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

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.

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.