Chemometric Modeling of Coffee Sensory Notes through Their Chemical Signatures: Potential and Limits in Defining an Analytical Tool for Quality Control

Comprehensive analysis of key aroma compounds enhanced by Tamarix ramosissima Ledeb in mutton roasted by air-frying roast technology by means of SAFE-GC-O-MS and lipidomics

Little information is known about the increased aroma compounds and possible mechanism in Tamarix ramosissima Ledeb roasted mutton (TRM). A comprehensive analysis of aroma compounds and lipids were firstly performed by lipidomics and sensomics approach. The results indicated that 9 out of 53 aroma compounds were considered as key odorants, including 5-methyl-2,3-diethylpyrazine. The roasted mutton contained highest levels of phosphatidylcholine (PC, 13.95%), triglyceride (TG, 13.50%), and phosphatidylethanolamine (PE, 12.25%). TG 18:0_18:0_18:1 and nine odorants were the potential biomarkers for discriminating differential samples due to variable importance in projection (VIP) > 1 and p < 0.05. PCs and TGs, including PC 21:0_13:1 and TG 16:0_18:1_18:1, might be predominantly responsible for the formation and retention of aroma compounds, respectively. This will clarify the enhanced effect of Tamarix ramosissima Ledeb on the presence of aroma compounds via lipid pathways in roasted mutton.

Exploring accurate mass measurements in pixel-based chemometrics: Advancing coffee classification with GC-HRMS-A proof of concept study

This paper presents a study that assesses the application of chemometrics for classifying coffee samples in a quality control context. High-resolution and accurate mass measurements were utilized as input for pixel-based orthogonal partial least squares discriminant analysis (OPLS-DA) models. The compositional data were acquired through a fully automated workflow combining headspace solid-phase microextraction and gas chromatography-high-resolution mass spectrometry (GC-HRMS) using an FT-Orbitrap® mass analyzer. A workflow centered on accurate mass measurements was successfully utilized for group-type analysis, offering an alternative to methods relying solely on MS similarity searches. The predictive models underwent thorough evaluation, demonstrating robust multivariate classification performance. Five key coffee attributes, bitterness, acidity, body, intensity, and roasting level were successfully predicted using GC-HRMS data. The results revealed strong predictive accuracy across all models, ranging from 88.9 % (bitterness) to 94.4 % (roasting level). This study represents a significant advancement in automating methods for coffee quality control, notably increasing the predictive ability of the models compared to existing literature.

SPME-GC-MS untargeted metabolomics approach to identify potential volatile compounds as markers for fraud detection in roasted and ground coffee

Roasted ground coffee has been intentionally adulterated for economic revenue. This work aims to use an untargeted strategy to process SPME-GC-MS data coupled with chemometrics to identify volatile compounds (VOCs) as possible markers to discriminate Arabica coffee and its main adulterants (corn, barley, soybean, rice, coffee husks, and Robusta coffee). Principal Component Analysis (PCA) showed the difference between roasted ground coffee and adulterants, while the Hierarchical Clustering of Principal Components (HCPC) and heat map showed a trend of adulterants separation. The partial Least-Squares Discriminant Analysis (PLS-DA) approach confirmed the PCA results. Finally, 24 VOCs were putatively identified, and 11 VOCs are candidates for potential markers to detect coffee fraud, found exclusively in one type of adulterant: coffee husks, soybean, and rice. The results for possible markers may be suitable for evaluating the authenticity of ground-roasted coffee, thus acting as a coffee fraud control and prevention tool.

