Comprehensive Chemical Fingerprinting of High-Quality Cocoa at Early Stages of Processing: Effectiveness of Combined Untargeted and Targeted Approaches for Classification and Discrimination

Solid-phase microextraction coupled to comprehensive multidimensional gas chromatography for food analysis

Solid-phase microextraction and comprehensive multidimensional gas chromatography represent two milestone innovations that occurred in the field of separation science in the 1990s. They have a common root in their introduction and have found a perfect coupling in their evolution and applications. This review will focus on food analysis, where the paradigm has changed significantly over time, moving from a targeted analysis, focusing on a limited number of analytes at the time, to a more holistic approach for assessing quality in a larger sense. Indeed, not only some major markers or contaminants are considered, but a large variety of compounds and their possible interaction, giving rise to the field of foodomics. In order to obtain such detailed information and to answer more sophisticated questions related to food quality and authenticity, the use of SPME-GC × GC-MS has become essential for the comprehensive analysis of volatile and semi-volatile analytes. This article provides a critical review of the various applications of SPME-GC × GC in food analysis, emphasizing the crucial role this coupling plays in this field. Additionally, this review dwells on the importance of appropriate data treatment to fully harness the results obtained to draw accurate and meaningful conclusions.

Cocoa quality: Chemical relationship of cocoa beans and liquors in origin identitation

In this study, HS-SPME-GC-MS was applied in combination with machine learning tools to the identitation of a set of cocoa samples of different origins. Untargeted fingerprinting and profiling approaches were tested for their informative, discriminative and classification ability provided by the volatilome of the raw beans and liquors inbound at the factory in search of robust tools exploitable for long-time studies. The ability to distinguish the country of origin on both beans and liquors is not so obvious due to processing steps accompanying the transformation of the beans, but this capacity is of particular interest to the chocolate industry as both beans and liquors can enter indifferently into the processing of chocolate. Both fingerprinting (untargeted) and profiling (targeted) strategies enable to decipher of the information contained in the complex dataset and the cross-validation of the results, affording to discriminate between the origins with effective classification models.

Authentication of Cocoa Products Based on Profiling and Fingerprinting Approaches: Assessment of Geographical, Varietal, Agricultural and Processing Features

Cocoa and its derivative products, especially chocolate, are highly appreciated by consumers for their exceptional organoleptic qualities, thus being often considered delicacies. They are also regarded as superfoods due to their nutritional and health properties. Cocoa is susceptible to adulteration to obtain illicit economic benefits, so strategies capable of authenticating its attributes are needed. Features such as cocoa variety, origin, fair trade, and organic production are increasingly important in our society, so they need to be guaranteed. Most of the methods dealing with food authentication rely on profiling and fingerprinting approaches. The compositional profiles of natural components -such as polyphenols, biogenic amines, amino acids, volatile organic compounds, and fatty acids- are the source of information to address these issues. As for fingerprinting, analytical techniques, such as chromatography, infrared, Raman, and mass spectrometry, generate rich fingerprints containing dozens of features to be used for discrimination purposes. In the two cases, the data generated are complex, so chemometric methods are usually applied to extract the underlying information. In this review, we present the state of the art of cocoa and chocolate authentication, highlighting the pros and cons of the different approaches. Besides, the relevance of the proposed methods in quality control and the novel trends for sample analysis are also discussed.

Link between Flavor Perception and Volatile Compound Composition of Dark Chocolates Derived from Trinitario Cocoa Beans from Dominican Republic

The chemical composition of dark chocolate has a significant impact on its complex flavor profile. This study aims to investigate the relationship between the volatile chemical composition and perceived flavor of 54 dark chocolate samples made from Trinitario cocoa beans from the Dominican Republic. The samples were evaluated by a trained panel and analyzed using gas chromatography-mass spectrometry (GC-MS) to identify and quantify the volatile compounds. Predictive models based on a partial least squares regression (PLS) allowed the identification of key compounds for predicting individual sensory attributes. The models were most successful in classifying samples based on the intensity of bitterness and astringency, even though these attributes are mostly linked to non-volatile compounds. Acetaldehyde, dimethyl sulfide, and 2,3-butanediol were found to be key predictors for various sensory attributes, while propylene glycol diacetate was identified as a possible marker for red fruit aroma. The study highlights the potential of using volatile compounds to accurately predict chocolate flavor potential.

Forastero and Criollo cocoa beans, differences on the profile of volatile and non-volatile compounds in the process from fermentation to liquor

Cocoa bean fermentation is an important process because during this process, aroma compounds are produced, the astringency decreases, and the embryo dies. The fermentation processes of the Criollo and Forastero types have been studied separately without comparing them at the same time and in the same place. The aim of this work was to determine differences in the profile of volatile and nonvolatile compounds of Criollo and Forastero cocoa from the fermentation process to the final stage of obtaining the liquor. The experiments were carried out at the same time in the Maya region. Volatile compounds were determined by HS-SPME GC-MS (headspace solid phase-microextraction with gas chromatography-mass spectrometry). Sugars, organic acids, and alkaloids were determined by ultrahigh-performance liquid chromatography (UHPLC-PDA/UV). Criollo cocoa liquor was defined by the volatile and nonvolatile compounds such as acetic acid, phenylethyl alcohol, benzaldehyde, 2-phenylethyl acetate, acetophenone and 3-methylbutanal., which are associated with sour, honey, almond, flowery and chocolate aroma. Forastero cocoa liquor was represented with a significant difference by acetic acid, isobutyl acetate, 2,3-diethyl-5-methylpyrazine and ethyl octanoate and these could provide aroma descriptors such as sour, fruity and nutty. This study characterized for the first time the dynamics of volatile compounds during the fermentation, drying, and roasting stages and in the final cocoa liquor of Criollo and Forastero from cocoa beans of the same origin.

