The potential of different ripeness of blood oranges (Citrus sinensis L. Osbeck) for sale in advance after low-temperature storage: Anthocyanin enhancements, volatile compounds, and taste attributes

To explore the optimal early harvest time similar to the ripe fruit qualities, the effects of storage temperatures on maturity indexes, weight losses, colour parameters, anthocyanin profiles, volatile and taste components of blood oranges at six different maturity levels were investigated. Total anthocyanin contents of cold-treated fruits increased to or exceed that of ripe fruits (0.24 ± 0.12 mg/100 g), and fruits harvested from 260 d and 280 d after anthesis shared similar individual anthocyanin profiles to ripe fruits during storage at 8 °C for 30 d and 20 d (III-30 d and IV-20 d groups), respectively. Moreover, comparative analyses of e-nose and e-tongue demonstrated the distances of volatile components and scores of taste attributes including sourness, saltiness, bitterness, sweetness, and umami in III-30 d and IV-20 d groups were close to that of ripe fruits, indicating that the fruits could be sold about 20 to 30 d ahead of the season.

Monitoring the baking quality of Tieguanyin via electronic nose combined with GC-MS

Roasting is extremely important for Tieguanyin oolong tea production because it strongly affects its chemical composition and sensory quality. In addition, there were significant differences in the preference for roasted tea among different people. However, the effect of roasting degree on the aroma characteristics and flavor quality of Tieguanyin tea is still unclear. To further study this, an electronic nose combined with gas chromatography-mass spectrometry (GC-MS) was used to monitor the baking process of Tieguanyin. The physicochemical indexes, sensory quality, and odor characteristics of the tea leaves subjected to different roasting conditions were measured. The increase in the roasting degree caused a decrease in the amount of taste substances such as tea polyphenols, catechins, and amino acids and a sharp increase in the phenol to ammonia ratio. Sensory evaluation results showed that moderate roasting could help improve the quality of the tea leaves. The results obtained using the electronic nose and GC-MS showed that there were substantial differences in the volatile substances, and 103 flavor compounds were highly correlated with the aroma characteristics of roasted tea with different roasting degrees. In addition, the electronic nose combined with various classification models could better distinguish tea leaves with different roasting degrees. Among them, the accuracy of the RF training set and prediction set reached>98.44%. The results of this study will aid in comprehensively monitoring the effects of the baking process on the flavor, chemical composition, and aroma of Tieguanyin as well as in distinguishing Tieguanyin tea leaves with different qualities.

An improved AdaBoost algorithm for identification of lung cancer based on electronic nose

The research developed an improved intelligent enhancement learning algorithm based on AdaBoost, that can be applied for lung cancer breath detection by the electronic nose (eNose). First, collected the breath signals from volunteers by eNose, including healthy individuals and people who had lung cancer. Additionally, the signals' features were extracted and optimized. Then, multi sub-classifiers were obtained, and their coefficients were derived from the training error. To improve generalization performance, K-fold cross-validation was used when constructing each sub-classifier. The prediction results of a sub-classifier on the test set were then achieved by the voting method. Thus, an improved AdaBoost classifier would be built through heterogeneous integration. The results shows that the average precision of the improved algorithm classifier for distinguishing between people with lung cancer and healthy individuals could reach 98.47%, with 98.33% sensitivity and 97% specificity. And in 100 independent and randomized tests, the coefficient of variation of the classifier's performance hardly exceeded 4%. Compared with other integrated algorithms, the generalization and stability of the improved algorithm classifier are more superior. It is clear that the improved AdaBoost algorithm may help screen out lung cancer more comprehensively. Additionally, it will significantly advance the use of eNose in the early identification of lung cancer.

