Electronic senses and UPLC-Q-TOF/MS combined with chemometrics analyses of Cynanchum species (Baishouwu).

INTRODUCTION: Baishouwu, derived from Cynanchum auriculatum (CA) Royle ex Wight, Cynanchum bungei (CB) Decne., and Cynanchum wilfordii (CW) (Maxim.) Hemsl., is a valuable traditional Chinese medicine. CA is also recognized as a new food resource by China's National Health Commission. Given the considerable variations in flavor and chemical composition among these species and lack of their qualitative assessments, accurately differentiating between the species constituting Baishouwu is essential. OBJECTIVE: To develop a method combining electronic tongue (E-tongue), electronic nose (E-nose), and ultra-performance liquid chromatography-quadrupole-time of flight/mass spectrometry (UPLC-Q-TOF/MS) to differentiate between Baishouwu samples. MATERIAL AND METHODS: Fifteen batches of Baishouwu samples were analyzed using E-tongue, E-nose, and UPLC-Q-TOF/MS. Flavor differences and key differential metabolites were determined through principal component analysis and orthogonal partial least squares discriminant analysis. RESULTS: E-tongue results revealed umami, sweetness, and richness as the predominant flavors of Baishouwu, with CA having the highest umami response, CW exhibiting the highest bitterness, and CB the highest sweetness. E-nose sensors showed consistent responses across species, with variations in signal strength; W1W and W2W sensors showed the highest response values. A total of 158 and 41 characteristic variables in the positive and negative ion modes, respectively, were selected as candidate differential metabolites, of which 29 and 14 were confirmed through database comparison. Eight critical differential metabolites, including C21 steroids and acetophenone compounds, were identified. CONCLUSION: This study presents a strategy for differentiating among the species constituting Baishouwu, providing a basis for broader application and establishing quality standards for these medicinal compounds.

Evaluating the Non-Invasive Measurement of Apple Aroma Using Electronic Nose Device through Comparison with Direct Mass Spectrometry, Sugar Content, and Ripeness Measurements.

To compare apple aroma intensities, apples were analyzed from the calyx side (on the opposite side of the stem) using an electronic nose (e-nose) sensor device and direct mass spectrometry. The results indicated that the sensor value tended to increase in accordance with the total intensity of apple aroma components measured by direct mass spectrometry. In addition, the e-nose sensor values for apple aroma did not correlate with the sugar content and ripeness measurements using optical sensors. Moreover, the relative standard deviations of repeatability and intermediate precision in the measurement of apple flavor (apple lip balm) were within 1.36-9.96%. Similar to the utilization of sugar content and ripeness values, the aroma measured from the calyx side can be potentially used for apple evaluation.

Ammonia Detection by Electronic Noses for a Safer Work Environment.

Providing employees with proper work conditions should be one of the main concerns of any employer. Even so, in many cases, work shifts chronically expose the workers to a wide range of potentially harmful compounds, such as ammonia. Ammonia has been present in the composition of products commonly used in a wide range of industries, namely production in lines, and also laboratories, schools, hospitals, and others. Chronic exposure to ammonia can yield several diseases, such as irritation and pruritus, as well as inflammation of ocular, cutaneous, and respiratory tissues. In more extreme cases, exposure to ammonia is also related to dyspnea, progressive cyanosis, and pulmonary edema. As such, the use of ammonia needs to be properly regulated and monitored to ensure safer work environments. The Occupational Safety and Health Administration and the European Agency for Safety and Health at Work have already commissioned regulations on the acceptable limits of exposure to ammonia. Nevertheless, the monitoring of ammonia gas is still not normalized because appropriate sensors can be difficult to find as commercially available products. To help promote promising methods of developing ammonia sensors, this work will compile and compare the results published so far.

Influence of the Degree of Fruitiness on the Quality Assessment of Virgin Olive Oils Using Electronic Nose Technology.

The electronic nose is a non-invasive technology suitable for the analysis of edible oils. One of the practical applications in the olive oil industry is the classification of virgin oils based on their sensory characteristics. Notwithstanding that this technology, at this stage, cannot realistically replace the currently used methods, it is fruitful for a preliminary analysis of the oil quality. This work makes use of this technology to develop a methodology for the detection of the threshold by which an extra-virgin olive oil (EVOO) drops into the virgin olive oil (VOO) category. With this aim, two features were studied: the level of fruitiness level and the type of defect. The results showed a greater influence of the level of fruitiness than the type of defect in the determination of the detection threshold. Furthermore, three of the sensors (S2, S7 and S9) of the commercial e-nose PEN3 were identified as the most discriminating in the classification between EVOO and VOO oils.

