Identification of changes in the volatile compounds of robusta coffee beans during drying based on HS-SPME/GC-MS and E-nose analyses with the aid of chemometrics

Early prediction of ochratoxigenic Aspergillus westerdijkiae on traditional Italian caciocavallo during ripening process by MS-based electronic nose

A rapid and non-invasive mass spectrometry-based electronic nose (MS-eNose) method, combined with chemometric analysis, was developed for the early detection of Aspergillus westerdijkiae on caciocavallo cheeses during ripening process. MS-eNose analyses were carried out on caciocavallo inoculated with ochratoxin A (OTA) non-producing species and artificially contaminated with A. westerdijkiae, an OTA producing species. Two classification models, i.e. PLS-DA and PC-LDA, were used to discriminate cheese samples in two classes, based on their contamination with toxigenic or non-toxigenic fungal species. Accuracy values were between 87 and 100 % and 86-100 %, in calibration and validation, respectively, with best results obtained at 15-ripening days with 98 % (PLS-DA) and 100 % (PC-LDA) of accuracy in validation. Moreover, eighteen potential volatile markers of the presence of A. westerdijkiae were identified by GC-MS analysis. Results show that MS-eNose represents a useful tool for a rapid screening in preventing A. westerdijkiae and related OTA contamination in caciocavallo cheese during ripening process.

Flavor profile analysis of grilled lamb seasoned with classic salt, chili pepper, and cumin (Cuminum cyminum) through HS-SPME-GC-MS, HS-GC-IMS, E-nose techniques, and sensory evaluation on Sonit sheep

In this study, the volatile flavor profiles of grilled lamb seasoned with salt, chili pepper, and cumin were analyzed employing HS-SPME-GC-MS, HS-GC-IMS, E-nose, and sensory evaluation techniques. The E-nose was found effective in differentiating the samples seasoned variously. A total of 67 volatile compounds were identified by HS-SPME-GC-MS, and 59 by HS-GC-IMS. The PCA demonstrated a correlation between the seasonings and the volatile compounds, with five principal components accounting for 99.54% of the total variance. 1-octen-3-ol, 3-furanmethanol, acetic acid, and heptanal were introduced by salt; compounds like propyl acetate were correlated with chili pepper; a broader range, including ethyl 3-methylbutanoate and high concentrations of alpha-pinene, was associated with cumin. Samples seasoned with all three ingredients showed similarities to those associated with cumin, alongside unique compounds such as gamma-octalactone and alpha-pinene. Sensory evaluations by consumers indicated that the combination of these seasonings significantly enhanced the overall acceptability of the grilled lamb. PRACTICAL APPLICATION: Utilizing modern analytical techniques, this study has successfully revealed the distinct impacts of seasonings-salt, chili pepper, and cumin-on the flavor profile of grilled lamb. By providing experimental data on how each seasonings influence the flavor profile of grilled lamb prepared with Sonit sheep. The research offers theoretical foundation for the development of grilled lamb products. By conducting a thorough comparison between GC-MS and GC-IMS, this study has expanded the understanding of the distinct characteristics of these two technologies. It has also provided a clearer analysis of some flavor compounds dimers produced in GC-IMS system.

Metagenomic and flavoromic profiling reveals the correlation between the microorganisms and volatile flavor compounds in Monascus-fermented cheese