Evaluation of vegetable sauerkraut quality during storage based on convolution neural network

Vegetable sauerkraut is a traditional fermented food. Due to oxidation reactions that occur during storage, the quality and flavor in different periods will change. In this study, the quality evaluation and flavor characteristics of 13 groups of vegetable sauerkraut samples with different storage time were analyzed by using physical and chemical parameters combined with electronic nose. Photographs of samples of various periods were collected, and a convolutional neural network (CNN) framework was established. The relationship between total phenol oxidative decomposition and flavor compounds was linearly negatively correlated. The vegetable sauerkraut during storage can be divided into three categories (full acceptance period, acceptance period and unacceptance period) by principal component analysis and Fisher discriminant analysis. The CNN parameters were fine-tuned based on the classification results, and its output results can reflect the quality changes and flavor characteristics of the samples, and have better fitting, prediction capabilities. After 50 epochs of the model, the accuracy of three sets of data namely training set, validation set and test set recorded 94%, 85% and 93%, respectively. In addition, the accuracy of CNN in identifying different quality sauerkraut was 95.30%. It is proved that the convolutional neural network has excellent performance in predicting the quality of Szechuan Sauerkraut with high reliability.

The development progress of multi-array colourimetric sensors based on the M13 bacteriophage

Techniques for detecting chemicals dispersed at low concentrations in air continue to evolve. These techniques can be applied not only to manage the quality of agricultural products using a post-ripening process but also to establish a safety prevention system by detecting harmful gases and diagnosing diseases. Recently, techniques for rapid response to various chemicals and detection in complex and noisy environments have been developed using M13 bacteriophage-based sensors. In this review, M13 bacteriophage-based multi-array colourimetric sensors for the development of an electronic nose is discussed. The self-templating process was adapted to fabricate a colour band structure consisting of an M13 bacteriophage. To detect diverse target chemicals, the colour band was utilised with wild and genetically engineered M13 bacteriophages to enhance their sensing abilities. Multi-array colourimetric sensors were optimised for application in complex and noisy environments based on simulation and deep learning analysis. The development of a multi-array colourimetric sensor platform based on the M13 bacteriophage is likely to result in significant advances in the detection of various harmful gases and the diagnosis of various diseases based on exhaled gas in the future.

Effect of ultrasonic power on the stability of low-molecular-weight oyster peptides functional-nutrition W1/O/W2 double emulsion

Ultrasonic-assisted treatment is an eco-friendly and cost-effective emulsification method, and the acoustic cavitation effect produced by ultrasonic equipment is conducive to the formation of stable emulsion. However, its effect on the underlying stability of low-molecular-weight oyster peptides (LOPs) functional-nutrition W₁/O/W₂ double emulsion has not been reported. The effects of different ultrasonic power (50, 75, 100, 125, and 150 W) on the stability of double emulsions and the ability to mask the fishy odor of LOPs were investigated. Low ultrasonic power (50 W and 75 W) treatment failed to form a well-stabilized double emulsion, and excessive ultrasound treatment (150 W) destroyed its structure. At an ultrasonic power of 125 W, smaller particle-sized double emulsion was formed with more uniform distribution, more whiteness, and a lower viscosity coefficient. Meanwhile, the cavitation effect generated by 125 W ultrasonic power improved storage, and oxidative stabilities, emulsifying properties of double emulsion by reducing the droplet size and improved sensorial acceptability by masking the undesirable flavor of LOPs. The structure of the double emulsion was further confirmed by optical microscopy and confocal laser scanning microscopy. The ultrasonic-assisted treatment is of potential value for the industrial application of double emulsion in functional-nutrition foods.

Analysis of flavor formation during the production of Jinhua dry-cured ham using headspace-gas chromatography-ion mobility spectrometry (HS-GC-IMS)

This study aimed to clarify the formation process of flavor compounds and identify the volatile substances present during a continuous period of Jinhua dry-cured ham (JDH) making. Via headspace-gas chromatography-ion mobility spectrometry (HS-GC-IMS), a total of 53 volatile organic compounds (VOCs), including 20 aldehydes, 16 alcohols, 11 ketones, 5 esters and 1 furan, were identified in JDH from seven sampling stages. The results showed that butanal, 3-methylbutanal, 2-methylbutanal, 2-hexanone, 2-pentanone and 2-butanone could be flavor markers in the evolution of aroma characteristics of JDH. Aldehydes (2-methylbutanal and 3-methylbutanal), alcohols (2-methylpropanol, 2-methylbutanol, 3-methylbutanol and 1-penten-3-ol), ketones (2-pentanone, 2-propanone, 2-butanone and 2-hexanone) and esters (ethyl acetate and ethyl 3-methylbutyrate) were considered the main VOCs in the mature JDH. Free fatty acid (FFA) analysis displayed the changes in intramuscular fat (IMF) of JDH. Additionally, principal component analysis (PCA) showed that drying-ripening was a critical stage in the flavor formation of JDH.

