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

Influence of Cheese Composition on Aroma Content, Release, and Perception

The quality of a cheese is determined by the balance of aroma compounds primarily produced by microorganisms during the transformation of milk into ripened cheese. The microorganisms, along with the technological parameters used in cheese production, influence aroma formation. The perception of these compounds is further influenced by the composition and structure of the cheese. This study aimed to characterize how cheese composition affects aroma compound production, release, and perception. Sixteen cheeses were produced under controlled conditions, followed by a quantitative descriptive analysis post ripening. Aroma composition was analyzed using HS-SPME-GC-MS, and a dynamic sensory evaluation (TCATA) was combined with nosespace analysis using PTR-ToF-MS. Image analysis was also conducted to characterize cheese structure. Cheese fat and whey lactose contents were identified as key factors in the variability of sensory attributes. GC-MS analyses identified 27 compounds correlated with sensory attributes. In terms of aroma compound release, 23 ions were monitored, with fat, salt, and lactose levels significantly affecting the release of most compounds. Therefore, cheese fat, salt, and whey lactose levels, as well as the types of microbial strains, play a role in influencing the composition, structure, release of aroma compounds, and sensory perception.

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.

Authenticating teas using multielement signatures, strontium isotope ratios, and volatile compound profiling

High value food products are subject to adulterations and frauds. This study aimed to combine, in our knowledge for the first time, inorganic chemical tracers (multi-elements and Sr isotopy) with volatile organic compound (VOCs) to discriminate the geographic origin, the varieties and transformation processes to authenticate 26 tea samples. By measuring Sr isotope ratio using the multi-collector inductively coupled plasma mass spectrometry (MC-ICP-MS), 6 out of 11 regions were successfully discriminated. The combination with the ICP-MS inorganic pattern allowed to discriminate 4 more regions with a significance level of 0.05. VOCs fingerprints, obtained with selected ion flow tube mass spectrometer (SIFT-MS), were not correlated with origin but with the cultivar and transformation processes. Green, oolong, and dark teas were clearly differentiated, with hexanal and hexanol contributing to the discrimination of oxidation levels. With this multi-instrumental approach, it is possible to certify the geographical origin and the tea conformity.

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.

On-Site Detection of Volatile Organic Compounds (VOCs)

Volatile organic compounds (VOCs) are of interest in many different fields. Among them are food and fragrance analysis, environmental and atmospheric research, industrial applications, security or medical and life science. In the past, the characterization of these compounds was mostly performed via sample collection and off-site analysis with gas chromatography coupled to mass spectrometry (GC-MS) as the gold standard. While powerful, this method also has several drawbacks such as being slow, expensive, and demanding on the user. For decades, intense research has been dedicated to find methods for fast VOC analysis on-site with time and spatial resolution. We present the working principles of the most important, utilized, and researched technologies for this purpose and highlight important publications from the last five years. In this overview, non-selective gas sensors, electronic noses, spectroscopic methods, miniaturized gas chromatography, ion mobility spectrometry and direct injection mass spectrometry are covered. The advantages and limitations of the different methods are compared. Finally, we give our outlook into the future progression of this field of research.

Effects of oenological tannins on aroma release and perception of oxidized and non-oxidized red wine: A dynamic real-time in-vivo study coupling sensory evaluation and analytical chemistry

Addition of oenological tannins claims to have a positive impact on wine stability, protection from oxidation and likely sensory persistence. However, their role on red wine aroma during oxidation is controversial. The present study aims at investigating the effect of addition of oenological tannins on wine flavour (mainly aroma) before and after air exposure. Temporal Dominance of Sensations, a dynamic sensory evaluation, was coupled with a dynamic chemical measurement (nosespace analysis) using a Proton-Transfer-Reaction Mass-Spectrometer connected to the nasal cavity of 17 assessors. Results showed that the oxidation of a non-oaked Pinot Noir red wine decreases the fruity aroma dominance and increases the maderised and prune one. A contextual decrease of the fruity ethyl decanoate and increase of oxidative Strecker aldehydes are observed. Ellagitannins but not proanthocyanidins preserved perception of fruitiness and prevented increase of maderised notes. Moreover, ellagitannins increase the aroma persistence mainly in the non-oxidized wine.