Changes in GC-MS metabolite profile, antioxidant capacity and anthocyanins content during fermentation of fine-flavor cacao beans from Ecuador

The fermentation of fine-flavor cacao beans is a key process contributing to the enhancement of organoleptic attributes and monetary benefits for cacao farmers. This work aimed to describe the dynamics of the gas chromatography-mass spectrometry (GC-MS) metabolite profile as well as the antioxidant capacity and anthocyanin contents during fermentation of fine-flavor cacao beans. Samples of Nacional x Trinitario cacao beans were obtained after 0, 24, 48, 72, 96, and 120 hours of spontaneous fermentation. Total phenolic content (TPC), ferric reducing antioxidant power (FRAP), and total anthocyanin content were measured by ultraviolet-visible (UV-Vis) spectrophotometry. Volatiles were adsorbed by headspace solid phase microextraction (HS-SPME) while other metabolites were assessed by an extraction-derivatization method followed by gas chromatography-mass spectrometry (GC-MS) detection and identification. Thirty-two aroma-active compounds were identified in the samples, including 17 fruity, and 9 floral-like volatiles as well as metabolites with caramel, chocolate, ethereal, nutty, sweet, and woody notes. Principal components analysis and Heatmap-cluster analysis of volatile metabolites grouped samples according to the fermentation time. Additionally, the total anthocyanin content declined during fermentation, and FRAP-TPC values showed a partial correlation. These results highlight the importance of fermentation for the improvement of the fine-flavor characteristics of cacao beans.

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.

SHS-GC-MS applied in Coffea arabica and Coffea canephora blend assessment

Considering the great economic significance of Coffea arabica (arabica) associated with the lower production cost of C. canephora (conilon), blends of these coffees are commercially available to reduce costs and combine sensory attributes. Thus, analytical tools are required to ensure consistency between real and labeled compositions. In this sense, chromatographic methods based on volatile analysis using static headspace-gas chromatography-mass spectrometry (SHS-GC-MS) and Fourier transform infrared (FTIR) spectroscopy associated with chemometric tools were proposed for the identification and quantification of arabica and conilon blends. The peak integration from the total ion chromatogram (TIC) and extracted ion chromatogram (EIC) was compared in multivariate and univariate scenarios. The optimized partial least squares (PLS) models with uninformative variable elimination (UVE) and chromatographic data (TIC and EIC) have similar accuracy according to a randomized test, with prediction errors between 3.3% and 4.7% and R_p² > 0.98. There was no difference between the univariate models for the TIC and EIC, but the FTIR model presented a lower performance than GC-MS. The multivariate and univariate models based on chromatographic data had similar accuracy. For the classification models, the FTIR, TIC, and EIC data presented accuracies from 96% to 100% and error rates from 0% to 5%. Multivariate and univariate analyses combined with chromatographic and spectroscopic data allow the investigation of coffee blends.

Discrimination of Green Coffee (Coffea arabica and Coffea canephora) of Different Geographical Origin Based on Antioxidant Activity, High-Throughput Metabolomics, and DNA RFLP Fingerprinting

The genus Coffea is known for the two species C. arabica (CA) and C. canephora (CC), which are used to prepare the beverage coffee. Proper identification of green beans of coffee varieties is based on phenotypic and phytochemical/molecular characteristics. In this work, a combination of chemical (UV/Vis, HPLC-DAD-MS/MS, GC-MS, and GC-FID) and molecular (PCR-RFLP) fingerprinting was used to discriminate commercial green coffee accessions from different geographical origin. The highest content of polyphenols and flavonoids was always found in CC accessions, whereas CA showed lower values. ABTS and FRAP assays showed a significant correlation between phenolic content and antioxidant activity in most CC accessions. We identified 32 different compounds, including 28 flavonoids and four N-containing compounds. The highest contents of caffeine and melatonin were detected in CC accessions, whereas the highest levels of quercetin and kaempferol derivatives were found in CA accessions. Fatty acids of CC accessions were characterized by low levels of linoleic and cis octadecenoic acid and high amounts of elaidic acid and myristic acid. Discrimination of species according to their geographical origin was achieved using high-throughput data analysis, combining all measured parameters. Lastly, PCR-RFLP analysis was instrumental for the identification of recognition markers for the majority of accessions. Using the restriction enzyme AluI on the trnL-trnF region, we clearly discriminated C. canephora from C. arabica, whereas the cleavage performed by the restriction enzymes MseI and XholI on the 5S-rRNA-NTS region produced specific discrimination patterns useful for the correct identification of the different coffee accessions. This work extends our previous studies and provides new information on the complete flavonoid profile, combining high-throughput data with DNA fingerprinting to assess the geographical discrimination of green coffee.