Characterization of odour emissions in a wastewater treatment plant using a drone-based chemical sensor system

Conventionally, odours emitted by different sources present in wastewater treatment plants (WWTPs) are measured by dynamic olfactometry, where a human panel sniffs and analyzes air bags collected from the plant. Although the method is considered the gold standard, the process is costly, slow, and infrequent, which does not allow operators to quickly identify and respond to problems. To better monitor and map WWTP odour emissions, here we propose a small rotary-wing drone equipped with a lightweight (1.3-kg) electronic nose. The "sniffing drone" sucks in air via a ten-meter (33-foot) tube and delivers it to a sensor chamber where it is analyzed in real-time by an array of 21 gas sensors. From the sensor signals, machine learning (ML) algorithms predict the odour concentration that a human panel using the EN13725 methodology would report. To calibrate and validate the predictive models, the drone also carries a remotely controlled sampling device (compliant with EN13725:2022) to collect sample air in bags for post-flight dynamic olfactometry. The feasibility of the proposed system is assessed in a WWTP in Spain through several measurement campaigns covering diverse operating regimes of the plant and meteorological conditions. We demonstrate that training the ML algorithms with dynamic (transient) sensor signals measured in flight conditions leads to better performance than the traditional approach of using steady-state signals measured in the lab via controlled exposures to odour bags. The comparison of the electronic nose predictions with dynamic olfactometry measurements indicates a negligible bias between the two measurement techniques and 95 % limits of agreement within a factor of four. This apparently large disagreement, partly caused by the high uncertainty of olfactometric measurements (typically a factor of two), is more than offset by the immediacy of the predictions and the practical advantages of using a drone-based system.

Gas Array Sensors based on Electronic Nose for Detection of Tuna (Euthynnus Affinis) Contaminated by Pseudomonas Aeruginosa

Background

Fish is a food ingredient that is consumed throughout the world. When fishes die, their freshness begins to decrease. The freshness of the fish can be determined by the aroma it produces. The purpose of this study is to monitor the odor of fish using a collection of gas sensors that can detect distinct odors.

Methods

The sensor was tested with three kinds of samples, namely Pseudomonas aeruginosa, tuna, and tuna that was contaminated with P. aeruginosa bacteria. During the process of collecting sensor data, all samples were placed in a vacuum so that the gas or aroma produced was not contaminated with other aromas. Eight sensors were used which were designed and implemented in an electronic nose (E-nose) device that can withstand aroma. The data collection process was carried out for 48 h, with an interval of 6 h for each data collection. Data processing was performed by using the principal component analysis and support vector machine (SVM) methods to obtain a plot score visualization and classification and to determine the aroma pattern of the fish.

Results

The results of this study indicate that the E-nose system is able to smell fish based on the hour with 95% of the cumulative variance of the main component in the classification test between fresh tuna and tuna fish contaminated with P. aeruginosa.

Conclusion

The SVM classifier was able to classify the healthy and unhealthy fish with an accuracy of 99%. The sensors that provided the highest response are the TGS 825 and TGS 826 sensors.