Volatile fingerprint of food products with untargeted SIFT-MS data coupled with mixOmics methods for profile discrimination: Application case on cheese

Volatile organic compounds (VOCs) emitted by food products are decisive for the perception of aroma and taste. The analysis of gaseous matrices is traditionally done by detection and quantification of few dozens of characteristic markers. Emerging direct injection mass spectrometry technologies offer rapid analysis based on a soft ionisation of VOCs without previous separation. The recent increase of selectivity offered by the use of several precursor ions coupled with untargeted analysis increases the potential power of these instruments. However, the analysis of complex gaseous matrix results in a large number of ion conflicts, making the quantification of markers difficult, and in a large volume of data. In this work, we present the exploitation of untargeted SIFT-MS volatile fingerprints of ewe PDO cheeses in a real farm model, using mixOmics methods allowing us to illustrate the typicality, the manufacturing processes reproducibility and the impact of the animals' diet on the final product.

A method for the identification of COVID-19 biomarkers in human breath using Proton Transfer Reaction Time-of-Flight Mass Spectrometry

BACKGROUND

COVID-19 has caused a worldwide pandemic, making the early detection of the virus crucial. We present an approach for the determination of COVID-19 infection based on breath analysis.

METHODS

A high sensitivity mass spectrometer was combined with artificial intelligence and used to develop a method for the identification of COVID-19 in human breath within seconds. A set of 1137 positive and negative subjects from different age groups, collected in two periods from two hospitals in the USA, from 26 August, 2020 until 15 September, 2020 and from 11 September, 2020 until 11 November, 2020, was used for the method development. The subjects exhaled in a Tedlar bag, and the exhaled breath samples were subsequently analyzed using a Proton Transfer Reaction Time-of-Flight Mass Spectrometer (PTR-ToF-MS). The produced mass spectra were introduced to a series of machine learning models. 70% of the data was used for these sub-models' training and 30% was used for testing.

FINDINGS

A set of 340 samples, 95 positives and 245 negatives, was used for the testing. The combined models successfully predicted 77 out of the 95 samples as positives and 199 out of the 245 samples as negatives. The overall accuracy of the model was 81.2%. Since over 50% of the total positive samples belonged to the age group of over 55 years old, the performance of the model in this category was also separately evaluated on 339 subjects (170 negative and 169 positive). The model correctly identified 166 out of the 170 negatives and 164 out of the 169 positives. The model accuracy in this case was 97.3%.

INTERPRETATION

The results showed that this method for the identification of COVID-19 infection is a promising tool, which can give fast and accurate results.

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.

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.

Phenylacetaldehyde real-time release kinetics in wine like model solutions

The present work shows key possibilities in modelling the kinetics of phenylacetaldehyde formation as a function of sugar, phenolic compounds, metals and sulphur dioxide. The release kinetics were measured online by proton transfer reaction-mass spectrometry (PTR-MS). Phenylacetaldehyde formation was fitted using Weibull models and an activation energy of 73 kJ/mol estimated. Also, a confirmation that glucose can inhibit the aldehyde formation was demonstrated, and the sequential additions in real time showed that the inhibition level was dependent on metal ions presence. Moreover, for the first time it was observed in real time the capacity of SO₂ to bind with phenylacetaldehyde, and by trapping it, lowering its release. Finally, the impact of pH and temperature in the stability of the formed adducts and underling release mechanism is also elucidated.

Respiratory CO2 Combined With a Blend of Volatiles Emitted by Endophytic Serendipita Strains Strongly Stimulate Growth of Arabidopsis Implicating Auxin and Cytokinin Signaling

Rhizospheric microorganisms can alter plant physiology and morphology in many different ways including through the emission of volatile organic compounds (VOCs). Here we demonstrate that VOCs from beneficial root endophytic Serendipita spp. are able to improve the performance of in vitro grown Arabidopsis seedlings, with an up to 9.3-fold increase in plant biomass. Additional changes in VOC-exposed plants comprised petiole elongation, epidermal cell and leaf area expansion, extension of the lateral root system, enhanced maximum quantum efficiency of photosystem II (Fv/Fm), and accumulation of high levels of anthocyanin. Notwithstanding that the magnitude of the effects was highly dependent on the test system and cultivation medium, the volatile blends of each of the examined strains, including the references S. indica and S. williamsii, exhibited comparable plant growth-promoting activities. By combining different approaches, we provide strong evidence that not only fungal respiratory CO2 accumulating in the headspace, but also other volatile compounds contribute to the observed plant responses. Volatile profiling identified methyl benzoate as the most abundant fungal VOC, released especially by Serendipita cultures that elicit plant growth promotion. However, under our experimental conditions, application of methyl benzoate as a sole volatile did not affect plant performance, suggesting that other compounds are involved or that the mixture of VOCs, rather than single molecules, accounts for the strong plant responses. Using Arabidopsis mutant and reporter lines in some of the major plant hormone signal transduction pathways further revealed the involvement of auxin and cytokinin signaling in Serendipita VOC-induced plant growth modulation. Although we are still far from translating the current knowledge into the implementation of Serendipita VOCs as biofertilizers and phytostimulants, volatile production is a novel mechanism by which sebacinoid fungi can trigger and control biological processes in plants, which might offer opportunities to address agricultural and environmental problems in the future.