The Monascus-fermented cheese (MC) is a unique cheese product that undergoes multi-strain fermentation, imparting it with distinct flavor qualities. To clarify the role of microorganisms in the formation of flavor in MC, this study employed SPME (arrow)-GC-MS, GC-O integrated with PLS-DA to investigate variations in cheese flavors represented by volatile flavor compounds across 90-day ripening periods. Metagenomic datasets were utilized to identify taxonomic and functional changes in the microorganisms. The results showed a total of 26 characteristic flavor compounds in MC at different ripening periods (VIP＞1, p < 0.05), including butanoic acid, hexanoic acid, butanoic acid ethyl ester, hexanoic acid butyl ester, 2-heptanone and 2-octanone. According to NR database annotation, the genera Monascus, Lactococcus, Aspergillus, Lactiplantibacillus, Staphylococcus, Flavobacterium, Bacillus, Clostridium, Meyerozyma, and Enterobacter were closely associated with flavor formation in MC. Ester compounds were linked to Monascus, Meyerozyma, Staphylococcus, Lactiplantibacillus, and Bacillus. Acid compounds were linked to Lactococcus, Lactobacillus, Staphylococcus, and Bacillus. The production of methyl ketones was closely related to the genera Monascus, Staphylococcus, Lactiplantibacillus, Lactococcus, Bacillus, and Flavobacterium. This study offers insights into the microorganisms of MC and its contribution to flavor development, thereby enriching our understanding of this fascinating dairy product.

Artificial Q-Grader: Machine Learning-Enabled Intelligent Olfactory and Gustatory Sensing System

Portable and personalized artificial intelligence (AI)-driven sensors mimicking human olfactory and gustatory systems have immense potential for the large-scale deployment and autonomous monitoring systems of Internet of Things (IoT) devices. In this study, an artificial Q-grader comprising surface-engineered zinc oxide (ZnO) thin films is developed as the artificial nose, tongue, and AI-based statistical data analysis as the artificial brain for identifying both aroma and flavor chemicals in coffee beans. A poly(vinylidene fluoride-co-hexafluoropropylene)/ZnO thin film transistor (TFT)-based liquid sensor is the artificial tongue, and an Au, Ag, or Pd nanoparticles/ZnO nanohybrid gas sensor is the artificial nose. In order to classify the flavor of coffee beans (acetic acid (sourness), ethyl butyrate and 2-furanmethanol (sweetness), caffeine (bitterness)) and the origin of coffee beans (Papua New Guinea, Brazil, Ethiopia, and Colombia-decaffeine), rational combination of TFT transfer and dynamic response curves capture the liquids and gases-dependent electrical transport behavior and principal component analysis (PCA)-assisted machine learning (ML) is implemented. A PCA-assisted ML model distinguished the four target flavors with >92% prediction accuracy. ML-based regression model predicts the flavor chemical concentrations with >99% accuracy. Also, the classification model successfully distinguished four different types of coffee-bean with 100% accuracy.

Effect of Different Drying Methods on the Quality of Oudemansiella raphanipes

In this study, we used fresh Oudemansiella raphanipes as raw materials and pre-treated through hot air drying (HD), infrared radiation drying (ID), and vacuum freeze drying (VD) to investigate the effects of different drying methods on the rehydration rate, appearance quality, microstructure, and volatile flavor components of the dried products, as well as to determine the physicochemical properties and bioactivities of the polysaccharides in the dried O. raphanipes. The results showed that the VD O. raphanipes had the highest rehydration rate and the least shrinkage in appearance, and it better maintained the original color of the gills, but their aroma was not as strong as that of the HD samples. The scanning electron microscopy results indicate that VD maintains a good porous structure in the tissue, while HD and ID exhibit varying degrees of shrinkage and collapse. Seventy-five common volatile substances were detected in the three dried samples, mainly alkanes, alcohols, and esters. The polysaccharides (PS-H, PS-I, and PS-V) extracted from the dried samples of these three species of O. raphanipes had similar infrared spectral features, indicating that their structures are basically consistent. The highest yield was obtained for PS-V, and the polysaccharide content and glucuronic acid content of PS-I were higher than those of the remaining two polysaccharides. In addition, PS-V also showed better antioxidant activity and inhibitory activity against α-glucosidase as well as α-amylase. In conclusion, among the above three drying methods, the quality of O. raphanipes obtained by vacuum freeze drying is the best, and this experiment provides a theoretical basis for the selection of drying methods for O. raphanipes.

