Characterization of the flavor profile of four major Chinese carps using HS-SPME-GC-MS combined with ultra-fasted gas chromatography-electronic nose.

The four major Chinese carps are highly popular for their distinctive nutritional benefits. However, the differences in flavor among these carps remain unclear. This study investigated the flavor profiles of these carps using headspace solid-phase micro-extraction gas chromatography-mass spectrometry (HS-SPME-GC/MS) combined with ultra-fasted gas chromatography electronic nose (GC E-nose). The four major Chinese carps had high protein content (16.68-18.61 %) and low fat levels (0.42-1.29 %). A total of 45 volatile compounds were identified in these carps. Both the GC E-nose and HS-SPME-GC-MS results consistently showed significant flavor profiles differences among these carps, with Ctenopharyngodon Idella (CI) exhibiting the most pronounced distinctions compared to the other three species. Based on VIP >1 and p < 0.05, 10 key compounds including 2-Nonanone, Cyclodecanol, Eugenol, 1,3-Cyclooctadiene, etc., largely contributed to the distinctive overall flavor profile of four major Chinese carps derived mainly from amino acid and fatty acid metabolism.

Identification of volatile differential markers in strong-aroma Baijiu based on gas chromatography-electronic nose combined with gas chromatography-time-of-flight mass spectrometry.

BACKGROUND: Baijiu is a traditional Chinese liquor produced from grains through fermentation, distillation, aging and blending. The flavor of Baijiu is influenced by factors such as raw materials, starter, processes and the environment, and since the relationship between these factors and the flavor of Baijiu is still being analyzed, the identification of different Baijiu is still somewhat difficult. In this paper, the volatile differential markers of 42 types of strong-aroma Baijiu of different origin, alcohol content and grade were explored. RESULTS: A total of 24 volatile substances were detected by gas chromatography-electronic nose (GC-E-Nose) and 99 volatile substances were detected by gas chromatography-time-of-flight mass spectrometry (GC-TOF MS). The peak areas of the substances obtained by GC-E-Nose were analyzed by the partial least squares (PLS) method, and the substances with variable importance in projection (VIP) >1 were screened out. Combined with the qualitative results of GC-TOF MS, four substances (isobutyric acid, 2-butanone, 2,3-butanediol and 3-methylbutyric acid) were selected as volatile differential markers for strong-aroma Baijiu. An external standard curve was established to accurately quantify these four substances, and the Kruskal-Wallis test confirmed that the absolute contents of these four substances varied significantly among different samples (P < 0.01). Principal component analysis and hierarchical cluster analysis based on the absolute content of these four substances showed that different samples were prioritized for different alcohol contents. CONCLUSION: These four substances can be used as volatile differential markers of strong-aroma Baijiu samples. This research provides theoretical support for the detection and improvement of Baijiu product quality. © 2024 Society of Chemical Industry.

Evaluation of the quality grade of Congou black tea by the fusion of GC-E-Nose, E-tongue, and E-eye.

In the present study, the comprehensive quality of Congou black tea (CBT) including aroma, taste, and liquid color was investigated by a combination of gas chromatography electronic nose (GC-E-Nose), electronic tongue (E-tongue), and electronic eye (E-eye). An excellent discrimination of different quality grades of CBT was accomplished through the fusion of GC-E-Nose, E-tongue, and E-eye combined with orthogonal partial least squares discriminant analysis, with parameters of R2Y = 0.803 and Q2 = 0.740. Moreover, the quantitative evaluation of CBT quality was successfully achieved by partial least squares regression analysis, with the absolute error within 1.39 point, and the relative error within 1.62%. Additionally, 12 key variables were screened out by stepwise multiple linear regression analysis, which significantly contributed to the comprehensive quality score of CBT. Our results suggest that the fusion of multiple intelligent sensory technologies offers great potential and practicability in the quality evaluation of black tea.

Volatolomics-assisted characterization of the key odorants in green off-flavor black tea and their dynamic changes during processing.

