Characterization of selected Chinese soybean paste based on flavor profiles using HS-SPME-GC/MS, E-nose and E-tongue combined with chemometrics

Investigating the triple-frequency ultrasound-assisted fermented rice lees: Impact on physicochemical, structural, morphological, and metabolic properties

This study examined the effect of triple-frequency ultrasound treatment (TFUT)-assisted lactic acid bacteria (LAB-L. plantarum and L. helveticus fermentation for 24-h and 48-h) on the chemical, structural, morphological, metabolic, and sensory properties of rice lees (RL). Ultrasonicated-assisted RL fermented with L. helveticus (URLH-48) had the greatest total phenolic contents (TPC) (112.1 mg GAE/m), total flavonoid contents (TFC) (163.62 mg RE/mL), and proanthocyanidin contents (PAC) (728.34 mg/mL) compared to RL (control) and other treatments. Furthermore, URLH-48 demonstrated an increase in the concentrations of quinic acid (486.96 mg/L) and gallic acid (201.42 mg/L), as determined by HPLC-UV analysis. Additionally, FTIR spectral analyses demonstrated that TFUT-assisted fermented RL exhibited a greater degree of flexibility and mobility in its secondary structures compared to RL (control). The amino acid's profile of RL was significantly increased as LAB degraded the RL proteins, and the function of TFUT facilitates bacterial activity. Moreover, SEM observation provides convincing evidence that TFUT improves and speeds up the breakdown of proteins' structures, resulting in irregular and dense structures. Correlation and molecular docking research suggest that TFUT has different impacts on specific RL and fermented RL characteristics. The analyses conducted using GC-MS and E-nose indicated the generation of highly volatile flavor compounds through fermentation. The sensory evaluation results show an increase in liking following fermentation and TFUT-assisted fermentation, which is attributed to the production of flavor compounds. Consequently, the combined use of TFUT-assisted fermentation markedly improves the polyphenolic composition, antioxidant capacity, flavor profile, micromorphology, and overall quality of RL, which may enhance their functionality and broaden their applications in the food industry.

Analysis of Flavor Differences in Yak Milk Powder at Different Milk Production Stages by Headspace Solid-Phase Microextraction and Gas Chromatography-Mass Spectrometry

The aroma of yak milk powder is a crucial sensory indicator for evaluating its quality and flavor. Yak milk powders collected from different lactation periods exhibit distinct flavors, but no studies have thoroughly investigated the aroma characteristics and variation patterns of yak milk powders across these periods. This study identified and analyzed the volatile compounds in freeze-dried colostrum powder (YCSP), freeze-dried mature milk powder (YMMP), and freeze-dried ending milk powder (YEMP) using headspace solid-phase microextraction combined with gas chromatography-mass spectrometry (HS-SPME-GC-MS) and multivariate statistical analysis. A total of 48 volatile compounds were identified, with significant differences in the types and contents of these compounds across the three samples. Compared to YCSP and YEMP, YMMP contained higher levels of acids and esters, while the levels of alkanes and alcohols were lower. Principal component analysis (PCA), orthogonal partial least squares discriminant analysis (OPLS-DA), and hierarchical clustering heatmap analysis revealed a high degree of differentiation and notable variation in volatile compounds between the samples from different lactation periods. Key compounds such as aldehydes, alcohols, and esters were found to distinguish the lactation stages, with certain compounds more prevalent in colostrum and others in mature and ending milk. These findings suggest that the methodologies employed-HS-SPME-GC-MS combined with multivariate analysis-can effectively distinguish flavor differences among yak milk powders from different lactation periods. This approach allows for the rapid and comprehensive analysis of volatile components in milk powders, aiding in the identification of collection periods and providing valuable insights for improving the flavor quality of dairy products. Furthermore, the results can benefit the dairy industry by enhancing product development, quality control, and flavor profiling of milk-based products across different stages of lactation.