E-nose and colorimetric sensor array combining homologous data fusion strategy discriminating the roasting degree of large-leaf yellow tea

Different degrees of roasting result in differences in the quality and flavor of large-leaf yellow tea. The current sensory evaluation and chemical detection methods cannot meet the requirement of online differentiation of LYT roasting degree, so an accurate and comprehensive assessment method needs to be developed urgently. First, the two aroma sensing technologies were compared. Two variable screening methods and three recognition algorithms were employed to build discriminant models. The results showed that the discrimination rate of the colorimetric sensor array (CSA) in the prediction set reached 91.89 %, outperforming that of the E-nose. Subsequently, three fusion strategies were applied to improve the discrimination accuracy. The discrimination rate of the middle fusion strategy resulted in an optimal resolution of 94.59 %. The results obtained from the homologous fusion were able to evaluate the roasting degree comprehensively and accurately, which provides a new method and idea for tea aroma quality.

Electronic Prediction of Chemical Contaminants in Aroma of Brewed Roasted Coffee and Quantification of Acrylamide Levels

The aim of this research was to apply an electronic device as indirect predictive technology to evaluate toxic chemical compounds in roasted espresso coffee. Fresh coffee beans were subjected to different thermal treatments and analyzed to determine volatile organic compounds, content of acrylamide and 5-hydroxymethylfurfural, sensory characteristics and electronic nose data. In total, 70 different volatile compounds were detected and grouped into 15 chemical families. The greatest percentage of these compounds were furans, pyrazines, pyridines and aldehydes. The positive aroma detected had the intensity of coffee odor and a roasted aroma, whereas the negative aroma was related to a burnt smell. A linear relationship between the toxic substances and the sensory defect was established. A high sensory defect implied a lower content of acrylamide and a higher content of 5-hydroxymethylfurfural. Finally, electronic signals were also correlated with the sensory defect. This relationship allowed us to predict the presence of these contaminants in the roasted coffee beverage with an indirect method by using this electronic device. Thus, this device may be useful to indirectly evaluate the chemical contaminants in coffee beverages according to their sensory characteristics.

Volatile profile of quinoa and lentil flour under fungal fermentation and drying

Solid-state fermentation reportedly improves the nutritional and sensory properties of legumes and pseudocereals. This study examined changes in the volatile profile using HS-SPME-GC-MS of two varieties of lentil and quinoa flour fermented with Pleurotus ostreatus and dried using hot-air drying and lyophilisation. Fermentation significantly increased the volatile profile. Pardina lentil flour showed a 570% increase in its volatile profile, and 10 compounds were created. In white quinoa, the total area rose from 96 to 4500, and 30 compounds were created. Compounds such as 1-octen-3-ol, benzaldehyde, 3-octanone and hexanal were generated during fermentation, providing a sweet, grassy, cocoa flavour. Hot-air drying led to decrease of over 40% in total peak area. Dried fermented flour retained higher levels of compounds that provide a sweet, cocoa aroma. Air-drying temperature had no significant influence on the volatile profile. This a allows the inclusion of these flours in a wide variety of food products.

