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Tan Y, Chen Y, Zhao Y, Liu M, Wang Z, Du L, Wu C, Xu X. Recent advances in signal processing algorithms for electronic noses. Talanta 2025; 283:127140. [PMID: 39489071 DOI: 10.1016/j.talanta.2024.127140] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/28/2024] [Revised: 09/25/2024] [Accepted: 10/30/2024] [Indexed: 11/05/2024]
Abstract
Electronic nose (e-nose) technology has emerged as a pivotal tool in various domains, which has been widely utilized for odor identification, concentration evaluation, and prediction tasks. This review provides a comprehensive survey on the most recent advances in the development of e-nose systems and their algorithmic applications, emphasizing the roles of various methodologies and deep learning technologies in odor classification and concentration forecasting. Additionally, we delve into model evaluation methods, including multidimensional performance assessment and cross-validation. Future trends encompass broader application domains, advanced drift correction techniques, comprehensive multifactorial analysis, and enhanced capabilities for dealing with unknown interferents. These trends are set to propel significant breakthroughs in e-nose technology within scientific research and practical applications, solidifying the e-nose system as a crucial tool in many areas such as environmental monitoring, biomedicine, and public safety.
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Othman Kombo K, Nur Hidayat S, Puspita M, Kusumaatmaja A, Roto R, Nirwati H, Susilowati R, Lutfia Haksari E, Wibowo T, Wandita S, Wahyono, Julia M, Triyana K. A machine learning-based electronic nose for detecting neonatal sepsis: Analysis of volatile organic compound biomarkers in fecal samples. Clin Chim Acta 2025; 565:119974. [PMID: 39326694 DOI: 10.1016/j.cca.2024.119974] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/01/2024] [Revised: 09/17/2024] [Accepted: 09/18/2024] [Indexed: 09/28/2024]
Abstract
BACKGROUND Neonatal sepsis is a global health threat, contributing to high morbidity and mortality rates among newborns. Recognizing the profound impact of neonatal sepsis on long-term health outcomes emphasizes the critical need for timely detection to mitigate its consequences and ensure optimal health for the affected newborns. Currently, various diagnostic approaches have been implemented, but they are limited by their invasiveness, high costs, centralized testing, frequent delays, inaccuracies in results, and the need for sophisticated laboratory equipment. METHODS We introduced a novel, non-invasive, cost-efficient, and easy-to-use technology that can provide rapid results at a point-of-care. The technology utilized a lab-built metal oxide semiconductor-based electronic nose (cNose) combined with volatile organic compound (VOC) biomarkers identified through gas chromatography-mass spectrometry (GC-MS) analysis. The system was evaluated using fecal profiling tests involving a total of 32 samples, including 17 positive and 15 negative sepsis, confirmed by blood culture. To assess the performance in discriminating patients from healthy controls, four machine learning algorithms were implemented. RESULTS Based on the cross-validation results, the MLPNN model provided the best results in distinguishing between neonates with positive and negative sepsis, achieving high-performance results of 90.63 % accuracy, 88.24 % sensitivity, and 93.33 % specificity at a 95 % confidence interval. Specific VOCs associated with neonatal sepsis, such as alcohols, acids, and esters, were successfully identified through GC-MS analysis, further validating the diagnostic capability of the cNose device. CONCLUSION The overall observations show the feasibility of using cNose system as a promising tool for real-time and bedside sepsis detection, potentially improving patient outcomes.
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El Kazzy M, Lalis M, Hurot C, Weerakkody JS, Mathey R, Saint-Pierre C, Buhot A, Livache T, Topin J, Moitrier L, Belloir C, Briand L, Hou Y. Study and optimization of the selectivity of an odorant binding protein-based bio electronic nose. Biosens Bioelectron 2025; 268:116879. [PMID: 39504883 DOI: 10.1016/j.bios.2024.116879] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/30/2024] [Revised: 10/22/2024] [Accepted: 10/23/2024] [Indexed: 11/08/2024]
Abstract
Over the past two decades, the use of odorant-binding proteins (OBPs) for the development of biosensors and bioelectronic noses (bioeNs) aimed at detecting and analyzing volatile organic compounds (VOCs) has been the subject of considerable research. However, there is a lack of fundamental studies for better understanding the interaction between OBPs and VOCs in gas phase. In this work, we investigated the effect of two key factors, namely relative humidity (RH) level and immobilization technique, on the selectivity of two OBP-based biosensors in gas phase. Concerning the effect of RH, the results showed that our active OBP (wild-type rat OBP3) lost its selectivity at 0% RH but retained good selectivity at 30% and 50% RH. To better understand the effect of this parameter, the hydration mechanism of the OBP was studied both experimentally and through molecular dynamics simulations. The effect of a cysteine residue, genetically added to the N-terminus of OBPs to control their orientation after immobilization on the chip, was evaluated. A significant reduction in selectivity was observed in the absence of cysteine. As expected, the introduction of this amino acid enabled to control the orientation of OBPs, making their binding pocket more accessible to VOCs and favoring specific interactions. Furthermore, we demonstrated that combining OBP-based biosensors with different properties can improve the discrimination capability of our bioeN. Finally, the ability of our system to detect essential oil vapors was tested, providing preliminary evidence that our bioeN is capable of detecting VOCs in complex media.