Formulation of a novel hot pot dipping sauce enriched with pepper seed press cake: Physical properties and flavor characteristics

Novel hot pot dipping sauces enriched with pepper seed press cake (PSPC) in five proportions were prepared and evaluated in terms of their physical properties and flavor characteristics. The findings indicated that enriching the sauce increased the content of palmitic and linoleic acids, enhanced storage stability, and improved the rheological behavior and textural properties. The maximum concentration of N-heterocyclic compounds was detected when PSPC was added at 5 g/100 g and 10 g/100 g. A suitable amount of PSPC could improve the mouthfeel and intensify the flavors of umami and saltiness. In comparing sauces with different amounts of PSPC added (0-20 g/100 g), the quality, aroma, and taste were better and overall acceptance was highest when PSPC was added in the range of 5 g/100 g to 10 g/100 g. This study provides a possible application of PSPC for improving the flavor, texture, nutritional quality, and storage stability of hot pot dipping sauce.

Multi-frequency sono-fermentation with mono and co-cultures of LAB synergistically enhance mulberry juice: Evidence from metabolic, micromorphological, sensorial, and computational approaches

The effect of multi-frequency ultrasound-assisted (20/28/40 KHz) lactic acid bacteria (LAB- Lacticaseibacillus casei, Lactiplantibacillus plantarum, Lacticaseibacillus paracasei, Lactobacillus acidophilus, and Lactobacillus helveticus) fermentation (mono and co-cultures) on the metabolic, structural, micromorphological, and sensorial properties of mulberry juice were evaluated. Results indicated that multi-frequency ultrasound-assisted fermentation significantly modified the microstructure of mulberry juice powder, resulting in more porous and rougher surfaces with irregular indentations. Total phenolic content in the best-performing sample (S10) increased to 365.36 mg GAE/mL, while total flavonoid content rose to 139.20 mg RE/mL (p < 0.05). Antioxidant activity, as measured by DPPH and FRAP assays, also showed considerable improvement, with DPPH scavenging activity increasing to 87.45 % and FRAP-value to 3.27 mM TE/mL (p < 0.05). Additionally, HPLC-UV analysis revealed that the amendment in the concentrations of cyanidin-3-rutinoside (47.47 mg/L) and peonidin-3-O-glucoside (66.86 mg/L) in the S2-based sample. E-nose analysis demonstrated intense flavor profiles in fermented samples, particularly in sample S15. Sensory evaluation also highlighted that the fruity and floral aromas in co-culture fermented samples were enhanced, notably in S10, S7, and S14. Thus, combining multifrequency ultrasonication and fermentation significantly enhances the antioxidants capacity, flavor profile, micro-morphology, and overall quality of mulberry juice.

Correlation between physicochemical properties, flavor characteristics and microbial community structure in Dushan shrimp sour paste

Dushan shrimp sour paste (DSSP), a traditional Guizhou condiment, and its unique flavor is determined by the fermentation microbiota. However, the relationship between the microbiota structure and its flavor remains unclear. This study identified 116 volatile flavor compounds using electronic nose and headspace solid-phase microextraction-gas chromatography mass spectrometry (HS-SPME-GC-MS) techniques, of which 19 were considered as key flavor compounds, mainly consisting of 13 esters and 1 alcohol. High-throughput sequencing technique, the bacterial community structure of nine groups of DSSPs was determined. Further analysis revealed Vagococcus, Lactococcus, and Tepidimicrobium as key bacteria involved in flavor formation. This study contributes to our understanding of the relationship between bacterial communities and the flavor formation, and provides guidance for screening starter culture that enhance the flavor of DSSP in industrial production.

Characterizing the flavor profiles of Linjiangsi broad bean (Vicia faba L.) paste using bionic sensory and multivariate statistics analyses based on ripening time and fermentation environment

The flavor profile of Linjiangsi broad bean paste (LBBP) is significantly influenced by fermentation environment and ripening time. This study aims to investigate the flavor of outdoor-treated (OT) and indoor-treated (IT) LBBP. Gas chromatography-mass spectrometry, electronic-nose, and electronic-tongue, combined with multivariate statistical analyses, were employed to identify the characteristic flavor profiles of OT and IT LBBP in ripening periods of one and three years. Overall, 95 volatile organic compounds (VOCs) were identified. Relative odor activity values and multivariate statistical analysis indicated that nine VOCs were responsible for the flavor differences. The most abundant VOCs in OT were aldehydes, providing caramel and nutty flavors, whereas the most abundant compounds in IT were esters, contributing fruity flavors to LBBP. Notably, three years of ripening significantly intensified the characteristic flavors of both OT and IT. These findings may elucidate the ripening time and fermentation environment effect on LBBP characteristic flavor profiles.