Flavor Characteristics of Ten Peanut Varieties from China

To investigate the flavor characteristics of peanuts grown in Jiangsu, China, ten local varieties were selected. The amino acids, 5'-nucleotides and volatile substances were detected, and the flavor and odor characteristics of these varieties were estimated using an electronic tongue and nose. The results showed that the fat and protein contents of ten peanut varieties changed significantly (p < 0.05), and may have been negatively correlated with those of the Taihua 6 variety-in particular, having the highest protein content and the lowest fat content. The amino acid contents of the peanuts were 20.08 g/100 g (Taihua 4)-27.18 g/100 g (Taihua 6). Taihua 6 also contained the highest bitter (10.41 g/100 g) and sweet (6.06 g/100 g) amino acids, and Taihua 10 had the highest monosodium glutamate-like amino acids (7.61 g/100 g). The content of 5'-nucleotides ranged from 0.08 mg/g (Taihua 9725) to 0.14 mg/g (Taihua 0122-601). Additionally, 5'-cytidylate monophosphate (5'-CMP) and 5'-adenosine monophosphate (5'-AMP) were the major 5'-nucleotides detected in the peanuts. A total of 42 kinds of volatile flavor compounds were detected, with both Taihua 4 and 6 showing the most (18 kinds) and the highest content being in Taihua 4 (7.46%). Both Taihua 9725 and 9922 exhibited the fewest kinds (nine kinds) of volatile components, and the lowest content was in Taihua 9725 (3.15%). Formic acid hexyl ester was the most abundant volatile substance in peanuts, and the highest level (3.63%) was detected in Taihua 7506. The electronic tongue and nose indicated that the greatest taste difference among the ten varieties of peanuts was mainly related to sourness, and Taihua 4 and Taihua 9922 had special taste characteristics. On the other hand, the greatest smell difference among the ten varieties of peanuts was mostly for methane and sulfur organic substances, and Taihua 0605-2 had a special and strong smell characteristic. In conclusion, the content and composition differences of the flavor substances of ten peanut varieties were responsible for their divergences in taste and smell. These results will provide guidelines for the further use (freshly consumed or processed) of these ten peanut varieties.

Novel Efficient Physical Technologies for Enhancing Freeze Drying of Fruits and Vegetables: A Review

Drying is the main technical means of fruit and vegetable processing and storage; freeze drying is one of the best dehydration processes for fruit and vegetables, and the quality of the final product obtained is the highest. The process is carried out under vacuum and at low temperatures, which inhibits enzymatic activity and the growth and multiplication of micro-organisms, and better preserves the nutrient content and flavor of the product. Despite its many advantages, freeze drying consumes approximately four to ten times more energy than hot-air drying, and is more costly, so freeze drying can be assisted by means of highly efficient physical fields. This paper reviews the definition, principles and steps of freeze drying, and introduces the application mechanisms of several efficient physical fields such as ultrasonic, microwave, infrared radiation and pulsed electric fields, as well as the application of efficient physical fields in the freeze drying of fruits and vegetables. The application of high efficiency physical fields with freeze drying can improve drying kinetics, increase drying rates and maintain maximum product quality, providing benefits in terms of energy, time and cost. Efficient physical field and freeze drying technologies can be well linked to sustainable deep processing of fruit and vegetables and have a wide range of development prospects.

Effect of Extraction Methods on Aroma Profile, Antioxidant Activity and Sensory Acceptability of Specialty Coffee Brews

Specialty coffees from various geographical origins were processed using different extraction methods. Four extraction techniques were employed: cold brew (CB), espresso (ES), French press (FR), and aeropress (AE). The potential health benefits of coffee brews were linked to their antioxidant activity, as determined by the DPPH assay, and total polyphenol content (TPC) measured through the Folin-Ciocalteu reducing-capacity assay. The Columbia (C) espresso coffee type (omni-roasting) exhibited the highest antioxidant activity (86.31 ± 0.70) μmol/100 mL, with a TPC value of (44.41 ± 0.35) mg GAE/g. Quantitative analyses of caffeine and chlorogenic acid were conducted using high-performance liquid chromatography (HPLC). The evaluation of coffee aroma profiles involved the application of headspace solid-phase microextraction/gas chromatography-mass spectrometry (HS-SPME/GC-MS) and was complemented by sensory analysis following the Specialty Coffee Association (SCA) standard protocol. The predominant volatile compounds found in all samples included furans, phenols, pyrazines, and terpenes. The EY espresso type (medium dark roasting) had the highest levels of most coffee volatiles. The C cold brew type (omni-roasting) was rated as the preferred coffee in terms of its sensory characteristics and flavour. In summary, ES and CB were found to be more effective extraction methods for the parameters assessed.

