative and quantitative analysis of EOs of Egyptian Xanthium strumarium leaves was done using GC-MS fingerprints [232]. These fingerprints, combined with ACA and PCA, allowed a geographical discrimination of the Egyptian EOs from Brazilian, Iranian and Pakistani ecospecies [232]. The chemical composition yield and biological activities of the EOs from plants have been widely reported. Studies about the biological activities, evaluation of the composition and variations of EOs were related to many factors, including environmental parameters, harvesting season and geographical locality [27, 233, 234]. The seasonal variation and chemical variability of the EOs of Corsican Senicio vulgaris aerial parts were investigated in [235] using GC-FID and GC-MS analyses. CA and PCA allowed the discrimination of two main clusters according to the samples location and environmental factors. The chemical composition as well as the biological activities of Mentha essential oils were found strongly affected by the harvesting season [236]. The variations in essential oil yield, and qualitative and quantitative compositions from Indian Ocimum taxa were linked to different stages of plants growth in [237]. The seasonal variation and climate conditions on the chemical composition of EOs of Aristolochia longa (subsp. paucinervis) were assessed by GC–FID and GC–MS in [238]. The results revealed that the EOs chemical composition and the biological characteristics strongly depend on the harvesting period [238]. The chemical composition and inflammatory activities of EOs extract from Lippia multiflora leaves varied as a function of the geographic area and harvesting time [239]. The chromatographical (GC-FID and GC- MS) data were interpreted with a PCA. The EO from clonal lines of Spanish Salvia lavandulifoliaJournal was monitored over four years Pre-proof of cultivation in [240]. The results based on PCA suggest that the EOs yield and their chemical composition were influenced by phenology and climatic conditions. HCA and PCA were used to assess the relationship between the chemical composition and the seasons in EOs extracted from Copaifera langsdorffi oil-resins [241].

The combination of GC-FID and GC/MS was used to examine the intraspecies variation of Calendula arvensis essential oils from different Corsican locations [242]. The seasonal variation and environmental factors that affect the EOs profile were evaluated with PCA and CA. The evaluation of effect of drying and the harvest season on the volatile constituents of the foliage and berries of Juniperus excelsa trees from Iran was done by GC-FID and GC–MS [243]. PCA discrimination based on chemical markers from the foliage and berries oils revealed a clear separation between the samples, according to the oil origin (foliage, berries) and the harvest season. Bioclimatic and environmental factors affect the diversity of Satureja pilosa subsp. origanita essential oils, PCA discrimination was carried out and suggest three clusters in these EOs [244]. Untargeted HS-GC-MS fingerprints of EOs of bitter orange (Citrus aurantium) were used to assess four ripening stages [245]. ANN classified the four ripening stages, while PCA did not allow a distinction between them.

4.2.2. Chemotaxonomic identification The chemotaxonomic variability of essential oils has been widely studied using chromatographic hyphenated techniques. Chromatographic analysis of the essential oil variability, coupled to PCA and CA, is discussed in many papers, for instance to study the taxonomy of several Thymus species, such as baeticus growing in the southeast of Spain [246], T. caespititius from the Azeros [247], T. capitatus from south Apulia (Italy) [248], T. carnosus from Portugal [249], T. hyemalis from southeastern Spain [250], T. mastichina from Portugal [251], T. moroderi and T. antoninae from Spain [252], T. lotocephalus and T. x mourae [253], T. piperilla from Spain [254], T. serpylloides subsp. gadorensis from southeast Spain [255], T. villosus from Portugal [256] and T. zygis from southeast Spain [257]. Another study concerning Thymus species used factor analysis, CA and DA to examine the taxonomy of Thymus praecox subsp. arcticus [258]. Thymus praeco subsp. arcticus results were evaluated by PCA, CA and FA [259]. MCR-ALS was applied to interpret the chromatographic fingerprints from Thymus vulgaris andJournal Thymus serpyllum [260]. Furthermore Pre-proof, PCA was applied to identify the garden and the wild Thymus. A chemometric study applying PCA, LDA and SIMCA was carried out on GC data from oil extracted from Eucalyptus species and their hybrids [261]. Another study concerning the taxonomy of the Eucalyptus genus used PCA and LDA for data interpretation [262]. PCA allowed the discrimination between the essential oils

36 extracted from eight taxa of the genus Angophora and to study possible relationship to the genus Eucalyptus [263]. The chemotaxonomic classification of Ocimum basilicum EOs was assessed by cluster analysis, for instances, in species from Italy [264], Iran [265] and Turkey [266]. GC-MS and genetic characterization using the amplified fragment-length polymorphism (AFLP) technique, coupled to CA were applied to study the EOs extracted from several Ocimum basilicum cultivars [267, 268]. A collection of 21 Ocimum basilicum cultivars was characterized by PCA to evaluate the morphological traits and the essential- oil chemical profiling [269]. The morphological and biochemical diversities in the EOs of 34 Ocimum taxa from India have been explored and their taxonomical categories were distinguished using both GC-FID and GC-MS with multiplicative scatter correction as data pretreatment [270]. The chemical composition and antibacterial activity from the essential oils of seven Ocimum taxa were investigated in [271], using GC-MS and PCA classification. The phytochemical variability of ten Indian Hedychium species was examined by using the GC-MS profiling of their EOs [272]. These fingerprints, combined with ACA and PCA, discriminated the ten Hedychium species into three groups, according to their chemical composition. Classification models were builts with PCA, LDA and SVM to classify the essential oils extracted from nine genotypes of Rosa damascene [273]. PCA and CA were applied to study the chemotaxonomic variability of the volatile oils from Stachys species [274]. The EOs chemical variability from some Satureja subspecies was assessed by PCA and CA in [275]. A chemotype classification study of Cupressus species (C. sempervirens var. horizontalis, C. sempervirens var. pyramidalis and C. macrocarpa) was assessed by GC-MS fingerprints of their EOs [276]. The acquired GC-MS data coupled to PCA, HCA, and OPLS- DA analysis, discriminated between EOs extracted from leaves and cones [276]. GC-MS and RAPD have been widely used to examine the volatile fractions from several plants. Ocimum gratissimum and Thymus caespititius taxonomic identification were assessed by CA and PCA methods in [247], and [277]. The data from the same techniques subjected to PCA have been applied to study the genotype of essential oils from Origanum onites andJournal Origanum vulgare and from a mixed Pre-proof population of putative hybrid Origanum × intercedens [278]. PCA, HCA and MSC were used to examine the composition of the EOs of ten endemic Centaurea species from Turkey [279]. The taxonomic classification of Aristolochia species was established based on GC–MS analysis of essential oils and PCA [280]. Chemotaxonomic profiling of the volatile compounds of 23 wild-growing taxa of

37 the Apioideae subfamily from Central Balkan using PCA was also evaluated in [281]. Taxonomical classification was also studied for the EOs of the Hypericum genus from Portugal [282], PCA and CA were able to identify four chemical groups for four studied species. Chemical variability in Pimenta pseudocaryophyllus EOs has been studied using PCA, CA and canonical discriminant analysis [283]. The chemical diversity and botanical taxonomic characterization of different Iranian Salvia chemotypes was assessed by PCA and AHC [284]. PCA and OPLS-DA applied to GC–MS data allowed a clear distinction between leaves and flowers essential oils extracted from Tussilago farfara [285]. Capillary GC-FID and GC/MS results were coupled to CA and PCA to study Corsican Teucrium essential oils regarding their chemotaxonomical diversity [286]. GC-EIMS and PCA were applied to study the chemotaxonomy and the antibacterial activity in essential oils from Origanum species (O. vulgare subsp. hirtum, O. onites, and O. marjorana) [287]. GC-MS results combined to PCA and OPLS-DA were used to study the taxonomical diversity in Cinnamomum cassia (bark and twig) [288]. The taxonomical classification based on the chemical profiling of fruit essential oils isolated from six Heracleum species was evaluated using PCA and CA [289]. The chemical variability in the essential-oil composition of five Dorema aucheri populations was interpreted by PCA, CA and CCA classification methods [290]. The chromatographic chemical composition of essential oils from Phagnalon sordidum were coupled to PCA and CA, which allowed identifying correlations between three populations of P. sordidum [291]. GC-FID fingerprints coupled to the multivariate methods SA, HCA, PCA and PLS-DA were used to classify Atractylodes lancea and A. koreana essential oils [292]. In [293], PCA was applied to discriminate and reveal the correlation between volatile components from Atractylodes species (A. lancea and A. koreana). A comparative study of the chemical difference between of volatile oils in the stems and roots of Ephedra sinica was carried out via GC-MS [294]. The chemical markers were selected using VIP variable selection, while PCA and OPLS-DA allowed the classification between the analyzed stems and roots. The phytochemical characterization of EO isolated from Espeletia Journalspecies (E. grandiflor, E. killipii ) Pre-proof was carried out using CA and OPLS-DA classification [295]. A comparative chemical study between two confused fruits of Amomum villosum Lour and Amomum villosum Lour. var. xanthioides T. L. Wu et Senjen was established using the GC-MS fingerprints of their EOs [296]. These fingerprints,

38 associated with HCA or PCA allowed a clear distinction between the two EOs and this the fruits, while PLS-DA indicated the potential biomarkers for their discrimination.

4.2.3. Processing and extraction methods The sample pre-processing and the extraction process are affecting the quantity and quality of the essential oils. The quality of the essential-oil is affected by various factors, such as harvesting stages, environmental factors, drying techniques, extraction methodologies, genetic factors and storage conditions [27, 233, 234, 297]. The essential- oil yield from Pogostemon cablin was maximized using a Box-Behnken experimental design, and then the GC-FID was applied for quantitative chemical fingerprinting [298]. Similarity analysis and CA were applied to assess the quality of this oil. A comprehensive study was performed on the essential oils from Cirsium japonicum and C. setosum two morphological- and aroma-similar species [299]. The hydro-distillation process was optimized using a Box-Behnken design to maximize the yield of EO extracted. GC-MS data and CA were used to discriminate the EOs obtained by the standard and the optimal methods. The microwave hydro-diffusion, hydro-distillation, cold pressing and the gravity were investigated for the extraction of essential oils from Citrus peels [300]. Canonical discriminant analysis on the chemical compounds was used to discriminate between the essential oils extracted from the different approaches. The volatile compounds from the peel of Citrus maxima (pummelo) were extracted by cold pressing, water distillation, simultaneous distillation and extraction, microwave-assisted extraction, ultrasonic- assisted extraction and super-critical-fluid extraction [301]. These EOs were evaluated using GC-MS data and PCA, where PCA was able to discriminate between the different EOs from the six extraction methods. The study of the effect of high-pressure, high temperature and thermal sterilization on the volatile fingerprint of pumpkin (Cucurbitaceae), red beet (Amaranthaceae), onion (AlliaceaeJournal) and potato (Solanaceae) using HSPre-proof-SPME for extraction and a GC-MS platform for profiling was discussed in [302]. The untargeted chemical fingerprinting was subjected to PLS-DA to classify the groups according to the preprocessing techniques. The solid-phase micro-extraction conditions for the extraction of essential oil of Eugenia uniflora leaves with orange, red and purple fruits color biotypes were optimized using

39 factorial design and response surface methodology [303]. The PCA and HCA were carried out to classify the three EOs groups from orange, red and purple fruits. Several investigations on drying and extraction variations in the essential-oil composition have been reported. The variation in the volatile oil compositions from fresh vs. dried foliage and berries of Juniperus excelsa trees was monitored by GC-FID, GC-MS with PCA analysis [243]. The comparison of these volatile oils revealed that the drying process affects the composition, while the seasonal variation affects the yield of berry oils more than of foliage oils. A comparative study of six different drying methods on the chemical composition of the essential oil of Laurus nobilis leaves was made using GC-MS [304]. The results showed a correlation between the chemical composition and the drying process. The data was subjected to CA discrimination and showed a clear separation between ambient air drying method at 22 °C and the subgroup with the five remaining methods (IR moisture analyser at 45 °C and 65 °C; Oven at 45 °C and 65 °C; microwave oven at 500 W). The composition and yield of the essential oil of Lippia alba leaves was influenced by both the geographical origin and the drying process [305]. The quality of volatile oils from two Ocimum basilicum varieties (purple and green), first subjected to six drying methods (sun, shade, oven at 40 °C and 60 °C, microwave at 500W and freeze-drying) was compared [306]. The results suggest that the drying methods affect directly the chemical composition of both basils. The cluster analysis of the chemical data allowed distinguishing four distinctive clusters. Besides the drying method, the temperature of drying and the biological characteristics of the plants affect the composition of the basil essential oils. The essential oil isolated from Thymus daenensis subsp. daenensis was also subjected to six drying methods (sun, microwave, shade, oven 50 ◦C, oven 70◦C and freeze–drying). GC-MS was coupled to CA for data analysis to find the relationship between different drying methods and the chemical composition [307]. GC– MS and PCA were applied to classify the Thymus vulgaris essential oil compositions from six different drying techniques (natural way, convective drying at 30 °C, 40 °C and 50 °C and lyophilization) [308]. The influence of natural and artificial drying methods on the essential oilJournal content from Mentha x villosa leavesPre-proof was investigated in [220], using GC-MS and PCA. The PCA treatment allowed the discrimination between the fresh and the dried leaves [220]. GC-MS fingerprints of 12 Nigella species (seeds) were investigated in order to study the effect of the roasting process on their volatiles composition (fixed oil) [309].

