Factors contributing to the variation in the volatile composition of chocolate: Botanical and geographical origins of the cocoa beans, and brand-related formulation and processing

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.

Key Aromatic Volatile Compounds from Roasted Cocoa Beans, Cocoa Liquor, and Chocolate

The characteristic aromas at each stage of chocolate processing change in quantity and quality depending on the cocoa variety, the chemical composition of the beans, the specific protein storage content, and the polysaccharides and polyphenols determining the type and quantity of the precursors formed during the fermentation and drying process, leading to the formation of specific chocolate aromas in the subsequent roasting and conching processes. Bean aroma is frequently profiled, identified, and semiquantified by headspace solid-phase microextraction combined with gas chromatography-mass spectrometry (HS-SPMEGC-MS) and by gas chromatography olfactometry (GC-O). In general, the flavors generated in chocolate processing include fruity, floral, chocolate, woody, caramel, earthy, and undesirable notes. Each processing stage contributes to or depletes the aroma compounds that may be desirable or undesirable, as discussed in this report.

Cocoa bean fingerprinting via correlation networks

Cocoa products have a remarkable chemical and sensory complexity. However, in contrast to other fermentation processes in the food industry, cocoa bean fermentation is left essentially uncontrolled and is devoid of standardization. Questions of food authenticity and food quality are hence particularly challenging for cocoa. Here we provide an illustration how network science can support food fingerprinting and food authenticity research. Using a large dataset of 140 cocoa samples comprising three cocoa fermentation/processing stages and eight countries, we obtain correlation networks between the cocoa samples by computing measures of pairwise correlation from their liquid chromatography-mass spectrometry (LC-MS) profiles. We find that the topology of correlation networks derived from untargeted LC-MS profiles is indicative of the fermentation and processing stage as well as the origin country of cocoa samples. Progressively increasing the correlation threshold firstly reveals network clusters based on processing stage and later country-based clusters. We present both, qualitative and quantitative evidence through network visualization, network statistics and concepts from machine learning. In our view, this network-based approach for classifying mass spectrometry data has broad applicability beyond cocoa.

Automated fragment formula annotation for electron ionisation, high resolution mass spectrometry: application to atmospheric measurements of halocarbons

Background

Non-target screening consists in searching a sample for all present substances, suspected or unknown, with very little prior knowledge about the sample. This approach has been introduced more than a decade ago in the field of water analysis, together with dedicated compound identification tools, but is still very scarce for indoor and atmospheric trace gas measurements, despite the clear need for a better understanding of the atmospheric trace gas composition. For a systematic detection of emerging trace gases in the atmosphere, a new and powerful analytical method is gas chromatography (GC) of preconcentrated samples, followed by electron ionisation, high resolution mass spectrometry (EI-HRMS). In this work, we present data analysis tools to enable automated fragment formula annotation for unknown compounds measured by GC-EI-HRMS.

Results

Based on co-eluting mass/charge fragments, we developed an innovative data analysis method to reliably reconstruct the chemical formulae of the fragments, using efficient combinatorics and graph theory. The method does not require the presence of the molecular ion, which is absent in \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\sim$$\end{document}∼40% of EI spectra. Our method has been trained and validated on >50 halocarbons and hydrocarbons, with 3–20 atoms and molar masses of 30–330 g mol\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$^{-1}$$\end{document}-1, measured with a mass resolution of approx. 3500. For >90% of the compounds, more than 90% of the annotated fragment formulae are correct. Cases of wrong identification can be attributed to the scarcity of detected fragments per compound or the lack of isotopic constraint (no minor isotopocule detected).

Conclusions

Our method enables to reconstruct most probable chemical formulae independently from spectral databases. Therefore, it demonstrates the suitability of EI-HRMS data for non-target analysis and paves the way for the identification of substances for which no EI mass spectrum is registered in databases. We illustrate the performances of our method for atmospheric trace gases and suggest that it may be well suited for many other types of samples. The L-GPL licenced Python code is released under the name ALPINAC for ALgorithmic Process for Identification of Non-targeted Atmospheric Compounds.

Supplementary Information

The online version contains supplementary material available at 10.1186/s13321-021-00544-w.

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.