Metabolomics as a tool for geographic origin assessment of roasted and green coffee beans

Coffee is widely consumed across the globe. The most sought out varieties are Arabica and Robusta which differ significantly in their aroma and taste. Furthermore, varieties cultivated in different regions are perceived to have distinct characteristics encouraging some producers to adopt the denomination of origin label. These differences arise from variations on metabolite content related to edaphoclimatic conditions and post-harvest management among other factors. Although sensory analysis is still standard for coffee brews, instrumental analysis of the roasted and green beans to assess the quality of the final product has been encouraged. Metabolomic profiling has risen as a promising approach not only for quality purposes but also for geographic origin assignment. Many techniques can be applied for sample analysis: chromatography, mass spectrometry, and NMR have been explored. The data collected is further sorted by multivariate analysis to identify similar characteristics among the samples, reduce dimensionality and/or even propose a model for predictive purposes. This review focuses on the evolution of metabolomic profiling for the geographic origin assessment of roasted and green coffee beans in the last 21 years, the techniques that are usually applied for sample analysis and also the most common approaches for the multivariate analysis of the collected data. The prospect of applying a wide range of analytical techniques is becoming an unbiased approach to determine the origin of different roasted and green coffee beans samples with great correlation. Predictive models worked accurately for the geographic assignment of unknown samples once the variety was known.

Recent developments of e-sensing devices coupled to data processing techniques in food quality evaluation: a critical review

A greater demand for high-quality food is being driven by the growth of economic and technological advancements. In this context, consumers are currently paying special attention to organoleptic characteristics such as smell, taste, and appearance. Motivated to mimic human senses, scientists developed electronic devices such as e-noses, e-tongues, and e-eyes, to spot signals relative to different chemical substances prevalent in food systems. To interpret the information provided by the sensors' responses, multiple chemometric approaches are used depending on the aim of the study. This review based on the Web of Science database, endeavored to scrutinize three e-sensing systems coupled to chemometric approaches for food quality evaluation. A total of 122 eligible articles pertaining to the e-nose, e-tongue and e-eye devices were selected to conduct this review. Most of the performed studies used exploratory analysis based on linear factorial methods, while classification and regression techniques came in the second position. Although their applications have been less common in food science, it is to be noted that nonlinear approaches based on artificial intelligence and machine learning deployed in a big-data context have generally yielded better results for classification and regression purposes, providing new perspectives for future studies.

Use and abuse of retention indices in gas chromatography

The value of the concept of retention indices (RI) to the practice of gas chromatography (GC) is highlighted, where the RI of a compound is one component of the strategy to identify the compound. The widespread reliance on GC and then on mass spectrometry for 'identification', may result in inadequate confirmation of molecular identity. However, RI do provide a useful tentative indication of the possible molecule(s). Thus, the RI value is a useful first measure of the molecule identity, and shown here to be valuable provided limitations are recognised. An author has a responsibility to correctly calculate the index and then use the values for (tentative) identification. Tables of reference RI values are useful in this respect, but finding an 'exact match' RI value does not confirm the identity. Hence, it is necessary to understand how the RI value may be incorrectly used in this respect. The reviewer of written research is charged with ensuring the index values are applied in a rigorous manner. Selected case studies from our own work, support the care that must be exercised when reporting RI values. In terms of advanced GC operations, mention is made of multidimensional gas chromatography and comprehensive two-dimensional gas chromatography to acquire RI values on both the first and second columns in the two-column separation experiment.