Roasting and Cacao Origin Affect the Formation of Volatile Organic Sulfur Compounds in 100% Chocolate

Chocolate is a highly appreciated food that develops its characteristic flavors in large part during the roasting of cacao beans. Many functional classes have been noted for their importance to chocolate flavor, including volatile organic sulfur compounds (VSCs). Despite this, the effect of roasting on the concentration of VSCs has never been thoroughly assessed. Here, we studied the effects of roasting temperature, time, and cacao origin on the formation of VSCs. Twenty-seven 100% chocolate samples made from cacao from three different origins and roasted according to an I-optimal experimental design were analyzed by comprehensive gas chromatography with sulfur-selective detection (GCxGC-SCD). For two compounds, dimethyl disulfide and dimethyl trisulfide, the effects of roasting time, roasting temperature, and cacao origin were modelled using response surface methodology and semi-quantified relative concentration. Overall, roasting increased the number of sulfur-containing volatiles present in chocolate, with a total of 28 detected, far more than previously thought. Increased roasting time and especially roasting temperature were found to significantly increase the concentration of VSCs (p < 0.05), while cacao origin effects were only seen for dimethyl disulfide (p < 0.05). The identity of most VSCs remains tentative, and more research is needed to unravel the impact of these volatiles on flavor perception in chocolate.

Extending the breadth of saliva metabolome fingerprinting by smart template strategies and effective pattern realignment on comprehensive two-dimensional gas chromatographic data

Comprehensive two-dimensional gas chromatography with time-of-flight mass spectrometry (GC × GC-TOFMS) is one the most powerful analytical platforms for chemical investigations of complex biological samples. It produces large datasets that are rich in information, but highly complex, and its consistency may be affected by random systemic fluctuations and/or changes in the experimental parameters. This study details the optimization of a data processing strategy that compensates for severe 2D pattern misalignments and detector response fluctuations for saliva samples analyzed across 2 years. The strategy was trained on two batches: one with samples from healthy subjects who had undergone dietary intervention with high/low-Maillard reaction products (dataset A), and the second from healthy/unhealthy obese individuals (dataset B). The combined untargeted and targeted pattern recognition algorithm (i.e., UT fingerprinting) was tuned for key process parameters, the signal-to-noise ratio (S/N), and MS spectrum similarity thresholds, and then tested for the best transform function (global or local, affine or low-degree polynomial) for pattern realignment in the temporal domain. Reliable peak detection achieved its best performance, computed as % of false negative/positive matches, with a S/N threshold of 50 and spectral similarity direct match factor (DMF) of 700. Cross-alignment of bi-dimensional (2D) peaks in the temporal domain was fully effective with a supervised operation including multiple centroids (reference peaks) and a match-and-transform strategy using affine functions. Regarding the performance-derived response fluctuations, the most promising strategy for cross-comparative analysis and data fusion included the mass spectral total useful signal (MSTUS) approach followed by Z-score normalization on the resulting matrix.

Metabolomics during the spontaneous fermentation in cocoa (Theobroma cacao L.): An exploraty review

Spontaneous fermentation is a process that depends on substrates' physical characteristics, crop variety, and postharvest practices; it induces variations in the metabolites that are responsible for the taste, aroma, and quality. Metabolomics makes it possible to detect key metabolites using chemometrics and makes it possible to establish patterns or identify biomarker behaviors under certain conditions at a given time. Therefore, sensitive and highly efficient analytical techniques allow for studying the metabolomic fingerprint changes during fermentation; which identify and quantify metabolites related to taste and aroma formation of an adequate processing time. This review shows that studying metabolomics in spontaneous fermentation permits the characterization of spontaneous fermentation in different stages. Also, it demonstrates the possibility of modulating the quality of cocoa by improving the spontaneous fermentation time (because of volatile aromatic compounds formation), thus standardizing the process to obtain attributes and quality that will later impact the chocolate quality.

