A feature selection strategy of E-nose data based on PCA coupled with Wilks Λ-statistic for discrimination of vinegar samples

Light-Activated Virtual Sensor Array with Machine Learning for Non-Invasive Diagnosis of Coronary Heart Disease

Early non-invasive diagnosis of coronary heart disease (CHD) is critical. However, it is challenging to achieve accurate CHD diagnosis via detecting breath. In this work, heterostructured complexes of black phosphorus (BP) and two-dimensional carbide and nitride (MXene) with high gas sensitivity and photo responsiveness were formulated using a self-assembly strategy. A light-activated virtual sensor array (LAVSA) based on BP/Ti3C2Tx was prepared under photomodulation and further assembled into an instant gas sensing platform (IGSP). In addition, a machine learning (ML) algorithm was introduced to help the IGSP detect and recognize the signals of breath samples to diagnose CHD. Due to the synergistic effect of BP and Ti3C2Tx as well as photo excitation, the synthesized heterostructured complexes exhibited higher performance than pristine Ti3C2Tx, with a response value 26% higher than that of pristine Ti3C2Tx. In addition, with the help of a pattern recognition algorithm, LAVSA successfully detected and identified 15 odor molecules affiliated with alcohols, ketones, aldehydes, esters, and acids. Meanwhile, with the assistance of ML, the IGSP achieved 69.2% accuracy in detecting the breath odor of 45 volunteers from healthy people and CHD patients. In conclusion, an immediate, low-cost, and accurate prototype was designed and fabricated for the noninvasive diagnosis of CHD, which provided a generalized solution for diagnosing other diseases and other more complex application scenarios.

Quality properties of fish ball with abalone and its relationship with sensory properties

In this work, whiteness, water-holding capacity, gel strength, textural profile analysis were performed to examine the quality of fish balls with abalone (FBA). In addition, a correlation between quality and sensory properties was established. The addition of abalone significantly increased the water holding capacity, gel strength and textural properties of FBA, and decreased their whiteness, the best overall quality was achieved at 9 % w/w abalone addition. The E-nose and E-tongue results revealed that the addition of abalone changed the flavour of FBA. HS-SPME-GC-MS identified 65 volatile organic compounds (VOCs) and proved to be effective in reducing fishy flavour. E-nose can distinguish between the VOCs in FBA. Moreover, Umami and 1-octen-3-ol can serve as important indicators to observe changes in the quality of FBA, as they were positively connected with WHC, gumminess, chewiness, resilience, a*, hexanal, etc. The results provided a theoretical basis for the development of abalone and surimi products.

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.

Assessment of flavor characteristics in snakehead (Ophiocephalus argus Cantor) surimi gels affected by atmospheric cold plasma treatment using GC-IMS

The gel formation ability of freshwater surimi is weak, resulting in its poor flavor and quality. Atmospheric cold plasma (ACP), a widely developed non-thermal processing technology in the food industry, is considered to have potential applications in maintaining and improving the flavor characteristics of surimi gels. In this study, the effect of ACP on snakehead surimi gels flavor at different treatment times was investigated by sensory evaluation and gas chromatography-ion mobility spectrometry (GC-IMS) analysis. The results showed that ACP could better maintain and improve the original appearance and tissue state characteristics of surimi gels, scoring about 1-2 points higher than the ACP-untreated group. GC-IMS analysis demonstrated the obvious difference in the volatile organic compounds (VOCs) among the treatment groups. Specifically, the samples treated for 120 s with ACP exhibited the most unique aroma characteristics, which probably related to the highest thiobarbituric acid reactive substances values (73.28 μmol MDA/kg sample). Meanwhile, the reduced TCA-soluble peptides content indicated that ACP could inhibit protein degradation to maintaining the tissue state and flavor characteristics of the surimi gels. In conclusion, the advantages of ACP treatment, such as little damage to nutrients, and maximum retention of original sensory properties, provide new ideas for its application in the flavor characteristics of the snakehead surimi gels.

Vinegar Volatile Organic Compounds: Analytical Methods, Constituents, and Formation Processes

Vinegar is an acid condiment shared all over the world. According to the raw materials, vinegar can be mainly divided into fruit and cereal ones, both of which possess unique aroma and flavor characteristics and corresponding volatile organic compounds (VOCs). Many studies about vinegar VOCs' (VVOCs) sorts, analytical methods, and forming mechanisms have been done. In this review, the main categories of vinegar and their distribution in the world are briefly introduced, then VVOCs' analytical and identified methods, types, and forming processes are summarized. Additionally, the VVOCs' research directions are discussed and prospected. According to the searched literatures, this study is the first to systematically review the analytical methods, sorts, and formation mechanisms of VVOCs, which will make the readers better understand the vinegar's aromas and flavors and their producing mechanisms.