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Yu KL, Yang HC, Lee CF, Wu SY, Ye ZK, Rai SK, Lee MR, Tang KT, Wang JY. Exhaled Breath Analysis Using a Novel Electronic Nose for Different Respiratory Disease Entities. Lung 2025; 203:14. [PMID: 39751629 DOI: 10.1007/s00408-024-00776-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/07/2024] [Accepted: 12/06/2024] [Indexed: 01/04/2025]
Abstract
PURPOSE Electronic noses (eNose) and gas chromatography mass spectrometry (GC-MS) are two important breath analysis approaches for differentiating between respiratory diseases. We evaluated the performance of a novel electronic nose for different respiratory diseases, and exhaled breath samples from patients were analyzed by GC-MS. MATERIALS AND METHODS Patients with lung cancer, pneumonia, structural lung diseases, and healthy controls were recruited (May 2019-July 2022). Exhaled breath samples were collected for eNose and GC-MS analysis. Breathprint features from eNose were analyzed using support vector machine model and leave-one-out cross-validation was performed. RESULTS A total of 263 participants (including 95 lung cancer, 59 pneumonia, 71 structural lung disease, and 38 healthy participants) were included. Three-dimensional linear discriminant analysis (LDA) showed a clear distribution of breathprints. The overall accuracy of eNose for four groups was 0.738 (194/263). The accuracy was 0.86 (61/71), 0.81 (77/95), 0.53 (31/59), and 0.66 (25/38) for structural lung disease, lung cancer, pneumonia, and control groups respectively. Pair-wise diagnostic performance comparison revealed excellent discriminant power (AUC: 1-0.813) among four groups. The best performance was between structural lung disease and healthy controls (AUC: 1), followed by lung cancer and structural lung disease (AUC: 0.958). Volatile organic compounds revealed a high individual occurrence rate of cyclohexanone and N,N-dimethylacetamide in pneumonic patients, ethyl acetate in structural lung disease, and 2,3,4-trimethylhexane in lung cancer patients. CONCLUSIONS Our study showed that the novel eNose effectively distinguishes respiratory diseases and holds potential as a point-of-care diagnostic tool, with GC-MS identifying candidate VOC biomarkers.
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Chen X, Yan F, Qu D, Wan T, Xi L, Hu CY. Aroma characterization of Sichuan and Cantonese sausages using electronic nose, gas chromatography-mass spectrometry, gas chromatography-olfactometry, odor activity values and metagenomic. Food Chem X 2024; 24:101924. [PMID: 39582659 PMCID: PMC11582465 DOI: 10.1016/j.fochx.2024.101924] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/06/2024] [Revised: 10/18/2024] [Accepted: 10/22/2024] [Indexed: 11/26/2024] Open
Abstract
The interest of Chinese consumers in meat-free sausages has increased considerably due to their health benefits, but the aroma quality is far from reaching the traditional fermented meat sausages. This study evaluated the aroma characterization of Sichuan and Cantonese sausages using electronic nose (E-nose), gas chromatography-mass spectrometry (GC-MS), gas chromatography-olfactometry (GC-O), odor activity values (OAVs) and metagenomic. Ninety-eight volatile compounds were identified. Among them, 23 odorants were perceived, and their intensity differed in the two groups of sausages. There was a significant difference in the volatile compound profile between Sichuan and Cantonese cooked sausages. E-nose sensors could differentiate them through specific responses to these volatile compounds. Furthermore, there was a significant difference in microbial communities between Sichuan and Cantonese sausages. For aroma quality improvement of meat-free sausages, studies should focus on controlling the formation of aroma compounds by aroma precursors and using different microorganisms to produce diverse meat aromas. Our results provide a reference for the implementation of these strategies.
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Cervellieri S, Longobardi F, Susca A, Anelli P, Ferrara M, Netti T, Haidukowski M, Moretti A, Lippolis V. Early prediction of ochratoxigenic Aspergillus westerdijkiae on traditional Italian caciocavallo during ripening process by MS-based electronic nose. Food Chem 2024; 468:142470. [PMID: 39700791 DOI: 10.1016/j.foodchem.2024.142470] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/01/2024] [Revised: 12/10/2024] [Accepted: 12/12/2024] [Indexed: 12/21/2024]
Abstract
A rapid and non-invasive mass spectrometry-based electronic nose (MS-eNose) method, combined with chemometric analysis, was developed for the early detection of Aspergillus westerdijkiae on caciocavallo cheeses during ripening process. MS-eNose analyses were carried out on caciocavallo inoculated with ochratoxin A (OTA) non-producing species and artificially contaminated with A. westerdijkiae, an OTA producing species. Two classification models, i.e. PLS-DA and PC-LDA, were used to discriminate cheese samples in two classes, based on their contamination with toxigenic or non-toxigenic fungal species. Accuracy values were between 87 and 100 % and 86-100 %, in calibration and validation, respectively, with best results obtained at 15-ripening days with 98 % (PLS-DA) and 100 % (PC-LDA) of accuracy in validation. Moreover, eighteen potential volatile markers of the presence of A. westerdijkiae were identified by GC-MS analysis. Results show that MS-eNose represents a useful tool for a rapid screening in preventing A. westerdijkiae and related OTA contamination in caciocavallo cheese during ripening process.