Application progress of intelligent flavor sensing system in the production process of fermented foods based on the flavor properties

Fermented foods are sensitive to the production conditions because of microbial and enzymatic activities, which requires intelligent flavor sensing system (IFSS) to monitor and optimize the production process based on the flavor properties. As the simulation system of human olfaction and gustation, IFSS has been widely used in the field of food with the characteristics of nondestructive, pollution-free, and real-time detection. This paper reviews the application of IFSS in the control of fermentation, ripening, and shelf life, and the potential in the identification of quality differences and flavor-producing microbes in fermented foods. The survey found that electronic nose (tongue) is suitable to monitor fermentation process and identify food authenticity in real time based on the changes of flavor profile. Gas chromatography-ion mobility spectrometry and nuclear magnetic resonance technology can be used to analyze the flavor metabolism of fermented foods at various production stages and explore the correlation between flavor substances and microorganisms.

Electronic nose and wireless sensor network for environmental monitoring application in pulp and paper industry: a review

Pulp and paper industries emit various odorous gases during the pulp production and paper-making phase, which are unpleasant and have harmful effects on the human body. The working staffs are continuously exposed to these gases and develop various health issues. Hence, regular monitoring and analysis of such gases are necessary to avoid any sudden high concentration exposure and to prevent adverse health effects on the staff. An electronic nose (EN) has an array of gas sensors with an alert system for early detection of gases. Various ENs have been developed for varying applications till date. The detailed knowledge of the sensors used, their sensitivity and technology is helpful in development of any EN. The objective of this study is to comprehensively review various developed ENs with respect to their gas sensing and pattern recognition (PR) technologies. The information on gases released from pulp and paper industries is also compiled. The evolution of EN technology, its various applications, challenges in developing EN and its utility in safeguarding the industrial workers' life have been described. Further, gap analysis among previously developed EN, contemporary EN and wireless sensor network (WSN) is elaborated. It will facilitate future researchers for better selection of sensors and PR technologies while developing EN. The commonly used sensing technologies are described with their advantages, disadvantages and working principles. Metal oxide semiconductor (MOS) gas sensor and ANN algorithm show better result and hence recommended in the development of EN, whereas ZigBee protocol has been widely used for WSN.

Fx-Net and PureNet: Convolutional Neural Network architecture for discrimination of Chronic Obstructive Pulmonary Disease from smokers and healthy subjects through electronic nose signals

As one of the most reliable and significant indicators, Chronic Obstructive Pulmonary Disease (COPD) becomes a robust predictor of lung cancer early detection, the world's leading cause of cancer death. One of the methods is to analyze the Volatile Organic Compounds (VOCs) in exhaled breath using electronic noses (E-noses), which have become emerging tools for analyzing breath because of their potential and promising technology for diagnosing. However, the signal processing of the E-Nose sensor becomes vital in exposing information about the subject condition, which most researchers strive to accomplish. We proposed a Convolutional Neural Network (CNN) architecture to classify COPD in smokers and non-smokers, healthy subjects, and smokers from E-Nose signals to contribute to this field. Two models were constructed following E-Nose signal processing state-of-the-arts. One was by combined feature extraction and classifier, and the second was by CNN, which directly processed the raw signal. In addition, various feature extraction and classifier (Machine Learning and CNN) used in prior research were investigated. Using 3K and 5K Fold cross-validation results demonstrated that our proposed models outperformed in Kernel Principal Component Analysis (KPCA) with Fx-ConvNet and Pure-ConvNet. They all reached maximum F1-Score with zero standard deviation values indicating a consistent result. Further experiments also showed that KPCA contributed to the increasing performance of some classifiers with average F1-Score 0.933 and 0.068 as standard deviation values.

Wireless portable bioelectronic nose device for multiplex monitoring toward food freshness/spoilage