These fingerprints combined with HCA, PCA and OPLS-DA distinguished the samples according to their processing method [309]. The essential oil composition from the stem and leaves of Coriandrum sativum was evaluated after applying seven drying methodologies (dried in sunlight, shade, mechanical ovens at 40 °C and 60 °C, microwave oven at 500 and 700 W, freeze–drying) [310]. The result from CA demonstrated that the grouping depended on the drying method and on temperature. GC-MS and PCA were used to study the influence of three drying methods (oven at 45 °C, air-drying at ambient temperature, freeze-drying) on the chemical content and flavor quality of spearmint (Mentha spicata) [311]. The microwave power in the drying procedure of Satureja bachtiarica EOs was optimized using response surface methodology [312]. Cluster analysis provided the relationships between the different drying methods and the chemical compositions [312].

4.2.4. Metabolomic, quality and authentication In recent years the essential-oil analysis has obtained an increased interest from the researchers. GC-MS was applied as first choice technique for characterizing the volatile components. However, because the essential oils are natural complex matrices (containing volatile and non-volatile fractions), the GC-MS profiles contain overlapping and embedded peaks [313]. In several papers, GC-MS and appropriate chemometric resolution methods have been used to analyze qualitatively and quantitatively the essential oils [314]. The quality control assessment of EOs from Chinese Juniperus rigida needles was established by using GC-MS fingerprints, combined with SA, PCA, HCA and DA techniques [315]. GC-MS combined with OPA and EWOP was applied for the quality control of Cortex cinnamomi essential oils [208]. The quality control essential oils of Pelargonium roseum were done by the GC-MS fingerprinting combined with chemometric resolution techniques [17]. The SBM, FSMWEFA and HELP techniques have been applied to resolve the overlapping peaks into pure chromatograms and pure mass spectra [17Journal]. The quality assessment of the DamaskPre-proof rose essential oil was investigated by GC-MS and the peak clusters were resolved using HELP and MCR-ALS tools [18]. GC-MS fingerprints of the essential oils from mandarin and lemon were handled and resolved using the mean field independent component analysis (MF-ICA) chemometric algorithm for the quality control purpose [15]. A 25 screening factorial design was applied to select

41 the important parameters of the MF-ICA algorithm. In addition, the work also reported a comparison between the MF-ICA, MCR-ALS, MCR-FMN and HELP methods [15]. A comparison between the HELP, OPR and MCR-ALS techniques was discussed in [34], where MCR-ALS was selected a reference algorithm to resolve the overlapping peaks in the GC-MS chromatograms of Rosmarinus officinalis essential oils quality analysis. Quality assessment of Teucrium polium essential oil was carried out by using GC–MS fingerprints combined with MCR and PARAFAC approaches [316]. The GC-MS fingerprints of citrus EOs were subjected to MCR-ALS analysis, and then PCA and KNN were able to discriminate between the EOs chemo-type classes [317]. Furthermore, the main chemical markers responsible for the classification were selected using CP-ANN. The MF-ICA resolution method was applied as algorithm to resolve the embedded peaks in the GC-MS Calligonum comosum essential oils for quality analysis [318]. A new pure-variable selection method, named MCR-FEQZ combined with MCR-ALS was applied to resolve the GC-MS peaks for chemical characterization of Spearmint essential oil [319]. The VSMW- EFA, FSMW-EFA, SIMPLISMA, OPA, HELP and MCR-ALS resolution methods were applied to resolve the chromatographic peaks and spectral profiles for chemical characterization of Lavender essential oil [35]. A quality control study of EOs extracted from Schizonepeta tenuifolia Briq was done using GC/MS combined with HCA and PCA analysis [218]. A metabolomic comparison study of EOs extracted from leaves, flower buds, and fully open flower of Tussilago farfara was investigated in [285], using GC-MS data combined with PCA and OPLS-DA. These chemometric tools allowed a clear discrimination between the three studied components [285]. The quality control of three Curcuma ecotypes (C. phaeocaulis, C. kwangsiensis and C. wenyujin), based on their essential oils, was assessed in [320], using the GC-MS technique. In this study, PCA, and PLS‐DA were applied to discriminate between the three groups, while curzerenone, germacrone, curdione and epicurzerenone were selected as chemical markers for the quality control and differentiation between the groups [320]. Both untargeted GC–MS and GC × GC-qMS fingerprints of Chaihu Shugan San EOs were applied inJournal [321], combined with HELP, SIA,Pre-proof AMWFA, and ATLD chromatographic resolution algorithms, for a qualitative phytochemical analysis (terpenoids and phthalides compounds). The PLS, PCR and OPLS multivariate regression methods were applied for recognition of antimicrobial constituents in Myrtus communis essential oils [322]. OPLS was selected as

42 preferred method to identify putative markers responsible for the antimicrobial potential. Different lavender essential oils were subjected to GC-MS and PCA to explore the occurrence of the chemo-types classes [323]. Then PLS regression was used to establish the relationship between the chemical information and biological activities (cytotoxicity, mutagenic, antioxidant and antifungal activity). A total of 158 commercial oils were evaluated in [324], using the GC-MS technique and measuring their antimicrobial activity. The biomarkers responsible for the antibacterial activity were successfully identified using the OPLS-DA multivariate technique, after VIP variable selection. In another study, a PLS model was established to predict the chemical markers responsible for the antifungal potential of Thapsia garganica essential oils [325]. GC-MS fingerprints resulted from EOs of Scutellaria barbata D. Don from different origins was investigated in [231], for adulteration control using both similarity analysis and PCA discrimination. These results were compared and applied to detect the adulteration in Scutellaria barbata EOs by two similar EOs adulterants Oldenlandia diffusa and Lobelia chinensis Lour. Untargeted GC-MS metabolomic fingerprints of several EOs were studied in [326], using OPLS-DA to identify potential biomarkers linked to anti-quorum sensing.

5. Conclusion & perspectives Herbal products (extracts and essential oils) are very complex chemical mixtures whose comprehensive quality evaluation is assessed using fingerprinting techniques combined with chemometric tools. This review provides an overview grouping a wide range of fingerprinting techniques (especially chromatographic and spectroscopic) of which the data is submitted to the chemometric tools. Although the use of fingerprints in untargeted/targeted approaches is still limited for the analysis of plant material, it provides promising possibilities for geographical origin discrimination, taxonomic identification, extraction-process optimization and quality control objectives for herbal extracts and essential oils. The choiceJournal of an accurate approach (untargeted Pre-proof or targeted) should be made to develop the desired fingerprint from a specific plant material (oil, plant extract, food…), and the result must fulfill several requirements (efficiency, reproducibility, robustness, time, and cost). In this review, the concept how untargeted/targeted approaches associated with chemometric tools became an important issue in the plant analysis, was discussed. Both

43 older and recent literature confirms clearly the applicability of the fingerprint concept for several objectives at laboratory and industrial scale. The development of validated fingerprint methods, combined with chemometric tools, may help to provide reliable analytical results for several issues in the certification of the plant materials. In addition to the qualitative and quantitative use, the developed fingerprint may play a key role in the quality guarantee of the plant’s raw material for international commercialization. Furthermore, future instrumental improvements may increase sensitivity and specificity of the fingerprint assays. Targeted/untargeted-based fingerprint acquisition can help to fill the gap between univariate and multivariate. Chemometric approaches are necessary to address all possible sources of variability in the data. Several limitations within the reviewed studies are identified, such as missing and unreported data, too limited numbers of samples or replicates for proper conclusions, lack of comparence with a reference standard, lack of variables optimization, mixing of univariate and multivariate analysis, lack of thourough interpretation, misunderstanding of the use of chemometric approaches and their proper application. The findings of this review suggest that in future, a collaborative research effort should be done by the concerned scientific community to create public fingerprint databases for the development of certified fingerprint (reference) plant materials and standard functional procedures. These include the careful reporting of the required untargeted/targeted approaches, the existing standards for the fingerprint objective, the availability of representative samples, and the proper and adequate chemometric tools. Optimization of factors from farming, over herbal treatment, extraction, analytical methods to industrial processes also can be considered.

Acknowledgement

The authors are grateful to the FWO-Vlaanderen, VLIR-UOS (Team project-VLIR 345 MA2017), the Vrije Universiteit Brussel (VUB) and the Faculty of Medicine and Pharmacy Rabat (FMPR)Journal for financial support. M.K is Pre-proof very thankful to Dr. Sophia Raiss for her unconditional support (IL).

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Figure1. The general analytical workflow and the chemometric tools used in the fingerprinting of plant material.

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Figure 2. The main chemometric tools applied on the fingerprint data obtained from untargeted and targeted techniques.

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Table 1. Overview of applications of untargeted and targeted fingerprinting techniques and chemometrics on herbal extracts peroids 2003-2019. A: discrimination of geographic origins or seasonal variations; B: differentiating similar species (taxonomic); C: quantitative analysis; D: evaluating the processing methods; E: authentication; F: prediction; G: quality control; sp: species; subsp: subspecies.

Extraction Pharmacologic Fingerprinting Analysed Chemometric Autors Plant name Part analysed Objectives References method activities technique components Analysis Acharya et al., Nymphaea pubescens Flowers Refluxing with Acetylcholinesterase GC-MS 71 metabolites C, G PCA, PLS-DA, 2018 methanol inhibitory activity OPLS-DA [138]

Agnolet et al., HPLC/DAD, 1H 15 phenolic Salix alba Bark Maceration - C, G PCA [147] 2012 NMR compounds

Ben Ahmed et Antioxidant Pistacia atlantica Leaves Maceration HPLC/UV Signal intensity A, F PLS, O-PLS [144] al., 2016 activities Alaerts et al., Ligusticum chuanxiong, Rhizome Ultrasound - HPLC/DAD - B, G HCA, PCA [111] 2010 Ligusticum jeholense Flavonols, ﬂavan-3- ols, Berries, seeds, Ali et al., 2011 Vitis species Ultrasound - 1H NMR phenylpropanoids, D PCA [54] pulp, skin amino acids, sugars and organic acids Ultrasound- assisted extraction Aliakbarzadeh Crocus sativus Stigmas coupled with - GC-FID Signal intensity E, G PLS-DA [327] et al., 2016 dispersive liquid-liquid microextraction Aliferis et al., PLS-DA, Avena sterilis Seeds Maceration - 1H NMR Spectral intensities D [81] 2006 SIMCA

Aliferis et al., Lemna minor Minor cultures Maceration - 1H NMR Spectral intensities D HCA, PLS-DA [83] 2009 Gallic, chlorogenic, Arceusz et al., syringic, caffeic, Melissa ofﬁcinalis Herb, Leaves Ultrasound - HPLC/UV G PCA [328] 2013 ferulic and rosmarinic acids Awin et al., Curcuma species (C. Rhizomes Ultrasound Antioxydant effect, 1H NMR 34 metabolites F, G PCA, PLS 2016 zedoaria, C. xanthorrhiza, C. α-Glucosidase [63] aeruginosa and JournalC. mangga) Pre-proofinhibition, nitric oxide inhibition Bailey et al., Suspension of Silene cucubalus - - 1H NMR Signal intensity D PCA [62] 2003 cells