Medium Roasting and Brewing Methods Differentially Modulate Global Metabolites, Lipids, Biogenic Amines, Minerals, and Antioxidant Capacity of Hawai'i-Grown Coffee (Coffea arabica)

In the United States, besides the US territory Puerto Rico, Hawai'i is the only state that grows commercial coffee. In Hawai'i, coffee is the second most valuable agricultural commodity. Health benefits associated with moderate coffee consumption, including its antioxidant capacity, have been correlated to its bioactive components. Post-harvest techniques, coffee variety, degree of roasting, and brewing methods significantly impact the metabolites, lipids, minerals, and/or antioxidant capacity of brewed coffees. The goal of our study was to understand the impact of roasting and brewing methods on metabolites, lipids, biogenic amines, minerals, and antioxidant capacity of two Hawai'i-grown coffee (Coffea arabica) varieties, "Kona Typica" and "Yellow Catuai". Our results indicated that both roasting and coffee variety significantly modulated several metabolites, lipids, and biogenic amines of the coffee brews. Furthermore, regardless of coffee variety, the antioxidant capacity of roasted coffee brews was higher in cold brews. Similarly, total minerals were higher in "Kona Typica" cold brews followed by "Yellow Catuai" cold brews. Hawai'i-grown coffees are considered "specialty coffees" since they are grown in unique volcanic soils and tropical microclimates with unique flavors. Our studies indicate that both Hawai'i-grown coffees contain several health-promoting components. However, future studies are warranted to compare Hawai'i-grown coffees with other popular brand coffees and their health benefits in vivo.

Potential Aroma Chemical Fingerprint of Oxidised Coffee Note by HS-SPME-GC-MS and Machine Learning

This study examines the volatilome of good and oxidised coffee samples from two commercial coffee species (i.e., Coffea arabica (arabica) and Coffea canephora (robusta)) in different packagings (i.e., standard with aluminium barrier and Eco-caps) to define a fingerprint potentially describing their oxidised note, independently of origin and packaging. The study was carried out using HS-SPME-GC-MS/FPD in conjunction with a machine learning data processing. PCA and PLS-DA were used to extrapolate 25 volatiles (out of 147) indicative of oxidised coffees, and their behaviour was compared with literature data and critically discussed. An increase in four volatiles was observed in all oxidised samples tested, albeit to varying degrees depending on the blend and packaging: acetic and propionic acids (pungent, acidic, rancid), 1-H-pyrrole-2-carboxaldehyde (musty), and 5-(hydroxymethyl)-dihydro-2(3H)-furanone.

Evaluation of key aroma compounds and protein secondary structure in the roasted Tan mutton during the traditional charcoal process

The traditional charcoal technique was used to determine the changes in the key aroma compounds of Tan mutton during the roasting process. The results showed that the samples at the different roasting time were distinguished using GC-MS in combination with PLS-DA. A total of 26 volatile compounds were identified, among which 14 compounds, including (E)-2-octenal, 1-heptanol, hexanal, 1-hexanol, heptanal, 1-octen-3-ol, 1-pentanol, (E)-2-nonenal, octanal, 2-undecenal, nonanal, pentanal, 2-pentylfuran and 2-methypyrazine, were confirmed as key aroma compounds through the odor activity values (OAV) and aroma recombination experiments. The OAV and contribution rate of the 14 key aroma compounds were maintained at high levels, and nonanal had the highest OAV (322.34) and contribution rate (27.74%) in the samples after roasting for 10 min. The content of α-helix significantly decreased (P < 0.05), while the β-sheet content significantly increased (P < 0.05) during the roasting process. The content of random coils significantly increased in the samples roasted for 0-8 min (P < 0.05), and then no obvious change was observed. At the same time, β-turn content had no obvious change. Correlation analysis showed that the 14 key aroma compounds were all positively correlated with the content of α-helix and negatively correlated with the contents of β-sheet and random coil, and also positively correlated with the content of β-turn, except hexanal and 2-methypyrazine. The results are helpful to promoting the industrialization of roasted Tan mutton.