Fisher ratio feature selection by manual peak area calculations on comprehensive two-dimensional gas chromatography data using standard mixtures with variable composition, storage, and interferences

Comprehensive two-dimensional gas chromatography (GC×GC) is becoming increasingly more common for non-targeted characterization of complex volatile mixtures. The information gained with higher peak capacity and sensitivity provides additional sample composition information when one-dimensional GC is not adequate. GC×GC generates complex multivariate data sets when using non-targeted analysis to discover analytes. Fisher ratio (FR) analysis is applied to discern class markers, limiting complex GC×GC profiles to the most discriminating compounds between classes. While many approaches for feature selection using FR analysis exist, FR can be calculated relatively easily directly on peak areas after any native software has performed peak detection. This study evaluated the success rates of manual FR calculation and comparison to a critical F-value for samples analyzed by GC×GC with defined concentration differences. Long-term storage of samples and other spiked interferences were also investigated to examine their impact on analyzing mixtures using this FR feature selection strategy. Success rates were generally high with mostly 90-100% success rates and some instances of percentages between 80 and 90%. There were rare cases of false positives present and a low occurrence of false negatives. When errors were made in the selection of a compound, it was typically due to chromatographic artifacts present in chromatograms and not from the FR approach itself. This work provides foundational experimental data on the use of manual FR calculations for feature selection from GC×GC data.

Multidimensional Gas Chromatography Moves Forward

In this extended special feature to celebrate the 35th anniversary edition of LCGC Europe, leading figures from the separation science community explore contemporary trends in separation science and identify possible future developments.

Non-targeted analysis of VOCs by HS-SPME-G C/MS coupled with chemometrics as a potential tool for authentication of White Kołuda oat goose

This study tested the possibility of using non-targeted analysis of volatile organic compounds by headspace solid-phase microextraction-gas chromatography-mass spectrometry coupled with chemometrics as a potential tool for differentiating leg meat of oat- and wheat-fed (ad libitum) White Kołuda geese. Thirty-six classification models were obtained for which the correct classification rate and classification accuracy for oatfed and wheat-fed geese were calculated based on a seven-fold cross-validation. Generally, the most advantageous method of the sample preparation was the high-temperature heat treatment version, whereas the highest correct classification rate was obtained when the chemometric analysis was carried out on the female, then male, and finally male + female variant of group comparisons (P<0.01). Furthermore, log-transformation appeared to be a slightly better data preprocessing technique in comparison to systematic ratio normalization. The obtained classification models can potentially differentiate the meat of oat-fattened from wheat-fattened White Kołuda geese.

Tile-Based Pairwise Analysis of GC × GC-TOFMS Data to Facilitate Analyte Discovery and Mass Spectrum Purification

A new tile-based pairwise analysis workflow, termed 1v1 analysis, is presented to discover and identify analytes that differentiate two chromatograms collected using comprehensive two-dimensional (2D) gas chromatography coupled with time-of-flight mass spectrometry (GC × GC-TOFMS). Tile-based 1v1 analysis easily discovered all 18 non-native analytes spiked in diesel fuel within the top 30 hits, outperforming standard pairwise chromatographic analyses. However, eight spiked analytes could not be identified with multivariate curve resolution-alternating least-squares (MCR-ALS) nor parallel factor analysis (PARAFAC) due to background contamination. Analyte identification was achieved with class comparison enabled-mass spectrum purification (CCE-MSP), which obtains a pure analyte spectrum by normalizing the spectra to an interferent mass channel (m/z) identified from 1v1 analysis and subtracting the two spectra. This report also details the development of CCE-MSP assisted MCR-ALS, which removes the identified interferent m/z from the data prior to decomposition. In total, 17 out of 18 spiked analytes had a match value (MV) > 800 with both versions of CCE-MSP. For example, MCR-ALS and PARAFAC were unable to decompose the pure spectrum of methyl decanoate (MVs < 200) due to its low 2D chromatographic resolution (∼0.34) and high interferent-to-analyte signal ratio (∼30:1). By leveraging information gained from 1v1 analysis, CCE-MSP and CCE-MSP assisted MCR-ALS obtained a pure spectrum with an average MV of 908 and 964, respectively. Furthermore, tile-based 1v1 analysis was applied to track moisture damage in cacao beans, where 86 analytes with at least a 2-fold concentration change were discovered between the unmolded and molded samples. This 1v1 analysis workflow is beneficial for studies where multiple replicates are either unavailable or undesirable to save analysis time.

Cocoa bean turning as a method for redirecting the aroma compound profile in artisanal cocoa fermentation

Two artisanal fermentation processes for Criollo cocoa beans with different turning start times (24 h and 48 h) were studied. The aromatic profile of cocoa turned every 24 h (B1) displayed volatile compounds associated with fermented, bready, and fruity aromas. When cocoa beans were fermented with a different turning technique with a start time of 48 h (B2), they provided volatile compounds mainly associated with descriptors of floral, woody, sweet, fruity and chocolate aromas. The turning start time of 48 h stimulated a microbial profile dominated by yeast such as Hanseniaspora opuntiae, Pichia manshurica, and Meyerozyma carpophila, favoring the production of several key aroma markers associated with cocoa bean fermentation quality, such as phenylethyl acetate, 2-phenylacetaldehyde, 3-methylbutanal, 2-phenylethyl alcohol, 2,3-butanedione, 3-methylbutanoic acid, and 2-methylpropanoic acid, while an immediate turning start time (24 h) favored an aerobic environment that stimulated the rapid growth of Acetobacter pasteurianus, Bacillus subtilis and a higher biodiversity of lactic acid bacteria (LAB) (e.g., Lactobacillus plantarum and Pediococcus acidilactici), which increased the production of ethyl acetate and 3-hydroxy-2-butanone. Volatile compound generation and microbial populations were evaluated and analyzed by multivariate analysis (principal component analysis and partial least squares discriminant analysis) to find correlations and significant differences. This study shows that the method of turning Criollo cacao beans can lead to the formation of desirable aromatic compounds.