Coffee bean processing: Emerging methods and their effects on chemical, biological and sensory properties

Emerging processing methods have been applied in coffee bean processing for improved sensory quality. The processes focus on optimizing the fermentation process of the coffee cherries and beans. This involves various pathways, including the formation of volatiles, flavor precursors and organic acids and the reduction in the concentrations of bioactive compounds. Comprehensive information regarding the effect of these emerging processes on the chemical, biological and sensory properties of the coffee beans is summarized. Emerging processes affected the coffee bean to various degrees depending on the raw material and the method used. The emerging methods promoted the reduction of bioactives such as caffeine and phenolics in coffee beans. Substantial improvement of these processes is needed to obtain coffee beans with improved biological activities. Effort to simplify the methods and optimize the post-fermentation process is crucial for the methods to be easily accessible by the producers and to produce defect-free coffee beans.

Changes in the Texture and Flavor of Lotus Root after Different Cooking Methods

The changes in the texture and flavor of lotus root were determined before and after boiling, steaming and frying. Compared to fresh lotus root, all three kinds of cooking decreased the hardness and springiness, and frying significantly enhanced the gumminess, chewiness and cohesiveness. The flavor components, such as flavor amino acids, nucleotides and their taste character in lotus roots, were determined by liquid chromatography and electronic tongue. The amino acids and nucleotide contents of fresh lotus root were 20.9 and 0.07 μg/kg, respectively. The content of flavor substances in lotus roots decreased obviously, and the texture characteristics decreased after boiling and steaming. After deep-frying for 2 min, the free amino acids and nucleotide contents of lotus root were 32.09 and 0.85 μg/kg, respectively, which were the highest in all cooking methods. The contents of volatile flavor components and their smell character in lotus roots were determined by GC-MS and electronic nose. There were 58 kinds of flavor compounds identified in fresh lotus root, mainly alcohols, esters and olefins. The total amount of volatile flavor compounds decreased, and new compounds, such as benzene derivatives, were produced in lotus roots after boiling and steaming. After deep-frying, the content of volatile flavor compounds in lotus root increased significantly, especially the aldehyde volatile flavor compounds. The production of pyran, pyrazine and pyridine volatile flavor compounds made the lotus root flavor unique and delicious. The taste and smell character of lotus roots before and after cooking were effectively separated by an electronic tongue, nose and PCA analysis; the results suggested the boiled lotus root exhibited the most natural and characteristic taste and smell among the four groups.

Burn Defect and Phenol Prediction for Flavoured Californian-Style Black Olives Using Digital Sensors

Californian-style black olives can undergo different chemical changes during the sterilization process that can affect their sensory and phenol characteristics. Thus, these olives were stuffed with flavoured hydrocolloids and submitted to different thermal sterilization treatments to assess sensory categories. The triangular test indicated that the panellists were able to discriminate between samples from different categories according to their aromas with more than 85% success. The results indicated that the negative aroma detected by tasters was related to burn defects. The highest level of defects was found in standard olives, while the lowest was identified in the extra category. Furthermore, olives submitted to the lowest thermal sterilization treatment (extra) presented significantly higher phenol profile content, such as for hydroxytyrosol, tyrosol, oleuropein and procyanidin B1. The electronic nose (E-nose) discriminated between samples from different categories according to the specific aroma (PC1 = 82.1% and PC2 = 15.1%). The PLS-DA classified the samples with 90.9% accuracy. Furthermore, the volatile organic compounds responsible for this discrimination were creosol, copaene, benzaldehyde and diallyl disulphide. Finally, the models established by the PLS analysis indicated that the E-nose could predict olives according to their aroma and total phenol profile (RCV2 values were 0.89 and 0.92, respectively). Thus, this device could be used at the industrial level to discriminate between olives with different sensory aromas to determine those with the highest quality.