Fast and Non-Destructive Profiling of Commercial Coffee Aroma under Three Conditions (Beans, Powder, and Brews) Using GC-IMS

The flavor of coffee can be affected by the preparation parameters. In this investigation, the flavor profiles of three coffee brands under three conditions (bean, powder, and brew) were analyzed by gas chromatography—ion mobility spectrometry (GC-IMS) and the electronic nose (E-nose). The flavor results were further studied using multiple factor analysis (MFA). A total of 117 peaks were identified in all coffee samples by GC-IMS, and the principal component analysis (PCA) showed these coffee samples could be grouped and separated. A total of 37 volatile organic compounds (VOCs) were selected as biomarkers to distinguish coffee samples, including 5 aldehydes, 10 ketones, 8 alcohols, 2 acids, 4 esters, 5 furans, and 3 other compounds. The comparison between E-nose and GC-IMS data using partial least squares regression (PLSR) and MFA showed GC-IMS could present very close sample spaces. Compared with E-nose, GC–IMS could not only be used to classify coffee samples in a very short time but also provide VOC bio-markers to discriminate coffee samples.

Recent advances in analytical strategies for coffee volatile studies: Opportunities and challenges

Coffee has attracted significant research interest owing to its complex volatile composition and aroma, which imparts a pleasant sensorial experience that remains challenging to analyse and interpret. This review summarises analytical challenges associated with coffee's volatile and matrix complexity, and recent developments in instrumental techniques to resolve them. The benefits of state-of-the-art analytical techniques applied to coffee volatile analysis from experimental design to sample preparation, separation, detection, and data analysis are evaluated. Complementary method selection coupled with progressive experimental design and data analysis are vital to unravel the increasing comprehensiveness of coffee volatile datasets. Considering this, analytical workflows for conventional, targeted, and untargeted coffee volatile analyses are thus proposed considering the trends towards sorptive extraction, multidimensional gas chromatography, and high-resolution mass spectrometry. In conclusion, no single analytical method addresses coffee's complexity in its entirely, and volatile analysis must be tailored to the key objectives and concerns of the analyst.

Characterization of Key Aroma Compounds in Fermented Bamboo Shoots Using Gas Chromatography-Olfactometry-Mass Spectrometry, Odor Activity Values, and Aroma Recombination Experiments

Guangxi fermented bamboo shoots (GFBS) are widely appreciated by consumers in China because of their unique aroma. In this study, the dominant aroma compounds of GFBS were investigated using gas chromatography-olfactometry-mass spectrometry, odor-activity values, and aroma recombination. The results show that 70 aroma compounds, including alcohols, esters, aldehydes, acids, phenols, ethers, ketones, alkenes, benzene derivatives, and furans, were identified in GFBS. Among them, 15 aroma compounds with odor-activity values (OAVs) > 1 were identified. Aroma-recombination-omission experiments and sensory evaluation demonstrated that octanal, (E)-2-octenal, acetic acid, guaiacol, phenylethyl alcohol, creosol, 4-ethylguaiacol, and p-cresol significantly contributed to the characteristic aroma of GFBS. Most importantly, p-cresol (34,997.95 ≤ OAV ≤ 71,409.51) and acetic acid (2155.79 ≤ OAV ≤ 3872.09) significantly contributed to its aroma (p < 0.001). The major aroma profile of GFBS included a strong fermented odor, which was pungent and sour. This study provides a theoretical basis for improving the flavor of GFBS.

Does Coffee Have Terroir and How Should It Be Assessed?