Real-Time Monitoring of Odour Emissions at the Fenceline of a Waste Treatment Plant by Instrumental Odour Monitoring Systems: Focus on Training Methods.

Waste treatment plants (WTPs) often generate odours that may cause nuisance to citizens living nearby. In general, people are becoming more sensitive to environmental issues, and particularly to odour pollution. Instrumental Odour Monitoring Systems (IOMSs) represent an emerging tool for continuous odour measurement and real-time identification of odour peaks, which can provide useful information about the process operation and indicate the occurrence of anomalous conditions likely to cause odour events in the surrounding territories. This paper describes the implementation of two IOMSs at the fenceline of a WTP, focusing on the definition of a specific experimental protocol and data processing procedure for dealing with the interferences of humidity and temperature affecting sensors' responses. Different approaches for data processing were compared and the optimal one was selected based on field performance testing. The humidity compensation model developed proved to be effective, bringing the IOMS classification accuracy above 95%. Also, the adoption of a class-specific regression model compared to a global regression model resulted in an odour quantification capability comparable with those of the reference method (i.e., dynamic olfactometry). Lastly, the validated models were used to process the monitoring data over a period of about one year.

Recent Advances and Future Perspectives in the E-Nose Technologies Addressed to the Wine Industry.

Electronic nose devices stand out as pioneering innovations in contemporary technological research, addressing the arduous challenge of replicating the complex sense of smell found in humans. Currently, sensor instruments find application in a variety of fields, including environmental, (bio)medical, food, pharmaceutical, and materials production. Particularly the latter, has seen a significant increase in the adoption of technological tools to assess food quality, gradually supplanting human panelists and thus reshaping the entire quality control paradigm in the sector. This process is happening even more rapidly in the world of wine, where olfactory sensory analysis has always played a central role in attributing certain qualities to a wine. In this review, conducted using sources such as PubMed, Science Direct, and Web of Science, we examined papers published between January 2015 and January 2024. The aim was to explore prevailing trends in the use of human panels and sensory tools (such as the E-nose) in the wine industry. The focus was on the evaluation of wine quality attributes by paying specific attention to geographical origin, sensory defects, and monitoring of production trends. Analyzed results show that the application of E-nose-type sensors performs satisfactorily in that trajectory. Nevertheless, the integration of this type of analysis with more classical methods, such as the trained sensory panel test and with the application of destructive instrument volatile compound (VOC) detection (e.g., gas chromatography), still seems necessary to better explore and investigate the aromatic characteristics of wines.

Analysis of Wheat Grain Infection by Fusarium Mycotoxin-Producing Fungi Using an Electronic Nose, GC-MS, and qPCR.

Fusarium graminearum and F. culmorum are considered some of the most dangerous pathogens of plant diseases. They are also considerably dangerous to humans as they contaminate stored grain, causing a reduction in yield and deterioration in grain quality by producing mycotoxins. Detecting Fusarium fungi is possible using various diagnostic methods. In the manuscript, qPCR tests were used to determine the level of wheat grain spoilage by estimating the amount of DNA present. High-performance liquid chromatography was performed to determine the concentration of DON and ZEA mycotoxins produced by the fungi. GC-MS analysis was used to identify volatile organic components produced by two studied species of Fusarium. A custom-made, low-cost, electronic nose was used for measurements of three categories of samples, and Random Forests machine learning models were trained for classification between healthy and infected samples. A detection performance with recall in the range of 88-94%, precision in the range of 90-96%, and accuracy in the range of 85-93% was achieved for various models. Two methods of data collection during electronic nose measurements were tested and compared: sensor response to immersion in the odor and response to sensor temperature modulation. An improvement in the detection performance was achieved when the temperature modulation profile with short rectangular steps of heater voltage change was applied.

Distinguishing between Wheat Grains Infested by Four Fusarium Species by Measuring with a Low-Cost Electronic Nose.