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Zaytsev V, Issainova A, Borisov RS, Shi X, Baideldinov MU, Zimens ME, Zhunusbekov AM, Lantsberg AV, Kondrashov VA, Nasibulin AG, Fedorov FS, Satybaldina DZ. Coding smell patterns of crude oil by the electronic nose: A soil pollution case. JOURNAL OF HAZARDOUS MATERIALS 2024; 480:135838. [PMID: 39307011 DOI: 10.1016/j.jhazmat.2024.135838] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/10/2024] [Revised: 08/25/2024] [Accepted: 09/13/2024] [Indexed: 12/01/2024]
Abstract
In our study, we leveraged an electronic nose to detect the patterns of crude oils and their mixtures, sourced from the oil fields from neighboring regions in pursuit of the task of environmental impact evaluation. The temporal dynamics of oil-related patterns acquired by an electronic nose was tracked to identify the influence of high or low emissions of volatiles that depend on the oil composition. Analyzing the oils by Fourier-transform IR-spectroscopy and GC×GC-MS, we confirmed the correlation between sensor responses and the oil compositions, significantly dependent on the ratio of aromatic compounds/alkanes. Using pattern recognition techniques, Random Forest classifier enabled good accuracy of classification of oil samples and contaminated soils underscoring a high-resolution distinction between the response data. Applying these principles to determine the oil origin, we observed that the studied oil samples and contaminated soil samples corroborate with the dynamic changes in odor patterns based only on volatile and semivolatile compounds. Crude oils from the border of two oil fields facilitate a change in the odor pattern to remain one of the fields depending on the weathering time. These proposed intelligent multisensor systems show great promise as a tool for estimating oil-contaminated soils, thereby potentially enhancing environmental monitoring practices.
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Li N, Xu J, Zhao Y, Zhao M, Liu Z, Wang K, Huang J, Zhu M. The influence of processing methods on polyphenol profiling of tea leaves from the same large-leaf cultivar (Camellia sinensis var. assamica cv. Yunkang-10): nontargeted/targeted polyphenomics and electronic sensory analysis. Food Chem 2024; 460:140515. [PMID: 39067433 DOI: 10.1016/j.foodchem.2024.140515] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/24/2024] [Revised: 07/07/2024] [Accepted: 07/16/2024] [Indexed: 07/30/2024]
Abstract
Tea polyphenols transform under processing methods, but a systematic study on their changes in the same large-leaf tea cultivar is lacking. Here, Camellia sinensis var. assamica cv. Yunkang-10 leaves underwent six processing methods and were assessed using optimized nontargeted (UHPLC-Q-Exactive Orbitrap-MS) and targeted (UHPLC-QqQ-MS) polyphenomics, along with molecular networking analysis. 903 and 52 polyphenolic compounds (catechins, flavones and flavonols, and phenolic acids) were respectively relatively and absolutely quantified for the first time. Dark and black teas, with the lowest polyphenol content, differed from the other four tea types, although variations existed among these four teas. However, some flavonol and flavone aglycones (e.g. kaempferol, apigenin), as well as some phenolic acids (e.g. ellagic acid, gallic acid), exhibited higher levels in dark and black teas. Correlations between polyphenolic composition and electronic sensory characteristics were observed using E-tongue and E-eye. This study enriches understanding of polyphenol profiles in Chinese teas post diverse processing.
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Seesaard T, Kamjornkittikoon K, Wongchoosuk C. A comprehensive review on advancements in sensors for air pollution applications. THE SCIENCE OF THE TOTAL ENVIRONMENT 2024; 951:175696. [PMID: 39197792 DOI: 10.1016/j.scitotenv.2024.175696] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/22/2024] [Revised: 08/18/2024] [Accepted: 08/20/2024] [Indexed: 09/01/2024]
Abstract
Air pollution, originating from both natural and human-made sources, presents significant threats to human health and the environment. This review explores the latest technological advancements in air quality sensors focusing on their applications in monitoring a wide range of pollution sources from volcanic eruptions and wildfires to industrial emissions, transportation, agricultural activities and indoor air quality. The review categorizes these sources and examines the operational principles, system architectures, and effectiveness of various air quality monitoring instruments including low-cost sensors, gas analyzers, weather stations, passive sampling devices and remote sensing technologies such as satellite and LiDAR. Key insights include the rapid evolution of sensor technology driven by the need for more accurate, real-time monitoring solutions that are both cost-effective and widely accessible. Despite significant advancements, challenges such as sensor calibration, standardization, and data integration remain critical for ensuring reliable air quality assessments. The manuscript concludes by emphasizing the need for continued innovation and the integration of advanced sensor technologies with regulatory frameworks to enhance environmental management and public health protection.