Effects of Wickerhamomyces anomalus Co-Fermented with Saccharomyces cerevisiae on Volatile Flavor Profiles during Steamed Bread Making Using Electronic Nose and HS-SPME-GC-MS

Steamed bread is a traditional staple food in China, and it has gradually become loved by people all over the world because of its healthy production methods. With the improvement in people's living standards, the light flavor of steamed bread fermented by single yeast cannot meet people's needs. Multi-strain co-fermentation is a feasible way to improve the flavor of steamed bread. Here, the dynamic change profiles of volatile substances in steamed bread co-fermented by Saccharomyces cerevisiae SQJ20 and Wickerhamomyces anomalus GZJ2 were analyzed using the electronic nose (E-nose) and headspace solid-phase microextraction combined with gas chromatography-mass spectrometry (HS-SPME-GC-MS). The five detectors of the E-nose rapidly detected the changes in volatile substances in different dough or steamed bread with the highest response value in co-fermented dough. A total of 236 volatile substances were detected in all the samples using HS-SPME-GC-MS, and alcohols were the most variable component, especially Phenylethyl alcohol. Significantly, more alcohols and esters were upregulated in co-fermented dough, and the addition of W. anomalus GZJ2 improved the key volatile aroma compounds of steamed bread using the relative odor activity value method (ROAV), especially the aldehydes and alcohols. Moreover, these key volatile aroma compounds can be quickly distinguished using the W2S detector of the E-nose, which can be used for the rapid detection of aroma components in steamed bread.

Preparation of edible antibacterial films based on corn starch /carbon nanodots for bioactive food packaging

Packaging plays an important role in protecting food from environmental impacts. However, traditional petroleum-based packaging has difficulty in meeting the antimicrobial and antioxidant requirements of prepared foods. This study introduced carbon dots (CDs), prepared by using carrot as a precursor, into corn starch (CS) to construct a bio-friendly composite film with high freshness retention properties. The scavenging of DPPH radicals reached 92.77 % at a CDs concentration of 512 µg/mL, and the antimicrobial activity of CS/5% CDs against Escherichia coli and Staphylococcus aureus was increased to 99.9 %. Notably, the homogeneous doping of CDs creates a dense surface and high carbon content inside the film, which promotes the elasticity and thermal stability of the composite film. Finally, we encapsulated deep-fried meatballs in CS-CDs films. The results showed that the CS-CDs films effectively protected the quality of deep-fried meatballs, and have excellent potential for application in food preservation.

Evaluation of a Voltametric E-Tongue Combined with Data Preprocessing for Fast and Effective Machine Learning-Based Classification of Tomato Purées by Cultivar

The potential of a voltametric E-tongue coupled with a custom data pre-processing stage to improve the performance of machine learning techniques for rapid discrimination of tomato purées between cultivars of different economic value has been investigated. To this aim, a sensor array with screen-printed carbon electrodes modified with gold nanoparticles (GNP), copper nanoparticles (CNP) and bulk gold subsequently modified with poly(3,4-ethylenedioxythiophene) (PEDOT), was developed to acquire data to be transformed by a custom pre-processing pipeline and then processed by a set of commonly used classifiers. The GNP and CNP-modified electrodes, selected based on their sensitivity to soluble monosaccharides, demonstrated good ability in discriminating samples of different cultivars. Among the different data analysis methods tested, Linear Discriminant Analysis (LDA) proved to be particularly suitable, obtaining an average F1 score of 99.26%. The pre-processing stage was beneficial in reducing the number of input features, decreasing the computational cost, i.e., the number of computing operations to be performed, of the entire method and aiding future cost-efficient hardware implementation. These findings proved that coupling the multi-sensing platform featuring properly modified sensors with the custom pre-processing method developed and LDA provided an optimal tradeoff between analytical problem solving and reliable chemical information, as well as accuracy and computational complexity. These results can be preliminary to the design of hardware solutions that could be embedded into low-cost portable devices.