Are the elemental fingerprints of organic and conventional food different? ED-XRF as screening technique

Research has been conducted the last years to assess whether organically grown food is chemically different from produce of conventional agriculture and which markers are appropriate to discriminate between them. Most articles focus on one single food commodity, produced under strict controlled organic farming conditions, leaving open the question whether the difference would be seen when applied to the same commodity under different growing conditions. In this work 118 organic and 151 conventional samples of commercially available paprika powder, cinnamon, coffee, tea, chocolate, rice, wheat flour, cane sugar, coconut water, honey and bovine milk were characterised for their elemental composition using energy dispersive X-ray fluorescence. Resulting profiles were analysed using univariate and multivariate statistical techniques. Organic samples of a given commodity clustered together and were separated from their conventional counterparts. Differences in the elemental composition of food, could be used to develop statistical models for verifying the agronomical production system.

The Evaluation of Dark Chocolate-Elicited Emotions and Their Relation with Physico Chemical Attributes of Chocolate

The aim of this study was to evaluate the effect of different origin (Venezuela, Ghana, Peru) dark chocolates on emotions induced for consumers, and to analyse the relationships among overall acceptability (OA), emotions, and physicochemical attributes of the chocolate (fatty acids (FAs) and volatile compounds (VC)). Chocolate-elicited emotions were measured with FaceReader 8 software, scaling 10 emotion patterns (neutral, happy, sad, angry, surprised, scared, disgusted, contempt, valence, arousal). The OA was carried out by using a 10-point hedonic scale, ranging from 1 (extremely dislike) to 10 (extremely like). The obtained results showed that, among all chocolate-elicited emotions, the intensity of “happy” was the highest. In most cases, the influence of the different origin chocolate on the emotions induced for consumers was significant (except on emotions “neutral”, “scared”, and “disgusted”). Significant differences between the tested chocolates OA were not found. The origin of chocolate had a significant effect on most of the identified VC and the content of the main FAs (methyl palmitate, methyl stearate, cis,trans-9- oleic acid methyl ester, and methyl linoleate). Significant correlations between chocolate-elicited emotions and separate FAs and VC were found. Finally, this study showed that the origin of dark chocolate significantly influenced most of chocolate-elicited emotions and physicochemical attributes of chocolate, while separate FAs or VC can be used as chocolate quality indicators related to the chocolate OA, as well as chocolate-elicited emotions.

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.

Functional role of yeasts, lactic acid bacteria and acetic acid bacteria in cocoa fermentation processes

Cured cocoa beans are obtained through a post-harvest, batchwise process of fermentation and drying carried out on farms in the equatorial zone. Fermentation of cocoa pulp-bean mass is performed mainly in heaps or boxes. It is made possible by a succession of yeast, lactic acid bacteria (LAB) and acetic acid bacteria (AAB) activities. Yeasts ferment the glucose of the cocoa pulp into ethanol, perform pectinolysis and produce flavour compounds, such as (higher) alcohols, aldehydes, organic acids and esters. LAB ferment the glucose, fructose and citric acid of the cocoa pulp into lactic acid, acetic acid, mannitol and pyruvate, generate a microbiologically stable fermentation environment, provide lactate as carbon source for the indispensable growth of AAB, and contribute to the cocoa and chocolate flavours by the production of sugar alcohols, organic acids, (higher) alcohols and aldehydes. AAB oxidize the ethanol into acetic acid, which penetrates into the bean cotyledons to prevent seed germination. Destruction of the subcellular seed structure in turn initiates enzymatic and non-enzymatic conversions inside the cocoa beans, which provides the necessary colour and flavour precursor molecules (hydrophilic peptides, hydrophobic amino acids and reducing sugars) for later roasting of the cured cocoa beans, the first step of the chocolate-making.

Effect of cocoa bean origin and conching time on the physicochemical and microstructural properties of Indonesian dark chocolate

Abstract Indonesian cocoa is cheaper and considered second grade compared with most other cocoa. However, the domestic chocolate industry is not well-developed due to significantly low consumption. To cope with these issues, product innovation through technical process improvement is required to stimulate the domestic chocolate industry. This study aimed to investigate the effect of cocoa bean origin and conching time on the physicochemical (water content, texture, color, crude fat content, and melting enthalpy) and microstructural properties of chocolate. The experiment was conducted under a completely randomized factorial design consisting of two factors: cocoa bean origin (100% fermented cocoa beans from Jember, 100% fermented cocoa beans from Southeast Sulawesi, 50% fermented cocoa beans from Jember + 50% non-fermented cocoa beans from Southeast Sulawesi, and 50% fermented cocoa beans from Southeast Sulawesi + 50% non-fermented cocoa beans from Southeast Sulawesi) and conching time (4, 6 and 8 h). The results showed that cocoa bean origin significantly affected the hardness, gumminess and color of chocolate, including the redness and yellowness level, whereas conching time affected water content, hardness, cohesiveness, elasticity, and crude fat content. Differential scanning calorimetry (DSC) analysis showed that the treatment with 100% fermented cocoa beans from Jember presented higher values of Tonset, ΔHmelt and area compared with those of the treatment with 100% fermented cocoa beans from Southeast Sulawesi, which presented higher values of Tpeak and Tend. Scanning Electron Microscopy (SEM) analysis showed a distribution of solid and dense particles with crystal interaction with chocolate structures.