Multi-block classification of chocolate and cocoa samples into sensory poles

The present study aims at developing an analytical methodology which allows correlating sensory poles of chocolate to their chemical characteristics and, eventually, to those of the cocoa beans used for its preparation. Trained panelists investigated several samples of chocolate, and they divided them into four sensorial poles (characterized by 36 different descriptors) attributable to chocolate flavor. The same samples were analyzed by six different techniques: Proton Transfer Reaction-Time of Flight-Mass Spectrometry (PTR-ToF-MS), Solid Phase Micro Extraction-Gas Chromatography-Mass Spectroscopy (SPME-GC-MS), High-Performance Liquid Chromatography (HPLC) (for the quantification of eight organic acids), Ultra High Performance Liquid Chromatography coupled to triple-quadrupole Mass Spectrometry (UHPLC-QqQ-MS) for polyphenol quantification, 3D front face fluorescence Spectroscopy and Near Infrared Spectroscopy (NIRS). A multi-block classification approach (Sequential and Orthogonalized-Partial Least Squares - SO-PLS) has been used, in order to exploit the chemical information to predict the sensorial poles of samples. Among thirty-one test samples, only two were misclassified.

VOC Emission Analysis of Bitumen Using Proton-Transfer Reaction Time-Of-Flight Mass Spectrometry

Bitumen is one of the most important materials used in roads. During asphalt pavement construction, workers can be affected by emissions, such as volatile organic compounds (VOCs), when bitumen is heated. Therefore, it is crucial to correctly identify and measure VOCs. This paper presents a novel, promising method to determine VOC emissions. The proposed method offers a way to standardize routine measurements on a lab scale, enabling reliable comparison across bitumen types and their modifications or additives. A proton-transfer reaction time-of-flight mass spectrometer (PTR-TOF-MS) was used to monitor VOC emissions from commercial unmodified bitumen and crumb rubber modified bitumen (CRMB) with heating of up to 180 °C. Results confirmed that the temperature range of 160-180 °C is a highly influential factor for VOC emissions from heated commercial bitumen and particularly CRMB. A significant increase in alkane and aromatic emission was detected when the binders were heated to 180 °C. Sulfur-containing VOCs were almost nonexistent for the base bitumen fumes, while a significant increase was detected in the fumes when two different types of CR were added to the bitumen, even at 120 °C. The additional CR in the bituminous binder contributed to the potentially harmful VOC emission of benzothiazole, which belongs to the class of sulfur-containing compounds. The concentration of benzothiazole was 65%, 38%, and 35% higher for CR1 in comparison to CR2 at 140, 160, and 180 °C, respectively. It is clear from the results that this method allows different bitumen sources or modifications to be quickly analyzed and their VOC emissions cross-compared. If adopted and confirmed further, the method could offer the asphalt industry a viable solution to monitor VOC emissions by analyzing samples in real time at different steps of the production process.

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.

Multiblock Analysis to Relate Polyphenol Targeted Mass Spectrometry and Sensory Properties of Chocolates and Cocoa Beans

Chocolate quality is largely due to the presence of polyphenols and especially of flavan-3-ols and their derivatives that contribute to bitterness and astringency. The aim of the present work was to assess the potential of a quantitative polyphenol targeted metabolomics analysis based on mass spectrometry for relating cocoa bean polyphenol composition corresponding chocolate polyphenol composition and sensory properties. One-hundred cocoa bean samples were transformed to chocolates using a standard process, and the latter were attributed to four different groups by sensory analysis. Polyphenols were analyzed by an ultra-high-performance liquid chromatography (UPLC) system hyphenated to a triple quadrupole mass spectrometer. A multiblock method called a Common Component and Specific Weights Analysis (CCSWA) was used to study relationships between the three datasets, i.e., cocoa polyphenols, chocolate polyphenols and sensory profiles. The CCSWA multiblock method coupling sensory and chocolate polyphenols differentiated the four sensory poles. It showed that polyphenolic and sensory data both contained information enabling the sensory poles' separation, even if they can be also complementary. A large amount of variance in the cocoa bean and corresponding chocolate polyphenols has been linked. The cocoa bean phenolic composition turned out to be a major factor in explaining the sensory pole separation.