Aroma plays a pivotal role in the quality of black tea. However, the acceptability of black tea is greatly limited by the green off-flavor (GOF) resulting from the inappropriate processing control. In this study, the key odorants causing GOF were investigated by volatolomics, and their dynamic changes and formation pathways were in-depth understood. Significant alterations in volatile metabolites were observed in the withering stage. A total of 14 key odorants were identified as contributors to GOF, including 2-methylpropanal, 3-methylbutanal, 1-hexanol, nonanal, (E, E)-2,4-heptadienal, benzaldehyde, linalool, (E, E)-3,5-octadiene-2-one, ß-cyclocitral, phenylacetaldehyde, (E, E)-2,4-nonadienal, methyl salicylate, geraniol, and ß-ionone. Among them, (E, E)-2,4-heptadienal (OAV = 3913), characterized by fatty, green, and oily aromas, was considered to be the most important contributor causing GOF. Moreover, it was found that lipid degradation served as the primary metabolic pathway for GOF. This study provides a theoretical foundation for off-flavor control and quality improvement of black tea.

Comprehensive investigation on the dynamic changes of volatile metabolites in fresh scent green tea during processing by GC-E-Nose, GC-MS, and GC × GC-TOFMS.

Processing technology plays a crucial role in the formation of tea aroma. The dynamic variations in volatile metabolites across different processing stages of fresh scent green tea (FSGT) were meticulously tracked utilizing advanced analytical techniques such as GC-E-Nose, GC-MS, and GC × GC-TOFMS. A total of 244 volatile metabolites were identified by GC-MS and GC × GC-TOFMS, among which 37 volatile compounds were concurrently detected by both methods. Spreading and fixation stages were deemed as pivotal processes for shaping the volatile profiles in FSGT. Notably, linalool, heptanal, 2-pentylfuran, nonanal, ß-myrcene, hexanal, 2-heptanone, pentanal, 1-octen-3-ol, and 1-octanol were highlighted as primary contributors to the aroma profiles of FSGT by combining odor activity value assessment. Furthermore, lipid degradation and glycoside hydrolysis were the main pathways for aroma formation of FSGT. The results not only elucidate the intricate variations in volatile metabolites but also offer valuable insights into enhancing the processing techniques for improved aroma quality of green tea.

Discrimination and screening of volatile metabolites in atractylodis rhizoma from different varieties using headspace solid-phase microextraction-gas chromatography-mass spectrometry and headspace gas chromatography-ion mobility spectrometry, and ultra-fast gas chromatography electronic nose.

Atractylodis rhizoma is a common bulk medicinal material with multiple species. Although different varieties of atractylodis rhizoma exhibit variations in their chemical constituents and pharmacological activities, they have not been adequately distinguished due to their similar morphological features. Hence, the purpose of this research is to analyze and characterize the volatile organic compounds (VOCs) in samples of atractylodis rhizoma using multiple techniques and to identify the key differential VOCs among different varieties of atractylodis rhizoma for effective discrimination. The identification of VOCs was carried out using headspace solid-phase microextraction-gas chromatography-mass spectrometry (HS-SPME-GC-MS) and headspace gas chromatography-ion mobility spectrometry (HS-GC-IMS), resulting in the identification of 60 and 53 VOCs, respectively. The orthogonal partial least squares discriminant analysis (OPLS-DA) model was employed to screen potential biomarkers and based on the variable importance in projection (VIP ≥ 1.2), 24 VOCs were identified as critical differential compounds. Random forest (RF), K-nearest neighbor (KNN) and back propagation neural network based on genetic algorithm (GA-BPNN) models based on potential volatile markers realized the greater than 90 % discriminant accuracies, which indicates that the obtained key differential VOCs are reliable. At the same time, the aroma characteristics of atractylodis rhizoma were also analyzed by ultra-fast gas chromatography electronic nose (Ultra-fast GC E-nose). This study indicated that the integration of HS-SPME-GC-MS, HS-GC-IMS and ultra-fast GC E-nose with chemometrics can comprehensively reflect the differences of VOCs in atractylodis rhizoma samples from different varieties, which will be a prospective tool for variety discrimination of atractylodis rhizoma.

Uncovering the Dynamic Alterations of Volatile Components in Sweet and Floral Aroma Black Tea during Processing.