The Impact of Wet Fermentation on Coffee Quality Traits and Volatile Compounds Using Digital Technologies

Fermentation is critical for developing coffee’s physicochemical properties. This study aimed to assess the differences in quality traits between fermented and unfermented coffee with four grinding sizes of coffee powder using multiple digital technologies. A total of N = 2 coffee treatments—(i) dry processing and (ii) wet fermentation—with grinding levels (250, 350, 550, and 750 µm) were analysed using near-infrared spectrometry (NIR), electronic nose (e-nose), and headspace/gas chromatography–mass spectrometry (HS-SPME-GC-MS) coupled with machine learning (ML) modelling. Most overtones detected by NIR were within the ranges of 1700–2000 nm and 2200–2396 nm, while the enhanced peak responses of fermented coffee were lower. The overall voltage of nine e-nose sensors obtained from fermented coffee (250 µm) was significantly higher. There were two ML classification models to classify processing and brewing methods using NIR (Model 1) and e-nose (Model 2) values as inputs that were highly accurate (93.9% and 91.2%, respectively). Highly precise ML regression Model 3 and Model 4 based on the same inputs for NIR (R = 0.96) and e-nose (R = 0.99) were developed, respectively, to assess 14 volatile aromatic compounds obtained by GC-MS. Fermented coffee showed higher 2-methylpyrazine (2.20 ng/mL) and furfuryl acetate (2.36 ng/mL) content, which induces a stronger fruity aroma. This proposed rapid, reliable, and low-cost method was shown to be effective in distinguishing coffee postharvest processing methods and evaluating their volatile compounds, which has the potential to be applied for coffee differentiation and quality assurance and control.

Evaluation of the Olfactory Quality of Roasted Coffee Beans Using a Digital Nose

The roasting process is one of the critical points to obtain a product of the highest quality with certain sensorial properties including the aroma of coffee. Samples of coffee beans were roasted at different thermal treatment intensities with the aim of obtaining aromatic compounds detected with an electronic device. Sensory analysis, volatile compound profiling, and electronic nose analysis were carried out. Through principal component analysis (95.8% of the total variance of the data was explained by PC1 and PC2) and partial least squares discriminant analysis (the sum of the diagonal elements gave a hit rate of 94%), it could be demonstrated that the E-nose is able to discriminate roasted coffee beans subjected to different thermal treatments. Aromatic profiling was carried out by a testing panel and volatile compounds (VOCs) for the discrimination of roasted coffee samples. Alcohols, aromatics, esters, ketones and furanone were found in higher proportions in samples at the lowest thermal treatment. The VOCs with positive attributes were 1-(4-nitrophenyl)-3-phenylamino-propenone, carboxylic acids, 2-methoxy-4-vinylphenol, and 2-phenylethyl alcohol, while the compounds with negative ones were 2-methyl-furan, 2,5-dimethyl-pyridine, 2-methyl-butanal, and 2-furfurylthiol. The PLS model allows for the quantification of the positive and negative aromas (RCV2 = 0.92) of roasted coffee by using the E-nose. Therefore, the E-nose, that is, an inexpensive and nondestructive instrument, could be a chemometric tool able to discriminate between different qualities of coffee during processing.

Characterization of Traditional Chinese Sesame Oil by Using Headspace Solid-Phase Microextraction/Gas Chromatography-Mass Spectrometry, Electronic Nose, Sensory Evaluation, and RapidOxy

Xiao Mo Xiang You (XMXY) is a traditional Chinese sesame oil variety that is obtained through a hot water flotation process. This unique process gives the oil a unique aroma, health benefits, and excellent product stability. Although XMXY is always the most expensive among all the sesame oil varieties, it is usually used as a flavoring in many traditional Chinese daily food products and is increasingly popular. In order to reveal the characteristics of the oil, the volatile components, sensory evaluation, and oxidation stability of five XMXY samples were, respectively, analyzed by using headspace solid-phase microextraction/gas chromatography−mass spectrometry, an electronic nose, sensory evaluation, and RapidOxy. Comparisons and multidimensional statistical analysis were also carried out to distinguish XMXY from roasted sesame oil (RSO) and cold-pressed sesame oil (CSO) samples. In total, 69 volatiles were identified from XMXY, RSO, and CSO samples. Some compounds possessed high odor activity value (OAV > 1) in XMXY, including heterocyclic compounds, phenols, and sulfur-containing compounds. Additionally, they were also the main volatile components that distinguish XMXY from RSO and CSO. Roasted and nutty aromas were the dominant aroma attributes of XMXY. XMXY had better flavor intensity and oxidation stability than the other two sesame oil samples. These results are very valuable for the quality control and product identification of traditional Chinese sesame oil.