PTR-ToF-MS for the Online Monitoring of Alcoholic Fermentation in Wine: Assessment of VOCs Variability Associated with Different Combinations of Saccharomyces/Non-Saccharomyces as a Case-Study

The management of the alcoholic fermentation (AF) in wine is crucial to shaping product quality. Numerous variables (e.g., grape varieties, yeast species/strains, technological parameters) can affect the performances of this fermentative bioprocess. The fact that these variables are often interdependent, with a high degree of interaction, leads to a huge ‘oenological space’ associated with AF that scientists and professionals have explored to obtain the desired quality standards in wine and to promote innovation. This challenge explains the high interest in approaches tested to monitor this bioprocess including those using volatile organic compounds (VOCs) as target molecules. Among direct injection mass spectrometry approaches, no study has proposed an untargeted online investigation of the diversity of volatiles associated with the wine headspace. This communication proposed the first application of proton-transfer reaction-mass spectrometry coupled to a time-of-flight mass analyzer (PTR-ToF-MS) to follow the progress of AF and evaluate the impact of the different variables of wine quality. As a case study, the assessment of VOC variability associated with different combinations of Saccharomyces/non-Saccharomyces was selected. The different combinations of microbial resources in wine are among the main factors susceptible to influencing the content of VOCs associated with the wine headspaces. In particular, this investigation explored the effect of multiple combinations of two Saccharomyces strains and two non-Saccharomyces strains (belonging to the species Metschnikowia pulcherrima and Torulaspora delbrueckii) on the content of VOCs in wine, inoculated both in commercial grape juice and fresh grape must. The results demonstrated the possible exploitation of non-invasive PTR-ToF-MS monitoring to explore, using VOCs as biomarkers, (i) the huge number of variables influencing AF in wine, and (ii) applications of single/mixed starter cultures in wine. Reported preliminary findings underlined the presence of different behaviors on grape juice and on must, respectively, and confirmed differences among the single yeast strains ‘volatomes’. It was one of the first studies to include the simultaneous inoculation on two non-Saccharomyces species together with a S. cerevisiae strain in terms of VOC contribution. Among the other outcomes, evidence suggests that the addition of M. pulcherrima to the coupled S. cerevisiae/T. delbrueckii can modify the global release of volatiles as a function of the characteristics of the fermented matrix.

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.

Discrimination and geo-spatial mapping of atmospheric VOC sources using full scan direct mass spectral data collected from a moving vehicle

Volatile and semi-volatile organic compounds (S/VOCs) are ubiquitous in the environment, come from a wide variety of anthropogenic and biogenic sources, and are important determinants of environmental and human health due to their impacts on air quality. They can be continuously measured by direct mass spectrometry techniques without chromatographic separation by membrane introduction mass spectrometry (MIMS) and proton-transfer reaction time-of-flight mass spectrometry (PTR-ToF-MS). We report the operation of these instruments in a moving vehicle, producing full scan mass spectral data to fingerprint ambient S/VOC mixtures with high temporal and spatial resolution. We describe two field campaigns in which chemometric techniques are applied to the full scan MIMS and PTR-ToF-MS data collected with a mobile mass spectrometry lab. Principal Component Analysis (PCA) has been successfully employed in a supervised analysis to discriminate VOC samples collected near known VOC sources including internal combustion engines, sawmill operations, composting facilities, and pulp mills. A Gaussian mixture model and a density-based spatial clustering of application with noise (DBSCAN) algorithm have been used to identify sample clusters within the full time series dataset collected and we present geospatial maps to visualize the distribution of VOC sources measured by PTR-ToF-MS.

Fast Discrimination of Chocolate Quality Based on Average-Mass-Spectra Fingerprints of Cocoa Polyphenols

This work aims to sort cocoa beans according to chocolate sensory quality and phenolic composition. Prior to the study, cocoa samples were processed into chocolate in a standard manner, and then the chocolate was characterized by sensory analysis, allowing sorting of the samples into four sensory groups. Two objectives were set: first to use average mass spectra as quick cocoa-polyphenol-extract fingerprints and second to use those fingerprints and chemometrics to select the molecules that discriminate chocolate sensory groups. Sixteen cocoa polyphenol extracts were analyzed by liquid chromatography-low-resolution mass spectrometry. Averaging each mass spectrum provided polyphenolic fingerprints, which were combined into a matrix and processed with chemometrics to select the most meaningful molecules for discrimination of the chocolate sensory groups. Forty-four additional cocoa samples were used to validate the previous results. The fingerprinting method proved to be quick and efficient, and the chemometrics highlighted 29 m/ z signals of known and unknown molecules, mainly flavan-3-ols, enabling sensory-group discrimination.