Fine cocoa beans production: Tracking aroma precursors through a comprehensive analysis of flavor attributes formation

The fine flavor cocoa (FFC) market offers cocoa farmers better monetary and nonmonetary benefits than the bulk market. In this work, during cocoa fermentation, flavor formation was studied at different fermentation times based on sensory profiles, volatile compound contents and untargeted metabolomics. It was observed that chocolate quality is influenced by fermentation time. Thus, at 72 h, the sensory profiles showed no outstanding attributes, while at 96 h, the global quality presented a stronger influence of fine attributes, such as fruitiness, florality, spices and nuttiness. Finally, at 120/144 h, these FFC features diminished. Metabolomic fingerprint of cocoa beans (related to peptides, sugars, amino acids, and phenolic compounds) and the volatile fingerprint of chocolate showed a change according to the fermentation time. This allowed the proposal of 96 h as the optimal fermentation time to produce FFC beans. Additionally, 20 volatiles and 48 discriminating metabolites were defined as potential quality biomarkers.

Influence of Taxonomic and Functional Content of Microbial Communities on the Quality of Fermented Cocoa Pulp-Bean Mass

Microbial metabolism drives changes in the physicochemical properties and, consequently, the sensory characteristics of fermented cocoa beans. In this context, information regarding the structure, function, and metabolic potential of microbial communities' present during cocoa pulp-bean mass fermentation is limited, especially concerning the formation of aromatic compounds. To bridge the gap, the metagenome of fermented cocoa pulp-bean mass (Criollo and Forastero) has been investigated using shotgun metagenomics coupled with physicochemical, microbiological, quality, and sensory analyses to explore the impact of microbial communities on the quality of fermented cocoa pulp-bean mass on one farm in one season and in one region under the same environmental conditions. Our findings showed that the metagenomic diversity in cocoa, the fermentation length, and the diversity and function of metagenome-assembled genomes (MAGs) greatly influence the resulting distinctive flavors. From the metabolic perspective, multiple indicators suggest that the heterolactic metabolism was more dominant in Criollo fermentations. KEGG genes were linked with the biosynthesis of acetic acid, ethanol, lactic acid, acetoin, and phenylacetaldehyde during Criollo and Forastero fermentations. MAGs belonging to Lactiplantibacillus plantarum, Limosilactobacillus reuteri, and Acetobacter pasteurianus were the most prevalent. Fermentation time and roasting are the most important determinants of cocoa quality, while the difference between the two varieties are relatively minor. The assessment of microbiological and chemical analysis is urgently needed for developing fermentation protocols according to regions, countries, and cocoa varieties to guarantee safety and desirable flavor development. IMPORTANCE Monitoring the composition, structure, functionalities, and metabolic potential encoded at the level of DNA of fermented cocoa pulp-bean mass metagenome is of great importance for food safety and quality implications.

Discrimination of French wine brandy origin by PTR-MS headspace analysis using ethanol ionization and sensory assessment

The headspace volatile organic compound (VOC) fingerprints (volatilome) of French wine brandies were investigated by proton transfer reaction time-of-flight mass spectrometry (PTR-ToF-MS). Protonated ethanol chemical ionization was used with dedicated experimental conditions that were previously validated for model wines. These included a reference vial containing a hydro-alcoholic solution with the same ethanol content (20% v/v) as the diluted sample spirits, which was used to establish steady-state ionization conditions. A low electric field strength to number density ratio E/N (85 Td) was used in the drift tube in order to limit the fragmentation of the protonated analytes. The obtained headspace fingerprints were used to investigate the origin of French brandies produced within a limited geographic production area. Brandies of two different vintages (one freshly distilled and one aged for 14 years in French oak barrels) were successfully classified according to their growth areas using unsupervised (principal component analysis, PCA) and supervised (partial least squares regression discriminant analysis, PLS-DA) multivariate analyses. The models obtained by PLS-DA allowed the identification of discriminant volatile compounds that were mainly characterised as key aroma compounds of wine brandies. The discrimination was supported by sensory evaluation conducted with free sorting tasks. The results showed that this ethanol ionization method was suitable for direct headspace analysis of brandies. They also demonstrated its ability to distinguish French brandies according to their growth areas, and this effect on brandy VOC composition was confirmed at a perceptive level.