Characterization of lamb shashliks with different roasting methods by intelligent sensory technologies and GC-MS to simulate human muti-sensation: Based on multimodal deep learning

To simulate the functions of olfaction, gustation, vision, and oral touch, intelligent sensory technologies have been developed. Headspace solid-phase microextraction gas chromatography-mass spectrometry (HS-SPME-GC/MS) with electronic noses (E-noses), electronic tongues (E-tongues), computer vision (CVs), and texture analyzers (TAs) was applied for sensory characterization of lamb shashliks (LSs) with various roasting methods. A total of 56 VOCs in lamb shashliks with five roasting methods were identified by HS-SPME/GC-MS, and 21 VOCs were identified as key compounds based on OAV (>1). Cross-channel sensory Transformer (CCST) was also proposed and used to predict 19 sensory attributes and their lamb shashlik scores with different roasting methods. The model achieved satisfactory results in the prediction set (R2 = 0.964). This study shows that a multimodal deep learning model can be used to simulate assessor, and it is feasible to guide and correct sensory evaluation.

Characterization of Flavor Profile of Sauced Pork from Different Regions of China Based on E-Nose, E-Tongue and Gas Chromatography-Ion Mobility Spectroscopy

This study aimed to investigate the volatile flavor compounds and tastes of six kinds of sauced pork from the southwest and eastern coastal areas of China using gas chromatography-ion mobility spectroscopy (GC-IMS) combined with an electronic nose (E-nose) and electronic tongue (E-tongue). The results showed that the combined use of the E-nose and E-tongue could effectively identify different kinds of sauced pork. A total of 52 volatile flavor compounds were identified, with aldehydes being the main flavor compounds in sauced pork. The relative odor activity value (ROAV) showed that seven key volatile compounds, including 2-methylbutanal, 2-ethyl-3, 5-dimethylpyrazine, 3-octanone, ethyl 3-methylbutanoate, dimethyl disulfide, 2,3-butanedione, and heptane, contributed the most to the flavor of sauced pork (ROAV ≥1). Multivariate data analysis showed that 13 volatile compounds with the variable importance in projection (VIP) values > 1 could be used as flavor markers to distinguish six kinds of sauced pork. Pearson correlation analysis revealed a significant link between the E-nose sensor and alcohols, aldehydes, terpenes, esters, and hetero-cycle compounds. The results of the current study provide insights into the volatile flavor compounds and tastes of sauced pork. Additionally, intelligent sensory technologies can be a promising tool for discriminating different types of sauced pork.

HS-SPME-GC-MS Combined with Orthogonal Partial Least Squares Identification to Analyze the Effect of LPL on Yak Milk's Flavor under Different Storage Temperatures and Times

Flavor is a crucial parameter for assessing the sensory quality of yak milk. However, there is limited information regarding the factors influencing its taste. In this study, the effects of endogenous lipoprotein lipase (LPL) on the volatile flavor components of yak milk under storage conditions of 4 °C, 18 °C and 65 °C were analyzed via headspace solid-phase microextraction gas chromatography-mass spectrometry (HS-SPME-GC-MS) combined with orthogonal partial least-squares (OPSL) discrimination, and the reasons for the changes in yak milk flavors were investigated. Combined with the difference in the changes in volatile flavor substance before and after the action of LPL, LPL was found to have a significant effect on the flavor of fresh yak milk. Fresh milk was best kept at 4 °C for 24 h and pasteurized for more than 24 h. Principal component analysis (PCA) and orthogonal partial least squares discriminant analysis (OPLS-DA) were employed to characterize the volatile components in yak milk under various treatment conditions. Twelve substances with significant influence on yak milk flavor were identified by measuring their VIP values. Notably, 2-nonanone, heptanal, and ethyl caprylate exhibited OAV values greater than 1, indicating their significant contribution to the flavor of yak milk. Conversely, 4-octanone and 2-heptanone displayed OAV values between 0.1 and 1, showing their important role in modifying the flavor of yak milk. These findings can serve as monitoring indicators for assessing the freshness of yak milk.