Monitoring food freshness/spoilage is important to ensure food quality and safety. Current methods of food quality monitoring are mostly time-consuming and labor intensive processes that require massive analytical equipment. In this study, we developed a portable bioelectronic nose (BE-nose) integrated with trace amine-associated receptor (TAAR) nanodiscs (NDs), allowing food quality monitoring via the detection of food spoilage indicators, including the biogenic amines cadaverine (CV) and putrescine (PT). The olfactory receptors TAAR13c and TAAR13d, which have specific affinities for CV and PT, were produced and successfully reconstituted in ND structures. TAAR13 NDs BE-nose-based side-gated field-effect transistor (SG-FET) system was constructed by utilizing a graphene micropattern (GM) into which two types of olfactory NDs (TAAR13c ND and TAAR13d ND) were introduced, and this system showed ultrahigh sensitivity for a limit of detection (LOD) of 1 fM for CV and PT. Moreover, the binding affinities between the TAAR13 NDs and the indicators were confirmed by a tryptophan fluorescence quenching assay and biosimulations, in which the specific binding site was confirmed. Gas-phase indicators were detected by the TAAR13 NDs BE-nose platform, and the LODs for CV and PT were confirmed to be 26.48 and 7.29 ppb, respectively. In addition, TAAR13 NDs BE-nose was fabricated with commercial gas sensors as a portable platform for the measurement of NH₃ and H₂S, multiplexed monitoring was achieved with similar performance, and the change ratio of the indicators was observed in a real sample. The integration of commercial gas sensors on a BE-nose enhanced the accuracy and reliability for the quality monitoring of real food samples. These results indicate that the portable TAAR13 NDs BE-nose can be used to monitor CV and PT over a wide range of concentrations, therefore, the electronic nose platform can be utilized for monitoring the freshness/spoilage step in various foods.

Electronic nose homogeneous data sets for beef quality classification and microbial population prediction

Objectives

In recent years, research on the use of electronic noses (e-nose) has developed rapidly, especially in the medical and food fields. Typically, e-nose is coupled with machine learning algorithms to detect or predict multiple sensory classes in a given sample. In many cases, comprehensive and complete experiments are required to ensure the generalizability of the predictive model. For this reason, homogeneous data sets are important to use. Homogeneous data sets refer to the data sets obtained from different observations in almost similar environmental condition. In this data article, e-nose homogeneous data sets are provided for beef quality classification and microbial population prediction.

Data description

This data set is originated from 12 type of beef cuts. The process of beef spoilage is recorded using 11 Metal-Oxide Semiconductor (MOS) gas sensors for 2220 min. The formal standards, issued by the Meat Standards Committee, are used as a reference in labeling beef quality. Based on the number of microbial populations, meat quality was grouped into four classes, namely excellent, good, acceptable, and spoiled. The data set is formatted in "xlsx" file. Each sheet represents one beef cut. Moreover, data sets are good cases for feature selection algorithm stability test, especially to solve sensor array optimization problems.

Hybrid learning method based on feature clustering and scoring for enhanced COVID-19 breath analysis by an electronic nose

Breath pattern analysis based on an electronic nose (e-nose), which is a noninvasive, fast, and low-cost method, has been continuously used for detecting human diseases, including the coronavirus disease 2019 (COVID-19). Nevertheless, having big data with several available features is not always beneficial because only a few of them will be relevant and useful to distinguish different breath samples (i.e., positive and negative COVID-19 samples). In this study, we develop a hybrid machine learning-based algorithm combining hierarchical agglomerative clustering analysis and permutation feature importance method to improve the data analysis of a portable e-nose for COVID-19 detection (GeNose C19). Utilizing this learning approach, we can obtain an effective and optimum feature combination, enabling the reduction by half of the number of employed sensors without downgrading the classification model performance. Based on the cross-validation test results on the training data, the hybrid algorithm can result in accuracy, sensitivity, and specificity values of (86 ± 3)%, (88 ± 6)%, and (84 ± 6)%, respectively. Meanwhile, for the testing data, a value of 87% is obtained for all the three metrics. These results exhibit the feasibility of using this hybrid filter-wrapper feature-selection method to pave the way for optimizing the GeNose C19 performance.