Cocoa origin classifiability through LC-MS data: A statistical approach for large and long-term datasets

Classification of food samples based upon their countries of origin is an important task in food industry for quality assurance and development of fine flavor products. Liquid chromatography -mass spectrometry (LC-MS) provides a fast technique for obtaining in-depth information about chemical composition of foods. However, in a large dataset that is gathered over a period of few years, multiple, incoherent and hard to avoid sources of variations e.g., experimental conditions, transportation, batch and instrumental effects, etc. pose technical challenges that make the study of origin classification a difficult problem. Here, we use a large dataset gathered over a period of four years containing 297 LC-MS profiles of cocoa sourced from 10 countries to demonstrate these challenges by using two popular multivariate analysis methods: principal component analysis (PCA) and linear discriminant analysis (LDA). We show that PCA provides a limited separation in bean origin, while LDA suffers from a strong non-linear dependence on the set of compounds. Further, we show for LDA that a compound selection criterion based on Gaussian distribution of intensities across samples dramatically enhances origin clustering of samples thereby suggesting possibilities for studying marker compounds in such a disparate dataset through this approach. In essence, we show and develop a new approach that maximizes, avoiding overfitting, the utility of multivariate analysis in a highly complex dataset.

Cocoa smoky off-flavour: A MS-based analytical decision maker for routine controls

Cocoa smoky off-flavour is generated from an inappropriate artificial drying applied on beans to speeding up the post-harvest process and it can affect the quality of the chocolate. The sensory tests are time-consuming, and at present, a fast analytical method to detect this defect in raw materials is not yet available. This study applies a HS-SPME-MS-enose in combination with chemometrics to obtain diagnostic mass-spectral patterns to detect smoked samples and/or as analytical decision maker. SIMCA models provide the best classification results, compared to PLS-DA, with sensitivities exceeding 90% and a high class specificity range of 89-100% depending on the matrix investigated (beans or liquors). Resulting diagnostic ions were related to phenolic derivatives. The discrimination ability of the method has been confirmed by a quantitative analysis through HS-SPME-GC-MS. HS-SPME-MS-enose turned out to be a fast, cost-effective and objective approach for high throughput analytical screening to discard defective cocoa samples.

Thermal properties and volatile compounds profile of commercial dark-chocolates from different genotypes of cocoa beans (Theobroma cacao L.) from Latin America

There is a growing interest in the identification of chemometric markers that allow the distinction and authentication of dark-chocolates according to their cocoa geographical origin and/or genotype. However, samples derived from Latin American cocoa, including specimens from North and South America, have not been studied in this context. An exploration of the melting behavior, fat composition, bioactive content, and volatile profile of commercial darkchocolates was conducted to identify possible patterns related to the genotype and/or origin of cocoa from Latin America. The melting properties were evaluated by DSC and related to fat content and fatty acids profile. Total polyphenol, anthocyanin, methylxanthine, and catechin content were analyzed. Finally, the volatile compounds were extracted and identified by HS-SPME/GC-MS and were analyzed through Principal Component Analysis (PCA) and the Hierarchical Cluster Analysis Heatmap (HCA Heatmap). The fatty acids profile showed a relationship with the melting properties of dark chocolate. The samples exhibited two glass-transition temperatures (T_g) at ≈19 °C and ≈25.5 °C, possibly related to traces of unstable polymorphic forms of monounsaturated triacylglycerides. The analysis of bioactive compounds demonstrated great variability among samples independent of the cocoa origin, genotype, and content. The PCA and HCA Heatmaps allowed discriminating against the chocolates in relation to the cocoa origin and genotype. Compounds like tetramethylpyrazine, trimethylpyrazine, benzaldehyde, and furfural could be considered as dark-chocolate aroma markers derived from Latin American cocoas (North American region). The 2-phenylethyl alcohol, 2-methylpropanoic acid, 2,3-butanediol, 2-nonanone, and limonene for derived from South America. And the 2-phenylethyl acetate, 3-methyl-butanal, and cinnamaldehyde could allow to distinguishing between regional genotypes.

Statistical inference of mass channel purity from Fisher ratio analysis using comprehensive two-dimensional gas chromatography with time of flight mass spectrometry data

Tile-based Fisher ratio (F-ratio) analysis has recently been developed and validated for discovery-based studies of highly complex data collected using comprehensive two-dimensional gas chromatography coupled with time-of-flight mass spectrometry (GC×GC-TOFMS). In previous studies, interpretation and utilization of F-ratio hit lists has relied upon manual decomposition and quantification performed by chemometric methods such as parallel factor analysis (PARAFAC), or via manual translation of the F-ratio hit list information to peak table quantitative information provided by the instrument software (ChromaTOF). Both of these quantification approaches are bottlenecks in the overall workflow. In order to address this issue, a more automatable approach to provide accurate relative quantification for F-ratio analyses was investigated, based upon the mass spectral selectivity provided via the F-ratio spectral output. Diesel fuel spiked with 15 analytes at four concentration levels (80, 40, 20, and 10 ppm) produced three sets of two class comparisons that were submitted to tile-based F-ratio analysis to obtain three hit lists, with an F-ratio spectrum for each hit. A novel algorithm which calculates the signal ratio (S-ratio) between two classes (eg., 80 ppm versus 40 ppm) was applied to all mass channels (m/z) in the F-ratio spectrum for each hit. A lack of fit (LOF) metric was utilized as a measure of peak purity and combined with F-ratio and p-values to study the relationship of each of these metrics with m/z purity. Application of a LOF threshold coupled with a p-value threshold yielded a subset of the most pure m/z for each of the 15 spiked analytes, evident by the low deviations (< 5%) in S-ratio relative to the true concentration ratio. A key outcome of this study was to demonstrate the isolation of pure m/z without the need for higher level signal decomposition algorithms.