Insights into the volatile flavor and quality profiles of loquat (Eriobotrya japonica Lindl.) during shelf-life via HS-GC-IMS, E-nose, and E-tongue

Loquat fruits are among the most popular Chinese fruits because of their unique taste and aroma. The quality profiles of these fruits during 18 days of shelf-life at 20 °C were elucidated by headspace gas chromatography-ion mobility spectrometry (HS-GC-IMS), E-nose, and E-tongue. During shelf-life period, the properties and variations of 43 (20 aldehydes, 7 esters, 6 ketones, 1 alcohol, and 1 furan) volatile flavored compounds were determined by GC-IMS, which showed that the contents of methyl 3-methyl butanoate, ethyl acetate, and dimethyl ketone gradually decrease with prolonged shelf-life time, while (E)-2-heptenal, heptanal, (E)-2-pentenal, 1-penten-3-one 3-pentanone and 2-pentylfuran increase. The PCA based on the signal intensity of GC-IMS and E-nose, revealed that loquat fruits are well distinguished at different shelf-life times. The taste profile alternates as the storage time increases, along with higher pH, and lower amounts of total soluble solids, vitamin C, and total phenolics. The visual plots of GC-IMS, E-nose, and E-tongue had good consistency, and they characterized the aroma characteristics of loquat fruits well during different shelf-life periods. The findings of this research provide a useful understanding of the flavors of loquat fruits during their prolonged shelf-life, and a potential research basis for advancements in the loquat industry.

Towards Verifying the Imported Soybeans of China Using Stable Isotope and Elemental Analysis Coupled with Chemometrics

Verifying the geographical origin of soybeans (Glycine max [Linn.] Merr.) is a major challenge as there is little available information regarding non-parametric statistical origin approaches for Chinese domestic and imported soybeans. Commercially procured soybean samples from China (n = 33) and soybeans imported from Brazil (n = 90), the United States of America (n = 6), and Argentina (n = 27) were collected to characterize different producing origins using stable isotopes (δ2H, δ18O, δ15N, δ13C, and δ34S), non-metallic element content (% N, % C, and % S), and 23 mineral elements. Chemometric techniques such as principal component analysis (PCA), linear discriminant analysis (LDA), and BP-artificial neural network (BP-ANN) were applied to classify each origin profile. The feasibility of stable isotopes and elemental analysis combined with chemometrics as a discrimination tool to determine the geographical origin of soybeans was evaluated, and origin traceability models were developed. A PCA model indicated that origin discriminant separation was possible between the four soybean origins. Soybean mineral element content was found to be more indicative of origin than stable isotopes or non-metallic element contents. A comparison of two chemometric discriminant models, LDA and BP-ANN, showed both achieved an overall accuracy of 100% for testing and training sets when using a combined isotope and elemental approach. Our findings elucidate the importance of a combined approach in developing a reliable origin labeling method for domestic and imported soybeans in China.

Advancing Insights into Probiotics during Vegetable Fermentation

Fermented vegetables have a long history and are enjoyed worldwide for their unique flavors and health benefits. The process of fermentation improves the nutritional value, taste, and shelf life of foods. Microorganisms play a crucial role in this process through the production of metabolites. The flavors of fermented vegetables are closely related to the evaluation and succession of microbiota. Lactic acid bacteria (LABs) are typically the dominant bacteria in fermented vegetables, and they help inhibit the growth of spoilage bacteria and maintain a healthy gut microbiota in humans. However, homemade and small-scale artisanal products rely on spontaneous fermentation using bacteria naturally present on fresh vegetables or from aged brine, which may introduce external microorganisms and lead to spoilage and substandard products. Hence, understanding the role of LABs and other probiotics in maintaining the quality and safety of fermented vegetables is essential. Additionally, selecting probiotic fermentation microbiota and isolating beneficial probiotics from fermented vegetables can facilitate the use of safe and healthy starter cultures for large-scale industrial production. This review provides insights into the traditional fermentation process of making fermented vegetables, explains the mechanisms involved, and discusses the use of modern microbiome technologies to regulate fermentation microorganisms and create probiotic fermentation microbiota for the production of highly effective, wholesome, safe, and healthy fermented vegetable foods.