Direct authentication and composition quantitation of red wines based on Tri-step infrared spectroscopy and multivariate data fusion

A robust data fusion strategy integrating Tri-step infrared spectroscopy (IR) with electronic nose (E-nose) was established for rapid qualitative authentication and quantitative evaluation of red wines using Cabernet Sauvignon as an example. The chemical fingerprints of four types of wines were thoroughly interpreted by Tri-step IR, and the defined spectral fingerprint region of alcohol and sugar was 1200-950 cm^-1. The wine types were authenticated by IR-based principal component analysis (PCA). Furthermore, ten quantitative models by partial least squares (PLS) were built to evaluate alcohol and total sugar contents. In particular, the model based on the fusion datasets of spectral fingerprint region and E-nose was superior to the others, in which RMSEP reduced by 47.95% (alcohol) and 79.90% (total sugar), r_p increased by 11.95% and 43.47%, and RPD >3.0. The developed methodology would be applicable for mass screening and rapid multi-chemical-component quantification of wines in a more comprehensive and efficient manner.

A comprehensive study for taste and odor compounds using electronic tongue and nose in broccoli stem with different thermal processing

This study analyzed taste and odor profiles in broccoli stems with different methods of thermal processing using electronic tongue and electronic nose. In electronic tongue analysis, umami and bitterness were obviously changed upon thermal processing, however, saltiness, sweetness, and sourness showed slight variations. Between raw and thermally processed broccolis, microwaved broccoli showed the highest changes of tastes based on raw broccoli, however, blanched broccoli showed similar tastes to raw broccoli compared with the others. In electronic nose analysis, a total of 21 volatiles in broccolis were analyzed. Sulfur-containing volatiles were changed via thermal steps, and the generation and reduction of sulfur-containing compounds have occurred (i.e. methnaethiol, 2,4,5-trimethylthiazole). In addition, some of the thermal steps (oven-heating, microwave heating, air-frying) have occurred Maillard reaction, and thus pyridine was generated. Therefore, this study can provide flavor data in broccoli, and contribute to further research for flavor characteristics in broccoli using electronic sensors.

Diversity of microbiota, microbial functions, and flavor in different types of low-temperature Daqu

Light-flavor Baijiu is made from grain materials using a combination of three types of low-temperature Daqu (Hongxin, Houhuo, and Qingcha). This study comprehensively examined the microbial structure, microbial functions, and flavor characteristics of the three types of low-temperature Daqu using high-throughput sequencing and electronic senses, and it further clarified the relationship between the microbiota and flavor in low-temperature Daqu. The results showed that Hongxin had the highest bacterial richness and diversity, while Houhuo had the lowest. Both fungal richness and diversity were significantly higher in Qingcha than in Hongxin and Houhuo. The differences in peak temperature during Daqu-making led to significant differences in the structure of microbial communities, microbial functions, and flavor quality in the three types of low-temperature Daqu, and could be leveraged for screening and enriching functional microorganisms for Baijiu-making. Co-exclusion patterns between lactic acid bacteria and Bacillus in low-temperature Daqu resulted in a negative correlation between amino acid transport metabolism and carbohydrate transport metabolism. The different types of low-temperature Daqu had distinct flavor profiles, and the differences in the taste profiles were more significant. Dominated by Thermoactinomyces and Lactobacillus, and together with Saccharopolyspora, Bacillus, Streptomyces, Saccharomycopsis, and Thermoascus, they formed the core microbiota that influencing the flavor of low-temperature Daqu. The bacteria mainly influenced the taste of low-temperature Daqu, whereas the fungi mainly influenced the aroma. Each type of low-temperature Daqu contributed to the flavor of light-flavor Baijiu: Hongxin could elevate the levels of aromatic compounds, Houhuo could regulate the bitterness and sourness, and Qingcha could inhibit the generation of sulfur organic compounds. The results of the present study enrich and refine our knowledge of low-temperature Daqu, promoting the further evolution of traditional brewing methods.