Key Aroma Compounds of Dark Chocolates Differing in Organoleptic Properties: A GC-O Comparative Study

Dark chocolate samples were previously classified into four sensory categories. The classification was modelled based on volatile compounds analyzed by direct introduction mass spectrometry of the chocolates' headspace. The purpose of the study was to identify the most discriminant odor-active compounds that should characterize the four sensory categories. To address the problem, a gas chromatography-olfactometry (GC-O) study was conducted by 12 assessors using a comparative detection frequency analysis (cDFA) approach on 12 exemplary samples. A nasal impact frequency (NIF) difference threshold combined with a statistical approach (Khi² test on k proportions) revealed 38 discriminative key odorants able to differentiate the samples and to characterize the sensory categories. A heatmap emphasized the 19 most discriminant key odorants, among which heterocyclic molecules (furanones, pyranones, lactones, one pyrrole, and one pyrazine) played a prominent role with secondary alcohols, acids, and esters. The initial sensory classes were retrieved using the discriminant key volatiles in a correspondence analysis (CA) and a hierarchical cluster analysis (HCA). Among the 38 discriminant key odorants, although previously identified in cocoa products, 21 were formally described for the first time as key aroma compounds of dark chocolate. Moreover, 13 key odorants were described for the first time in a cocoa product.

Analysis of volatile compounds of five varieties of Maya cocoa during fermentation and drying processes by Venn diagram and PCA

Fermented cocoa beans can be described as a complex matrix that integrates the chemical history of beans, their processing, and environmental factors. This study presents an analysis that aims to identify volatile compounds of five varieties of fine-aroma cocoa types. The cocoa types studied were Carmelo, Rojo Samuel, Lagarto, Arcoiris, Regalo de Dios, that grow in the Maya lands of Chiapas, Mexico. Profile of volatile compounds was obtained from each cacao type during fermentation and drying process. This profile of volatile compounds also was compared with beans unfermented, using a statistical analysis of Venn diagram and a multivariate Analysis of Principal Components (PCA). One hundred nine different compounds were identified by SPME-HS GC-MS, these compounds mainly related to desirable aromatic notes generated by esters, aldehydes, ketones, and alcohols. The differences in chemical composition of the volatile compounds were associated mainly with the process and not to cocoa varieties. Fermented dry cocoa beans showed a higher content of esters, aldehydes, pyrazines, alcohols, some acids, and furans where Lagarto (CL), Rojo Samuel (CR), and Regalo de Dios (TRD) cocoas type showed a more interesting aromatic profile. On the other hand, as expected dry unfermented cocoas presented a few numbers of aroma compounds, in the five cacao types, where alcohols, ketones and hydrocarbons predominated.

Highly Informative Fingerprinting of Extra-Virgin Olive Oil Volatiles: The Role of High Concentration-Capacity Sampling in Combination with Comprehensive Two-Dimensional Gas Chromatography

The study explores the complex volatile fraction of extra-virgin olive oil by combining high concentration-capacity headspace approaches with comprehensive two-dimensional gas chromatography, which is coupled with time of flight mass spectrometry. The static headspace techniques in this study are: (a) Solid-phase microextraction, with multi-polymer coating (SPME- Divinylbenzene/Carboxen/Polydimethylsiloxane), which is taken as the reference technique; (b) headspace sorptive extraction (HSSE) with either a single-material coating (polydimethylsiloxane—PDMS) or a dual-phase coating that combines PDMS/Carbopack and PDMS/EG (ethyleneglycol); (c) monolithic material sorptive extraction (MMSE), using octa-decyl silica combined with graphite carbon (ODS/CB); and dynamic headspace (d) with either PDMS foam, operating in partition mode, or Tenax TA™, operating in adsorption mode. The coverage of both targeted and untargeted 2D-peak-region features, which corresponds to detectable analytes, was examined, while concentration factors (CF) for a selection of informative analytes, including key-odorants and off-odors, and homolog-series relative ratios were calculated and the information capacity was discussed. The results highlighted the differences in concentration capacities, which were mainly caused by polymer-accumulation characteristics (sorptive/adsorptive materials) and its amount. The relative concentration capacity for homologues and potent odorants was also discussed, while headspace linearity and the relative distribution of analytes, as a function of different sampling amounts, was examined. This last point is of particular interest in quantitative studies where accurate data is needed to derive consistent conclusions.

Microencapsulation of cocoa liquor nanoemulsion with whey protein using spray drying to protection of volatile compounds and antioxidant capacity

The aim of this study was microencapsulated a nanoemulsion of cocoa liquor with whey protein by spray drying, and evaluate the effect of different inlet drying temperatures on the properties of microcapsules. The nanoemulsion showed a particle size of 202.13 nm, PdI of 0.424, and ζ-potential of -25.20 mV. The inlet drying temperature showed differences in physicochemical properties of microcapsules. Microcapsules presented good thermal stability and protection against the melting of cocoa liquor. Microcapsules obtained showed excellent yields of polyphenolic compounds (78-93%), and high retention of volatile compounds, especially of pyrazines. Greater microencapsulation yield of bioactive compounds and retention of volatile compounds was obtained at higher drying temperature (180 °C). Excellent stability of polyphenols content, antioxidant capacity, and volatile compounds of cocoa liquor were observed during storage of the microcapsules at different temperature conditions, indicating the feasibility of this powder for its incorporation into functional foods.