Characterization of different grades of Jiuqu hongmei tea based on flavor profiles using HS-SPME-GC-MS combined with E-nose and E-tongue

In order to distinguish different grades of Jiuqu hongmei tea (black tea), four different grades of Jiuqu hongmei tea were used as materials in this study: Super Grade (SuG), First Grade (FG), Second Grade (SG), and Third Grade (TG). HS-SPME-GC-MS combined with electronic nose (E-nose) and electronic tongue (E-tongue) technology was used to detect and analyze tea samples. The results showed that 162 volatile substances were identified, mainly alcohols, followed by hydrocarbons, aldehydes, ketones and esters. Twenty-nine volatile compounds were found in all grades of tea samples. The results of heat map analysis showed that the relative contents of five volatile compounds in different grades of Jiuqu hongmei tea were positively correlated with the grades of Jiuqu hongmei tea. By orthogonal partial least squares discriminant analysis (OPLS-DA), 35 different compounds of SuG and FG, 30 different compounds of SG and TG, 34 different compounds of FG and SG were found. Overall, the results indicated that there were significant differences in volatile compounds among different grades of Jiuqu hongmei tea, and the use of HS-SPME-GC-MS combined with E-nose and E-tongue could provide a scientific reference method as an effective tool for detecting flavor characteristics of other types of black tea grades.

Optimization of thermal desorption conditions of stir bar sorptive extraction facilitated by machine learning

Stir bar sorptive extraction is an effective technique for trapping odorants, but there are limited studies on the effect of varying thermal desorption conditions on desorption efficiency of odorants. Therefore, we conducted this study to explore the relationship between desorption conditions and desorption efficiency for 18 odorants with diverse physicochemical properties using instrumental analysis and mathematical modeling. We trained four types of machine learning models using a dataset comprising 864 different combinations of four desorption conditions (each three levels) and physicochemical properties. The prediction value of the selected model was validated using a validation dataset of six new odorants. The Random Forest model had the highest performance (R = 0.910). The order of feature importance using this model was as follows: cryo-focusing temperature, molecular weight, log P, boiling point, desorption temperature, desorption time, and helium flow. For testing on new odorants, the correlations between predicted and experimental data for terpene (R = 0.99), alcohol (R = 0.98), ester (R = 0.92), sulfide (R = 0.89), phenol (R = 0.88), and aldehyde (R = 0.61) were determined.

Flavor Characterization of Traditional Fermented Soybean Pastes from Northeast China and Korea

This study compares the physicochemical properties, taste, and volatile compounds of Northeastern Chinese dajiang (C) and Korean doenjang (K) and distinguishes the discriminant volatile metabolites between them. The result revealed that compared to group C, group K exhibited more similar physicochemical properties and had lower pH, moisture, and amino acid nitrogen content, while demonstrating higher titratable acidity, salt content, and reduced sugar content. The electronic tongue analysis showed that the saltiness and umami of soybean pastes had high response values, enabling clear differentiation of the overall taste between the two types of soybean pastes. A total of 71 volatile substances from the soybean pastes were identified through solid-phase microextraction gas chromatography-mass spectrometry. Furthermore, orthogonal partial least squares discriminant analysis revealed 19 volatile compounds as differentially flavored metabolites. Our study provides a basis for explaining the differences in flavor difference of Northeastern Chinese dajiang and Korean doenjang from the perspective of volatile metabolites.

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.