Aroma is an indispensable factor that substantially impacts the quality assessment of black tea. This study aims to uncover the dynamic alterations in the sweet and floral aroma black tea (SFABT) throughout various manufacturing stages using a comprehensive analytical approach integrating gas chromatography electronic nose, gas chromatography-ion mobility spectrometry (GC-IMS), and gas chromatography-mass spectrometry (GC-MS). Notable alterations in volatile components were discerned during processing, predominantly during the rolling stage. A total of 59 typical volatile compounds were identified through GC-IMS, whereas 106 volatile components were recognized via GC-MS throughout the entire manufacturing process. Among them, 14 volatile compounds, such as linalool, ß-ionone, dimethyl sulfide, and 1-octen-3-ol, stood out as characteristic components responsible for SFABT with relative odor activity values exceeding one. This study serves as an invaluable theoretical platform for strategic controllable processing of superior-quality black tea.

Comparative Analysis of Volatile Compounds in the Flower Buds of Three Panax Species Using Fast Gas Chromatography Electronic Nose, Headspace-Gas Chromatography-Ion Mobility Spectrometry, and Headspace Solid Phase Microextraction-Gas Chromatography-Mass Spectrometry Coupled with Multivariate Statistical Analysis.

The flower buds of three Panax species (PGF: P. ginseng; PQF: P. quinquefolius; PNF: P. notoginseng) widely consumed as health tea are easily confused in market circulation. We aimed to develop a green, fast, and easy analysis strategy to distinguish PGF, PQF, and PNF. In this work, fast gas chromatography electronic nose (fast GC e-nose), headspace-gas chromatography-ion mobility spectrometry (HS-GC-IMS), and headspace solid phase microextraction-gas chromatography-mass spectrometry (HS-SPME-GC-MS) were utilized to comprehensively analyze the volatile organic components (VOCs) of three flowers. Meanwhile, a principal component analysis (PCA) and heatmap were applied to distinguish the VOCs identified in PGF, PQF, and PNF. A random forest (RF) analysis was used to screen key factors affecting the discrimination. As a result, 39, 68, and 78 VOCs were identified in three flowers using fast GC e-nose, HS-GC-IMS, and HS-SPME-GC-MS. Nine VOCs were selected as potential chemical markers based on a model of RF for distinguishing these three species. Conclusively, a complete VOC analysis strategy was created to provide a methodological reference for the rapid, simple, and environmentally friendly detection and identification of food products (tea, oil, honey, etc.) and herbs with flavor characteristics and to provide a basis for further specification of their quality and base sources.

Comparative study on Angelica sinensis after different processing with yellow rice wine in color, aromas, chemical components, and antioxidant activities.

This study aimed to explore the differences in raw Angelica Sinensis (RAS), wine washing AS (WAS), and wine stir-frying AS (WSAS). The results showed there were differences among the three AS in color and aroma, and 34 aroma compounds were identified. The content determination results revealed the ferulic acid and Z-ligustilide levels of RAS decreased after processing, and those in WAS were higher than in WSAS. Furthermore, 85 representative common components and 37 unique components were tentatively identified in three AS. Finally, the free radical scavenging assay results indicated the antioxidant capacity of RAS was reduced after processing, and the antioxidant capacity of WAS was better than WSAS. Collectively, the RAS undergoes significant changes in color, aromas, components, and antioxidant ability after processing, and the different processing methods also result in significant differences between WAS and WSAS.

Correlations between flavor and fermentation days and changes in quality-related physiochemical characteristics of fermented Aurantii Fructus.

The effects of different fermentation times (0, 1, 2, 3, 4, and 5 days) on the physicochemical properties and flavor components of fermented Aurantii Fructus (FAF) were evaluated. Component analysis identified 66 compounds in positive ion mode and 32 compounds in negative ion mode. Flash GC e-nose results showed that propanal, (+)-limonene and n-nonanal may be the flavor characteristic components that distinguish FAF with different fermentation days. Furthermore, we found that the change of total flavonoid content was closely related to colony growth vitality. The total flavonoid content of FAF gradually decreased from 3rd day and then increased from 5th day (3rd day: 0.766 ± 0.123 mg/100 g; 4th day: 0.464 ± 0.001 mg/100 g; 5th day: 0.850 ± 0.192 mg/100 g). Finally, according to antioxidant activity correlation analysis, meranzin, (+)-limonene and total flavonoids were found to be the key substances affecting the fermentation days of FAF. Overall, the optimal fermentation time for FAF was 4 days.