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Li H, Covington JA, Tian F, Wu Z, Liu Y, Hu L. Development and analysis of an artificial olfactory bulb. Talanta 2024; 279:126551. [PMID: 39018948 DOI: 10.1016/j.talanta.2024.126551] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/26/2024] [Revised: 06/24/2024] [Accepted: 07/10/2024] [Indexed: 07/19/2024]
Abstract
This article presents the development of an artificial olfactory bulb (OB) using an electronic nose with thermally modulated metal-oxide sensors. Inspired by animal OBs, our approach employs thermal modulation to replicate the spatial encoding patterns of glomeruli clusters and subclusters. This new approach enhances the classification capabilities of traditional electronic noses and offers new insights for biomimetic olfaction. Molecular receptive range (MRR) analysis confirms that our artificial OB effectively mimics the glomerular distribution of animal OBs. Additionally, the incorporation of a short axon cell (SAC) network, inspired by the animal olfactory system, significantly improves lifetime sparseness and qualitative ability of the artificial OB through extensive lateral inhibition, providing a theoretical framework for enhanced olfactory performance.
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Song X, Liao D, Zhou Y, Huang Q, Lei S, Li X. Correlation between physicochemical properties, flavor characteristics and microbial community structure in Dushan shrimp sour paste. Food Chem X 2024; 23:101543. [PMID: 39022783 PMCID: PMC11252767 DOI: 10.1016/j.fochx.2024.101543] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/16/2024] [Revised: 05/28/2024] [Accepted: 06/07/2024] [Indexed: 07/20/2024] Open
Abstract
Dushan shrimp sour paste (DSSP), a traditional Guizhou condiment, and its unique flavor is determined by the fermentation microbiota. However, the relationship between the microbiota structure and its flavor remains unclear. This study identified 116 volatile flavor compounds using electronic nose and headspace solid-phase microextraction-gas chromatography mass spectrometry (HS-SPME-GC-MS) techniques, of which 19 were considered as key flavor compounds, mainly consisting of 13 esters and 1 alcohol. High-throughput sequencing technique, the bacterial community structure of nine groups of DSSPs was determined. Further analysis revealed Vagococcus, Lactococcus, and Tepidimicrobium as key bacteria involved in flavor formation. This study contributes to our understanding of the relationship between bacterial communities and the flavor formation, and provides guidance for screening starter culture that enhance the flavor of DSSP in industrial production.
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Yu Y, Liu H, Gong W, Chen Y, An X, Zhang H, Liang Y, Wang J. Change in volatile profiles of wheat flour during maturation. Food Res Int 2024; 194:114936. [PMID: 39232547 DOI: 10.1016/j.foodres.2024.114936] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/07/2024] [Revised: 08/06/2024] [Accepted: 08/14/2024] [Indexed: 09/06/2024]
Abstract
The volatile profiles of wheat flour during maturation were examined through headspace solid-phase micro-extraction gas chromatography-mass spectrometry (HS-SPME-GC/MS) combined with electronic nose (E-nose) and electronic tongue (E-tongue) analyses. The wheat flour underwent maturation under three distinct conditions for predetermined durations. While GC/MS coupled with E-tongue exhibited discernment capability among wheat flour samples subjected to varying maturation conditions, E-nose analysis solely relying on principal component analysis failed to achieve discrimination. 83 volatile compounds were identified in wheat flour, with the highest abundance observed in samples matured for 50 d at 25 °C. Notably, trans-2-Nonenal, decanal, and nonanal were the main contributors to the characteristic flavor profile of wheat flour. Integration of HS-SPME-GC/MS with E-tongue indicated superior flavor development and practical viability in wheat flour matured for 50 d at 25 °C. This study furnishes a theoretical groundwork for enhancing the flavor profiles of wheat flour and its derivative products.
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Liu J, Wan Y, Chen Y, Fan H, Li M, Jiang Q, Fu G. Evaluation of the effect of Torulaspora delbrueckii on important volatile compounds in navel orange original brandy using E-nose combined with HS-SPME-GC-MS. Food Chem 2024; 453:139625. [PMID: 38754349 DOI: 10.1016/j.foodchem.2024.139625] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/11/2024] [Revised: 04/03/2024] [Accepted: 05/08/2024] [Indexed: 05/18/2024]
Abstract
Simultaneous inoculation of non-Saccharomyces cerevisiae during the alcoholic fermentation process has been found to be an effective strategy for enhancing wine flavor. This study aimed to investigate the effect of Torulaspora delbrueckii NCUF305.2 on the flavor of navel orange original brandy (NOOB) using E-nose combined with HS-SPME-GC-MS. The results showed a significant increase (p < 0.05) in the sensitivity of NOOB to W5C, W3C, W1S, and W3S sensors by mixed fermentation (MF). Esters in NOOB increased by 4.13%, while higher alcohols increased by 21.93% (p < 0.001), terpenes and others increased by 52.07% and 40.99% (p < 0.01), respectively. Notably, several important volatile compounds with relative odor activity values above 10 showed an increase. Sensory analysis revealed that a more pronounced citrus-like flavor and higher overall appearance scores were found in MF than in pure fermentation (PF). These findings offer valuable theoretical guidance for enhancing the quality of fruit brandies.
