Application of the voltammetric electronic tongue based on nanocomposite modified electrodes for identifying rice wines of different geographical origins

Determination of heavy metals in edible oils by a novel voltammetry taste sensor array

Herein, a novel voltammetry taste sensor array (VTSA) using pencil graphite electrode, screen-printed electrode, and glassy carbon electrode was used to identify heavy metals (HM) including Cad, Pb, Sn and Ni in soybean and rapeseed oils. HMs were added to edible oils at three concentrations of 0.05, 0.1 and 0.25 ppm, and then, the output of the device was classified using a chemometric classification method. According to the principal component analysis results, PG electrode explains 96% and 81% of the variance between the data in rapeseed and soybean edible oils, respectively. Additionally, the SP electrode explains 91% of the variance between the data in rapeseed and soybean oils. Moreover, the GC electrode explains 100% and 99% of the variance between the data in rapeseed and soybean edible oils, respectively. K-nearest neighbor exhibited high capability in classifying HMs in edible oils. In addition, partial least squares in the combine of VTSA shows a predict 99% in rapeseed oil. The best electrode for soybean edible oil was GC.

Research Progress on Flavor and Quality of Chinese Rice Wine in the Brewing Process

Chinese rice wine (CRW) is a traditional and unique alcoholic beverage in China, favored by many consumers for its rich aroma, unique taste, and complex ingredients. Its flavor is primarily composed of volatile and nonvolatile compounds. These flavor compounds are partly derived from grains and starters (Qu), while the other part is produced by microbial metabolism and chemical reactions during the brewing process. Additionally, ethyl carbamate (EC) in CRW, a hazardous chemical, necessitates controlling its concentration during brewing. In recent years, numerous new brewing techniques for CRW have emerged. Therefore, this paper aims to collect aroma descriptions and thresholds of flavor compounds in CRW, summarize the relationship between the brewing process of CRW and flavor formation, outline methods for reducing the concentration of EC in the brewing process of CRW, and summarize the four stages (pretreatment of grains, fermentation, sterilization, and aging process) of new techniques. Furthermore, we will compare the advantages and disadvantages of different approaches, with the expectation of providing a valuable reference for improving the quality of CRW.

Hierarchical tungsten-doped bimetallic selenides nanosheets arrays/nickel foam composite electrode as efficient gallic acid electrochemical sensor

The development of effective and accurate gallic acid (GA) electrochemical sensors is critical for food and pharmaceutical industry and health perspectives. Multi-step hydrothermal treatments of bimetallic (Ni/Co) flaky bimetallic hydroxides (NiCo FBHs) were employed to prepare tungsten-doped cobalt-nickel selenides nanosheets arrays (W-Co_0.5Ni_0.5Se₂ NSAs) serving  as the main active substance of GA detection. The morphology and composition of the W-Co_0.5Ni_0.5Se₂ NSAs/NF were characterized by scanning electron microscopy (SEM), transmission electron microscopy (TEM), Fourier transform infrared spectroscopy (FT-IR), Raman spectroscopy, X-ray powder diffraction (XRD) and X-ray photoelectron spectroscopy (XPS). The GA electrochemical sensor constructed by the W-Co_0.5Ni_0.5Se₂ NSAs/NF composite electrode exhibits two linear concentration ranges of 1.00-36.2 μM and 36.2-1.00×10³ μM for GA electrochemical detection with a limit of detection of  0.120 μM (S/N=3) at the working potential of 0.05 V (vs. SCE). The W-Co_0.5Ni_0.5Se₂ NSAs/NF shows high selectivity, good long-term stability, high recovery in the range 97.9-105%, and a relative standard deviation (RSD) between 0.60 and 2.7%.

Flavor compounds with high odor activity values (OAV > 1) dominate the aroma of aged Chinese rice wine (Huangjiu) by molecular association

Aging is an essential operation to perfect the flavor quality of Hungjiu. In this study, formation mechanism of flavor compounds responsible for the characteristic flavor of aged Huangjiu was investigated. The contents of umami and bitter free amino acids (FAA) increased with the storage period prolonged, while that of sweet FAA showed downward trend. Gas chromatograph-mass spectrometry and principal component analysis indicated that the volatile flavor compounds with OAV exceed 1, especially middle-chain fatty-acid-ethyl-esters and aromatic compounds, dominated the characteristic flavor of aged Huangjiu. Low field-NMR was firstly applied to characterize the molecular association between water and dissolved flavor compounds in aged Huangjiu. The results showed that basic amino acids contributed greatly to the flavor formation of aged Huangjiu via molecular association. In addition, the molecular association significantly promoted the accumulation of flavor compounds with OAV > 1, especially ethyl esters.