Anti-plasmodial Bailey et al., SIMCA-P, PLS- Artemisia annua - Maceration activity, cytotoxicity 1H NMR Signal intensity F [329] 2004 DA , PCA assay Bernal et al., PCA, HCA, Piper species leaves, fruits Maceration - HPLC-DAD Signal intensity A, B [94] 2012 PARAFAC Boccard et al., Ball mill Arabidopsis thaliana leaves - UPLC-TOF/MS - E PCA [131] 2010 extraction Maceration, Thirty-one ultrasound, compounds reﬂux (anthocyanins, Brighenti et Mesocarp, juice, Punica granatum extraction, - HPLC-MS hydrolyzable tannins, E, D PCA [125] al., 2017 peel microwave- vanillic acid and assisted hydroxycinnamic extraction acid) Chang et al., Thymus Herbs Ultrasound Antioxidant activity UPLC-MS/MS, 12 phenolic acids and A HCA, PCA 2019 quinquecostatus Celak HPLC-DAD 23 flavonoids [127]

HCA, PCA, Chen et al., fruiting bodies, Reflux Ganoderma lucidum - HPLC-DAD - G PLS-DA, [330] 2008 mycelia, spores extraction SIMCA Chen et al., Reflux PCA, OSC- Codonopsis Radix Roots - HPLC-DAD 25 common peaks G [331] 2013 extraction PLS-DA Chen et al., Zanthoxylum bungeanum Leaves Reflux Antioxidant activity, HPLC-DAD 8 flavonoids G SA, HCA, PCA, 2019 extraction antibacterial activity DA [134]

Choi et al., Tobacco mosaic Nicotiana tabacum Leaves Sonication 1H NMR Spectral intensities F PCA [53] 2004 virus infection Chlorogenic acid, Ultrasonic SA, HCA, PCA, Cui et al., 2016 Pyrrosia species Aerial part - HPLC-DAD mangiferin, G [133] extraction QAMS kaempferol and rutin Epimedium subp., Custers et al., Aerial part, leaves, Ultrasonic HPLC- Pausinystalia yohimbe and Signal intensity G - [135] 2017 fruit extraction PDA, HPLC-MS Tribulus terrestris

Daolio et al., 1H-NMR, HPLC- Signal overlap, Signal Trichilia catigua Juss bark, root Maceration - G PCA, HCA [332] 2008 UV, 1H-HR-MAS intensity

Davey et al. Arabidopsis lyrata ssp. Seeds Frozen Amino acids and Maceration - MS A, C PCA [49] 2008 petraea foliage carbohydrates Maceration, De Combarieu supercritical CO2 Saw palmetto Berries - 1H NMR Spectral intensities D, G PCA, COW [60] et al., 2015 extraction, Journalreflux extraction Pre-proof Defernez et Potato, tomato, tea leaves Fruit, leaves - 1H NMR Spectral intensities G PCA [333] al., 2003

Deng et al., Ilex asprella, Lophatherum - Decoction - PS-MS Signal intensity E, G PCA [90] 2013 gracile Brongn, 74

Adenosma glutinosum (Linn) Druce, Bidens pilosa Linn, Lysimachia christinae Hance, Ilex rotunda Thunb., Vitex negundo Acute toxicity study, Dey et al., HPLC/DAD, 36 volatiles Dioscorea alata Aerial tuber Maceration immunomodulatory C, G PCA, HCA [334] 2016 FTIR, GC-MS components activities 5 polyphenolic classes; 1 terpenic Donno et al., class; Lycium species Berry fruit Maceration Antioxidant HPLC/DAD C, F, G PCA [149] 2015 (a) organic acids and vitamin C

5 polyphenolic classes; 1 terpenic Donno et al., Maceration class; Morus species Fruit Antioxidant HPLC/DAD C, E, G PCA [150] 2015 (b) Organic acids and vitamin C

Commercial tanning Dos Santos extracts; 10 samples of Grasel et al., - - - FTIR Spectral intensities A HCA, PCA [57] black wattle and 8 of 2016 quebracho alpha-glucosidase Easmin et al., Phaleria macrocarpa Fruits, seeds Ultrasound inhibitory FTIR-ATR Spectral intensities F, G OPLS [73] 2016 activity Species from the Ernst et al., Vernonieae, Eupatorieae Leaves Ultrasound - MALDI-TOF-MS Signal intensity B, G PCA, HCA [48] 2015 Heliantheae Asteraceae families

UPLC/PDA/ESI– Farag et al., PCA, OPLS- Phoenix dactylifera Fruits Ultrasound - qTOF-MS, GC– Quercetin and luteolin A [99] 2014 DA, HCA MS

Farag et al., Leaves, ﬂowers UPLC-qTOF- 10 phenolic E, C, F Brachychiton acerifolius Ultrasound Antioxidant effect PCA, OPLS-DA [145] 2015 and seeds PDA-MS, GC–MS compounds

Farag et al., Myristica fragrans Fruit, arillus and Solid-phase - GC-MS 35 voltile compounds C, G PCA, HCA, 2018 (Nutmeg) seed microextraction OPLS-DA [137]

Farag et al., Cinnamomum verum J. Barks Solid-liquid - 1H NMR 9 metabolites G PCA, OPLS-DA 2018 Presl, Cinnamomum cassia [85] (L.) J. Presl

Feng et al., Journal Pre-proof Zingiber officinale Rhizome Ultrasound - HPLC-PDA 16 compounds G PCA, HCA, SA [335] 2014

Fischedick et Cannabinoids 36 compounds Cannabis sativa Flower - GC–FID B, C, G PCA, HCA [336] al., 2010 extraction

Fraige et al., Vitis vinifera Fruits Maceration - HPLC–DAD–MS 20 anthocyanins A PCA [97] 2014 Gad et Curcuma longa Commercial Soxhlet - UV, FT-IR, 1H - G PCA, HCA Bouzbta., powder extraction NMR, HPLC- [87] 2017 DAD Gang et al., Rutin, quercetin, and Fagopyrum tataricum Seeds Ultrasound Antioxidant activity HPLC F, G HCA, SA, MLR [337] 2012 kaempferol

Gidman et al. Brachypodium distachyion, Leaves - - FT–IR - G PCA, CVA [64] 2003 Arabidopsis thaliana

Gifen et al., Salvia ssp, S. apiana, S. Leaves - - DART‐HRMS, 14 metabolites G KDA, SIMCA 2017 dominica, S. officinalis, S. GC-MS [46] farinacea, S. elegans, S. patens Gogna et al., Carica 1H NMR, UPLC- Leaves, seeds - Antioxidant activity - A, C PLS-DA [338] 2015 papaya ESI–MS Gomes et al., Lippia gracilis Leaves, stems Maceration - HPLC/DAD - A, B PCA [101] 2010

Goodacre et PCA, DFA, Pharbitis nil Leaves Infusion - ESI-MS - A [339] al., 2003 ANN Grata et al., HPLC-ESI-TOF- PLS-DA, PCA, Arabidopsis thaliana Leaves Ball mill - 4 oxylipins E [71] 2007 MS HCA Grebenstein Daucus carota Seeds Ultrasonication 1H NMR Signal intensity B, C PCA, PLS–DA [51] et al., 2011 Apigenin 7-O- glucoside, luteolin 7- Guzelmeric et Matricaria recutita Flower Infusion - HPTLC O-glucoside, 5,7- B, C PCA [140] al., 2015 dihydroxy-4- methylcoumarin Apigenin 7-O- Guzelmeric et Reflux glucoside and 5,7- Matricaria recutita Flower - HPTLC B, C, G PCA, HCA [141] al., 2017 extraction dihydroxy-4- methylcoumarin PCA, MCR- Hakimzadeh Leaves, ﬂowers, Ultrasonic Salvia reuterana - HPLC-DAD - A ALS, kNN, CP- [100] et al., 2014 Buds extraction ANN

Han et al., Reflux HPLC-DAD-ESI- Gardenia jasminoides Ellis Fruits - 14 compounds A, C, G, HCA, PCA [128] 2015 extraction MS

Hasan, et Asparagus racemosus Roots Soxhlet, Antioxidant activity, HPLC-DAD Polyphenols D HCA, PCA, Panda., 2019 Journalmaceration Pre-proof and anti-diabetic activity PLS-DA [126] Supercritical fluid (CO2)

43 compounds including 17 monoterpenoid HPLC–DAD, He et al., 2014 Paeonia species Root cortex Maceration - glucosides, 11 galloyl A, B PCA, HCA [105] HPLC–QTOF-MS glucoses, 5 ﬂavonoids, 6 paeonols and 4 phenols Ultrasonic 2 flavonoids and 4 He et al., 2016 Chrysanthemum Indicum Flower - HPLC G PCA, SA [340] extraction phenolic acids

Hendriks et Ultrasonic COW, DTW, Salix species Twigs - HPLC/UV - G [341] al., 2005 extraction PCA Hoai et al., Ultrasonic Mallotus species Leaves Antioxidant effect HPLC-UV Myricetin, quercetin G, F PCA, PLS [342] 2009 extraction beta-patchoulene, caryophyllene, alpha- guaiene, seychellene, Pressurized Hu et al., 2006 Pogostemon cablin Stem, leaves - GC–MS beta-guaiene, delta- G PCA, CA [343] liquid extraction guaiene, spathulenol, patchouli alcohol and pogostone Huang et al., Actaea racemosa, A. Ultrasonic - - FIMS - B, E PCA [344] 2015 podocarpa, A. dahurica extraction Islam et al., Reflux Epimedium koreanum Leaves Estrogenic activity HPLC-UV - G, F CDA [345] 2012 extraction Izadiyan et al., Thymus vulgaris, T. fallax, T. Leaves Ultrasound Antioxidant activity FTIR - F, G PCA, PCA-DA, 2018 pubescens, T. PLS-DA, PCR, kotschyanus, T.hymus iPLS, PLS [76] lancifolius, T. trautvetteri, and T. carmanicus Piper longum, Cuminum Jayasundar, et Aerial Part, root, FTIR, LIBS, E- cyminum and Capsicum Maceration - - G PCA [346] al., 2016 fruit seed tongue, NMR annum Microwave- NIR, GC-MS, Fatty acids, amino Jiao et al., Oryza sativa Seeds assisted - HPLC/UV, ICP- acids, protein, phytic B, C PCA, PLS-DA [58] 2010 extraction AES acid Johnson et al., B, D Solanum lycopersicum Fruit, pulp - - FT-IR - PCA, DFA, GA [66] 2003 Glycolic acid, malic acid, pyroglutamic Jumhawan et acid, citric acid, quinic Kopi Luwak Beans Solid-liquid - GC-FID, GC-MS E, G OPLS-DA [154] al., 2015 acid, inositol, caffeine, sucrose, chlorogenic acid Mulberroside A, Journal7 differentPre-proof oxyresveratrol, Kang et al., HPLC-DAD-ESI- Morus alba Root bark extraction Tyrosinase mulberrofuran G, F, G PLS [347] 2013 MS methods kuwanon G, kuwanon H, morusin,