Combining e-nose and e-tongue for improved recognition of instant starch noodles seasonings

Seasonings play a key role in determining sensory attributes of instant starch noodles. Controlling and improving the quality of seasoning is becoming important. In this study, five different brands along with fifteen instant starch noodles seasonings (seasoning powder, seasoning mixture sauce and the mixture of powder and sauce) were characterized by electronic nose (e-nose) and electronic tongue (e-tongue). Feature-level fusion for the integration of the signals was introduced to integrate the e-nose and e-tongue signals, aiming at improving the performances of identification and prediction models. Principal component analysis (PCA) explained over 85.00% of the total variance in e-nose data and e-tongue data, discriminated all samples. Multilayer perceptron neural networks analysis (MLPN) modeling demonstrated that the identification rate of the combined data was basically 100%. PCA, cluster analysis (CA), and MLPN proved that the classification results acquired from the combined e-nose and e-tongue data were better than individual e-nose and e-tongue result. This work demonstrated that in combination e-nose and e-tongue provided more comprehensive information about the seasonings compared to each individual e-nose and e-tongue. E-nose and e-tongue technologies hold great potential in the production, quality control, and flavor detection of instant starch noodles seasonings.

Preparation and Characterization of Flavored Sauces from Chinese Mitten Crab Processing By-Products

To achieve high-value utilization of Chinese mitten crab processing by-products, different types of sauces were prepared using crab legs (CLs) and crab bodies (CBs). Two processing methods, enzymatic hydrolysis and enzymatic hydrolysis coupled with the Maillard reaction, were employed to prepare these sauces. An electronic nose (E-nose) and electronic tongue (E-tongue) were used to measure the changes in the taste and odor, an automatic amino acid analyzer was used to measure the amino acid content, and a headspace solid-phase microextraction GC/MS (HS-SPME-GC/MS) was used to analyze the volatile compounds, qualitatively and quantitatively. The results showed that the sour, bitter, and other disagreeable odors of the enzymatic hydrolysis solution (EHS) were reduced following the Maillard reaction; meanwhile, the umami and saltiness were considerably enhanced. The quantity of free acidic amino acids with an umami and sweet taste in the CL and CB sauces after enzymatic hydrolysis and the Maillard reaction was substantially higher than that in the homogenate (HO). The Maillard reaction solution (MRS) produced more volatile compounds than the HO and EHS, such as aldehydes, pyrazines, ketones, etc. These compounds not only impart a unique flavor but also have antioxidant capabilities, making them a prototype for the high-value utilization of crab processing by-products.

Evaluation of dual-frequency multi-angle ultrasound on physicochemical properties of tofu gel and its finished product by TOPSIS-entropy weight method

The effects of dual-frequency (40 + 20 kHz) and multi-angle ultrasound (0°, 30°, 45°) on the coagulation state, network structure, flavor and protein conformation of tofu gel were studied. The results showed that the gel flavor of 40 + 20 kHz 0° group was the best and fluorescence intensity was low. The gel flavor in the 40 + 20 kHz 30° group was better than the group without ultrasound, and hydrophobic interaction and disulfide bond content was the largest. Meanwhile, the degree of protein cross-link was increased. The gel in 40 + 20 kHz 45° group had tightly gel state, high thermal stability, but poor flavor. Combined with The Order Preference by Similarity to Ideal Solution (TOPSIS)-entropy weight method, the 40 + 20 kHz 30° group, was the best ultrasonic treatment of gel. It can change the interaction between proteins, promote protein cross-link, and form a uniform and dense gel network. Finally, the hardness and moisture content of finished tofu were increased significantly, and the quality was improved.

Assessing the Levels of Robusta and Arabica in Roasted Ground Coffee Using NIR Hyperspectral Imaging and FTIR Spectroscopy

It has been reported that some brands of roasted ground coffee, whose ingredients are labeled as 100% Arabica coffee, may also contain the cheaper Robusta coffee. Thus, the objective of this research was to test whether near-infrared spectroscopy hyperspectral imaging (NIR-HSI) or Fourier transform infrared spectroscopy (FTIRs) could be used to test whether samples of coffee were pure Arabica or whether they contained Robusta, and if so, what were the levels of Robusta they contained. Qualitative models of both the NIR-HSI and FTIRs techniques were established with support vector machine classification (SVMC). Results showed that the highest levels of accuracy in the prediction set were 98.04 and 97.06%, respectively. Quantitative models of both techniques for predicting the concentration of Robusta in the samples of Arabica with Robusta were established using support vector machine regression (SVMR), which gave the highest levels of accuracy in the prediction set with a coefficient of determination for prediction (Rp2) of 0.964 and 0.956 and root mean square error of prediction (RMSEP) of 5.47 and 6.07%, respectively. It was therefore concluded that the results showed that both techniques (NIR-HSI and FTIRs) have the potential for use in the inspection of roasted ground coffee to classify and determine the respective levels of Arabica and Robusta within the mixture.