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Wijaya DR, Handayani R, Badri MD, Shabri S, Rahadi VP. Data set for Gambung green tea aroma using on electronic nose. BMC Res Notes 2024; 17:244. [PMID: 39227855 PMCID: PMC11373295 DOI: 10.1186/s13104-024-06905-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/12/2023] [Accepted: 08/21/2024] [Indexed: 09/05/2024] Open
Abstract
OBJECTIVES In recent years, there has been much discussion and research on electronic nose (e-nose). This topic has developed mainly in the medical and food fields. Typically, e-nose is combined with machine learning algorithms to predict or detect multiple sensory classes in each tea sample. Therefore, in e-nose systems, e-nose signal processing is an important part. In many situations, a comprehensive set of experiments is required to ensure the prediction model can be generalized well. This data set specifically focuses on two main goals such as classification of green tea quality and prediction of organoleptic score. In this experiment, Gambung dry green tea samples were used. The challenge is that dry tea does not emit as strong an aroma as tea infusions, making it more difficult for the e-nose system to detect and identify the aromas. This data set offers a valuable resource for researchers and developers to conduct investigations and experiments by classifying and detecting organoleptic scores that aim to categorize and identify organoleptic ratings. This enables a deeper understanding of the quality of dry green tea and encourages further integration of e-nose technology in the tea industry. DATA DESCRIPTION This experiment focused on analyzing green tea aroma using six gas sensors. Seventy-eight green tea samples were tested, each observed three times, using a tea chamber connected to a sensor chamber via a hose and an intake micro air pump. Air flowed from the tea chamber to the sensor chamber for 60 s, followed by 60 s of aroma data recording. This data was saved into CSV files and labeled according to the Indonesian National Standard (SNI) 3945:2016, which includes special and general requirements for green tea quality. An organoleptic test by a tea tester further labeled the data set into "good" or "quality defect" for classification and provided organoleptic scores based on dry appearance, brew color, taste, aroma, and dregs of brewing for continuous label.
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Piochi M, Nervo C, Savo F, Chirilli C, Brunori A, Torri L. Firewood as a tool to valorize meat: A sensory and instrumental approach to grilled veal, lamb, and pork meat. Food Res Int 2024; 192:114719. [PMID: 39147545 DOI: 10.1016/j.foodres.2024.114719] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/03/2024] [Revised: 06/28/2024] [Accepted: 06/28/2024] [Indexed: 08/17/2024]
Abstract
Two firewood species (beech and olive) were used for grilling three meat types (lamb, pork, and veal) to assess their influence on the sensorial properties of meat. A multimethod approach was adopted, including sensory evaluation with consumers and two analytical techniques to characterize the volatile fraction (Solid-Phase Micro-Extraction Gas Chromatography-Mass Spectrometry [SPME-GC/MS] and electronic nose [e-nose]). The sensory session included three pairwise preference tests (one for each type of meat), an overall liking test, a Rate-All-That-Apply test, and a questionnaire on the interest and perceived value of using sustainably certified firewood in food preparation. The firewood species significantly affected the perception of a few crucial attributes. In particular, olive wood increased the roasted meat flavor perception in lamb and veal, while beech wood increased the perceived intensity of a vegetable/herbaceous flavor in veal. No effect of firewood was observed on preference within each pair of meat samples. Lamb was the significantly most liked meat by consumers, followed by pork; veal was the least liked meat type. Positive and negative drivers of preference were discussed. 36 volatile organic compounds were identified from SPME-GC/MS in meats. Congruently with sensory data, the two veal samples showed a greater distance in terms of volatile composition. Relative distances among samples on maps obtained from SPME-GC/MS and the e-nose were similar. This multi-method approach innovatively showed the potential of using firewood as a 'gastronomic' tool to sensorially characterize and valorize cooked meat.