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.

From Extra Virgin Olive Oil to Refined Products: Intensity and Balance Shifts of the Volatile Compounds versus Odor

To explore relationships between the volatile organic compounds (VOCs) of different grades of olive oils (OOs) (extra virgin olive oil (EVOO), refined olive oil (ROO), and pomace olive oil (POO)) and odor quality, VOCs were measured in the headspace of the oils by proton transfer reaction quadrupole ion guide time-of-flight mass spectrometry. The concentrations of most VOCs differed significantly between the grades (EVOO > ROO > POO), whereas the abundance of m/z 47.012 (formic acid), m/z 49.016 (fragments), m/z 49.027 (fragments), and m/z 115.111 (heptanal/heptanone) increased in that order. Although the refined oils had considerably lower VOC abundance, the extent of the decline varied with the VOCs. This results in differences in VOCs proportions. The high VOC abundance in the EVOO headspace in comparison to ROO and POO results in a richer and more complex odor. The identified C5–C6 compounds are expected to contribute mainly to the green odor notes, while the identified C1–C4 and C7–C15 are mainly responsible for odor defects of OOs. Current results reveal that processing strongly affects both the quantitative and relative abundance of the VOCs and, therefore, the odor quality of the various grades of OOs.

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.

Novel experimental approach to study aroma release upon reconstitution of instant coffee products

This study presents an experimental approach to study the kinetics and fast release of volatile organic compounds (VOCs) upon reconstitution of instant coffee products. A sampling setup coupled to PTR-ToF-MS (Proton Transfer Reaction Time-of-Flight Mass Spectrometry) for the automated and reproducible reconstitution of instant coffee products was developed to monitor the dynamic release of VOCs. A rapid release of aroma compounds was observed in the first seconds upon hot water addition ("aroma burst"), followed by subsequent decrease in headspace (HS) intensities over the course of analysis. Differences in time-intensity release profiles of individual VOCs were correlated to their Henry's Law constant, vapor pressure and water solubility. The setup and approach proposed here have shown to be sensitive and to respond to fast dynamic changes in aroma release. It allows studying VOCs release upon reconstitution and supports the development of novel technologies and formulations for instant products with improved aroma release properties.

An overview of the physical and biochemical transformation of cocoa seeds to beans and to chocolate: Flavor formation

Chocolate is a widely consumed product worldwide due to its exquisite flavor, which comes from the unique and fascinating cocoa flavor. This flavor depends on little controllable variables such as the genotype and the agroecological niche, and on the other side, on postharvest operations: (1) cocoa transformation from seeds to beans that comprises cocoa seeds preconditioning, fermentation, and drying, and (2) the production of chocolate from the bean in which roasting is highlighted. Postharvest transformation operations are critically important because during these, cocoa flavor is formed, allowing the differentiation of two categories: bulk and specialty cocoa. In this sense, this article presents an overview of cocoa postharvest operations, the variables and phenomena that influence and control the physical and biochemical transformation from seeds to cocoa beans, and their relation to the formation of chocolate flavor. Moreover, research perspectives in terms of control and management of postharvest practices in order to obtain cocoa with differentiated and specialty characteristics "from bean to bar" are discussed.

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.

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.