Thermal Strecker aldehydes, Pumpkin (Cucurbitacea), processing, such as 2- Kebede et al., red beet (Amaranthaceae), Fresh vegetables high pressure - GC–MS methylbutanal, 2- D PLS-DA [302] 2014 onion (Alliaceae) and (market) high methylpropanal and potato (Solanaceae) temperature 3-methylbutanal processing gendarucin A, gendarucin A isomer, Antioxidant and α- 3,3-di-O- Khoo et al., Ultrasound, NMR, UPLC-MS- Clinacanthus nutans Stem, leaves glucosidase methylellagic acid, D, F PLS, PCA [84] 2015 soaking MS inhibitory activities ascorbic acid, two isomeric oxoprolinates Cholinesterase Klein-Júnior inhibitory, COW, PLS, Psychotria nemorosa Leaves Ultrasound UPLC-DAD Signal intensity F [348] et al., 2016 monoamine oxidase OPLS inhibitory Kong et al., Anti-bacterial Radix Isatidis Root Ultrasound HPLC-DAD - F HCA, PCA [349] 2008 activity Jateorrhizine, Kong et al., Reflux epiberberine, Rhizoma Coptidischinensis Root - UPLC-PAD C, G PCA, HCA, SA [350] 2009 extraction coptisine, palmatine, berberine Thermal Peroxidase and Koutidou et Brassica oleracea L. italica Florets treatments and lipoxygenase GC-MS - D PCA, PLS-DA [351] al., 2017 cold break activity Kuhnen et al., Maize Grains - - ATR-FTIR - A PCA [74] 2010 Reserpine, ajmalicine, Kumar et al., HPLC-ESI- Rauwolfia species Roots Ultrasound - ajmaline, serpentine, B PCA [113] 2016 QTOF-MS/MS yohimbine PCA, PLS, BP- Lai et al., 2010 46 Cassia Seeds Ultrasound - HPLC-UV - E, G ANN, RBF- [352] ANN Digestion and Laursen et al., Oryza sativa Seeds microwave - ICP-MS 73 elements C, E, G PCA [50] 2009 extraction Lebot & Roots, stumps, Ultrasound HPTLC, UV- Legendre Piper methysticum - - A, G PCA [353] basal stems extraction Visible (2016) Glutamine, arginine, Lee et al., Panax ginseng, Panax Roots Maceration - 1H NMR sucrose, malate, myo- B, G PCA, PLS-DA [80] 2009 quinquefolius inositol Ligustilide, Angelica tenuissima, Lee et al., unidentiﬁed Angelica gigas, Angelica Roots - - DART-TOF-MS G OPLS [45] 2012 Journal Pre-proofmolecular, dahurica, Cnidium ofﬁcinale senkyunolide Datura ceratocaula; D. Lesiak et al. DART-HR-TOF- discolor; D. ferox; D. inoxia; Seeds Ultrasound - B LDA [44] 2015 MS D. leichhardtii; D. metel; D.

quercifolia; D. stramonium; D. wrightii. Banisteripsis caapi, Lesiak and Peganum harmala, Mimosa Root bark, seeds, Reflux - DART-HRMS 15 compounds E PCA [354] Musah., 2016 hostilis, Psychotria viridis, leaves extraction Diplopterys cabrerana HPLC–DAD– Li et al., 2007 Cephalotaxus sinensis Leaf, twig Ultrasound - 8 compounds A, G PCA [355] MS/MS UPLC/Q-TOF- Li et al., 2010 Phellodendri amurensis Cortex Ultrasound - 10 compounds G PCA, HCA [356] MS Ultrasound Levisticum officinale, Blood enriching Li et al., 2015 Roots extraction and 1H NMR - F PCA [357] Angelicae sinensis effect fractionation Reflux - Liu et al., 2006 Angelica sinensis Roots - FT-IR D - [67] extraction Vortex extraction, reflux extraction, Liu et al., 2013 Chrysanthemum species Flowers, leaves pressurized hot - HPLC-UV - D, G PCA [358] water extraction, pressurized fluid extraction Cell wall Polysaccharide Liu et al., 2014 Citrus sinensis Leaves - XPS, FT-IR A PCA [69] preparation structures UV, FTIR, Soxhlet Liu et al., 2015 Lycium barbarum Fruits - HPSEC, PCD- 4 monosaccharides G PCA [359] extraction HPLC Methyl benzoate-2-O- beta- D - Gaultheria species glucopyranosyl (1-2)- Leaves, stems Liu et al., 2015 (Gaultheria leucocarpa var. Ultrasound - UPLC-PDA [O-beta- D - B SA, HCA, PCA [116] flowers yunnanensis) xylopyranosyl (1-6)]- O- beta- D - glucopyranoside Liu et al., 2016 Reflux COW, PLS, Turpinia arguta Leaves Antioxidant activity HPLC-DAD Signal intensity F [360] (a) extraction PCA Liu et al., 2016 Reflux Hippophae species Fruits - FTIR - B PCA [361] (b) extraction Liu et al., 2019 Hydrastis canadensis, Root and rhizome, - - FT-NIR - E, F PLS, MW-PCA, Coptis chinensis, Coptis yellow root, SIMCA deltoidea, Berberis oregon grape, [77] aquifolium, Rumex crispus, yellow dock, and Xanthorhiza simplicissimaJournal coptis Pre-proof Angelica sinensis, Ligusticum chuanxiong, Lu et al., 2005 Root, rhizome Ultrasound - HPLC-DAD 7 compounds A, C PCA [362] Szechwan lovage, Cnidium officinale

Luteolin 5-O- glucoside, caffeic acid derivative, caffeic acid, rutin, chicoric acid, kaempferol 3-O- maonylglucoside, Lu et al., 2014 Ocimum basilicum Leaves Ultrasound - HPLC-UV, FIMS G PCA [114] luteolin acetyl- glucuronide, rosmarinic acid, ursolic acid, naringin, hesperidin, neohesperidin Different pre- 15 flavonoids and Commercial Lucini et al., treatments UHPLC/Q-TOF- chalcones, 6 lignans, 4 Tomatoes homogenous Antioxidant capacity B, G PLS-DA [363] 2016 (cold, warm and MS phenylpropanoids, 7 mixture hot break) other Lucio- COW, PCA, Chromatographic Gutiérrez et Valeriana ofﬁcinalis Roots Ultrasound - HPLC-DAD B, G SIMCA, PLS- [110] signals al., 2012 (a) DA Lucio- Aerial parts Vortex Gutiérrez et Turnera diffusa Antioxidant activity HPLC-DAD - F, G PLS [152] (leaves and stems) extraction al., 2012 (b) Lucio- DA, PCA, Number of principal Gutiérrez et Eleutherococcus senticosus Roots - - NIR, FTIR E SIMCA, [78] components al., 2011 PLS-DA Luo et al., SA, HCA, PCA, Salvia miltiorrhiza -- Ultrasound - UHPLC-PDA 5 compounds G [364] 2015 PLS-DA Luthria et al., Brasscia oleracea Florets Ultrasound - UV Signal intensity B, D ANOVA-PCA [36] 2008 (a)

Luthria et al., Brasscia oleracea Florets Ultrasound - HPLC-MS - B ANOVA-PCA [43] 2008 (b) Leaves including Ma et al., 2008 Artemisia annua Ultrasound - GC–MS - A PLS-DA, PCA [93] flowers Magagna et al., Camellia sinensis Leaves Infusion - GC×GC-MS 123 components G PCA [365] 2017 Maldini et al., HPTLC, LC– Rosmarinus ofﬁcinalis - Soxhlet Antioxidant activity 12 compounds A, F PCA [98] 2016 MS/MS

Masson et al., 32 secondary Iris species Rhizomes Soxhlet - UHPL-TOF-MS A, G OPLS-DA, PCA [366] 2014 metabolites Cynara scolymus, Cimicifuga racemosa Nutt, Filipendula vulgaris Leaves, black Mattoli et al., Moench, Filipendula Journal Pre-proof1 cohosh root, Maceration - ESI-MS, H-NMR 5 compounds G CA, PCA [92] 2006 hexapetala Gilib, rhizom, ﬂowers Helichrysum italicum, Spiraea ulmaria, Filipendula ulmaria, Salvia

ofﬁcinalis, Helianthus annuus, Achillea millefolium

Elichrysum italicum, Mattoli et al., Plantago lanceolata, Flowered head, MSPC, PCA, Ultrasound - FIA-ESI-MS - G [367] 2016 Grindelia robusta and leaves PLS-DA honey Mazina et al., SFS, CE-DAD, β-asarone, trans- PARAFAC, 47 medicinal herbs - Ultrasound - E, G [368] 2015 HPLC-DAD-MS anethole PCA, CA Mendonça et Coffea species Beans Soxhlet - ESI-MS Phenolic compounds G PCA [91] al., 2008 3-O-caffeoylquinic, 5- O-caffeoylquinic, 3,4- Mhlongo et al., UHPLC-qTOF- Nicotiana tabacum Cell Ultrasound - dicaffeoylquinic, and D PCA [122] 2014 MS 4,5-dicaffeoylquinic acids Moreira and HPLC-DAD, Caffeine, trigonelline Scarminio., Coffea arabica Grains Ultrasound - A, D PCA [56] FTIR and chlorogenic acids 2013 In vivo solid Psychotria species, phase Musteata et Tabernaemontana sp., - microextraction, - LC-MS - B, D, C PCA [108] al., 2016 Uncaria sp., Solanum ex vivo solvent wrightii extraction HPLC-DAD, ICP- PCA, KNN, Ni et al., 2008 Atractylis chinensis - Ultrasound - 7 compounds A, C, D [369] AES LDA Radix bupleuri, Flos lonicerae, Fructus Decoction, Ni et al., 2009 Fruit, rhizomes Antipyretic, UV - F PCA, CCA [88] forsythiae, and Radix hydrodistillation isatidis GA-PLS, PCA, Ni et al., 2011 Rhizoma curcumae Rhizome Ultrasound - HPLC-DAD 11 common peaks A, G, D [370] KNN GC–MS, HPLC– 27 volatile PCA, LDA, BP- Ni et al., 2012 Rhizoma Curcumae Rhizome Ultrasound - B [115] DAD compounds ANN, LS-SVM, 25 compounds in the Novotná et al., Solanum lycopersicum, tomato extracts, 38 Fruit Ice-cold - DART-TOF-MS A, B, E PCA, LDA [89] 2012 Capsicum annuum compounds in pepper extracts Orland et al., Effect on HepG2 cells Chelidonii herba Aerial parts Maceration 1H-NMR Signal overlap D PCA [59] 2014 proliferation

Orzel et al., 12 Antioxidant PCA, UVE- Aspalathus linearis Fermented tea Infusions Antioxidant activity HPLC-DAD F, G [371] 2014 Journal Pre-proofmarkers PLS, PLS Dry herb, capsule Owen et al., Ba, Ca, Cd, Mg, Mo, Ni Hypericum perforatum samples, tablet Microwave - ICP-OES C, G PCA [372] 2016 and Y samples

Pankoke et al., Plantago lanceolata, P. Beta-glucosidase Iridoid glycoside Leaves Maceration UPLC–TOF-MS A, B PCA [95] 2013 major activity aucubin Iridoid glycosides: (aucubin, catalpol), chlorogenic acid, Pankoke et al., Plantago lanceolata Leaves Ice-cold - UHPLC–TOF-MS phenylpropanoid C, D PCA [120] 2015 glycosides verbascoside, isoverbascoside UHPLC-PDA, Panusa et al., Arctostaphylos pungens, Leaves Ultrasound - UHPLC-ESI- 84 compounds B, C PCA [106] 2015 Arctostaphylos uva-ursi TOF/MS Pardo-Mates Raspberry, blueberry or 17 polyphenols and E, F Fruit Ultrasound - HPLC-UV PCA, PLS [153] et al., 2017 grape fruit phenolic acids Park et al., Mentha species: Mentha × Aerial parts Solid-liquid Antioxidant activity GC-MS, HPLC- Phenolic compounds, C, G, F HCA, PCA, PLS 2018 piperita, M. aquatica, M. DAD volatile compound, suaveolens, M. spicata, M. riboflavin, and piperita ‘Chocolate’, M. carotenoids [155] suaveolens ‘Variegata’, M. longifolia, M. piperita f. citrata, M. pulegium Peng et al., Artemisia selengensis Herbs (stems) Microwave - HPLC–PAD 11 compounds G PCA, HCA, SA [373] 2011 Peng et al., Reflux Fagopyrum species Grains - HPLC-UV - E, G PCA, HCA, SA [374] 2012 extraction Phenolic compounds, fructose, glucose, Pereira et al., Vitis species Grape berries Ice extraction - 1H NMR gama-amino-n- A, G PCA [37] 2005 butyric acid, proline, arginine, glutamine Sucre (glucose, fructose and sucrose), organic acids (tartaric, malic, citric Grape berries: ‘Merlot noir’, Pereira et al., and succinic acids), ‘Carbernet franc’ or Grape berries Ice extraction - 1H NMR A, G PCA, PLS [61] 2006 and amino acids ‘Cabernet Sauvignon’ (proline, arginine, GABA, valine, alanine, leucine and isoleucine) OPLS-DA, Petrakis et al., Crocus sativus Stigmas Vortex - 1H NMR Curcuminoid signals E, G O2PLS-DA, [79] 2015 PCA, PLS-DA, Pieters et al., 39 Mallotus samples from Leaves, Roots Ultrasound Antioxidant activity pCEC, HPLC-UV Characteristic peaks F PLS [375] 2011 17 different species Journal Pre-proof1560 compounds (Epigallocatechin, Pongsuwan et Japanese green tea Leaves liquid-liquid - UPLC-TOF/MS epigallocatechin F, G PCA, PLS [146] al., 2008 gallate and epicatechin gallate)