Early Discrimination and Prediction of C. fimbriata-Infected Sweetpotatoes during the Asymptomatic Period Using Electronic Nose

Sweetpotato is prone to disease caused by C. fimbriata without obvious lesions on the surface in the early period of infection. Therefore, it is necessary to explore the possibility of developing an efficient early disease detection method for sweetpotatoes that can be used before symptoms are observed. In this study, sweetpotatoes were inoculated with C. fimbriata and stored for different lengths of time. The total colony count was detected every 8 h; HS-SPME/GC–MS and E-nose were used simultaneously to detect volatile compounds. The results indicated that the growth of C. fimbriata entered the exponential phase at 48 h, resulting in significant differences in concentrations of volatile compounds in infected sweetpotatoes at different times, especially toxic ipomeamarone in ketones. The contents of volatile compounds were related to the responses of the sensors. E-nose was combined with multiple chemometrics methods to discriminate and predict infected sweetpotatoes at 0 h, 48 h, 64 h, and 72 h. Among the methods used, linear discriminant analysis (LDA) had the best discriminant effect, with sensitivity, specificity, precision, and accuracy scores of 100%. E-nose combined with K-nearest neighbours (KNN) achieved the best predictions for ipomeamarone contents and total colony counts. This study illustrates that E-nose is a feasible and promising technology for the early detection of C. fimbriata infection in sweetpotatoes during the asymptomatic period.

Recent Progress in Electronic Noses for Fermented Foods and Beverages Applications

Fermented foods and beverages have become a part of daily diets in several societies around the world. Emitted volatile organic compounds play an important role in the determination of the chemical composition and other information of fermented foods and beverages. Electronic nose (E-nose) technologies enable non-destructive measurement and fast analysis, have low operating costs and simplicity, and have been employed for this purpose over the past decades. In this work, a comprehensive review of the recent progress in E-noses is presented according to the end products of the main fermentation types, including alcohol fermentation, lactic acid fermentation, acetic acid fermentation and alkaline fermentation. The benefits, research directions, limitations and challenges of current E-nose systems are investigated and highlighted for fermented foods and beverage applications.

Shelf-Life Prediction and Critical Value of Quality Index of Sichuan Sauerkraut Based on Kinetic Model and Principal Component Analysis

Kinetic models and accelerated shelf-life testing were employed to estimate the shelf-life of Sichuan sauerkraut. The texture, color, total acid, microbe, near-infrared analysis, volatile components, taste, and sensory evaluation of Sichuan sauerkraut stored at 25, 35, and 45 °C were determined. Principal component analysis (PCA) and Fisher discriminant analysis (FDA) were used to analyze the e-tongue data. According to the above analysis, Sichuan sauerkraut with different storage times can be divided into three types: completely acceptable period, acceptable period, and unacceptable period. The model was found to be useful to determine the critical values of various quality indicators. Furthermore, the zero-order kinetic reaction model (R², 0.8699-0.9895) was fitted better than the first-order kinetic reaction model. The Arrhenius model (E_a value was 47.23-72.09 kJ/mol, k_ref value was 1.076 × 10⁶-9.220 × 10¹⁰ d^-1) exhibited a higher fitting degree than the Eyring model. Based on the analysis of physical properties, the shelf-life of Sichuan sauerkraut was more accurately predicted by the combination of the zero-order kinetic reaction model and the Arrhenius model, while the error back propagation artificial neural network (BP-ANN) model could better predict the chemical properties. It is a better choice for dealers and consumers to judge the shelf life and edibility of food by shelf-life model.