The terroir of coffee is defined as the unique sensory experience derived from a single origin roasted coffee that embodies its source. Environmental conditions such as temperature, altitude, shade cover, rainfall, and agronomy are considered the major parameters that define coffee terroir. However, many other parameters such as post-harvest processing, roasting, grinding, and brewing can combine to influence the perception of terroir. In this review, we discuss the contribution of these parameters and their influence on coffee terroir. Assessment of terroir requires defined sensory descriptors, as provided by the World Coffee Research Lexicon, and standardized roast level, grind size, and brew method. The choice of the post-harvest processing method is often environmentally dependent, suggesting that an inclusion into the coffee terroir definition is warranted. Coffee terroir is often not intentionally created but results from the contributions of the Coffea species and variety planted, environmental and agricultural parameters, and both the harvest and post-harvest method used. The unique combination of these parameters gives the consumer a unique cup of coffee, reminiscent of the place the coffee was produced.

The formation of key aroma compounds in roasted mutton during the traditional charcoal process

The formation of key aroma compounds in roasted mutton during the traditional charcoal process were investigated. The results indicated that the samples roasted for 0-15 min could be discriminated using a flash GC E-nose and GC-O-MS combined with multivariate data analysis. A total of 37 odorants were identified, among which 15 odorants were confirmed as key aroma compounds by aroma recombination experiments. Significant increases in key aroma compositions and concentrations in samples were observed during the roasting process, in which hexanal had the highest concentration. The odour activity values (OAVs) of 15 key aroma compounds were maintained at high levels in the samples after roasting for 10 min. The roasted mutton had typical aromas of meaty, fatty, roasty, grassy, and sweet odours. The multivariate linear modeling indicated that a lower specific heat capacity and lower water activity could contribute to the formation of aroma compounds of samples.

Multivariate calibration applied to study of volatile predictors of arabica coffee quality

The chemical complexity of coffee influences the sensory evaluation of the beverage, the main method used to define the quality of the coffee. In view of the subjectivity that method offers, we propose the association of an instrumental method with multivariate calibration (PLS and GA-SVR) to predict the quality of arabica coffee as support for sensory analysis. Arabica coffee samples were submitted to sensory evaluation using the Specialty Coffee Association (SCA) protocol and HS-SPME-GC/MS analysis. The models presented RMSEp results from 0.20 to 0.25, within the evaluation range the quality levels of sensory attributes (0.25). For the fragrance/aroma attribute, a value of R²p equal to 0.8503 was reached. 15 volatile compounds were identified as responsible for predicting the quality of arabica coffee, among which, 1-nonadecene was first reported as an impact compound in the prediction of important sensory attributes.

Hyper-fast gas chromatography and single-photon ionisation time-of-flight mass spectrometry with integrated electrical modulator-based sampling for headspace and online VOC analyses

We developed a novel fast gas chromatography (fastGC) instrument with integrated sampling of volatile organic compounds (VOCs) and detection by single-photon ionisation (SPI) time-of-flight mass spectrometry (TOFMS). A consumable-free electrical modulator rapidly cools down to -55 °C to trap VOCs and inject them on a short chromatographic column by prompt heating to 300 °C, followed by carrier gas exchange from air to helium. Due to the low thermal mass and optical heating, the fastGC is operated within total runtimes including cooling for 30 s and 15 s, referring to hyper-fast GC, and at a constantly increasing temperature ramp from 30 °C to 280 °C. The application of soft SPI-TOFMS allows the detection of co-eluting VOCs of different molecular compositions, which cannot be resolved by conventional GC (cGC) with electron ionisation (EI). Among other analytical figures of merit, we achieved limits of detection for toluene and p-xylene of 2 ppb and 0.5 ppb, respectively, at a signal-to-noise ratio of 3 and a linear response over a range of more than five orders of magnitude. Furthermore, we demonstrate the performance of the instrument on samples from the fields of environmental research and food science by headspace analysis of roasted coffee beans and needles from coniferous trees as well as by quasi-real-time analysis of biomass burning emissions and coffee roast gas.

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.