Maceration, heat-reflux HPLC-UV/DAD, Prencipe et al., Female strobiles extraction, Prenylflavonoids and Humulus lupulus - HPLC-ESI-MS D, G PCA [376] 2014 (cones) microwave prenylphloroglucinols and MS/MS power

Qian et al., UPLC-TOF/MS, Angelica sinensis Leaf, root Ultrasound - 13 compounds G PCA [104] 2013 UPLC-UV Radulescu Hippophae rhamnoides Berries Ultrasound- - Raman, FTIR Phenolic compounds D PCA, PLS-DA et al., 2019 assisted extraction, rapid extraction under [70] pressure, subcritical fluid extraction Ren et al., Arabidopsis Leaves Maceration - 1H NMR - B PLS-DA, OSC [52] 2009 Riedl et al., Variation in the Myriophyllum spicatum Leaves, ﬂower Liquid-liquide - GC-MS A PCA [96] 2012 signals Populus nigra, Populus tremula, Betula pendula, Quercus Robur, Salix Ristivojević et herbaceae, Prunus avium, Resin Ultrasound - HPTLC Phenolic compounds B PCA [148] al., 2017 Malus domestica, Cidonia oblongata, Prunus armeniaca, Prunus mahaleb Sabir et al., Oryza species Rice brans Ultrasound Antioxidant activity HPLC-UV 9 common peaks B, G PCA, DA [107] 2017 Safer et al., Leontopodium species Roots Maceration - 1H NMR, LC-MS - B PCA, PLS-DA [82] 2011 Sajewicz et al., Reflux Salvia species Plants - HPLC/DAD Flavonoids B PCA [102] 2012 extraction Sánchez- 31 polyphenolic Salcedo et al., Morus nigra, Morus alba Leaves Ultrasound - UHPLC-MS B, E, G PCA, HCA [118] compounds 2016 Sârbu et al., Actinidia chinensis, Citrus HPLC-DAD/MS, Fruits Ultrasound - - B PCA, LDA [42] 2012 maxima UV–Vis Schiozer et al., ESI-MS, Deoxyanthocyanidin Arrabidaea chica Leaves - - B PLS-DA [41] 2012 ESI-MS/MS ions Schulze et al., Solid-liquid HPLC-DAD, LC- 15 phenolic Cyclopia maculata Seeds - C, G SA, PCA [124] 2014 (Water) MS compounds hypoxanthine, uridine, progoitrin, Reflux epiprogoitrin, Shi et al., 2012 Isatis indigotica Roots - UPLC-PDA G SA, HCA PCA [377] Journalextraction Pre-proofadenosine, guanosine, R, S-goitrin, and gluconapin Orthosiphon stamineus Sim et al.,2004 Leaves Vortex - FT-IR - A PCA, SIMCA [72] benth

Seven Singh et al., Antioxidant activity, Ocimum species Leaves Maceration HPLC-PDA phenolics acids and B, C PCA [378] 2016 behavioral studies, quercetin Soares and Bauhinia species, B. PCA, SIMCA, Scarminio., Leaves Maceration - HPLC-DAD Polar substances B [379] andicans and B. forﬁcata HCA 2008 Soares et al., Chromatographic PCA, Tuker3, Erythrina speciosa Leaves Ultrasound - HPLC-DAD D [123] 2012 peaks PARAFAC Yohimbine (main Sun and Chen., Tablet, capsule, Pausinystalia johimbe Ultrasound - UHPLC-UV/MS active chemical) and C, G PCA [380] 2012 liquid forms other contents Peaks potentially Thiangthum Mallotus species, Anti-oxidant PCA, HCA, Leaves Ultrasound HPLC-DAD responsible for the F [381] et al., 2012 Phyllanthus sp. capacity PLS, O-PLS antioxidant activity Tianniam et Thermal Angelica acutiloba Roots - GC-MS - A, G PLS-DA, PCA [139] al., 2010 desorption UVE-PLS, Peaks potentially Tistaert et al., Anti-oxidant PCA, PLS, Step Mallotus species Leaves, roots, bark Ultrasound HPLC-UV responsible for the F [382] 2009 capacity MLR, PCR, antioxidant activity OPLS Mallonanoside A, mallonanoside B, Tistaert et al., Anti-oxidant COW, PCA, Mallotus species Leaves, roots, bark Ultrasound HPLC–MS quercitrin and B, F [143] 2012 capacity OPLS kaempferol-3-O-l- rhamnosyl Castanea sativa, Schinopsis balancae, Quercus robur, Presence of Tondi et al., Acacia mearnsii, Pinus Leave, tara, bark Industrial tannin - ATR FT-MIR hydrolyzable or G PCA, CA [65] 2015 pinaster, Uncaria gambir, and wood extracts condensed tannins Rus coriaria, Cesalpinea spinosa, Presence of opiates, Turner et al., Heads without Methanolic FTIR-ATR, Poppy (market) - morphine and C, G PCA, SIMPLS [75] 2009 seeds extraction HPLC–MS thebaine Thermal and Vervoort et al., Daucus carota Fresh carrots high-pressure - GC-MS Volatile compounds C, D PLS-DA [121] 2013 processing PCA, LDA, Viaene et al., Mallotus Phyllanthus sp. Leaves Ultrasound - HPLC-UV Signal intensity B QDA, [109] 2015 CART, SIMCA Aromatic acids, Wang et al., Roasted aliphatic acids, Beans Solid-liquid - FT-IR A PCA, SIMCA [68] 2011 Arabica Coffees ketones, aldehydes, and aliphatic esters Journal Pre-proof COW, BP- Wang et al., Epimedium koreanum and Leaves Ultrasound - HPLC-DAD Target signal B ANN, PCA, [117] 2012 E. wushanense HCA, SIMCA Wang et al., Reflux Treat kidney yin 34 chemical Rehmanniae glutinosa - LC-MS B, C, F, G PCA, PLS [383] 2013 extraction deﬁciency components

Radix Paeoniae Alba and Wang et al., Radix Paeoniae Rubra, - Maceration - UPLC-MS-MS, IR - B, G PCA [384] 2015 Paeonia lactifloras Harbohydrate, Ward et al., Micro- aliphatic (sugars), Arabidopsis thaliana Aerial part - 1H NMR A PCA [39] 2003 centrifugation organic and amino acids regions Amino acids, Wen et al., triterpenes, and Ganoderma lucidum - Ultrasound - 1H NMR A, G PCA, OPLS-DA [38] 2010 hydroxylated compounds Windarish et Curcuma longa and C. Rhizomes Ultrasound - 1H NMR 8 E PCA, OPLS-DA al., 2019 heyneana [86] Wirasuta et Piper betle Leaves Ultrasound Antifungal activity TLC - E, G PCA, HCA [151] al., 2017 Pinoresinol-beta- D - glucoside; Reflux Xia et al., 2009 Forsythia suspensa Fruits - HPLC-PAD forsythiaside; C, G HCA, BDA [385] extraction phillyrin and phillygenin Panax species, of Terminalia chebula, Fruits, leaves, Reflux HPTLC, HPLC- Xie et al., 2006 Ophiopogon japonicus, - Rutin, flavonoids E, G PCA [3] roots, rhizome extraction DAD Schisandra chinensis, Ginkgo biloba Esculin, esculetin, Xie et al., 2013 Fraxini Cortex Cortex Ultrasound - HPLC-DAD G HCA [386] fraxetin HPLC-DAD-ESI- Rutin, narcissin, Xie et al., 2014 Sophora japonica Flower buds Ultrasound - C, G SA, HCA, PCA [129] MS/MS quercetin Maceration, 16 characteristic Xu et al., 2009 Paeonia lactiflora, P. veitchii Root Reflux - HPLC-PDA B PCA [112] peaks extraction Naringin and Xu et al., 2010 Fructus aurantii immaturus Fruit Ultrasound - HPLC-DAD A, C, G PCA, HCA, SA [387] hesperidin,

Cistanche deserticola, C. Glycoside and Xu et al., 2013 Stems Solid-liquid - FTIR B SIMCA [55] tubulosa, C. salsa, C. sinensis saccharide

Yang et al., Standardized HPLC-UV-DAD- Ginkgo biloba - - Flavonoids C, E, G PCA [388] 2016 extract powders MS

Yang et al., Anti-oxidant HPLC-DAD-CAD, 8 discriminating Rhizoma smilacis Glabrae - Solid-liquid C, G PCA, HCA [389] 2017 capacity HPLC-ECD-DAD components Citrus reticulata, C. Hesperidin, nobiletin Yi et al., 2009 Fruits Ultrasound - HPLC–DAD B HELP, PCA [119] erythrosa Journal Pre-proofand tangeretin Yudthavorasit HCA, PCA, Zingiber ofﬁcinale Rhizomes Ultrasound - HPLC-DAD 9 compounds A [390] et al., 2014 LDA Active garlic Zhang et al., Reflux Allium sativum Garlic cloves - IEESI-MS substances and G PCA [391] 2014 extraction saccharides 85

Zhang et al., Salvia miltiorrhiza Roots Ultrasound Antioxidant activity UPLC-Qqq- 12 phenolic acids and C, G PCA [136] 2019 MS/MS 5 tanshinones Ultrasound, Zhao et al., Viscum coloratum - Reflux - HPLC-UV 18 compounds D, G SA, HCA [392] 2011 extraction Zhao et al., Anti-oxidant Gynostemma pentaphyllum Leaves Ultrasound HPLC-UV-MS 9 compounds G PCA [393] 2012 capacity Zhao et Leaves, whole- Gynostemma pentaphyllum Ultrasound - FIMS 11 characteristic ions E, G PCA, PLS-DA [47] al.,2013 plant Aurantii fructus immaturus, Zhao et Poniciri trifoliatae fructus - Ultrasound - FIMS 8 characteristic ions E PCA, SIMCA [394] al.,2015 immaturus Aconitine, Bioactivity to hypaconitine Zheng et al., Aconitum carmichaelii Roots Decoction mitochondrial UPLC-UV mesaconitine, F, G PCA, CCA [395] 2014 growth. benzoylaconitine, benzoylhypacoitine Zhong et al., HPLC–DAD–ESI- Aloe barbadensis - Ultrasound - 27 common peaks G HCA, PCA, SA [132] 2015 MS/MS Zhou et al., HPLC/DAD/ESI- Evodia rutaecarpa Fruit Ultrasound - 15 compounds G HCA, PCA [103] 2010 MS HPLC–ESI- Zhou et al., Sarcandra glabra Stems, leaves Ultrasound - MS/MS and ESI- 16 phenolic compounds G HCA, PCA [130] 2013 TOF-MS

Table 2. Applications of untargeted and targeted fingerprinting and chemometrics on essential oils (1994-2019). A: discrimination of geographic origins or seasonal variations; B: differentiatingJournal similar species (taxonomic); C: quantitative Pre-proof analysis; D: evaluating the processing methods; E: authentication; F: prediction; G: quality control; sp: species; subsp: subspecies.