An electronic device based on the detection of volatile substances was developed in response to the need to distinguish between fungal infestations in food and was applied to wheat grains. The most common pathogens belong to the fungi of the genus Fusarium: F. avenaceum, F. langsethiae, F. poae, and F. sporotrichioides. The electronic nose prototype is a low-cost device based on commercially available TGS series sensors from Figaro Corp. Two types of gas sensors that respond to the perturbation are used to collect signals useful for discriminating between the samples under study. First, an electronic nose detects the transient response of the sensors to a change in operating conditions from clean air to the presence of the gas being measured. A simple gas chamber was used to create a sudden change in gas composition near the sensors. An inexpensive pneumatic system consisting of a pump and a carbon filter was used to supply the system with clean air. It was also used to clean the sensors between measurement cycles. The second function of the electronic nose is to detect the response of the sensor to temperature disturbances of the sensor heater in the presence of the gas to be measured. It has been shown that features extracted from the transient response of the sensor to perturbations by modulating the temperature of the sensor heater resulted in better classification performance than when the machine learning model was built from features extracted from the response of the sensor in the gas adsorption phase. By combining features from both phases of the sensor response, a further improvement in classification performance was achieved. The E-nose enabled the differentiation of F. poae from the other fungal species tested with excellent performance. The overall classification rate using the Support Vector Machine model reached 70 per cent between the four fungal categories tested.

Development of a Neural Network for Target Gas Detection in Interdigitated Electrode Sensor-Based E-Nose Systems.

In this study, a neural network was developed for the detection of acetone, ethanol, chloroform, and air pollutant NO2 gases using an Interdigitated Electrode (IDE) sensor-based e-nose system. A bioimpedance spectroscopy (BIS)-based interface circuit was used to measure sensor responses in the e-nose system. The sensor was fed with a sinusoidal voltage at 10 MHz frequency and 0.707 V amplitude. Sensor responses were sampled at 100 Hz frequency and converted to digital data with 16-bit resolution. The highest change in impedance magnitude obtained in the e-nose system against chloroform gas was recorded as 24.86 Ω over a concentration range of 0-11,720 ppm. The highest gas detection sensitivity of the e-nose system was calculated as 0.7825 Ω/ppm against 6.7 ppm NO2 gas. Before training with the neural network, data were filtered from noise using Kalman filtering. Principal Component Analysis (PCA) was applied to the improved signal data for dimensionality reduction, separating them from noise and outliers with low variance and non-informative characteristics. The neural network model created is multi-layered and employs the backpropagation algorithm. The Xavier initialization method was used for determining the initial weights of neurons. The neural network successfully classified NO2 (6.7 ppm), acetone (1820 ppm), ethanol (1820 ppm), and chloroform (1465 ppm) gases with a test accuracy of 87.16%. The neural network achieved this test accuracy in a training time of 239.54 milliseconds. As sensor sensitivity increases, the detection capability of the neural network also improves.

Sub-PPB Detection with Gas-Phase Multiphoton Electron Extraction Spectroscopy under Ambient Conditions.

Multiphoton electron extraction spectroscopy (MEES) is an advanced analytical technique that has demonstrated exceptional sensitivity and specificity for detecting molecular traces on solid and liquid surfaces. Building upon the solid-state MEES foundations, this study introduces the first application of MEES in the gas phase (gas-phase MEES), specifically designed for quantitative detection of gas traces at sub-part per billion (sub-PPB) concentrations under ambient atmospheric conditions. Our experimental setup utilizes resonant multiphoton ionization processes using ns laser pulses under a high electrical field. The generated photoelectron charges are recorded as a function of the laser's wavelength. This research showcases the high sensitivity of gas-phase MEES, achieving high spectral resolution with resonant peak widths less than 0.02 nm FWHM. We present results from quantitative analysis of benzene and aniline, two industrially and environmentally significant compounds, demonstrating linear responses in the sub-PPM and sub-PPB ranges. The enhanced sensitivity and resolution of gas-phase MEES offer a powerful approach to trace gas analysis, with potential applications in environmental monitoring, industrial safety, security screening, and medical diagnostics. This study confirms the advantages of gas-phase MEES over many traditional optical spectroscopic methods and demonstrates its potential in direct gas-trace sensing in ambient atmosphere.

Comprehensive Analysis Reveals the Difference in Volatile Oil between Bupleurum marginatum var. stenophyllum (Wolff) Shan et Y. Li and the Other Four Medicinal Bupleurum Species.