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Li H, Wang Q, Han L, Chen Z, Wang G, Wang Q, Ma S, Ai B, Xi G. Quality characterization of tobacco flavor and tobacco leaf position identification based on homemade electronic nose. Sci Rep 2024; 14:19229. [PMID: 39164410 PMCID: PMC11336110 DOI: 10.1038/s41598-024-70180-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/08/2024] [Accepted: 08/13/2024] [Indexed: 08/22/2024] Open
Abstract
A set of nine unique tobacco extract samples was analyzed using a self-developed electronic nose (E-nose) system, a commercial E-nose, and gas chromatography-mass spectrometry (GC-MS). The evaluation employed principal component analysis, statistical quality control, and soft independent modeling of class analogies (SIMCA). These multifaceted statistical methods scrutinized the collected data. Subsequently, a quality control model was devised to assess the stability of the sample quality. The results showed that the custom E-nose system could successfully distinguish between tobacco extracts with similar odors. After further training and the development of a quality control model for accepted tobacco extracts, it was possible to identify samples with normal and abnormal quality. To further validate our E-nose and extend its use within the tobacco industry, we collected and accurately classified the flavors of different tobacco leaf positions, with a remarkable accuracy rate of 0.9744. This finding facilitates the practical application of our E-nose system for the efficient identification of tobacco leaf positions.
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Zhu T, Wu X, Ma L, Zeng Y, Lian J, Liu J, Chen X, Zhong L, Chang J, Hui G. Rapid Mold Detection in Chinese Herbal Medicine Using Enhanced Deep Learning Technology. J Med Food 2024; 27:797-806. [PMID: 38919153 DOI: 10.1089/jmf.2024.k.0004] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/27/2024] Open
Abstract
Mold contamination poses a significant challenge in the processing and storage of Chinese herbal medicines (CHM), leading to quality degradation and reduced efficacy. To address this issue, we propose a rapid and accurate detection method for molds in CHM, with a specific focus on Atractylodes macrocephala, using electronic nose (e-nose) technology. The proposed method introduces an eccentric temporal convolutional network (ETCN) model, which effectively captures temporal and spatial information from the e-nose data, enabling efficient and precise mold detection in CHM. In our approach, we employ the stochastic resonance (SR) technique to eliminate noise from the raw e-nose data. By comprehensively analyzing data from eight sensors, the SR-enhanced ETCN (SR-ETCN) method achieves an impressive accuracy of 94.3%, outperforming seven other comparative models that use only the response time of 7.0 seconds before the rise phase. The experimental results showcase the ETCN model's accuracy and efficiency, providing a reliable solution for mold detection in Chinese herbal medicine. This study contributes significantly to expediting the assessment of herbal medicine quality, thereby helping to ensure the safety and efficacy of traditional medicinal practices.
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Sun X, Yu Y, Wang Z, Akhtar KH, Saleh ASM, Li W, Zhang D. Insights into flavor formation of braised chicken: Based on E-nose, GC-MS, GC-IMS, and UPLC-Q-Exactive-MS/MS. Food Chem 2024; 448:138972. [PMID: 38555691 DOI: 10.1016/j.foodchem.2024.138972] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/12/2023] [Revised: 02/20/2024] [Accepted: 03/06/2024] [Indexed: 04/02/2024]
Abstract
Effects of braising duration on volatile organic compounds (VOCs) and lipids in chicken were investigated. Aroma profiles identified by an electronic nose were effective in differentiating braising stages. During braising process, a total of 25 key VOCs were detected in braised chicken, and sample braised for 210 min exhibited the highest level of key VOCs. Additionally, a gas chromatography mass spectrometry fingerprint was established to evaluate the distribution of VOCs throughout the braising process. Partial least square discriminant analysis indicated that 2-heptanone, 3-methyl-2-butanone, octanal, nonanal, butanal, (E)-2-pentenal, 1-octen-3-ol, 1-hexanol, pentanal, hexanal, and 1-pentanol significantly affected flavor characteristics of braised chicken. Furthermore, 88 differential lipids were screened, and glycerolipids metabolic was found to be main metabolic pathway during braising process. Triglycerides (TG) and phosphatidyl ethanolamine (PE), such as TG (16:0/18:1/18:2), TG (18:0/18:1/18:2), TG (18:1/18:2/18:3), TG (18:1/18:1/18:2), PE (O-18:2/18:2), PE(O-18:2/18:1), and TG (16:0/16:1/18:2), played a vital role in the generation of VOCs.
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Zhou Y, Zhang Z, He Y, Gao P, Zhang H, Ma X. Integration of electronic nose, electronic tongue, and colorimeter in combination with chemometrics for monitoring the fermentation process of Tremella fuciformis. Talanta 2024; 274:126006. [PMID: 38569371 DOI: 10.1016/j.talanta.2024.126006] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/31/2023] [Revised: 03/22/2024] [Accepted: 03/26/2024] [Indexed: 04/05/2024]
Abstract
This study proposes an efficient method for monitoring the submerged fermentation process of Tremella fuciformis (T. fuciformis) by integrating electronic nose (e-nose), electronic tongue (e-tongue), and colorimeter sensors using a data fusion strategy. Chemometrics was employed to establish qualitative identification and quantitative prediction models. The Pearson correlation analysis was applied to extract features from the e-nose and tongue sensor arrays. The optimal sensor arrays for monitoring the submerged fermentation process of T. fuciformis were obtained, and four different data fusion methods were developed by incorporating the colorimeter data features. To achieve qualitative identification, the physicochemical data and principal component analysis (PCA) results were utilized to determine three stages of the fermentation process. The fusion signal based on full features proved to be the optimal data fusion method, exhibiting the highest accuracy across different models. Notably, random forest (RF) was shown to be the most accurate pattern recognition method in this paper. For quantitative prediction, partial least squares regression (PLSR) and support vector regression (SVR) were employed to predict the sugar content and dry cell weight during fermentation. The best respective predictive R2 values for reducing sugar, tremella polysaccharide and dry cell weight were found to be 0.965, 0.988, and 0.970. Furthermore, due to its ability to capture nonlinear data relationships, SVR had superior performance in prediction modeling than PLSR. The results demonstrated that the combination of electronic sensor fusion signals and chemometrics provided a promising method for effectively monitoring T. fuciformis fermentation.