Investigation of fermentation-induced changes in the volatile compounds of Trachinotus ovatus (meixiangyu) based on molecular sensory and interpretable machine-learning techniques: Comparison of different fermentation stages

Fermented golden pomfret (Trachinotus ovatus) is appreciated by local consumers owing to its distinct flavor. Electronic nose (E-nose) and gas chromatography-ion mobility spectrometry (GC-IMS) technologies were used to analyze the changes in volatile compounds responsible for evolution of the golden pomfret odor profile during fermentation. Forty-five ion peaks were detected using GC-IMS. Although aldehydes represented the major initial volatile compound group, their levels decreased as fermentation proceeded. Between 3 and 15 days, increased levels of esters contributed to a stable volatile organic compounds profile. After 18 days, high levels of indole and pyrazines were detected. Eleven key volatile compounds were screened based on partial least squares discriminant analysis (PLS-DA). Back propagation artificial neural network (BP-ANN) predicted the fermentation stage enabling the development of better strategies to regulate golden pomfret fermentation. This study provided a theoretical basis for real-time monitoring and quality control of Chinese fermented golden pomfet.

Applying the electronic nose for pre-operative SARS-CoV-2 screening

BACKGROUND

Infection with SARS-CoV-2 causes corona virus disease (COVID-19). The most standard diagnostic method is reverse transcription-polymerase chain reaction (RT-PCR) on a nasopharyngeal and/or an oropharyngeal swab. The high occurrence of false-negative results due to the non-presence of SARS-CoV-2 in the oropharyngeal environment renders this sampling method not ideal. Therefore, a new sampling device is desirable. This proof-of-principle study investigated the possibility to train machine-learning classifiers with an electronic nose (Aeonose) to differentiate between COVID-19-positive and negative persons based on volatile organic compounds (VOCs) analysis.

METHODS

Between April and June 2020, participants were invited for breath analysis when a swab for RT-PCR was collected. If the RT-PCR resulted negative, the presence of SARS-CoV-2-specific antibodies was checked to confirm the negative result. All participants breathed through the Aeonose for five minutes. This device contains metal-oxide sensors that change in conductivity upon reaction with VOCs in exhaled breath. These conductivity changes are input data for machine learning and used for pattern recognition. The result is a value between - 1 and + 1, indicating the infection probability.

RESULTS

219 participants were included, 57 of which COVID-19 positive. A sensitivity of 0.86 and a negative predictive value (NPV) of 0.92 were found. Adding clinical variables to machine-learning classifier via multivariate logistic regression analysis, the NPV improved to 0.96.

CONCLUSIONS

The Aeonose can distinguish COVID-19 positive from negative participants based on VOC patterns in exhaled breath with a high NPV. The Aeonose might be a promising, non-invasive, and low-cost triage tool for excluding SARS-CoV-2 infection in patients elected for surgery.

Discrimination and characterization of the volatile organic compounds in eight kinds of huajiao with geographical indication of China using electronic nose, HS-GC-IMS and HS-SPME-GC-MS

Huajiao (Zanthoxylum bungeanum maxim. and Zanthoxylum armatum DC.) is a highly prized spice in China due to its distinctive aroma and taste. The volatile organic compounds (VOCs) of eight kinds of red and green huajiao which varied according to geographical indication of P.R. China were evaluated 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). Results showed that red huajiao emitted more terpenes, esters, and fewer alcohols than green huajiao. Partial least squares-discriminant analysis based on GC-MS and GC-IMS data was revealed a good classifying tool for huajiao from different original habitats. Four and eight aroma substances were selected as the potential markers by the variable importance in projection (VIP) variable selection method, respectively. The results of the current study provide a useful basis in the huajiao aroma difference study. Additionally, a rapid huajiao aroma analysis method using GC-IMS was developed.

Classification of geographical origin of kimchi by volatile compounds analysis using an electronic nose

Food authenticity is one of the largest concerns in recent days. As kimchi has been a global food, its production origin has been important issue, particularly due to the large import from China. Among the potential methods, electronic nose which can measure volatile compounds in foods is considered to be a powerful device for identifying country of production. This study is to classify 69 kinds of kimchi produced in South Korea (39) and China (30) by analyzing volatile compounds in kimchi using electronic nose-mass spectrometry. Two widely used multivariate analyses, discriminant function analysis and principal component analysis, were used. Results showed that both multivariate analyses can completely separate Korean and Chinese kimchi using 10 kinds of molecular weights among 10-160 amu. The results indicate that the volatile compounds in kimchi are a suitable target to determine the geographical origin of kimchi.