Volatile oil serves as a traditional antipyretic component of Bupleuri Radix. Bupleurum marginatum var. stenophyllum (Wolff) Shan et Y. Li belongs to the genus Bupleurum and is distinguished for its high level of saikosaponins and volatile oils; nonetheless, prevailing evidence remains inconclusive regarding its viability as an alternative resource of other official species. This study aims to systematically compare the volatile oil components of both dried and fresh roots of B. marginatum var. stenophyllum and the four legally available Bupleurum species across their chemical, molecular, bionics, and anatomical structures. A total of 962 compounds were determined via GC-MS from the dried roots; B. marginatum var. stenophyllum showed the greatest differences from other species in terms of hydrocarbons, esters, and ketones, which was consistent with the results of fresh roots and the e-nose analysis. A large number of DEGs were identified from the key enzyme family of the monoterpene synthesis pathway in B. marginatum var. stenophyllum via transcriptome analysis. The microscopic observation results, using different staining methods, further showed the distinctive high proportion of phloem in B. marginatum var. stenophyllum, the structure which produces volatile oils. Together, these pieces of evidence hold substantial significance in guiding the judicious development and utilization of Bupleurum genus resources.

Comparative Analysis of Volatile Compounds in the Flower Buds of Three Panax Species Using Fast Gas Chromatography Electronic Nose, Headspace-Gas Chromatography-Ion Mobility Spectrometry, and Headspace Solid Phase Microextraction-Gas Chromatography-Mass Spectrometry Coupled with Multivariate Statistical Analysis.

The flower buds of three Panax species (PGF: P. ginseng; PQF: P. quinquefolius; PNF: P. notoginseng) widely consumed as health tea are easily confused in market circulation. We aimed to develop a green, fast, and easy analysis strategy to distinguish PGF, PQF, and PNF. In this work, fast gas chromatography electronic nose (fast GC e-nose), headspace-gas chromatography-ion mobility spectrometry (HS-GC-IMS), and headspace solid phase microextraction-gas chromatography-mass spectrometry (HS-SPME-GC-MS) were utilized to comprehensively analyze the volatile organic components (VOCs) of three flowers. Meanwhile, a principal component analysis (PCA) and heatmap were applied to distinguish the VOCs identified in PGF, PQF, and PNF. A random forest (RF) analysis was used to screen key factors affecting the discrimination. As a result, 39, 68, and 78 VOCs were identified in three flowers using fast GC e-nose, HS-GC-IMS, and HS-SPME-GC-MS. Nine VOCs were selected as potential chemical markers based on a model of RF for distinguishing these three species. Conclusively, a complete VOC analysis strategy was created to provide a methodological reference for the rapid, simple, and environmentally friendly detection and identification of food products (tea, oil, honey, etc.) and herbs with flavor characteristics and to provide a basis for further specification of their quality and base sources.

Destructive and non-destructive early detection of postharvest noble rot (Botrytis cinerea) in wine grapes aimed at producing high-quality wines.

BACKGROUND: Botrytis cinerea (Bc) is the causative agent of gray mold disease in wine grape bunches. Under particular climatic and edaphic conditions, typical of some wine regions, the grapes infected by this fungus can develop noble rot, the basic phenomenon for the production of sweet botrytized wines or some high-quality dry wines, such as Amarone. The possibility of early detection of noble rot on plants and at postharvest is an interesting option for managing botrytized wines. RESULTS: The present work aimed at early detection of noble rot and monitoring its development, at postharvest, on Trebbiano wine grapes by means of destructive and non-destructive analytical approaches (e.g., electronic nose and near-infrared spectroscopy). The development of Bc led to substantial modifications in grape composition, including dehydration, biosynthesis, and accumulation of different compounds due to Bc metabolism, grape stress responses, or both. However, these modifications are appreciable, notably at advanced stages of infection. Consequently, a specific focus was to monitor the infection in the first 72 h post inoculation for testing, potentially through non-destructive technologies, and to identify the real early stages of Bc development. CONCLUSION: The destructive chemical analyses performed over the 16 monitored days confirmed what is widely reported in the literature regarding the metabolic/compositional changes that occur following the development of Bc. Moreover, non-destructive technologies allowed us to identify the evolution of Bc, even at early stages of its presence. © 2023 The Authors. Journal of The Science of Food and Agriculture published by John Wiley & Sons Ltd on behalf of Society of Chemical Industry.

Optimizing the winemaking process: NIR spectroscopy and e-nose analysis for the online monitoring of fermentation.