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Li H, Chen J, Zhang Y, Jiang Y, Sun D, Piao C, Li T, Wang J, Li H, Mu B, Li G. Evaluation of the flavor profiles of Yanbian-style sauced beef from differently treated raw beef samples. Food Chem X 2024; 22:101505. [PMID: 38883915 PMCID: PMC11178982 DOI: 10.1016/j.fochx.2024.101505] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/18/2023] [Revised: 05/08/2024] [Accepted: 05/22/2024] [Indexed: 06/18/2024] Open
Abstract
In this study, we investigated the volatile flavor compounds and sensory perceptions of Yanbian-style sauced beef prepared from raw meats subjected to different treatments (hot fresh, chilled, and frozen beef). The results indicated that the treatment of raw beef significantly impacted the quality and flavor of sauced beef. Sauced chilled beef (CRSB) exhibited the highest content of fatty acids and total amino acids. A total of 48 volatile compounds were identified. Moreover, a relative odor activity value analysis identified hexanal, nonanal, heptanal, 1-octen-3-ol, and 2,3-octanedione as the characteristic flavor compounds in Yanbian-style sauced beef. The sensory evaluation demonstrated that CRSB was the most palatable and flavorful. Additionally, correlation loading plot analysis indicated strong correlations between sensory evaluation, fatty acids, amino acids, and volatile flavor compounds. These results suggest that chilled beef meat is the best raw material for the production of Yanbian-style sauced beef.
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Yin X, Zhang M, Wang S, Wang Z, Wen H, Sun Z, Zhang Y. Characterization and discrimination of the taste and aroma of Tibetan Qingke baijiu using electronic tongue, electronic nose and gas chromatography-mass spectrometry. Food Chem X 2024; 22:101443. [PMID: 38846797 PMCID: PMC11154201 DOI: 10.1016/j.fochx.2024.101443] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/10/2023] [Revised: 05/01/2024] [Accepted: 05/02/2024] [Indexed: 06/09/2024] Open
Abstract
Consumers rely on flavor characteristics to distinguish different types of Qingke Baijiu (QKBJ). Clarifying QKBJ's traits enhances its recognition and long-term growth. Thus, this study analyzed eight QKBJ samples from different regions of Tibet (Lhasa, Sannan, Shigatse, and Qamdo) using GC-MS, electronic nose and electronic tongue. The radar charts of the electronic tongue and electronic nose revealed highly similar profiles for all eight samples. Fifteen common compounds were found in all samples, with the main alcohol compounds being 3-Methyl-1-butanol, 1-hexanol, isobutanol, 1-butanol, 1-nonanol, and phenylethyl alcohol, imparting fruity, floral, and herbal aromas. However, the Sannan samples had higher total alcohol content than total ester content, emphasizing bitterness. Lhasa1 exhibited the most prominent sweetness, Lhasa2 the most noticeable sourness, and Qamdo the most pronounced umami. Lhasa3 and Lhasa4 had total acid content second only to total ester content. Tyd had the highest alkanes, while Lhasa had most aldehydes among samples.
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Sun H, Hua Z, Yin C, Li F, Shi Y. Geographical traceability of soybean: An electronic nose coupled with an effective deep learning method. Food Chem 2024; 440:138207. [PMID: 38104451 DOI: 10.1016/j.foodchem.2023.138207] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/05/2023] [Revised: 11/05/2023] [Accepted: 12/11/2023] [Indexed: 12/19/2023]
Abstract
The quality of soybeans is correlated with their geographical origin. It is a common phenomenon to replace low-quality soybeans from substandard origins with superior ones. This paper proposes the adaptive convolutional kernel channel attention network (AKCA-Net) combined with an electronic nose (e-nose) to achieve soybean quality traceability. First, the e-nose system is used to collect soybean gas information from different origins. Second, depending on the characteristics of the gas information, we propose the adaptive convolutional kernel channel attention (AKCA) module, which focuses on key gas channel features adaptively. Finally, the AKCA-Net is proposed, which is capable of modeling deep gas channel interdependency efficiently, realizing high-precision recognition of soybean quality. In comparative experiments with other attention mechanisms, AKCA-Net demonstrated superior performance, achieving an accuracy of 98.21%, precision of 98.57%, and recall of 98.60%. In conclusion, the combination of the AKCA-Net and e-nose provides an effective strategy for soybean quality traceability.