Chromatographic Fingerprinting Strategy to Delineate Chemical Patterns Correlated to Coffee Odor and Taste Attributes

Coffee cupping includes both aroma and taste, and its evaluation considers several different attributes simultaneously to define flavor quality and therefore requires complementary data from aroma and taste. This study investigates the potential and limits of a data-driven approach to describe the sensory quality of coffee using complementary analytical techniques usually available in routine quality control laboratories. Coffee flavor chemical data from 155 samples were obtained by analyzing volatile (headspace-solid-phase microextraction-gas chromatography-mass spectrometry (HS-SPME-GC-MS)) and nonvolatile (liquid chromatography-ultraviolet/diode array detector (LC-UV/DAD)) fractions, as well as from sensory data. Chemometric tools were used to explore the data sets, select relevant features, predict sensory scores, and investigate the networks between features. A comparison of the Q model parameter and root-mean-squared error prediction (RMSEP) highlights the variable influence that the nonvolatile fraction has on prediction, showing that it has a higher impact on describing acid, bitter, and woody notes than on flowery and fruity. The data fusion emphasized the aroma contribution to driving sensory perceptions, although the correlative networks highlighted from the volatile and nonvolatile data deserve a thorough investigation to verify the potential of odor-taste integration.

Analytical Determination of the Severity of Potato Taste Defect in Roasted East African Arabica Coffee

The quality of East African coffee beans has been significantly reduced by a flavor defect known as potato taste defect (PTD) due to the presence of 2-isopropyl-3-methoxypyrazine (IPMP) and 2-isobutyl-3-methoxypyrazine (IBMP). Therefore, the aims of this study were to determine the correlation between these methoxypyrazines and the severity of odor attributed to PTD and discover additional analytes that may be correlated with PTD using Fisher ratio analysis, a supervised discovery-based data analysis method. Specialty ground roasted coffees from East Africa were classified as clean (i.e., no off-odor), mild, medium, or strong PTD. For the samples examined, IPMP was found to discriminate between non-defective and defective samples, while IBMP did not do so. Samples affected by PTD exhibited a wide range of IPMP concentration (1.6-529.9 ng/g). Except for one sample, the IPMP concentration in defective samples was greater than the average IPMP concentration in the non-defective samples (2.0 ng/g). Also, an analysis of variance found that IPMP concentrations were significantly different based on the severity of odor attributed to PTD (p < 0.05). Fisher ratio analysis discovered 21 additional analytes whose concentrations were statistically different based on the severity of PTD odor (p < 0.05). Generally, analytes that were positively correlated with odor severity generally had unpleasant sensory descriptions, while analytes typically associated with desirable aromas were found to be negatively correlated with odor severity. These findings not only show that IPMP concentration can differentiate the severity of PTD but also that changes in the volatile analyte profile of coffee beans induced by PTD can contribute to odor severity.

Chemometrics-based aroma profiling for revealing origin, roasting indices, and brewing method in coffee seeds and its commercial blends in the Middle East

Coffee is among the most consumed beverages worldwide. The present study reports on the aroma composition associated with coffee seeds brewing. Aroma of authentic coffee specimens of Coffea arabica and C. robusta alongside with typical products consumed in the Middle East were analyzed using HS-SPME coupled with GC-MS. In addition, multivariate data analysis (MVA) was employed. Results revealed for 102 volatiles with a distinct aroma profile between the different brewing methods. Infusion demonstrated higher esters level, while decoction and maceration were more abundant in sesquiterpenes and terpene alcohols, respectively. Besides, heat-induced products, i.e., 4-vinyl guaiacol was identified as potential roasting index in instant coffee and roasted C. robusta brews. Blending with cardamom further masked the smoky odor of such compounds by its fragrant terpinyl acetate. This study provides the first report on the chemical sensory attributes of Middle Eastern coffee blends and further reveal for the impact of brewing, roasting on its aroma composition.