Which cocoa bean traits persist when eating chocolate? Real-time nosespace analysis by PTR-QiToF-MS

More consumers have become aware of the existence of different cocoa genotypes and their origins, which resulted in a growing market of premium chocolates with single-origin beans. The question is whether traits of cocoa botanical and geographical origins still persist in the end product, especially when it is consumed. By analysing the concentrations of volatile organic compounds (VOCs) in the nose of subjects over time while they are eating, new insights about aroma release can be gained. In the current study, in vivo release of VOCs during consumption of dark chocolates with different botanical and geographical origins was examined. Proton Transfer Reaction-Quadrupole interface Time of Flight- Mass Spectrometry (PTR-Qi ToF- MS) was applied to analyse nosespace VOC profiles of 10 subjects while they were eating 10 different chocolates manufactured with beans of different botanical origins (Criollo-Forastero-Trinitario) and geographical origins (Africa-South America-Asia). The headspace of the chocolates were also analysed for comparison. Cocoa botanical information appeared to affect the nosespace profiles more than geographical information. The subjects varied considerable in their VOC release, and inter-individual differences were larger than cocoa beans differences. Nevertheless, the botanical origin was consistently reflected in the nosespace profile during eating. It was clearly possible to distinguish Criollo chocolates from the nosespace profiles despite inter-individual differences.

Similarities and differences of the volatile profiles of six spices explored by Proton Transfer Reaction Mass Spectrometry

Aroma properties of spices are related to the volatile organic compounds (VOCs) present, which can provide distinct analytical signatures. The aim of the study was to examine similarity and diversity of VOC profiles of six common market spices (black/white pepper, chili paprika, cinnamon, nutmeg and saffron). The key volatiles were identified by PTR-TOFMS. Twelve samples per spice were subjected to PTR-Quadrupole MS (PTR-QMS) and Principal Component Analysis to compare the groups and examine diversity. With PTR-TOFMS, 101 volatile compounds were identified as total sum across all samples by mass and comparing them with literature data. Some spices comprised key character aroma compounds, e.g. cinnamaldehyde in cinnamon. For others, VOC groups, such as terpenes, acids and aldehydes topped the list. The PTR-QMS in combination with variables selection resulted in distinct PCA patterns for each spice. Variation within the spice groups was observed, but varied with the kind of spice. The results are valuable for future authentication studies.

Time-Related Changes in Volatile Compounds during Fermentation of Bulk and Fine-Flavor Cocoa (Theobroma cacao) Beans

Chocolate is one of the most consumed foods worldwide and cacao fermentation contributes to the unique sensory characteristics of chocolate products. However, comparative changes in volatiles occurring during fermentation of Criollo, Forastero, and Nacional cacao—three of the most representative cultivars worldwide—have not been reported. Beans of each cultivar were fermented for five days and samples were taken every 24 hours. Volatiles from each sample were adsorbed into a solid phase microextraction fiber and analyzed by gas chromatography-mass spectrometry. Aroma potential of each compound was determined using available databases. Multivariate data analyses showed partial clustering of samples according to cultivars at the start of the fermentation but complete clustering was observed at the end of the fermentation. The Criollo cacao produced floral, fruity, and woody aroma volatiles including linalool, epoxylinalool, benzeneethanol, pentanol acetate, germacrene, α-copaene, aromadendrene, 3,6-heptanedione, butanal, 1-phenyl ethenone, 2-nonanone, and 2-pentanone. Nacional cacao produced fruity, green, and woody aroma volatiles including 2-nonanone, 3-octen-1-ol, 2-octanol acetate, 2-undecanone, valencene, and aromadendrene. The Forastero cacao yielded floral and sweet aroma volatiles such as epoxylinalool, pentanoic acid, benzeneacetaldehyde, and benzaldehyde. This is the first report of volatiles produced during fermentation of Criollo, Forastero, and Nacional cacao from the same origin.

Chocolate Classification by an Electronic Nose with Pressure Controlled Generated Stimulation

In this work, we will analyze the response of a Metal Oxide Gas Sensor (MOGS) array to a flow controlled stimulus generated in a pressure controlled canister produced by a homemade olfactometer to build an E-nose. The built E-nose is capable of chocolate identification between the 26 analyzed chocolate bar samples and four features recognition (chocolate type, extra ingredient, sweetener and expiration date status). The data analysis tools used were Principal Components Analysis (PCA) and Artificial Neural Networks (ANNs). The chocolate identification E-nose average classification rate was of 81.3% with 0.99 accuracy (Acc), 0.86 precision (Prc), 0.84 sensitivity (Sen) and 0.99 specificity (Spe) for test. The chocolate feature recognition E-nose gives a classification rate of 85.36% with 0.96 Acc, 0.86 Prc, 0.85 Sen and 0.96 Spe. In addition, a preliminary sample aging analysis was made. The results prove the pressure controlled generated stimulus is reliable for this type of studies.