Characterization of volatile metabolites in Pu-erh teas with different storage years by combining GC-E-Nose, GC-MS, and GC-IMS.

Storage time is one of the important factors affecting the aroma quality of Pu-erh tea. In this study, the dynamic changes of volatile profiles of Pu-erh teas stored for different years were investigated by combining gas chromatography electronic nose (GC-E-Nose), gas chromatography-mass spectrometry (GC-MS), and gas chromatography-ion mobility spectrometry (GC-IMS). GC-E-Nose combined with partial least squares-discriminant analysis (PLS-DA) realized the rapid discrimination of Pu-erh tea with different storage time (R2Y = 0.992, Q2 = 0.968). There were 43 and 91 volatile compounds identified by GC-MS and GC-IMS, respectively. A satisfactory discrimination (R2Y = 0.991, and Q2 = 0.966) was achieved by using PLS-DA based on the volatile fingerprints of GC-IMS. Moreover, according to the multivariate analysis of VIP > 1.2 and univariate analysis of p < 0.05, 9 volatile components such as linalool and (E)-2-hexenal were selected as key variables to distinguish Pu-erh teas with different storage years. The results provide theoretical support for the quality control of Pu-erh tea.

Characterization of the key odorants in floral aroma green tea based on GC-E-Nose, GC-IMS, GC-MS and aroma recombination and investigation of the dynamic changes and aroma formation during processing.

To characterize the key odorants of floral aroma green tea (FAGT) and reveal its dynamic evolution during processing, the volatile metabolites in FAGT during the whole processing were analyzed by integrated volatolomics techniques, relative odor activity value (rOAV), aroma recombination, and multivariate statistical analysis. The volatile profiles undergone significant changes during processing, especially in the withering and fixation stages. A total of 184 volatile compounds were identified (∼53.26% by GC-MS). Among them, 7 volatiles with rOAV > 1 were identified as characteristic odorants of FAGT, and most of these compounds reached the highest in withering stage. According to the formation pathways, these key odorants could be divided into four categories: fatty acid-derived volatiles, glycoside-derived volatiles, amino acid-derived volatiles, and carotenoid-derived volatiles. Our study provides a comprehensive strategy to elucidate changes in volatile profiles during processing and lays a theoretical foundation for the targeted processing of high-quality green tea.

Characterization and intrinsic quality correlation of raw and vinegar-processed Curcumae Radix.

Among the existing criteria, the traits of Curcumae Radix (CW) rely on traditional empirical identification, and the correlation between extrinsic traits and intrinsic components hasn't been systematically studied. In this study, a spectrophotometer, HS-GC-MS, and fast GC e-nose, combined with chemometrics were used to correlate the trait characteristics and intrinsic qualities of CW and vinegar-processed CW (VCW). The overall color of VCW was dark, red, and yellow, but the powder color was similar and difficult to distinguish with the naked eye. The exclusive discriminatory functional equations were established for the characterization between the two. 31 odor components were identified by fast GC e-nose. After vinegar preparation, 3 odor components disappeared and 8 odor components were generated. In addition, there were significant differences between the common components. 27 volatile components were identified by HS-GC-MS, 21 of which were terpenoids. Meanwhile, the difference discrimination models could be used for the rapid and accurate identification of CW and VCW. Through the comprehensive analysis of the color-odor-component, it was speculated that curzerene, germacrene D, and germacrone were potential chemical markers. The quality evaluation model based on the color-odor-composition of trait characteristics combined with internal components provided a basis for rapid identification and quality control of CW and VCW.

E-eye, flash GC E-nose and HS-GC-MS combined with chemometrics to identify the adulterants and geographical origins of Ziziphi Spinosae Semen.