Coffee flavour modification through controlled fermentation of green coffee beans by Saccharomyces cerevisiae and Pichia kluyveri: Part II. Mixed cultures with or without lactic acid bacteria

This study attempted to achieve coffee flavour biotransformation through controlled fermentation of sterilsed green coffee beans with a coculture of Saccharomyces cerevisiae and Pichia kluyveri (FYco) and a sequential inoculation of Lc. lactis subsp. cremoris and the yeast coculture (FLYco). Isoamyl acetate, 2-phenylethyl acetate, and ethyl octanoate were produced by 5.76, 1.35 and 0.54 mg/kg, respectively, in FYco fermented green coffee beans. Compared to the green coffee bean fermented by the yeast monocultures in previous study, FYco led to a 1.2- and 4.1-times elevation in production of isoamyl acetate and 2-phenylethyl acetate, respectively. FLYco further increased acetate ester production by more than 2 times relative to FYco. The esters produced in FYco and FLYco partially survived the roasting process and imparted the roasted coffees with considerable fruity and winey aromas. The lactic acid fermentation in FLYco increased the acidity in green coffee beans, which promoted the formation of caramel-smelling furfurals and preservation of acidity and sweetness in the roasted coffees. Apart from the mere additions of flavour modification from individual strains, the proper combination of multiple strains can result in synergistic effects that enhanced the modulating activities of individual strains and further enhance flavour complexity of the resulted coffee.

Characterization of the Key Aroma Compounds in Dog Foods by Gas Chromatography-Mass Spectrometry, Acceptance Test, and Preference Test

Gas chromatography-mass spectrometry (GC-MS) coupled with the acceptance test, partial least-squares regression (PLSR) analysis, validation experiment, and preference test was used to identify the key aroma compounds in dog foods (DFs). Six DFs were evaluated by spraying six palatability enhancers onto a basal DF. The differently flavored palatability enhancers were prepared by the Maillard reaction using different protein sources and reaction conditions. The intake ratios of the six DFs were tested by six adult beagle dogs and were classified into high, medium, and low levels. A total of 55 volatile compounds were identified using headspace solid-phase microextraction (HS-SPME) GC-MS. Correlation analysis of the volatile compounds associated with intake ratios using partial least-squares regression (PLSR) found nine significantly positive and three significantly negative compounds that made a significant contribution to the palatability of DFs. Validation tests undertaken by adding three significantly positive compounds, one significantly negative compound, and one nonsignificant compound into the odorless matrix successfully verified the accuracy prediction of the PLSR model. The nine significantly positive compounds were heptanal, nonanal, octanal, (E)-2-hexenal, (E,E)-2,4-decadienal, 2-pentylfuran, 4-methyl-5-thiazoleethanol, 2-furfurylthiol, and (E)-2-decenal. The contributions of nine key aroma compounds were further analyzed by the preference test. (E)-2-decenal, 2-furfurylthiol, and 4-methyl-5-thiazoleethanol showed higher first choice, consumption ratio, and unit contribution rate and were vital to the overall preferred aroma of DFs.

HS-SPME-MS-Enose Coupled with Chemometrics as an Analytical Decision Maker to Predict In-Cup Coffee Sensory Quality in Routine Controls: Possibilities and Limits