BACKGROUND: In the winemaking process, the rapid determination of specific quality parameters such as sugar content, pH, acidity, concentrations of phenolic compounds, anthocyanins and volatile organic compounds is crucial for high-quality wine production. Traditional analytical methods allow for precise quantification of these parameters but are time-consuming and expensive. This article explores the potential application of non-destructive analytical technique (NDAT) (near infra-red [NIR] and e-nose), as efficient alternatives for online monitoring of fermentation working on two different winemaking tanks and applying chemometrics to develop predictive models to correlate non-destructive and analytical data. RESULTS: NIR measurements have been used to build principal components regression models, showing good prediction capability for polyphenols, anthocyanins, glucose and fructose. Both offline and online e-nose applications demonstrate good capability of discriminating different fermentation phases, in agreement with aromatic profile changes observed via gas chromatography-mass spectrometry analysis. Moreover, correlation analysis reveals the potential of quartz microbalances, Taguchi Gas Sensors and H2 S sensors in predicting the concentration of compounds of great interest for winemaking (e.g. C6 alcohols, ketones, terpenes and ethyl esters) highlighting the robust connection between sensor data and specific chemical classes. CONCLUSION: This research aims to showcase the potential employment of NDAT for online monitoring the evolution of must composition during fermentation. The proposed methods could potentially fulfil a longstanding requirement of winemakers, enabling them to closely monitor fermentation allowing the timely making of important technical decisions aimed at achieving oenological objectives in wine production. © 2024 Society of Chemical Industry.

The effect of terroir on volatilome fingerprinting and qualitative attributes of non-irrigated grapes reveals differences on glycosylated aroma compounds.

BACKGROUND: 'Xynisteri' is considered as the reference white grape cultivar in Cyprus with remarkable adaptation to adverse edaphoclimatic conditions and appreciable oenological properties that renders it as an appropriate cultivar for studies within a global context due to climate change. To this aim, two distinct non-irrigated plots with different climatic conditions, soil properties and levels of rainfall were selected; Koilani [KO, altitude 800 m, 76% calcium carbonate (CaCO3) content, pH 7.97, average temperature: 16.5 °C, rainfall: 229 mm] and Kyperounda (KY, altitude 1200 m, CaCO3-free soil, pH 6.47, average temperature: 14.9 °C, rainfall: 658 mm). An array of physiological, biochemical and qualitative indices during successive developmental stages (BBCH 75-89) were determined. During the advanced on-vine developmental stages (BBCH 85-89), the aromatic profile of grapes was assessed with the employment of gas chromatography-mass spectrometry (GC-MS). Such analysis was complemented with non-destructive chemometric analyses. RESULTS: Berry ripening process substantially differed on the examined plots; BBCH 89 stage reached at 267 and 303 Julian days for KO and KY, respectively. Results indicated that berry weight, soluble solids content (SSC) and α-amino nitrogen were higher in KO than in KY, with exception made for ammonium nitrogen content. A total of 75 compounds, including aliphatic alcohols, benzenic compounds, phenols, vanillins, monoterpenes and C13-norisoprenoids were identified and quantified. The variations of mesoclimatic conditions affected the volatile organic compound (VOC) profiles at the fully-ripe stage, showing a considerable rise in glycosylated aroma compounds, especially monoterpenes and benzenic compounds. In particular, the higher amount of glycosylated aroma compounds were obtained in KY berries up to mid-ripe, whereas KO showed higher glycosylated aroma compounds at fully-ripe stage. Results reported herein indicate that aroma profile of 'Xynisteri' grapes varied substantially in the examined terroirs. Interestingly, the limited rainfall in KΟ non-irrigated vine did not compromise qualitative and aromatic properties of berries. CONCLUSIONS: The present study aimed at dissecting the impact of terroir on bush-trained, non-irrigated grapevines of a cultivar appropriate for extreme climate change scenarios. The volatilome fingerprint was highly variable among the examined plots; such results can be further exploited at vinification level towards production of single vineyard premium end products. © 2024 The Author(s). Journal of the Science of Food and Agriculture published by John Wiley & Sons Ltd on behalf of Society of Chemical Industry.

Identification of volatile differential markers in strong-aroma Baijiu based on gas chromatography-electronic nose combined with gas chromatography-time-of-flight mass spectrometry.