Ziziphi Spinosae Semen (ZSS), a valuable seed food, has faced increasing authenticity issues. In this study, the adulterants and geographical origins of ZSS were successfully identified by electronic eye, flash gas chromatography electronic nose (Flash GC e-nose) and headspace gas chromatography-mass spectrometry (HS-GC-MS). As a result, there were color differences between ZSS and adulterants, mainly represented by the a* value of ZSS was less than adulterants. In ZSS, 29 and 32 compounds were detected by Flash GC e-nose and HS-GC-MS. Spicy, sweety, fruity and herbal were the main flavor of ZSS. Five compounds were determined to be responsible for flavor differences between different geographical origins. In the HS-GC-MS analysis, the relative content of Hexanoic acid was the highest in ZSS from Hebei and Shandong, while 2,4-Decadien-1-ol was the highest in Shaanxi. Overall, this study provided a meaningful strategy for addressing authenticity problems of ZSS and other seed foods.

Performance evaluation of E-nose and E-tongue combined with machine learning for qualitative and quantitative assessment of bear bile powder.

Bear bile powder (BBP) is a valuable animal-derived product with a huge adulteration problem on market. It is a crucially important task to identify BBP and its counterfeit. Electronic sensory technologies are the inheritance and development of traditional empirical identification. Considering that each drug has its own specific odor and taste characteristics, electronic tongue (E-tongue), electronic nose (E-nose) and GC-MS were used to evaluate the aroma and taste of BBP and its common counterfeit. Two active components of BBP, namely tauroursodeoxycholic acid (TUDCA) and taurochenodeoxycholic acid (TCDCA) were measured and linked with the electronic sensory data. The results showed that bitterness was the main flavor of TUDCA in BBP, saltiness and umami were the main flavor of TCDCA. The volatiles detected by E-nose and GC-MS were mainly aldehydes, ketones, alcohols, hydrocarbons, carboxylic acids, heterocyclic, lipids, and amines, mainly earthy, musty, coffee, bitter almond, burnt, pungent odor descriptions. Four different machine learning algorithms (backpropagation neural network, support vector machine, K-nearest neighbor, and random forest) were used to identify BBP and its counterfeit, and the regression performance of these four algorithms was also evaluated. For qualitative identification, the algorithm of random forest has shown the best performance, with 100% accuracy, precision, recall and F1-score. Also, the random forest algorithm has the best R2 and the lowest RMSE in terms of quantitative prediction.

Headspace GC/MS and fast GC e-nose combined with chemometric analysis to identify the varieties and geographical origins of ginger (Zingiber officinale Roscoe).

Food authenticity regarding different varieties and geographical origins is increasingly becoming a concern for consumers. In this study, headspace gas chromatography-mass spectrometry (HS-GC-MS) and fast gas chromatography electronic nose (fast GC e-nose) were used to successfully distinguish the varieties and geographical origins of dried gingers from seven major production areas in China. By chemometric analysis, a distinct separation between the two varieties of ginger was achieved based on HS-GC-MS. Furthermore, flavor information extracted by fast GC e-nose realized the discrimination of geographical origins, and some potential flavor components were selected as important factors for origin certification. Moreover, several pattern recognition algorithms were compared in varietal and regional identification, and random forest (RF) led to the highest accuracies for discrimination. Overall, a rapid and precise method combining multivariate chemometrics and algorithms was developed to determine varieties and geographical origins of ginger, and it could also be applied to other agricultural products.

Comparison of Different Drying Methods on the Volatile Components of Ginger (Zingiber officinale Roscoe) by HS-GC-MS Coupled with Fast GC E-Nose.

Ginger (Zingiber officinale Roscoe) is one of the most popular spices in the world, with its unique odor. Due to its health benefits, ginger is also widely used as a dietary supplement and herbal medicine. In this study, the main flavor components of gingers processed by different drying methods including hot air drying, vacuum drying, sun-drying, and vacuum-freeze drying, were identified on the basis of headspace-gas chromatography coupled with mass spectrometry (HS-GC-MS) and fast gas chromatography electronic-nose (fast GC e-nose) techniques. The results showed that the ginger dried by hot air drying exhibited high contents of volatile compounds and retained the richest odor in comparison with those dried by other methods, which indicated that hot air drying is more suitable for the production of dried ginger. Sensory description by fast GC e-nose exhibited that ginger flavor was mainly concentrated in the spicy, sweet, minty, fruity, and herbaceous odor. The relative content of the zingiberene was significantly higher in the hot air drying sample than those by other methods, suggesting that dried ginger by hot air drying can retain more unique spicy and pungent odorants. Furthermore, the results of chemometrics analyses showed that the main variance components among the samples by different drying methods were α-naginatene, (+)-cyclosativene, and sulcatone in HS-GC-MS analysis, and α-terpinen-7-al, dimethyl sulfide, and citronellal in fast GC e-nose analysis. For comparison of fresh and dried gingers, terpinolene, terpinen-4-ol, 2,4-decadienal, (E, Z)-, and linalool were considered the main variance components. This study generated a better understanding of the flavor characteristics of gingers by different drying methods and could provide a guide for drying and processing of ginger.