Assessment of volatile fingerprint by HS-SPME/GC-qMS and E-nose for the classification of cocoa bean shells using chemometrics

The cocoa bean shell (CBS) is a main by-product of cocoa processing, with great potential to be used as an ingredient for functional foods because of its nutritional and flavour properties. This study aimed to characterise and classify CBSs obtained from cocoa beans of diverse cultivars and collected in different geographical origins through their volatile profile assessed using headspace solid-phase microextraction gas chromatography-mass spectrometry (HS-SPME/GC-qMS) and E-nose combined with principal component analysis (PCA). The study provides, for the first time in a representative set of samples, a comprehensive fingerprint and semi-quantitative data for >100 volatile organic compounds (VOCs), such as aldehydes, ketones, pyrazines, alcohols, and acids. Through PCA, a clear separation of the Criollo cultivar from the other cultivars was achieved with both GC-qMS and E-nose analytical techniques because of the high content of key-aroma VOCs. Several biomarkers identified by GC-qMS, such as 2-hepanol, 2-methylpropanoic acid, and 2,3,5-trimethylpyrazine, recognized as key-aroma compounds for cocoa beans, were found suitable for the classification of CBSs according to their quality and origin. GC-qMS and E-nose appeared to be suitable analytical approaches to classify CBSs, with a high correlation between both analytical techniques. The volatile fingerprint and classification of CBSs could allow for the selection of samples with a specific flavour profile according to the food application and, therefore, constitute an interesting approach to valorise this by-product as a food ingredient.

Untargeted and Targeted Fingerprinting of Extra Virgin Olive Oil Volatiles by Comprehensive Two-Dimensional Gas Chromatography with Mass Spectrometry: Challenges in Long-Term Studies

Comprehensive two-dimensional gas chromatography coupled with mass spectrometric detection (GC × GC-MS) offers an information-rich basis for effective chemical fingerprinting of food. However, GC × GC-MS yields 2D-peak patterns (i.e., sample 2D fingerprints) whose consistency may be affected by variables related to either the analytical platform or to the experimental parameters adopted for the analysis. This study focuses on the complex volatile fraction of extra-virgin olive oil and addresses 2D-peak patterns variations, including MS signal fluctuations, as they may occur in long-term studies where pedo-climatic, harvest year, or shelf life changes are studied. The 2D-pattern misalignments are forced by changing chromatographic settings and MS acquisition. All procedural steps, preceding pattern recognition by template matching, are analyzed and a rational workflow defined to accurately realign patterns and analytes metadata. Signal-to-noise ratio (SNR) detection threshold, reference spectra extraction, and similarity match factor threshold are critical to avoid false-negative matches. Distance thresholds and polynomial transform parameters are key for effective template matching. In targeted analysis (supervised workflow) with optimized parameters, method accuracy reaches 92.5% (i.e., % of true-positive matches) while for combined untargeted and targeted ( UT) fingerprinting (unsupervised workflow), accuracy reaches 97.9%. Response normalization also is examined, evidencing good performance of multiple internal standard normalization that effectively compensates for discriminations occurring during injection of highly volatile compounds. The resulting workflow is simple, effective, and time efficient.

Traceability of Functional Volatile Compounds Generated on Inoculated Cocoa Fermentation and Its Potential Health Benefits

Microbial communities are responsible for the unique functional properties of chocolate. During microbial growth, several antimicrobial and antioxidant metabolites are produced and can influence human wellbeing. In the last decades, the use of starter cultures in cocoa fermentation has been pushed to improve nutritional value, quality, and the overall product safety. However, it must be noted that unpredictable changes in cocoa flavor have been reported between the different strains from the same species used as a starter, causing a loss of desirable notes and flavors. Thus, the importance of an accurate selection of the starter cultures based on the biogenic effect to complement and optimize chocolate quality has become a major interest for the chocolate industry. This paper aimed to review the microbial communities identified from spontaneous cocoa fermentations and focused on the yeast starter strains used in cocoa beans and their sensorial and flavor profile. The potential compounds that could have health-promoting benefits like limonene, benzaldehyde, 2-phenylethanol, 2-methylbutanal, phenylacetaldehyde, and 2-phenylethyl acetate were also evaluated as their presence remained constant after roasting. Further research is needed to highlight the future perspectives of microbial volatile compounds as biomarkers to warrant food quality and safety.