BACKGROUND: Baijiu is a traditional Chinese liquor produced from grains through fermentation, distillation, aging and blending. The flavor of Baijiu is influenced by factors such as raw materials, starter, processes and the environment, and since the relationship between these factors and the flavor of Baijiu is still being analyzed, the identification of different Baijiu is still somewhat difficult. In this paper, the volatile differential markers of 42 types of strong-aroma Baijiu of different origin, alcohol content and grade were explored. RESULTS: A total of 24 volatile substances were detected by gas chromatography-electronic nose (GC-E-Nose) and 99 volatile substances were detected by gas chromatography-time-of-flight mass spectrometry (GC-TOF MS). The peak areas of the substances obtained by GC-E-Nose were analyzed by the partial least squares (PLS) method, and the substances with variable importance in projection (VIP) >1 were screened out. Combined with the qualitative results of GC-TOF MS, four substances (isobutyric acid, 2-butanone, 2,3-butanediol and 3-methylbutyric acid) were selected as volatile differential markers for strong-aroma Baijiu. An external standard curve was established to accurately quantify these four substances, and the Kruskal-Wallis test confirmed that the absolute contents of these four substances varied significantly among different samples (P < 0.01). Principal component analysis and hierarchical cluster analysis based on the absolute content of these four substances showed that different samples were prioritized for different alcohol contents. CONCLUSION: These four substances can be used as volatile differential markers of strong-aroma Baijiu samples. This research provides theoretical support for the detection and improvement of Baijiu product quality. © 2024 Society of Chemical Industry.

Machine-Learning-Aided NO2 Discrimination with an Array of Graphene Chemiresistors Covalently Functionalized by Diazonium Chemistry.

Boosted by the emerging need for highly integrated gas sensors in the internet of things (IoT) ecosystems, electronic noses (e-noses) are gaining interest for the detection of specific molecules over a background of interfering gases. The sensing of nitrogen dioxide is particularly relevant for applications in environmental monitoring and precision medicine. Here we present an easy and efficient functionalization procedure to covalently modify graphene layers, taking advantage of diazonium chemistry. Separate graphene layers were functionalized with one of three different aryl rings: 4-nitrophenyl, 4-carboxyphenyl and 4-bromophenyl. The distinct modified graphene layers were assembled with a pristine layer into an e-nose for NO2 discrimination. A remarkable sensitivity to NO2 was demonstrated through exposure to gaseous solutions with NO2 concentrations in the 1-10 ppm range at room temperature. Then, the discrimination capability of the sensor array was tested by carrying out exposure to several interfering gases and analyzing the data through multivariate statistical analysis. This analysis showed that the e-nose can discriminate NO2 among all the interfering gases in a two-dimensional principal component analysis space. Finally, the e-nose was trained to accurately recognize NO2 contributions with a linear discriminant analysis approach, thus providing a metric for discrimination assessment with a prediction accuracy above 95 %.

Trends in artificial aroma sensing by means of electronic nose technologies to advance dairy production - a review.

Controversies surrounding the name and how the electronics nose (e-nose) works have been at the center stage since the advent of the technology. Notwithstanding the controversies, the technology has gained popularity in the sensory analysis of dairy foods, because of its rapid results delivery on product aroma profile or pattern, which can be used to assess quality. This review critically evaluated the advances made in the application of the e-nose or artificial sensory system in the dairy industry, focusing on the evaluation of milk, yoghurt and cheese properties, and the trends and prospects of the technology. Most of the e-nose devices applied in the available scientific publications used sensors such as metal oxide semiconductor sensors (MOS), metal-oxide-semiconductor field-effect transistor (MOSFET), conducting polymers composites and quartz microbalance (QMB), and flame ionization detector FID, in a recent study. Though known for aroma sensing, the technology has been applied to evaluate the shelf life or microbial spoilage and to discriminate dairy products based on the volatile profile composition, as determined by the sensors. In most cases, the limitation of the technology is the inability of it to provide information on the nature of constituting compounds, except in gas chromatography and mass spectrometry-based e-nose systems.

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.

Prostate cancer detection using e-nose and AI for high probability assessment.

This research aims to develop a diagnostic tool that can quickly and accurately detect prostate cancer using electronic nose technology and a neural network trained on a dataset of urine samples from patients diagnosed with both prostate cancer and benign prostatic hyperplasia, which incorporates a unique data redundancy method. By analyzing signals from these samples, we were able to significantly reduce the number of unnecessary biopsies and improve the classification method, resulting in a recall rate of 91% for detecting prostate cancer. The goal is to make this technology widely available for use in primary care centers, to allow for rapid and non-invasive diagnoses.