Characterization and Discrimination of Apples by Flash GC E-Nose: Geographical Regions and Botanical Origins Studies in China.

Forty-one apple samples from 7 geographical regions and 3 botanical origins in China were investigated. A total of 29 volatile compounds have been identified by flash GC E-nose. They are 17 esters, 5 alcohols, 3 aldehydes, 1 ketone, and 3 others. A principal component analysis was employed to study the relationship between varieties and volatiles. A partial least squares discriminant analysis (PLS-DA), stepwise linear discriminant analysis (SLDA), and decision tree (DT) are used to discriminate apples from 4 geographical regions (34 apple samples) and 3 botanical origins (36 apple samples). The most influential markers identified by PLS-DA are 2-hexadecanone, methyl decanoate, tetradecanal, 1,8-cineole, hexyl 2-butenoate, (Z)-2-octenal, methyl 2-methylbutanoate, ethyl butyrate, dimethyl trisulfide, methyl formate, ethanol, S(-)2-methyl-1-butanol, ethyl acetate, pentyl acetate, butyl butanoate, butyl acetate, and ethyl octanoate. From the present work, SLDA reveals the best discrimination results in geographical regions and botanical origins, which are 88.2% and 88.9%, respectively. Although machine learning DT is attempted to classify apple samples, the results are not satisfactory.

Insight into aroma dynamic changes during the whole manufacturing process of chestnut-like aroma green tea by combining GC-E-Nose, GC-IMS, and GC × GC-TOFMS.

Processing is the crucial factor for green tea aroma quality. In this study, the aroma dynamic changes throughout the manufacturing process of chestnut-like aroma green tea were investigated with gas chromatography electronic nose (GC-E-Nose), gas chromatography-ion mobility spectrometry (GC-IMS), and comprehensive two-dimensional gas chromatography coupled to time-of-flight mass spectrometry (GC × GC-TOFMS). GC-IMS identified 33 volatile compounds while GC × GC-TOFMS identified 211 volatile components. Drying exerted the greatest influence on the volatile components of chestnut-like aroma green tea, and promoted the generation of heterocyclic compounds and sulfur compounds which were commonly generated via the Maillard reaction during the roasting stage. A large number of heterocyclic compounds such as 1-methyl-1H-pyrrole, pyrrole, methylpyrazine, furfural, 2-ethyl-5-methylpyrazine, 1-ethyl-1H-pyrrole-2-carboxaldehyde, and 3-acetylpyrrole were newly formed during the drying process. This study also validated the suitability of GC-E-Nose combined with GC-IMS and GC × GC-TOFMS for tracking the changes in volatile components of green tea throughout the manufacturing process.

Physicochemical parameters combined flash GC e-nose and artificial neural network for quality and volatile characterization of vinegar with different brewing techniques.

Vinegar is a kind of traditional fermented food, there are significant variances in quality and flavor due to differences in raw ingredients and processes. The quality assessment and flavor characteristics of 69 vinegar samples with 5 brewing processes were analyzed by physicochemical parameters combined with flash gas chromatography (GC) e-nose. The evaluation system of quality and the detection method of flavor profile were established. 17 volatile flavor compounds and potential flavor differential compounds of each brewing process were identified. The artificial neural network (ANN) analysis model was established based on the physicochemical parameters and the analysis of flash GC e-nose. Although the physicochemical parameters were more intuitive in quality evaluating, the flash GC e-nose could better reflect the flavor characteristics of vinegar samples and had better fitting, prediction and discrimination ability, the correct rates of training and prediction of flash GC e-nose trained ANN model were 98.6% and 96.7%, respectively.