The quality assessment of the green coffee that you will go to buy cannot be disregarded from a sensory evaluation, although this practice is time consuming and requires a trained professional panel. This study aims to investigate both the potential and the limits of the direct headspace solid phase microextraction, mass spectrometry electronic nose technique (HS-SPME-MS or MS-EN) combined with chemometrics for use as an objective, diagnostic and high-throughput technique to be used as an analytical decision maker to predict the in-cup coffee sensory quality of incoming raw beans. The challenge of this study lies in the ability of the analytical approach to predict the sensory qualities of very different coffee types, as is usual in industry for the qualification and selection of incoming coffees. Coffees have been analysed using HS-SPME-MS and sensory analyses. The mass spectral fingerprints (MS-EN data) obtained were elaborated using: (i) unsupervised principal component analysis (PCA); (ii) supervised partial least square discriminant analysis (PLS-DA) to select the ions that are most related to the sensory notes investigated; and (iii) cross-validated partial least square regression (PLS), to predict the sensory attribute in new samples. The regression models were built with a training set of 150 coffee samples and an external test set of 34. The most reliable results were obtained with acid, bitter, spicy and aromatic intensity attributes. The mean error in the sensory-score predictions on the test set with the available data always fell within a limit of ±2. The results show that the combination of HS-SPME-MS fingerprints and chemometrics is an effective approach that can be used as a Total Analysis System (TAS) for the high-throughput definition of in-cup coffee sensory quality. Limitations in the method are found in the compromises that are accepted when applying a screening method, as opposed to human evaluation, in the sensory assessment of incoming raw material. The cost-benefit relationship of this and other screening instrumental approaches must be considered and weighed against the advantages of the potency of human response which could thus be better exploited in modulating blends for sensory experiences outside routine.

Characterization of Key Aroma Compounds in Beijing Roasted Duck by Gas Chromatography-Olfactometry-Mass Spectrometry, Odor-Activity Values, and Aroma-Recombination Experiments

The dominant aroma compounds in the breast skin and breast muscle of Beijing roasted duck were investigated by gas chromatography-olfactometry-mass spectrometry (GC-O-MS), odor-activity values, and aroma recombination. The results demonstrated that a total of 42 aroma compounds were identified in Beijing roasted duck, including aldehydes, ketones, alcohols, acids, phenols, sulfur-containing compounds, and nitrogen-containing compounds. Among the 42 aroma compounds, 18 were identified as important odorants with odor-activity values (OAVs) greater than 1. Aroma-recombination-omission experiments and sensory evaluation demonstrated that nine aroma compounds significantly contributed to the characteristic aroma of Beijing roasted duck. These nine key aroma compounds were 2-furfurylthiol, dimethyl trisulfide, hexanal, heptanal, octanal, nonanal, methional, 1-octen-3-ol, and ( E, E)-2,4-decadienal. Among these, 2-furfurylthiol (3620 ≤ OAV ≤ 31 606) and dimethyl trisulfide (2515 ≤ OAV ≤ 23 470) significantly contributed to the aroma of roasted duck ( p < 0.01). Sensory evaluation of the recombination model with the nine aroma compounds scored 4.5 out of 5 points. The major aroma profile of Beijing roasted duck included strong fatty, roasty, and meaty aromas. The key aroma compounds of Beijing roasted duck were concluded to be 2-furfurylthiol, dimethyl trisulfide, hexanal, heptanal, octanal, nonanal, methional, 1-octen-3-ol, and ( E, E)-2,4-decadienal.

Anti-inflammatory Mechanism Involved in 4-Ethylguaiacol-Mediated Inhibition of LPS-Induced Inflammation in THP-1 Cells

4-Ethylguaiacol, a common aroma compound of baijiu (a traditional Chinese alcoholic beverage), was assessed for its potential anti-inflammatory effects in an LPS-induced THP-1 cell model. To characterize the effect of 4-ethylguaiacol on the LPS-induced inflammatory response, the mRNA and protein expression of the TLR4-MAPKs-NF-κB-IκBα-AP-1, Nrf2-HO-1, and AMPK-SIRT1 pathways were monitored by ELISA, real-time PCR, and Western blotting. On the basis of the result, 4-ethylguaiacol exerted anti-inflammatory effects at doses of 10, 100, and 500 μM (the concentration of 4-ethylguaiacol in gujinggong baijiu is in the range of 1044 ± 44 to 1661 ± 63 μg/L) and significantly mitigated LPS-induced inflammation via activation of the Nrf2-HO-1 and AMPK-SIRT1 pathways and inhibition of NF-κB and AP-1 activation, thereby markedly inhibiting the activation of inflammasomes and down-regulating the production of inflammatory cytokines. These results indicated that 4-ethylguaiacol could reverse LPS-induced inflammatory responses and is a natural, potent anti-inflammatory component in baijiu.

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.