Authors Plant name Part analysed Extraction method Pharmacologic Fingerprintin Number of Objective Chemometric Reference activities g technique analysed s Analysis s components Abd El-Gawad et Xanthium Strumarium Leaves Hydrostillation Allelopathic GC-MS 43 A, C ACA, PCA [232] al., 2019 activity, antioxidant potential

[396] Aghaei et al. 2013 Stachys lavandulifolia Aerial parts Hydrodistillation - GC-FID, GC-MS 49 B, C CA

Agostini et al. Cunila incisa Flowering plant Steam distillation - GC-FID, GC-MS 19 A, C PCA, HCA [216]

2006 Akbarian et al. Dorema aucheri Leaves Hydrodistillation - GC-FID, GC-MS 31 B, C HCA, PCA, CCA [290]

2016 Almeida et al. Aniba rosaeodora Vood, leaves, Steam distillation - Raman - B PCA, PLS-DA, [179]

2013 branches VIP Al Riza et al., 2019 Pogostemon cablin (Patchouli) Commercial oils Hydrodistillation - Fluorecence - A PCA [167]

Andreani et al. Xanthium italicum Aerial parts Hydrodistillation - GC-FID, GC-MS 107 A, C PCA, CA [219]

2012 Andreani et al. Senecio vulgaris Aerial parts Hydrodistillation - - 54 A, C PCA, HCA [235]

2015

Angioni et al. 2004 Rosmarinus officinalis Leaves Hydro, steam distillation Antimicrobial, GC-FID, RAPD 32 A, C PCA [213] Antifungal Ao et al., 2019 Amomum villosum Lour, Amomum villosum Fruits Steam distillation GC-MS 13 B, G HCA, PCA, PLS- [296] Lour. var. xanthioides T. L. Wu et Senjen DA Asadollahi-Baboli Thymus vulgaris, T. serpyllum Plants Solid-phase - GC-MS 47 B, C MCR-ALS, [260]

et al. 2016 microextraction OPA- SIMPLISMA, PCA Baranska et al. Origanum majorana, O. vulgare Aerial parts Hydrodistillation - FT-Raman, 13 B, C CA, PLS [172]

2005 ATR-IR, NIR, GC-FID Baranska et al. Eucalyptus species Leaves - - GC-FID, FT- 9 B, C, F PLS, CA [177]

2006 Raman, ATR- IR Belabbes et al. Calendula arvensis Aerial parts Hydrodistillation Antioxydant, GC-FID, GC-MS 53 A, C CA [228]

2017 antifungal Bischof‐Deichnik Thymus praecox species. polytrichus Aerial parts Hydrodistillation - GC-FID, GC-MS 60 C FA, CA, DA [258]

et al. 2000 Blagojević et al. Artemisia absinthium Aerial parts Hydrodistillation - GC-MS 35 C, D, F PCA, HCA [397] 2015 Journal Pre-proof Blanquer et al. THymus piperilla Aerial parts Hydrodistillation - GLC-FID, GC- 20 B, C PCA, CA, DA [254]

1998 MS Bombarda et al. Lavandula angustifolia × L. latifolia Commercial oil - - Mid-IR 13 A, C, F PCA, PLS, PLS- [398]

2008 DA 87

Boszormenyi et al. Salvia officinalis, S. judaica Boiss Leaves Steam distillation - GC-FID, GC- 37 C, A HCA [222]

2009 MS, RAPD Bousbia et al. 2009 Citrus limon, C. aurantifolia, C. sinensis, Peels Microwave - GC-MS 30 C, D CDA [300] Citrusparadisi hydrodiffusion and gravity

Brunel et al. 2016 Phagnalon sordidum Aerial parts Hydrodistillation Antimicrobial GC-FID, GC-MS 76 B, C PCA, CA [291]

Cañigueral et al. Thymus moroderi, T. antoninae Aerial parts Hydrodistillation - GC-FID, GC-MS 48 and 39 B, C PCA, CA [252]

1994

Carović-Stanko et Ocimum species (O. americanum L., O. Aerial parts Hydrodistillation Antibacterial GC-FID, GC-MS 69 B, C, G PCA [271]

al. 2010 basilicum L., O. champechianum, O. x citriodorum Vis., O. kilimandscharicum)

Chizzola et al. Thymus vulgaris Leaves Hydrodistillation Antioxydant GC-FID, GC-MS 38 B, C, G HCA [399]

2008

Chun et al. 2011 Schizonepeta tenuifolia Aerial stems, flow Sonication extraction - GC-FID, GC-MS 5 A, C PCA, HCA [218] stalk Dardioti et al. Satureja pilosa subsp. Origanita Aerial parts Hydrodistillation - GC-FID, GC-MS 6 B, C PCA [244]

2012 de Oliveira et al. Copaifera langsdorffii Oil resins Hydrodistillation - GC-FID, GC-MS 38 A, C HCA, PCA [241]

2017

Deng et al. 2014 Cinnamomum cassia bark, C. cassia twig Barks, twig Hydrodistillation - GC-MS 53 B, C PCA, OPLS-DA [288]

Dhouioui et al. Aristolochia longa subsp. Paucinervis Roots Hydrodistillation Antimicrobial GC-FID, GC-MS 103 A, C ANOVA [238]

2016 Díaz-Maroto et al. Mentha spicata Leaves Simultaneous distillation- - GC-MS 28 C, D PCA [311] 2003 extraction Djabou et al. 2011 Teucrium species (T. marum, T. scorodonia Aerial parts Hydrodistillation Antibacterial Capillary-GC, 49 B, C CA, PCA, [400] subsp. scorodonia, T. flavum subsp. glaucum, GC-MS T. massiliense, T. flavum subsp. flavu)

Dunlop et al. 1995 Eucalyptus camaldulensis, E. woodwardii, E. Leaves Vacuum distillation - GC-MS - B PCA, LDA, [261] torquata, E. torwood SIMCA

Dunlop et al. 1999 Angophora Leaves Vacuum distillation - GC-MS 52 B, C PCA [263]

Dunlop et al. 2003 Eucalyptus subser. Euglobulares Blakly, E. Leaves Vacuum distillation - GC-FID, GC-MS 23 B, C PCA, LDA [262] globulus, E. bicostata, E. maidenii, E. pseudoglobulus

Dupuy et al. 2013 Myristica fragrans (nutmug) Kernels (ripe, Steam distillation - MID-IR, GC-MS 21 A, C PCA, CA [162] unripe)

Dupuy et al. 2014 Lavandula angustifolia Aerial parts Hydrodistillation - GC-FID, FT- 14 C, F, G PCA, H-PLS, [191] NIR, FT-MIR MB-PLS Ebrahimabadi et Myrtus communis Aerial parts Hydrodistillation Antimicrobial GC-FID, GC-MS 22 C, F, G COW, PCR, PLS, [322]

Journal Pre-proof al. 2016 OPLS Echeverrigaray et Thymus vulgaris Leaves Steam distillation - GC-MS, RAPD 25 A, C CA [221]

al. 2001 Echeverrigaray et Cunila galioides Flowering plant Steam distillation - GC-FID, GC-MS 40 A, C PCA, HCA [212]

al. 2003 88

Egerton- Chamelaucium uncinatum Leaves Steam distillation - GLC-FID, GC- 33 A, C CA, PCA [206] Warburton et al. MS

1998 [401] Anti- mycobacterial, Pachira aquatica Aubl and Pachira leaves anti-helicobacter El-Din et al, 2018 gabra Pasq and stems Hydrodistillation pylori GC-FID, GC-MS 84 C, G HCA, PCA Farag et al., 2017 Nigella ssp (N. sativa, N. orientalis, N. Seeds Solid-phase - GC-MS 34 D HCA, PCA, [309] damascena, N. arvensi and N. nigellastrum) microextraction OPLS-DA Farag et al., 2018 Myrtaceae genera (Eucalyptus, Corymbia, Plants Hydrostillation - ATR-IR, GC- 9 B, C, G HCA, PCA [174] Melaleuca, Syzygium, and Eugenia) FID, GC-MS Fernando Rolim Copaifera langsdorff (copaiba) Leaves Hydrodistillation - GC-FID, GC-MS 24 A, C CA [402] de Almeida et al.

2014

Feudjio et al. 2014 Thymus vulgaris, Mentha piperita, Myrtus Commercial oil - - Fluorescence - G UPCA, [203] communis, Citrus aurantium, C. sinensis, PARAFAC Rosmarinus officinalis, Schinus terebinthifolius, Artemisia absinthium, Lavandula angustifolia, Zingiber officinali

Feudjio et al. 2017 Citrus aurantium (neroli), Mentha piperita 89 anuginose oils - - Fluorescence - E, F ANNs, SOMs, [204] (peppermint), Piper nigrum (black pepper), MLP Citrus aurantium (petitgrain), Zingiber officinalis (ginger), Helianthus annuus (sunflower)

Flamini et al. 2006 Centaurea subsp., C. aladaghensis, C. Aerial parts Hydrodistillation - GC-FID, GC-MS 141 B, C PCA, MSC, HCA [279] antiochia var. prealta, C. antitauri, C. babylonica, C. balsamita, C. cheirolepidoides, C. deflexa, C. iconiensis, C. lanigera, C. ptosimopappoides Francisco et al. Aristolochia species Roots Hydrodistillation - GC-MS 75 B, C PCA [280]

2008 Freitas et al., 2018 Ocimum americanum, O. basilicum L., O. Aerial parts Hydrostillation - 1H NMR, GC- 5 B HCA, PCA, PLS- [187] basilicum var. Purpuracea Bent, O. MS, GC-FID DA gratissimum L., O. micranthum Willd, O. selloi Benth, and O, tenuiflorum)

Gong et al. 2001 Cortex cinnamomi Herbs Hydrodistillation - GC-MS - A OPR, HELP, [207] SFA, IOP

Gong et al. 2004 Cortex cinnamomi Herbs Hydrodistillation - GC-MS 80 A, C HELP, IPREP [208] Gong et al. 2001 Rhizoma ctractylodis, pericarpium citri Crude herbs Hydrodistillation - GC-MS 65 C, G PCA, FSMW, [313] Reticulatae, cortex magnoliae officinalis, HELP Radix glycyrrhizae Guo et al. 2003 Notoptergium inciumJournal Herbs Pre-proofHydrodistillation - GC-MS 65 C, G OPA, EWOP [403] Guo et al. 2004 Artemisia capillaris Herbs Hydrodistillation - GC-MS 51 A, C, G SFA, OPA [209] Guo et al. 2004 Artemisia capillaris Herbs Hydrodistillation - GC-MS 51 A, C, G FSMWFA, [210] EWOP

Gorji-Chakespari Rosa damascena Petals Hydrodistillation - GC-FID, GC- 18 B, C PCA, LDA, SVM [273]

et al. 2016 MS, E-nose Gounaris et al. Origanum onites, O. vulgare subsp. hirtum, Leaves Hydrodistillation - GC-MS, RAPD 27 B, C PCA [278]

2002 and their putative hybrid Origanum x intercedens

Gudi et al. 2014 Foeniculum vulgare (fennel) Fruits Hydrodistillation - GC-FID, GC- 14 B, C HCA [181] MS, ATR-FTIR, NIR, FT- Raman

Gudi et al. 2015 Salvia officinalis Leaves Hydrodistillation - GC-FID, GC- 14 C, F, G PCA, PLS [197] MS, ATR-FTIR Hajdari et al., 2018 Hyssopus officinalis subsp. aristatus Plants Hydrodistillation Antioxydant effect GC-FID, GC- 110 A, C HCA, PCA [225] MS, E-nose, HPLC–UV– DAD Hammoda et al., Cupressus sempervirens var. horizontalis, C. Leaves and cones Hydrostillation Insecticidal GC-MS 75 B, C PCA, HCA, [276] 2019 sempervirens var. pyramidalis and C. activity, acetyl OPLS-DA macrocarpa cholinesterase inhibition

Hassen et al. 2015 Thapsia garganica Roots, stems, leaves, Hydrodistillation Antifungal GC-FID, GC-MS 137 B, C HCA, PCA [404] fruits He et al., 2017 Chaihu Shugan San Herbs Hydrodistillation, - GC-MS, GC × 216 G HELP, SIA, [321] supercritical fluid GC-q MS AMWFA, ATLD extraction, soxhlet extraction

Hu et al. 2014 Curcumae longae Rhizome Hydrodistillation - GC-MS 46 A, C HCA, PCA, PLS- [223] DA

Hu et al. 2015 Curcuma longa Herbals Hydrodistillation Antifungal SH-GC-MS 18 C, F, G PLS, VIP [325]

Jalali-Heravi et al. Pelargonium roseum Aerial parts Hydrodistillation - GC-FID, GC-MS 85 B, G SBM, [17]

2006 FSMWEFA, HELP Jalali-Heravi et al. Cuminum cyminum (cumin), Carum carvi Aerial parts Hydrodistillation - GC-MS 49 and 98 C, G OPR, DS-MCR- [405] 2007 (caraway) ALS Jalali-Heravi et al. Rosa gallica and Rosa moschata (Damask Air-dried flowers Hydrodistillation - GC-MS 95 C, G HELP, MCR- [18]

