Evaluation of fish meal freshness using a metal-oxide semiconductor electronic nose combined with the long short-term memory feature extraction method.

To improve the classification and regression performance of the total volatile basic nitrogen (TVB-N) and acid value (AV) of different freshness fish meal samples detected by a metal-oxide semiconductor electronic nose (MOS e-nose), 402 original features, 62 manually extracted features, manually extracted and selected features by the RFRFE method, and the features extracted by the long short-term memory (LSTM) network were used as inputs to identify the freshness. The classification performance of the freshness grades and the estimation performance of the TVB-N and AV values of fish meal with different freshness were compared. According to the sensor response curve, preprocessing and feature extraction steps were first applied to the original data. Then, five classification algorithms and four regression algorithms were used for modeling. The results showed that a total of 30 features were extracted using the LSTM network, and the number of extracted features was significantly reduced. In the classification, the highest accuracy rate of 95.4% was obtained using the support vector machine method. In the regression, the least squares support vector regression method obtained the best root mean square error (RMSE). The coefficient of determination (R2), RMSE, and relative standard deviation (RSD) between the predicted value of TVBN and the actual value were 0.963, 11.01, and 7.9%, respectively. The R2, RMSE, and RSD between the predicted value of AV and the actual value were 0.972, 0.170, and 6.05%, respectively. The LSTM feature extraction method provided a new method and reference for feature extraction using an E-nose to identify other animal-derived material samples.

High-Oleic Sunflower Oil as a Potential Substitute for Palm Oil in Sugar Coatings-A Comparative Quality Determination Using Multispectral Imaging and an Electronic Nose.

Palm oil has a bad reputation due to the exploitation of farmers and the destruction of endangered animal habitats. Therefore, many consumers wish to avoid the use of palm oil. Decorative sugar contains a small amount of palm oil to prevent the sugar from melting on hot bakery products. High-oleic sunflower oil used as a substitute for palm oil was analyzed in this study via multispectral imaging and an electronic nose, two methods suitable for potential large-batch analysis of sugar/oil coatings. Multispectral imaging is a nondestructive method for comparing the wavelength reflections of the surface of a sample. Reference samples enabled the estimation of the quality of unknown samples, which were confirmed via acid value measurements. Additionally, for quality determination, volatile compounds from decorative sugars were measured with an electronic nose. Both applications provide comparable data that provide information about the quality of decorative sugars.

Characterization of Key Odor-Active Compounds in Draft Beers for the Chinese Market Using Molecular Sensory Science Approaches.

Beer is a popular alcoholic beverage worldwide. However, limited research has been conducted on identifying key odor-active components in lager-type draft beers for the Chinese market. Therefore, this study aims to elucidate the odor characteristics of the four most popular draft beer brands through a sensory evaluation and an electronic nose. Subsequently, the four draft beers were analyzed through solid-phase microextraction and liquid-liquid extraction using a two-dimensional comprehensive gas chromatography-olfactometry-mass spectrometry analysis (GC×GC-O-MS). Fifty-five volatile odor compounds were detected through GC×GC-O-MS. Through an Aroma Extract Dilution Analysis, 22 key odor-active compounds with flavor dilution factors ≥ 16 were identified, with 11 compounds having odor activity values > one. An electronic nose analysis revealed significant disparities in the odor characteristics of the four samples, enabling their distinct identification. These findings help us to better understand the flavor characteristics of draft beer and the stylistic differences between different brands of products and provide a theoretical basis for objectively evaluating the quality differences between different brands of draft beer.

Characterization and discrimination of the taste and aroma of Tibetan Qingke baijiu using electronic tongue, electronic nose and gas chromatography-mass spectrometry.

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.

AI-Enabled Portable E-Nose Regression Predicting Harmful Molecules in a Gas Mixture.