Fabrication and application of three-dimensional nanocomposites modified electrodes for evaluating the aging process of Huangjiu (Chinese rice wine)

In this study, three modified glassy carbon electrodes based on three-dimensional conducting polymer nanocomposites (TDCPNs) were fabricated for evaluating the aging process of Huangjiu (Chinese rice wines). The electrochemical activity and experimental conditions of the TDCPNs modified electrodes were investigated by cyclic voltammetry, the aging information obtained by the modified electrodes were optimized by variance inflation factor (VIF). Principal components analysis (PCA), locally linear embedding (LLE), and locality preserving projection (LPP, which presented the best classification result) based on the optimized data were applied to classify the wine samples. Then, the dimensionality reduction data of PCA, LLE, and LPP were used as input variables of the logistic regression and extreme learning machine (ELM) for evaluating the aging process of Huangjiu, and the LLE-ELM method exhibited the best prediction results. These results demonstrated that the TDCPNs modified electrodes presented the potential for the quality analysis of food and beverages.

A Carbon Nanotube Sensor Array for the Label-Free Discrimination of Live and Dead Cells with Machine Learning

Developing robust cell recognition strategies is important in biochemical research, but the lack of well-defined target molecules creates a bottleneck in some applications. In this paper, a carbon nanotube sensor array was constructed for the label-free discrimination of live and dead mammalian cells. Three types of carbon nanotube field-effect transistors were fabricated, and different features were extracted from the transfer characteristic curves for model training with linear discriminant analysis (LDA) and support-vector machines (SVM). Live and dead cells were accurately classified in more than 90% of samples in each sensor group using LDA as the algorithm. The recursive feature elimination with cross-validation (RFECV) method was applied to handle the overfitting and optimize the model, and cells could be successfully classified with as few as four features and a higher validation accuracy (up to 97.9%) after model optimization. The RFECV method also revealed the crucial features in the classification, indicating the participation of different sensing mechanisms in the classification. Finally, the optimized LDA model was applied for the prediction of unknown samples with an accuracy of 87.5-93.8%, indicating that live and dead cell samples could be well-recognized with the constructed model.

Metal-Organic-Framework- and MXene-Based Taste Sensors and Glucose Detection

Taste sensors can identify various tastes, including saltiness, bitterness, sweetness, sourness, and umami, and have been useful in the food and beverage industry. Metal-organic frameworks (MOFs) and MXenes have recently received considerable attention for the fabrication of high-performance biosensors owing to their large surface area, high ion transfer ability, adjustable chemical structure. Notably, MOFs with large surface areas, tunable chemical structures, and high stability have been explored in various applications, whereas MXenes with good conductivity, excellent ion-transport characteristics, and ease of modification have exhibited great potential in biochemical sensing. This review first outlines the importance of taste sensors, their operation mechanism, and measuring methods in sensing utilization. Then, recent studies focusing on MOFs and MXenes for the detection of different tastes are discussed. Finally, future directions for biomimetic tongues based on MOFs and MXenes are discussed.

Machine learning and chemometrics for electrochemical sensors: moving forward to the future of analytical chemistry

Electrochemical sensors and biosensors have been successfully used in a wide range of applications, but systematic optimization and nonlinear relationships have been compromised for electrode fabrication and data analysis. Machine learning and experimental designs are chemometric tools that have been proved to be useful in method development and data analysis. This minireview summarizes recent applications of machine learning and experimental designs in electroanalytical chemistry. First, experimental designs, e.g., full factorial, central composite, and Box-Behnken are discussed as systematic approaches to optimize electrode fabrication to consider the effects from individual variables and their interactions. Then, the principles of machine learning algorithms, including linear and logistic regressions, neural network, and support vector machine, are introduced. These machine learning models have been implemented to extract complex relationships between chemical structures and their electrochemical properties and to analyze complicated electrochemical data to improve calibration and analyte classification, such as in electronic tongues. Lastly, the future of machine learning and experimental designs in electrochemical sensors is outlined. These chemometric strategies will accelerate the development and enhance the performance of electrochemical devices for point-of-care diagnostics and commercialization.

E-Tongues/Noses Based on Conducting Polymers and Composite Materials: Expanding the Possibilities in Complex Analytical Sensing

Conducting polymers (CPs) are extensively studied due to their high versatility and electrical properties, as well as their high environmental stability. Based on the above, their applications as electronic devices are promoted and constitute an interesting matter of research. This review summarizes their application in common electronic devices and their implementation in electronic tongues and noses systems (E-tongues and E-noses, respectively). The monitoring of diverse factors with these devices by multivariate calibration methods for different applications is also included. Lastly, a critical discussion about the enclosed analytical potential of several conducting polymer-based devices in electronic systems reported in literature will be offered.