2008 rose) ALS Jalali-Heravi et al. Rosmarinus officinalis Aerial parts Hydrodistillation - GC-MS 100 C, G MCR-ALS, [34]

2011 HELP, OPR Jalali-Heravi et al. Mentha spicata (spearmint) Ground plants Hydrodistillation - GC-MS 24 C, G HELP, MCR- [319]

2014 ALS, MCR- FQEZ Jalali-Heravi et al. Lavandula latifolia Ground plants Hydrodistillation - GC-MS 47 C, G VSMW-EFA, [35]

2015 MCR-ALS

Jassbi et al. 2012 Salvia aegyptica, S. eremophila, S. Aerial parts Hydrodistillation - GC-FID-GC-MS 113 B, C PCA, AHC [284] santolinifolia Journal Pre-proof

Jentzsch et al. Pimpinella anisum, Ocimum basilicum, Comemrcial oil - - Raman - G PCA [201]

2015 Pelargonium graveolens, Zingiber officinale, Lavandula hybrida, angustifolia, Citrus limon, Myristica fragrans, Citrus auroantium amara, C. sinensis, Pogostemon cablin, Mentha piperita, Salvia officinalis, Thymus vulgaris

Kanakis et al. 2012 Mentha pulegium Aerial parts Ultrasonication extraction - GC-FID-FTIR 20 A, C DA [161]

Kapetanos et al. Apioideae subsp. (23 taxa) Air-dried plant Steam distillation - GC-FID, GC-MS 100 B, C PCA [281]

2008 Karousou et al. Coridothymus capitatus, Satureja thymbra Aerial parts Hydrodistillation - GC-FID, GC-MS 4 A, C, G PCA [215]

2005

Kasrati et al. 2015 Mentha suaveolens subsp. Timija Aerial parts Hydrodistillation Insecticidal GC-MS 44 A, C CA [227]

Khoshroo et al. Thymus daenensis Aerial parts Hydrodistillation - GC-MS, 5 B, C CA [183]

2015 Fluorescence, Raman

Kokkini et al. 2004 Origanum vulgare ssp. hirtum, O. onites Aerial parts Hydrodistillation - GC-FID, GC-MS 38 A, B, C PCA [406]

Kuriakose and Joe Santalum album (sandalwood) Heartwood, roots Steam distillation - NIR - A, C, E, F, G, PCA, HCA, [166]

2012 SOM, SVMR Kuriakose and Joe Santalum album, S. spicatum Heartwood, Roots Steam distillation - NIR - E, F, G PLSR, LWR [190]

2013

Labra et al. 2004 Ocimum basilicum Leaves Hydrodistillation - GC-FID, AFLP 12 B, C CA [268]

Lafhal et al. 2015 Lavandula angustifolia, L. angustifolia P. Commercial oil - - GC-FID, GC- 56 B, C, E, F, G PCA, PLS, PLS- [184] Mill. × Lavandula latifolia MS, Raman DA

Lafhal et al. 2016 Lavandula angustifolia, L. angustifolia P. Commercial oil - - GC-FID, GC- 29 B, C, F, G PCA, PLS, PLS- [186] Mill. × Lavandula latifolia MS, NIR DA

Lafhal et al. 2016 Lavandula angustifolia, L. angustifolia × L. Commercial oil - - GC-FID, GC- 56 B, C, F, G PCA, PLS, PLS- [186] latifolia MS, MIR, DA Raman

Li et al. 2013 Cinnamomum verum, C. cassia, C. loureirii Dried barks Hydrodistillation - GC-MS, FTIR 38 A, C HCA, PCA [163]

Li et al. 2015 Salvia miltiorrhiza, S. przewalskii, S. Leaves, flowers Simultaneous distillation - GC-MS 48 B, C PCA, CA [407] officinalis, S. deserta extraction

Li et al. 2016 Citrus aurantium (Aurantii fructus Dried immature Steam distillation - GC-FID, GC-MS 18 B, C PCA, PLS-DA [408] immaturus, Aurantii fructus) fruits, dried ripe fruits Liber et al. 2011 Ocimum basilicumJournal Aerial parts Pre-proofHydrodistillation - GC-MS, AFLP 86 B, C HCA [267] Lira et al. 2009 Laurus nobilis Leaves, stalks Steam distillation - GC-MS 30 A, C PCA [409]

Liu et al. 2016 Atractylodes (A. lancea, A. koreana) Rhizomes Hydrodistillation - GC-FID, GC-MS 46 B, C HCA, PCA, PLS- [292] DA

Liu et al. 2016 Atractylodes (A. lancea, A. koreana) Rhizomes Hydrodistillation - GC-MS 52 B, C ANOVA, PCA [293]

Liu et al., 2019 Juniperus rigida Sieb. et Zucc Needles Supercritical CO2 fluid Antioxidant GC-MS 41 C, G SA, PCA, HCA, [315] extraction activity, DA antibacterial activity Lorigooini et al. Satureja bachtiarica Aerial parts Hydrodistillation - GC-MS 31 C, D CA [312]

2017 Lota et al. 2000 Citrus reticulata Essential oil (peel, Hydrodistillation - GC-MS, NMR 69 B, C PCA, HCA [410] leaf)

Maggio et al. 2016 Ocimum basilicum Aerial parts Hydrodistillation Antimicrobial GC-FID, GC-MS 80 B, C PCA [269]

Maietti et al. 2013 Lavandula angustifolia, L. hybrida cultivars Flowering parts Hydrodistillation Cytotoxic, GC-MS 75 C, F, G PCA, PLS [323] (cv. Ordinario, cv. Alardii, cv. Abrialis, cv. C.r, mutagenic, cv. Super z) antifungal, antioxydant

Maree et al. 2014 158 different commercial essential oils Commercial oil - Antimicrobial GC-MS - F, G PCA, OPLS-DA, [324] VIP Marincas Lavandula angustifolia, Lavandula x Leaves Steam distillation - GC-MS 31 E, G CA, LDA [411] et Feher., 2018 Intermedia

Marotti et al. 1996 Ocimum basilicum Leaves Hydrodistillation - GC-FID, GC-MS 40 B, C CA [264]

Marti et al. 2014 Citrus limon Commercial oil Brown Oil Extractor - GC-FID/MS, 36 A, F PCA, OPLS-DA [164] FT-MIR, H- NMR, UHPLC- TOF-MS Masoum et al. Calligonum comosum Various parts Simultaneous distillation - GC-MS 105 C, G MF-ICA, [318]

2013 extraction SIMPLISMA, OPR Mehl et al. 2014 Citrus lemons Essential oil (92 Brown Oil Extractor - GC-FID/MS, 36 A, B PCA, HCA, [165] anuginose oil) FT-MIR, H- OPLS-DA NMR, UHPLC- TOF-MS Meng-Ying et al. Ephedra sinica Stems, roots Hydrodistillation Antioxydant GC-MS 32 B, C PCA, OPLS-DA, [294]

2016 VIP Mesquita et al. Eugenia uniflora Leaves HS-SPME - GC-MS 33 B, C, D PCA, HCA [303]

2017

Miceli et al. 2006 Thymus capitatus (Thymbra capitata) Aerial parts Hydrodistillation - GC-FID, GC-MS 40 B, C PCA, CA [248]

Mokhetho et al., 40 essential oils Commercial oils - Antiquorum GC-FID-MS - G OPLS-DA [326] 2018 sensing activity Mundina et al. Piper lanceaefolium Leaves, spikes Hydrodistillation - GC-FID, GC- 52 A, C PCA, CA [211] 2001 Journal Pre-proofMS, C-NMR

Ni et al. 2012 Curcumae species (C. kwangsiensis, C. Rhizomes Headspace extraction - GC-MS, HPLC- - B, C PCA, LS-SVM, [115] phaeocaulis, C. wenyujin) DAD LDA, BP-ANN Nikpour et al., Teucrium polium Aerial or top Hydrodistillation GC-MS 106 C, G MCR-ALS, 2018 flowering part PARAFAC [316]

Nogueira et al. Hypericum species (H. perfoliatum, H. Aerial parts Hydrodistillation - GC-FID, GC-MS 95 B, C CA, PCA [282]

2008 humifusum, H. linarifolium, H. pulchrum)

Nsuala et al. 2017 Leonotis leonurus Aerial parts Hydrodistillation - GC-MS-FID, 26 A, C HCA, PCA, [229] GCxGC-TOF- OPLS-DA MS

Omar et al. 2012 Rosmarinus officinalis Leaves Supercritical fluid - Raman - F, G PLS [199] extraction, ultrasounds, hydrodistillation

Padalia et al. 2013 Ocimum taxa (O. basilicum, O. americanum, Aerial parts Hydrodistillation - GC-FID, GC-MS 70 A, C CA [237] O. gratissimum, O. tenuiflorum, O. kilimandscharicum) Padilla-González Espeletia species (E. grandiflor, E. killipii) Leaves Hydrodistillation - GC-FID, GC-MS 40 B, C OPLS-DA, HCA [295]

et al. 2016

Pan et al. 2011 Scutellaria barbata Dried herbals Hydrodistillation - GC-MS 86 C, E, F, G PCA [231]

Paolini et al. 2010 Calendula arvensis Aerial parts Hydrodistillation - GC-FID, GC-MS 85 A, C PCA, CA [242]

Parastar et al. Citrus fruits (C. limon, C. Sinensis, C. Air dried peel Hydrodistillation - GC-MS 37 C, G MCR-ALS, PCA, [317]

2012 reticulata, C. paradisi) KNN, CPANN

Paula et al. 2011 Pimenta pseudocaryophyllus Dried leaf Hydrodistillation - GC-FID, GC-MS 57 B, C PCA, CA, CDA [283]

Pereira et al. 2000 Tymus caespititius Aerial parts Hydrodistillation - GC-FID, GC-MS 49 B, C CA [412]

Petretto et al. Myrtus communis varieties (melanocarpa, Leaves Hydrodistillation Antioxydant GC-FID, GC-MS 31 B, C PCA [413]

2016 leucocarpa)

Pietraś et al. 2012 Aniba rosaeodora, Teucrium species (T. Commercial oil - - TLC, GC-FID, - G PCA, CA [192] marum, T. scorodonia subsp. scorodonia, T. HPLC, ATR- flavum subsp. glaucum, T. massiliense, T. FTIR, DSC flavum subsp. flavu) Pirbalouti et al. Thymus vulgaris, T. daenensis Aerial parts Hydrodistillation - GC-FID, GC-MS 24 A, C CA [414]

2013 Pirbalouti et al. Ocimum basilicum Steam, leaves Hydrodistillation - GC-FID, GC-MS 59 C, D CA [306]

2013 Pirbalouti et al. Coriandrum sativum Aerial parts Hydrodistillation - GC-MS 10 C, D ANOVA, HCA [310]

2016 Pirmoradi et al. Ocimum basilicum varieties (var. Aerial parts Hydrodistillation - GC-FID, GC-MS 49 B, C CA [265]

2013 purpurascens, var. dianatnejadii) Radjabian et al. Heracleum persicum, H. rechingeri, H. Ground fruits - - GC-FID, GC-MS 36 B, C HCA, PCA [289]

2014 gorganicum, H. rawianum, H. pastinacifolium, H. anisactis Rahimmalek and Thymus daenensis subsp. Daenensis Aerial parts Hydrodistillation - GC-MS 30 C, D CA [307]

Goli 2013 Rahimmalek et al. TrachyspermumJournal ammi Seeds Pre-proofHydrodistillation - GC-MS 14 A, C HCA, PCA, CCA [230]

2017

Ray et al., 2018 Hedychium species (H. gardnerianum, H. Rhizomes Hydrodistillation - GC-FID, GC-MS 59 B, C ACA, PCA [272] flavescens, H. thyrisiforme, H. flavum, H. ellipticum, H. aurantiacum, H. gracile, H. greenii, H. spicatum, and H. coronarium) Rodríguez-Solana Satureja hortensis, S. pilosa, S. thrymba, Leaves, flowers Hydrodistillation, steam - GC-FID, GC- 47 B, C HCA, LDA [182]

et al. 2014 Mentha pulegium, Thymus longicaulis subsp. distillation – solvent MS, FTIR, chaubardii, T. vulgaris Thymus sp., Origanum extraction dispersive- vulgare subsp. hirtum, O. onites Raman Rohman et al. Pandanus conoideus Red fruit (papua) - - FTIR - F PLSR, DA [193]