Portable Electronic Nose Based on Digital and Analog Chemical Sensors for 2,4,6-Trichloroanisole Discrimination

2,4,6-trichloroanisole (TCA) is mainly responsible for cork taint in wine, which causes significant economic losses; therefore, the wine and cork industries demand an immediate, economic, noninvasive and on-the-spot solution. In this work, we present a novel prototype of an electronic nose (e-nose) using an array of digital and analog metal-oxide gas sensors with a total of 31 signals, capable of detecting TCA, and classifying cork samples with low TCA concentrations (≤15.1 ng/L). The results show that the device responds to low concentrations of TCA in laboratory conditions. It also differentiates among the inner and outer layers of cork bark (81.5% success) and distinguishes among six different samples of granulated cork (83.3% success). Finally, the device can predict the concentration of a new sample within a ±10% error margin.

Optimization of Electronic Nose Sensor Array for Tea Aroma Detecting Based on Correlation Coefficient and Cluster Analysis

The electronic nose system is widely used in tea aroma detecting, and the sensor array plays a fundamental role for obtaining good results. Here, a sensor array optimization (SAO) method based on correlation coefficient and cluster analysis (CA) is proposed. First, correlation coefficient and distinguishing performance value (DPV) are calculated to eliminate redundant sensors. Then, the sensor independence is obtained through cluster analysis and the number of sensors is confirmed. Finally, the optimized sensor array is constructed. According to the results of the proposed method, sensor array for green tea (LG), fried green tea (LF) and baked green tea (LB) are constructed, and validation experiments are carried out. The classification accuracy using methods of linear discriminant analysis (LDA) based on the average value (LDA-ave) combined with nearest-neighbor classifier (NNC) can almost reach 94.44~100%. When the proposed method is used to discriminate between various grades of West Lake Longjing tea, LF can show comparable performance to that of the German PEN2 electronic nose. The electronic nose SAO method proposed in this paper can effectively eliminate redundant sensors and improve the quality of original tea aroma data. With fewer sensors, the optimized sensor array contributes to the miniaturization and cost reduction of the electronic nose system.

Noninvasive detection of COPD and Lung Cancer through breath analysis using MOS Sensor array based e-nose

INTRODUCTION

This paper describes the research work done toward the development of a breath analyzing electronic nose (e-nose), and the results obtained from testing patients with lung cancer, patients with chronic obstructive pulmonary disease (COPD), and healthy controls. Pulmonary diseases like COPD and lung cancer are detected with MOS sensor array-based e-noses. The e-nose device with the sensor array, data acquisition system, and pattern recognition can detect the variations of volatile organic compounds (VOC) present in the expelled breath of patients and healthy controls.

MATERIALS AND METHODS

This work presents the e-nose equipment design, study subjects selection, breath sampling procedures, and various data analysis tools. The developed e-nose system is tested in 40 patients with lung cancer, 48 patients with COPD, and 90 healthy controls.

RESULTS

In differentiating lung cancer and COPD from controls, support vector machine (SVM) with 3-fold cross-validation outperformed all other classifiers with an accuracy of 92.3% in cross-validation. In external validation, the same discrimination was achieved by k-nearest neighbors (k-NN) with 75.0% accuracy.

CONCLUSION

The reported results show that the VOC analysis with an e-nose system holds exceptional possibilities in noninvasive disease diagnosis applications.

Discrimination of COPD and lung cancer from controls through breath analysis using a self-developed e-nose

This work details the application of a metal oxide semiconductor (MOS) sensor based electronic nose (e-nose) system in the discrimination of lung cancer and chronic obstructive pulmonary disease (COPD) from healthy controls. The sensor array integrated with supervised classification algorithms was able to detect and classify exhaled breath samples from healthy controls, patients with COPD, and lung cancer by recognizing the amount of volatile organic compounds present in it. This paper details the e-nose design, participant selection, sampling methods, and data analysis. The clinical feasibility of the system was checked in 32 lung cancer patients, 38 COPD patients, and 72 healthy controls including smokers and non-smokers. One of the advantages of the equipment design was portability and robustness since the system was conditioned with elements that allowed its easy movement. In the discrimination of lung cancer from controls, the k-nearest neighbors gave an acceptable accuracy, sensitivity, and specificity of 91.3%, 84.4%, and 94.4% respectively. The support vector machine gave better results for COPD discrimination from controls with 90.9% accuracy, 81.6% sensitivity, and 95.8% specificity. Even though the attained results were good, further examinations are essential to enhance the sensor array system, investigate the long-run reproducibility, repeatability, and enlarge its relevancy.