Analysis of Volatile Compounds, Composition, and Thermal Behavior of Coffee Beans According to Variety and Roasting Intensity

This study aimed to investigate the ways in which the thermal behavior, composition, and volatile compound contents of roasted coffee beans depend on variety and roasting intensity. The thermal analysis revealed various transformations in coffee composition, namely, drying, water loss, and decomposition of polysaccharides, lipids, amino acids, and proteins. The results showed that volatile compounds are released differently in coffee depending on coffee type and degree of roasting. The most abundant volatile compounds present in the samples were 2-butanone, furan, 2-methylfuran, methyl formate, 2.3-pentanedione, methylpyrazine, acetic acid, furfural, 5-methyl furfural, and 2-furanmethanol. The total polyphenol contents ranged between 13.3 and 18.9 g gallic acid/kg, being slightly higher in Robusta than in Arabica varieties and in more intensely roasted beans compared to medium-roasted beans. The Robusta variety has higher mineral contents than Arabica, and the contents of most minerals (K, Ca, Mg, Fe, Cu, P, N, and S) increased with roasting intensity. Discrimination between coffee varieties and roasting intensities is possible based on mineral and polyphenol contents.

Technology characteristics and flavor changes of traditional green wheat product nian zhuan in Northern China

Nian zhuan has its aroma as one of the perceived principal characteristics. The current study was aimed mainly to investigate the potential to include the aroma of nian zhuan as a new target criterion into the green wheat product chain. By improving the conditions for the traditional processing of nian zhuan, the optimal processing conditions were determined as green wheat (GW) 14 d, steaming the green wheat with the skin (SGWS) 26 min and cooked green wheat peeled (CGWP) 280 min, to evaluate the feasibility of using electronic nose (E-nose) and gas chromatography mass spectrometry (GC-MS) to discriminate nian zhuan in different stages. E-nose was used to recognize nian zhuan odors in different processing stages, and GC-MS to identify the individual volatile compounds. A total of 139 volatile compounds were detected by GC-MS, of which 71 key were screened by t-test (P &lt; 0.01). The W1W, W1S, W2W and W2S sensors of E-nose gave higher responses to all samples, and effectively discriminated the samples. The most volatile compounds were produced in the millstone milling (MSM) stage of nian zhuan, and millstone could promote the release of volatile compounds from cooked green wheat by milling.

Potential valorisation of baobab (Adansonia digitata) seeds as a coffee substitute: Insights and comparisons on the effect of roasting on quality, sensory profiles, and characterisation of volatile aroma compounds by HS-SPME/GC-MS

The seeds of Africa's majestic baobab are often discarded or poorly utilized. Few studies explored its potential as a coffee substitute, while the key volatile compounds are still unknown. These compounds were hypothesized to be responsible for baobab's sensory acceptance. In this study, the physicochemical, sensory, and key volatile composition of brews from coffee beans and baobab seeds subjected to different roasting conditions were reported. Roasting increases pH while reducing acidity, total soluble solids, lightness (L*), redness/greenness (a*), and yellowness/blueness (b*) in coffee and baobab brews. Phenolic contents increased significantly (p < 0.05) with increased roasting intensity in baobab while degrading in coffee. Significant variability of volatile composition existed among coffee and baobab matrices and the roasting conditions. Nevertheless, the presence of several key coffee odorants in baobab from pyrazines, phenols, and furans chemical families, owing to their odour active value ≥ 1, likely contributed to its sensory acceptance.