Detection of COPD and Lung Cancer with electronic nose using ensemble learning methods

BACKGROUND AND AIMS

The chemical gas sensor array based electronic-nose (e-nose) devices with machine learning algorithms can detect and differentiate expelled breath samples of patients with various respiratory ailments and controls. It is by the recognition of levels and variations of volatile organic compounds (VOC) in the exhaled air. Here, we aimed to differentiate chronic obstructive pulmonary disease (COPD) and lung cancer from controls.

MATERIALS AND METHODS

This work presents the details of the developed e-nose system, selection of the study subjects, exhaled breath sampling method and detection, and the data analysis algorithms. The developed device is tested in 199 participants including 93 controls, 55 COPD patients, and 51 lung cancer patients. The main advantage of the device is robustness and portability and cost-effectiveness.

RESULTS

In the training phase and model validation phase, the ensemble learning method XGBoost outperformed the other two models. In the prediction of lung cancer, XGBoost method attained a classification accuracy of 79.31%. In COPD prediction also the same method had given the better results with 76.67% accuracy.

CONCLUSION

The e-nose system developed with TGS gas sensors was portable, low cost, and gave a rapid response. It has been demonstrated that the VOC profiles of patients with pulmonary diseases and healthy controls are different and hence the e-nose system can be used as a potential diagnostic device for patients with lung diseases.

Detection of Salmonella Typhimurium contamination levels in fresh pork samples using electronic nose smellprints in tandem with support vector machine regression and metaheuristic optimization algorithms

Rapid detection and quantification of bacterial foodborne pathogens are crucial in reducing the incidence of diseases associated with meat products contaminated with pathogens. For the identification, discrimination and quantification of Salmonella Typhimurium contamination in pork samples, a commercial electronic nose with ten (10) metal oxide semiconductor sensor array is applied. Principal component analysis was successfully applied for discrimination of inoculated samples and inoculated samples at different contaminant levels. Support vector machine regression (SVMR) together with a metaheuristic framework using genetic algorithm (GA), particle swarm optimization (PSO), and grid searching (GS) optimization algorithms were applied for S. Typhimurium quantification. Although SVMR results were satisfactory, SVMR hyperparameter tuning (c and g) by PSO, GA and GS showed superior performance of the models. The order of the prediction accuracy based on the prediction set was GA-SVMR (R P 2 = 0.989; RMSEP = 0.137; RPD = 14.93) > PSO-SVMR (R P 2 = 0.986; RMSEP = 0.145; RPD = 14.11) > GS-SVMR (R P 2 = 0.966; RMSEP = 0.148; RPD = 13.82) > SVMR (R P 2 = 0.949; RMSEP = 0.162; RPD = 12.63). GA-SVMR's proposed approach was fairly more effective and retained an excellent prediction accuracy. A clear relationship was identified between odor analysis results, and reference traditional microbial test, indicating that the electronic nose is useful for accurate microbial volatile organic compound evaluation in the quantification of S. Typhimurium in a food matrix.

The smell of lung disease: a review of the current status of electronic nose technology

There is a need for timely, accurate diagnosis, and personalised management in lung diseases. Exhaled breath reflects inflammatory and metabolic processes in the human body, especially in the lungs. The analysis of exhaled breath using electronic nose (eNose) technology has gained increasing attention in the past years. This technique has great potential to be used in clinical practice as a real-time non-invasive diagnostic tool, and for monitoring disease course and therapeutic effects. To date, multiple eNoses have been developed and evaluated in clinical studies across a wide spectrum of lung diseases, mainly for diagnostic purposes. Heterogeneity in study design, analysis techniques, and differences between eNose devices currently hamper generalization and comparison of study results. Moreover, many pilot studies have been performed, while validation and implementation studies are scarce. These studies are needed before implementation in clinical practice can be realised. This review summarises the technical aspects of available eNose devices and the available evidence for clinical application of eNose technology in different lung diseases. Furthermore, recommendations for future research to pave the way for clinical implementation of eNose technology are provided.