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Xia H, Chen W, Hu D, Miao A, Qiao X, Qiu G, Liang J, Guo W, Ma C. Rapid discrimination of quality grade of black tea based on near-infrared spectroscopy (NIRS), electronic nose (E-nose) and data fusion. Food Chem 2024; 440:138242. [PMID: 38154280 DOI: 10.1016/j.foodchem.2023.138242] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/04/2023] [Revised: 11/25/2023] [Accepted: 12/18/2023] [Indexed: 12/30/2023]
Abstract
For the manufacturing and sale of tea, rapid discrimination of overall quality grade is of great importance. However, present evaluation methods are time-consuming and labor-intensive. This study investigated the feasibility of combining advantages of near-infrared spectroscopy (NIRS) and electronic nose (E-nose) to assess the tea quality. We found that NIRS and E-nose models effectively identify taste and aroma quality grades, with the highest accuracies of 99.63% and 97.00%, respectively, by comparing different principal component numbers and classification algorithms. Additionally, the quantitative models based on NIRS predicted the contents of key substances. Based on this, NIRS and E-nose data were fused in the feature-level to build the overall quality evaluation model, achieving accuracies of 98.13%, 96.63% and 97.75% by support vector machine, K-nearest neighbors, and artificial neural network, respectively. This study reveals that the integration of NIRS and E-nose presents a novel and effective approach for rapidly identifying tea quality.
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Rong L, Wang Y, Wang Y, Jiang D, Bai J, Wu Z, Li L, Wang T, Tan H. A fresh-cut papaya freshness prediction model based on partial least squares regression and support vector machine regression. Heliyon 2024; 10:e30255. [PMID: 38707326 PMCID: PMC11068816 DOI: 10.1016/j.heliyon.2024.e30255] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/21/2023] [Revised: 04/19/2024] [Accepted: 04/23/2024] [Indexed: 05/07/2024] Open
Abstract
This study investigated the physicochemical and flavor quality changes in fresh-cut papaya that was stored at 4 °C. Multivariate statistical analysis was used to evaluate the freshness of fresh-cut papaya. Aerobic plate counts were selected as a predictor of freshness of fresh-cut papaya, and a prediction model for freshness was established using partial least squares regression (PLSR), and support vector machine regression (SVMR) algorithms. Freshness of fresh-cut papaya could be well distinguished based on physicochemical and flavor quality analyses. The aerobic plate counts, as a predictor of freshness of fresh-cut papaya, significantly correlated with storage time. The SVMR model had a higher prediction accuracy than the PLSR model. Combining flavor quality with multivariate statistical analysis can be effectively used for evaluating the freshness of fresh-cut papaya.
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Lee MR, Kao MH, Hsieh YC, Sun M, Tang KT, Wang JY, Ho CC, Shih JY, Yu CJ. Cross-site validation of lung cancer diagnosis by electronic nose with deep learning: a multicenter prospective study. Respir Res 2024; 25:203. [PMID: 38730430 PMCID: PMC11084132 DOI: 10.1186/s12931-024-02840-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/29/2024] [Accepted: 05/06/2024] [Indexed: 05/12/2024] Open
Abstract
BACKGROUND Although electronic nose (eNose) has been intensively investigated for diagnosing lung cancer, cross-site validation remains a major obstacle to be overcome and no studies have yet been performed. METHODS Patients with lung cancer, as well as healthy control and diseased control groups, were prospectively recruited from two referral centers between 2019 and 2022. Deep learning models for detecting lung cancer with eNose breathprint were developed using training cohort from one site and then tested on cohort from the other site. Semi-Supervised Domain-Generalized (Semi-DG) Augmentation (SDA) and Noise-Shift Augmentation (NSA) methods with or without fine-tuning was applied to improve performance. RESULTS In this study, 231 participants were enrolled, comprising a training/validation cohort of 168 individuals (90 with lung cancer, 16 healthy controls, and 62 diseased controls) and a test cohort of 63 individuals (28 with lung cancer, 10 healthy controls, and 25 diseased controls). The model has satisfactory results in the validation cohort from the same hospital while directly applying the trained model to the test cohort yielded suboptimal results (AUC, 0.61, 95% CI: 0.47─0.76). The performance improved after applying data augmentation methods in the training cohort (SDA, AUC: 0.89 [0.81─0.97]; NSA, AUC:0.90 [0.89─1.00]). Additionally, after applying fine-tuning methods, the performance further improved (SDA plus fine-tuning, AUC:0.95 [0.89─1.00]; NSA plus fine-tuning, AUC:0.95 [0.90─1.00]). CONCLUSION Our study revealed that deep learning models developed for eNose breathprint can achieve cross-site validation with data augmentation and fine-tuning. Accordingly, eNose breathprints emerge as a convenient, non-invasive, and potentially generalizable solution for lung cancer detection. CLINICAL TRIAL REGISTRATION This study is not a clinical trial and was therefore not registered.
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