Comparison between voltammetric detection methods for abalone-flavoring liquid

This article attempts to determine the most accurate classification method for different abalone-flavoring liquids. Three common voltammetric detection methods, namely, linear sweep voltammetry (LSV), cyclic voltammetry (CV), and square-wave voltammetry (SWV), were considered. To compare their classification accuracies of abalone-flavoring liquids, three methods were separately adopted to classify five different abalone-flavoring liquids, using a four-electrode (Au, Pt, Pd, and W) sensor array. Then the data acquired by each method were subject to the principal component analysis (PCA): the first three principal components whose eigenvalues were greater than 1 were extracted from each set of data; the cumulative variance contribution rate and the principal component scores of each method were obtained. The PCA results show that the first three principal components obtained by the CV had the highest cumulative variance contribution rate (91.307%), indicating that the CV can more comprehensively characterize the information of abalone-flavoring liquid samples than the other two methods. According to the principal component scores, compared with those of LSV and SWV, the same kind of samples detected by the CV were highly clustered and the different kinds of samples detected by the CV were greatly dispersed. This indicates that the CV can effectively distinguish between the five abalone-flavoring liquids. Finally, the detection data were further verified through probabilistic neural network and a support vector machine algorithm optimized by genetic algorithm. The results further confirm that the CV is more accurate than the other two methods in the classification of abalone-flavoring liquids. Therefore, the CV was recommended for the classification of abalone-flavoring liquids.

Recent Developments in Smart Food Packaging Focused on Biobased and Biodegradable Polymers

Food packaging has a crucial function in the modern food industry. New food packaging technologies seek to meet consumers and industrial's demands. Changes related to food production, sale practices and consumers' lifestyles, along with environmental awareness and the advance in new areas of knowledge (such as nanotechnology or biotechnology), act as driving forces to develop smart packages that can extend food shelf-life, keeping and supervising their innocuousness and quality and also taking care of the environment. This review describes the main concepts and types of active and intelligent food packaging, focusing on recent progress and new trends using biodegradable and biobased polymers. Numerous studies show the great possibilities of these materials. Future research needs to focus on some important aspects such as possibilities to scale-up, costs, regulatory aspects, and consumers' acceptance, to make these systems commercially viable.

Fuzzy Evaluation Output of Taste Information for Liquor Using Electronic Tongue Based on Cloud Model

As a taste bionic system, electronic tongues can be used to derive taste information for different types of food. On this basis, we have carried forward the work by making it, in addition to the ability of accurately distinguish samples, be more expressive by speaking evaluative language like human beings. Thus, this paper demonstrates the correlation between the qualitative digital output of the taste bionic system and the fuzzy evaluation language that conform to the human perception mode. First, through principal component analysis (PCA), backward cloud generator and forward cloud generator, two-dimensional cloud droplet groups of different flavor information were established by using liquor taste data collected by electronic tongue. Second, the frequency and order of the evaluation words for different flavor of liquor were obtained by counting and analyzing the data appeared in the artificial sensory evaluation experiment. According to the frequency and order of words, the cloud droplet range corresponding to each word was calculated in the cloud drop group. Finally, the fuzzy evaluations that originated from the eight groups of liquor data with different flavor were compared with the artificial sense, and the results indicated that the model developed in this work is capable of outputting fuzzy evaluation that is consistent with human perception rather than digital output. To sum up, this method enabled the electronic tongue system to generate an output, which conforms to human's descriptive language, making food detection technology a step closer to human perception.

Taste Sensor: Electronic Tongue with Lipid Membranes

Taste is of five basic types, namely, sourness, saltiness, sweetness, bitterness and umami. In this review, we focus on a potentiometric taste sensor that we developed and fabricated using lipid polymer membranes. The taste sensor can measure the taste perceived by humans and is called an electronic tongue with global selectivity, which is the property to discriminate taste qualities and quantify them without discriminating each chemical substance. This property is similar to the gustatory system; hence, the taste sensor is a type of biomimetic device. In this paper, we first explain the sensing mechanism of the taste sensor, its application to beer evaluation and the measurement mechanism. Second, results recently obtained are introduced; i.e., the application of the senor to high-potency sweeteners and the improvement of the bitterness sensor are explained. Last, quantification of the bitterness-masking effect of high-potency sweeteners is explained using a regression analysis based on both the outputs of bitterness and sweetness sensors. The taste sensor provides a biomimetic method different from conventional analytical methods.

Voltammetric Electronic Tongues in Food Analysis

A critical revision is made on recent applications of voltammetric electronic tongues in the field of food analysis. Relevant works are discussed dealing with the discrimination of food samples of different type, origin, age and quality and with the prediction of the concentration of key substances and significant indexes related to food quality.