PTR-QiToF-MS and HSI for the characterization of fermented cocoa beans from different origins

The wide range of geographical cocoa production areas and the increasing consumption trend towards single origin products induced the necessity to verify and certify cocoa beans origin for quality assurance purposes. In this study cocoa beans of various origins were examined by machine olfaction and machine vision techniques. Fifty-nine fermented and dried Forastero cocoa beans from 23 different geographical origins (Africa, Americas, Southeast Asia) were investigated using Proton Transfer Reaction-Quadrupole interface-Time of Flight-Mass Spectrometry and Hyperspectral Imaging to elucidate the geographical information in the beans. The volatile and spectral fingerprints showed the same tendency in clustering samples from Africa separate from those from the Americas. High variability was observed for the Southeast Asian samples, which is most likely related to differences in fermentation. Machine olfaction and machine vision characterization provided a similar degree of separation but are complementary rapid techniques, which may be further developed for use in practical settings.

Strategies for Accurate Quantitation of Volatiles from Foods and Plant-Origin Materials: A Challenging Task

The volatile fraction of foods and of plant-origin materials provides functional information on sample-related variables, and gas-phase extractions are ideal approaches for its accurate chemical characterization. However, for gas-phase sampling, the usual procedures adopted to standardize results from solvent extraction methods are not appropriate: headspace (HS) composition depends on the intrinsic physicochemical analyte properties (volatility, polarity, partition coefficient(s)) and matrix effects. Method development, design, and expression of the results are therefore challenging. This review article focuses on volatile vapor-phase quantitation methods (internal standard normalization, standard addition, stable isotope dilution assay, multiple headspace extraction) and their suitability in different applications. Because of the analyte informative role, the different ways of expressing results (normalized chromatographic area, percent normalized chromatographic areas, and absolute concentrations) are discussed and critically evaluated with examples of quality markers in chamomile, process contaminants (furan and 2-methylfuran) in roasted coffee, and key-aroma compounds from high-quality cocoa.

Phenolic variation among Chamaecrista nictitans subspecies and varieties revealed through UPLC-ESI(-)-MS/MS chemical fingerprinting

INTRODUCTION

Comparative analysis of metabolic features of plants has a high potential for determination of quality control of active ingredients, ecological or chemotaxonomic purposes. Specifically, the development of efficient and rapid analytical tools that allow the differentiation among species, subspecies and varieties of plants is a relevant issue. Here we describe a multivariate model based on LC-MS/MS fingerprinting capable of discriminating between subspecies and varieties of the medicinal plant Chamaecrista nictitans, a rare distributed species in Costa Rica.

METHODS

Determination of the chemical fingerprint was carried out on a LC-MS (ESI-QTOF) in negative ionization mode, main detected and putatively identified compounds included proanthocyanidin oligomers, several flavonoid C- and O-glycosides, and flavonoid acetates. Principal component analysis (PCA), partial least square-discriminant analysis (PLS-DA) and cluster analysis of chemical profiles were performed.

RESULTS

Our method showed a clear discrimination between the subspecies and varieties of Chamaecrista nictitans, separating the samples into four fair differentiated groups: M1 = C. nictitans ssp. patellaria; M2 = C. nictitans ssp. disadena; M3 = C. nictitans ssp. nictitans var. jaliscensis and M4 = C. nictitans ssp. disadena var. pilosa. LC-MS/MS fingerprint data was validated using both morphological characters and DNA barcoding with ITS2 region. The comparison of the morphological characters against the chemical profiles and DNA barcoding shows a 63% coincidence, evidencing the morphological similarity in C. nictitans. On the other hand, genetic data and chemical profiles grouped all samples in a similar pattern, validating the functionality of our metabolomic approach.

CONCLUSION

The metabolomic method described in this study allows a reliably differentiation between subspecies and varieties of C. nictitans using a straightforward protocol that lacks extensive purification steps.

Volatile compounds profiling by using proton transfer reaction-time of flight-mass spectrometry (PTR-ToF-MS). The case study of dark chocolates organoleptic differences

Direct-injection mass spectrometry (DIMS) techniques have evolved into powerful methods to analyse volatile organic compounds (VOCs) without the need of chromatographic separation. Combined to chemometrics, they have been used in many domains to solve sample categorization issues based on volatilome determination. In this paper, different DIMS methods that have largely outperformed conventional electronic noses (e-noses) in classification tasks are briefly reviewed, with an emphasis on food-related applications. A particular attention is paid to proton transfer reaction mass spectrometry (PTR-MS), and many results obtained using the powerful PTR-time of flight-MS (PTR-ToF-MS) instrument are reviewed. Data analysis and feature selection issues are also summarized and discussed. As a case study, a challenging problem of classification of dark chocolates that has been previously assessed by sensory evaluation in four distinct categories is presented. The VOC profiles of a set of 206 chocolate samples classified in the four sensory categories were analysed by PTR-ToF-MS. A supervised multivariate data analysis based on partial least squares regression-discriminant analysis allowed the construction of a classification model that showed excellent prediction capability: 97% of a test set of 62 samples were correctly predicted in the sensory categories. Tentative identification of ions aided characterisation of chocolate classes. Variable selection using dedicated methods pinpointed some volatile compounds important for the discrimination of the chocolates. Among them, the CovSel method was used for the first time on PTR-MS data resulting in a selection of 10 features that allowed a good prediction to be achieved. Finally, challenges and future needs in the field are discussed.