2011

Rouis et al. 2012 Hypericum triquetrifolium Aerial parts Hydrodistillation - GC/EI-MS 147 A, C CA, PCA [415]

Ruan and Li. 2007 Xanthium strumarium (Fructus xanthii) Fruits Microwave assisted - GC-MS - A SA, PCA [217] extraction

Sadeghi et al. 2014 Teucrium polium Aerial parts Hydrodistillation - GC-FID, GC-MS 19 A, C PCA [224]

Sáez 1995 Thymus hyemalis Aerial parts Steam distillation - GC-FID, GC-MS 37 B, C PCA, CA [250]

Sáez 1995 Thymus zygis Aerial parts Steam distillation - GC-FID, GC-MS 36 B, C CA, PCA [257]

Sáez 1999 Tymus baeticus Aerial parts Steam distillation - GLC-FID, GC- 36 B, C PCA, CA [246] MS

Sáez 2001 Thymus serpylloides subsp. Gadorensis Aerial parts Steam distillation - GC-FID, GC-MS 32 B, C PCA, CA [255]

Salgueiro et al. Thymus carnosus Aerial parts Hydrodistillation - GC-FID, GC- 74 B, C PCA, CA [249]

1995 MS, H-NMR Salgueiro et al. Thymus mastichina subsp. Mastichina, Flowering branches Hydrodistillation - GLC-FID, GC- 93 B, C PCA, CA [251]

1997 Thymus mastichina subsp, donyanae, T. MS albicans Salgueiro et al. Thymus lotocephalus, T. mastichina Aerial parts Hydrodistillation - GC-FID, GC-MS 92 B, C PCA, CA [253]

2000

Salgueiro et al. Thymus villosus subsp. Lusitanicus, T. villosus Aerial parts Hydrodistillation - GC-FID, GC-MS 86 B, C PCA, CA [256]

2000 subsp. Villosus

Sandasi et al. 2010 Agathosma betulina, A. crenulata Aerial parts Hydrodistillation - GC-MS, NIR, 12 C, F, G OPL-DA, PLS [198] MIR, FT- Raman

Sandasi et al. 2013 Lippia species (L. javanica, L. scaberrima, L. Aerial parts Hydrodistillation - GC-MS, MIR, - B PCA, OPLS-DA [180] rehmannii and L. wilmsii) NIR

Santos et al. 2005 Thymus caespititius Aerial parts Hydrodistillation - GC-FID, GC-MS 70 B, C CA [416]

Sárosi et al. 2013 Thymus vulgarisJournal Aerial parts Pre-proofHydrodistillation - GC-MS 20 C, D PCA [308] Schmidt et al. Thymus praecox subsp. Arcticus Aerial parts Hydrodistillation - GC-FID, GC-MS 69 B, C FA, CA, PCA [259]

2004

Schulz et al. 1999 Mentha piperita, M. peppermint, M. Leaves Hydrodistillation - GC-FID, NIR 9 B, C, F PLS, PCA [168] cornmint, M. spearmint, M. pennyroyal, M. Menthu citrutu

Schulz et al. 2002 Citrus sinensis, Citrus x paradisi, Citrus Essential oil Hydrodistillation - GC-FID, ATR- 7 B, C PCA, PLS [171] deliciosa, Citrus limon, Citrus aurantifolia FTIR, NIR-FT Raman

Schulz et al. 2003 Thyme, Origano, Chamomile Air-dried plant Hydrodistillation - GC-FID, ATR- 8 B, C PCA, PLS, CA [171] FTIR, NIR

Schulz et al. 2004 Ocimum species, Chamomilla recutita, Air-dried plant - - ATR-IR, NIR, - B, C CA, PLS [26] Thymus vulgaris, Origanum species Raman

Schulz et al. 2005 Origanum, Satureja, Salvia, Sideritis, Air-dried plant Hydrodistillation - GC-FID, GC- 21 B, C PCA, CA [176] Thymus, Calamintha, Lavandula, Ziziphora, MS, ATR/FT- Thymbra IR, NIR-FT- Raman

Sellami et al. 2011 Laurus nobilis Leaves Hydrodistillation - GC-FID, GC-MS 47 C, D CA [304]

Shanjani et al. Juniperus excelsa foliage and berries Steam distillation - GC-FID, GC-MS 22 C, D PCA [243]

2010 Sharopov et al. Ocimum basilicum Aerial parts Hydrodistillation Antimicrobial, GC-MS 99 A, C AHCA [417]

2016 cytotoxic, antioxidant, invertebrate toxicity

Skaltsa et al. 2001 Stachys ionica, S. spruneri, S. swainsonii ssp. Air-dried plant Steam distillation - GC-MS 223 B, C PCA, CA [274] Swainsonii, S. swainsonii subsp. Argolica, S. swainsonii ssp. Melangavica, S. swainsonii ssp. Scyronica Slavkovska et al. Satureja montana ssp. (S. montana, S. Aerial parts Steam distillation - GC-MS 19 B, C PCA, CA [275]

2001 pisidica, S. kitaibelii)

Socaci et al. 2013 Hippophae rhamnoides ssp. Carpatica Volatile oil (berries) In-tube extraction - GC-MS 46 B, C PCA [418]

Socaci et al. 2014 Solanum lycopersicum Volatile oil (fruits) In-tube extraction - GC-MS 61 B, C PCA, CA [419]

Sonboli et al. 2016 Salvia hypoleuca Aerial flowering Hydrodistillation - GC-FID, GC-MS 64 B, C CA [420] parts

Soro et al. 2016 Lippia multiflora Leaves Hydrodistillation Anti- GC-FID, GC-MS 54 A, C PCA [239] inflammatory, Stashenko et al. Lippia origanoides Flowers, leaves, Microwave-assisted - GC-MS, GC- 139 B, C PCA [421] 2010 stems hydrodistillation, FID, HPLC supercritical fluid extraction Stefanakis et al. Origanum vulgare subsp. (O. hirtum, O. Aerial parts Hydrodistillation antimicrobial GC-EIMS 56 B, C PCA [287] 2013 onites, O. marjorana)Journal Pre-proof

Strehle et al. 2006 Pimpinella anisum, Foenicum vulgare, Commercial oil - - GC-MS, Raman - B, C CA [178] Cuminum cyminum, Anethum graveolens, Carum carvi, Coriandrum sativum, Daucus carota, Lavandula angustifolia, Lavandula spica

Subki et al. 2013 Cinnamomum species (C. mollissimum, C. Leaves Hydrodistillation - GC-MS 50 B, C PCA, HCA [422] porrectum, C. verum) Taghadomi-Saberi Citrus aurantium Peels Steam distillation - HS-GC-MS 15 A PCA, ANN [245] et al., 2018

Tankeu et al. 2013 Tagetes minuta Aerial parts Hydrodistillation - GC-MS-FID 7 A, C PCA, HCA, [423] OPLS-DA

Tankeu et al. 2014 Lavandula angustifolia Comercial oil - - GC –MS-FID, 6 C, F, G PCA, PLS [196] MIR, NIR

Tankeu et al. 2014 Melaleuca alternifolia Comercial oil - - GC-MS/FID, 7 C, G, F PCA, PLS [195] MIR, NIR

Telci et al. 2006 Ocimum basilicum Leaves Hydrodistillation - GC-FID, GC-MS 67 B, C CA [266]

Teles et al. 2012 Lippia alba Leaves Hydrodistillation - GC-FID, GC-MS 24 A, C, D ANOVA [305]

Teles et al. 2013 Mentha × villosa Leaves Hydrodistillation - GC-MS 18 A, C, D ANOVA, PCA [220]

Tian et al. 2014 Perilla frutescens Aerial parts Hydrodistillation Antioxidant, GC-FID, GC-MS 119 A, C HCA, PCA [226] antifungal Trindade et al. Thymus caespititius Aerial parts Distillation-extraction - GC-FID, GC- 13 B, C PCA, CA [247]

2008 MS, RAPD Trindade et al. Thymus caespititius Aerial parts Distillation extraction - GC-FID, GC- 10 B, C PCA, CA [424]

2009 MS, RAPD Usano-Alemany et Salvia lavandulifolia Aerial parts Hydrodistillation - GC-FID, GC-MS 16 A, C PCA [240]

al. 2016

Verma et al. 2013 Ocimum species (O. basilicum, O. Aerial parts Hydrodistillation - GC-FID, GC-MS 106 B, C MDS [270] tenuiflorum, O. kilimandscharicum, O. gratissimum, O. americanum) Vieira and Simon Ocimum species (O. basilicum, O. × Aerial parts Hydrodistillation - GC-FID, GC-MS 42 B, C PCA [425]

2006 citriodorum, O. americanum var. americanum, O. americanum var. pilosum, O. minimum, O. kilimandscharicum)

Vieira et al. 2001 Ocimum gratissimum (var. gratissimum and Leaves Hydrodistillation - GC-FID, GC- 17 B, C PCA, CA [277] var. macrophyllum) MS, RAPD

Wang et al. 2008 Pericarpium citri Reticulatae viride, Dried fruits Hydrodistillation - GC-MS 61 B, C AMWFA [13] Pericarpium citri reticulatae Wang et al. 2014 Chamomile Flowers Steam distillation - GC-MS - B PCA, PLS-DA [426]

Wang et al. 2014 Myristica fragrans, Cananga odorata, Commercial oil - GC-FID, GC- - F, G CA [427] Cinnamomum Journal cassia, Santalum album, Pre-proofMS, ATR-FTIR, Coriandrum sativum, Rosa damascena, Raman Ocimum basilicum, Cinnamomum zeylanicum, Zingiber officinal, Melissa officinali, Foeniculum vulgare, F. dulce,

Thymus vulgaris, Pimpinella anisum, Eugenia caryophyllata, Syzygium aromaticum, Origanum majorana

Wu et al. 2008 Honghua Commercial oil - - GC-FID, FT- 3 C, F, G PLS [188] MIR, FT-NIR

Xiang et al. 2011 Curcuma phaeocaulis, C. kwangsiensis, C. Rizomes Steam distillation - GC-MS 61 C, G PCA, PLS-DA [70] wenyujin Xu et al. 2005 Houttuynia cordata Herbs Hydrodistillation - GC-MS 30 C, G FSMWFA, SFA [428]

Xu et al. 2006 Houttuynia cordata Herbs Hydrodistillation - GC-MS 30 C, G TPA, MSC, PCA, [7] SA

Xue et al. 2012 Tussilago farfara Leaves, flower buds, Petroleum-ether - GC-MS - B PCA, OPLS-DA [285] fully open flowers extraction

Yang et al. 2016 Pogostemon cablin Aerial parts Hydrodistillation - GC-FID 16 D SA, HCA [298]

You et al. 2015 Murraya species (M. tetramera, M. Branches, leaves Hydro-distillation Repellent against GC-FID, GC-MS 36 B, C HCA [429] euchrestifolia, M. koenigii, M. kwangsiensis, Tribolium M. exotica, M. alata) castaneum

Yu et al. 2016 Litsea coreana variety (Hawk tea) Leaves Hydrodistillation Antioxydant, GC-FID, GC-MS 72 A, C ANOVA, PCA [430] antimicrobial Zaouli et al. 2010 Rosmarinus officinalis varieties (var. typicus, Dried leaves Hydrodistillation Antioxydant, GC-FID, GC-MS 25 B, C PCA [431] var. troglodytorum) antimicrobial Zeng et al. 2007 Clematis species (C. serratifolia, C. Roots, stems Hydrodistillation - GC-MS 153 C, G FSMWEA, [432] brevicaudata, C. fusca, C. hexapetala, C. AMWFA chinensis)

Zeng et al. 2016 Cirsium japonicum, C. setosum Aerial parts HS-SPME Antioxydant GC-MS 119 B, C, D HCA [299]

Zhao et al. 2013 Mentha haplocalyx, M. spicata Aerial parts Hydrodistillation - GC-FID, GC-MS 37 B, C HCA [101] Zhu et al. 2010 Portulaca oleracea Whole herb Hydrodistillation - GC-MS, FTIR 38 A, C SA, HCA [40]

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