Influence of Triazole Pesticides on Wine Flavor and Quality Based on Multidimensional Analysis Technology

Triazole pesticides are widely used to control grapevine diseases. In this study, we investigated the impact of three triazole pesticides-triadimefon, tebuconazole, and paclobutrazol-on the concentrations of wine aroma compounds. All three triazole pesticides significantly affected the ester and acid aroma components. Among them, paclobutrazol exhibited the greatest negative influence on the wine aroma quality through its effect on the ester and acid aroma substances, followed by tebuconazole and triadimefon. Qualitative and quantitative analysis by solid-phase micro-extraction gas chromatography coupled with mass spectrometry revealed that the triazole pesticides also changed the flower and fruit flavor component contents of the wines. This was attributed to changes in the yeast fermentation activity caused by the pesticide residues. The study reveals that triazole pesticides negatively impact on the volatile composition of wines with a potential undesirable effect on wine quality, underlining the desirability of stricter control by the food industry over pesticide residues in winemaking.

Variations of volatile flavour compounds in finger citron (Citrus medica L. var. sarcodactylis) pickling process revealed by E-nose, HS-SPME-GC-MS and HS-GC-IMS

The pickled products of finger citron are famous in southern China for their unique taste and flavor. Although pickling process involves complex treatments including salting, desalting, sugaring, cooking and drying, extended shelf-life up to ten years after pickling can be achieved. In this study, the variations of volatile flavour components in the pickling process of finger citron were investigated by electronic nose (E-nose), headspace solid phase microextraction-gas chromatography-mass spectrometry (HS-SPME-GC-MS) and headspace-gas chromatography-ion mobility spectrometry (HS-GC-IMS). HS-SPME-GC-MS identified 85 substances, and HS-GC-IMS identified 81 substances, including terpenoids (21), aromatic hydrocarbons (11), alcohols (11), aldehydes (10), esters (7), phenols (6), acids (5), ethers (2), ketones (2), and other species (10). Linalool, limonene, (E)-3,7-dimethyl-1,3,6-octatriene, myrcene, 3-carene, β-pinene, α-pinene, terpinolene, 1-methyl-4-(1-methylethyl)-1,4-cyclohexadiene, α-terpinene, (S)-β-bisabolene, 1-isopropyl-2-methylbenzene and 1-methyl-4-(1-methylethenyl)-benzene were the stable substances at relatively high contents in finger citron at different pickling process. Salting and drying steps in the pickling process exerted greatest influence on the volatile components of finger citron. Salting promoted the generation of aldehydes, esters and acids, but led to the disappearance of alcohols, while drying promoted the generation of alcohols, phenols, aldehydes and acids at the expense of reduction in terpenoids. Our study revealed that the characteristic volatile compounds of finger citron pickled products was mainly formed by the biological reactions in the salting stage and thermal chemical transformations in the drying stage. This study also validated the suitability of E-nose combined with HS-SPME-GC-MS and HS-GC-IMS in tracking the changes of volatile components in finger citron during the pickling process.

Cross-Category Tea Polyphenols Evaluation Model Based on Feature Fusion of Electronic Nose and Hyperspectral Imagery

Tea polyphenols are important ingredients for evaluating tea quality. The rapid development of sensors provides an efficient method for nondestructive detection of tea polyphenols. Previous studies have shown that features obtained from single or multiple sensors yield better results in detecting interior tea quality. However, due to their lack of external features, it is difficult to meet the general evaluation model for the quality of the interior and exterior of tea. In addition, some features do not fully reflect the sensor signals of tea for several categories. Therefore, a feature fusion method based on time and frequency domains from electronic nose (E-nose) and hyperspectral imagery (HSI) is proposed to estimate the polyphenol content of tea for cross-category evaluation. The random forest and the gradient boosting decision tree (GBDT) are used to evaluate the feature importance to obtain the optimized features. Three models based on different features for cross-category tea (black tea, green tea, and yellow tea) were compared, including grid support vector regression (Grid-SVR), random forest (RF), and extreme gradient boosting (XGBoost). The results show that the accuracy of fusion features based on the time and frequency domain from the electronic nose and hyperspectral image system is higher than that of the features from single sensor. Whether based on all original features or optimized features, the performance of XGBoost is the best among the three regression algorithms (R² = 0.998, RMSE = 0.434). Results indicate that the proposed method in this study can improve the estimation accuracy of tea polyphenol content for cross-category evaluation, which provides a technical basis for predicting other components of tea.