The biomimetic electronic nose (e-nose) technology is a novel technology used for the identification and monitoring of complex gas molecules, and it is gaining significance in this field. However, due to the complexity and multiplicity of gas mixtures, the accuracy of electronic noses in predicting gas concentrations using traditional regression algorithms is not ideal. This paper presents a solution to the difficulty by introducing a fusion network model that utilizes a transformer-based multikernel feature fusion (TMKFF) module combined with a 1DCNN_LSTM network to enhance the accuracy of regression prediction for gas mixture concentrations using a portable electronic nose. The experimental findings demonstrate that the regression prediction performance of the fusion network is significantly superior to that of single models such as convolutional neural network (CNN) and long short-term memory (LSTM). The present study demonstrates the efficacy of our fusion network model in accurately predicting the concentrations of multiple target gases, such as SO2, NO2, and CO, in a gas mixture. Specifically, our algorithm exhibits substantial benefits in enhancing the prediction performance of low-concentration SO2 gas, which is a noteworthy achievement. The determination coefficient (R2) values of 93, 98, and 99% correspondingly demonstrate that the model is very capable of explaining the variation in the concentration of the target gases. The root-mean-square errors (RMSE) are 0.0760, 0.0711, and 3.3825, respectively, while the mean absolute errors (MAE) are 0.0507, 0.0549, and 2.5874, respectively. These results indicate that the model has relatively small prediction errors. The method we have developed holds significant potential for practical applications in detecting atmospheric pollution detection and other molecular detection areas in complex environments.

Rapid Mold Detection in Chinese Herbal Medicine Using Enhanced Deep Learning Technology.

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.

Evaluation of the flavor profiles of Yanbian-style sauced beef from differently treated raw beef samples.

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.

Ultralow-Power Single-Sensor-Based E-Nose System Powered by Duty Cycling and Deep Learning for Real-Time Gas Identification.

This study presents a novel, ultralow-power single-sensor-based electronic nose (e-nose) system for real-time gas identification, distinguishing itself from conventional sensor-array-based e-nose systems, whose power consumption and cost increase with the number of sensors. Our system employs a single metal oxide semiconductor (MOS) sensor built on a suspended 1D nanoheater, driven by duty cycling─characterized by repeated pulsed power inputs. The sensor's ultrafast thermal response, enabled by its small size, effectively decouples the effects of temperature and surface charge exchange on the MOS nanomaterial's conductivity. This provides distinct sensing signals that alternate between responses coupled with and decoupled from the thermally enhanced conductivity, all within a single time domain during duty cycling. The magnitude and ratio of these dual responses vary depending on the gas type and concentration, facilitating the early stage gas identification of five gas types within 30 s via a convolutional neural network (classification accuracy = 93.9%, concentration regression error = 19.8%). Additionally, the duty-cycling mode significantly reduces power consumption by up to 90%, lowering it to 160 µW to heat the sensor to 250 °C. Manufactured using only wafer-level batch microfabrication processes, this innovative e-nose system promises the facile implementation of battery-driven, long-term, and cost-effective IoT monitoring systems.

Cross-site validation of lung cancer diagnosis by electronic nose with deep learning: a multicenter prospective study.

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.

The Potential of Fecal Volatile Organic Compound Analysis for the Early Diagnosis of Late-Onset Sepsis in Preterm Infants: A Narrative Review.

Early diagnosis and treatment of late-onset sepsis (LOS) is crucial for survival, but challenging. Intestinal microbiota and metabolome alterations precede the clinical onset of LOS, and the preterm gut is considered an important source of bacterial pathogens. Fecal volatile organic compounds (VOCs), formed by physiologic and pathophysiologic metabolic processes in the preterm gut, reflect a complex interplay between the human host, the environment, and microbiota. Disease-associated fecal VOCs can be detected with an array of devices with various potential for the development of a point-of-care test (POCT) for preclinical LOS detection. While characteristic VOCs for common LOS pathogens have been described, their VOC profiles often overlap with other pathogens due to similarities in metabolic pathways, hampering the construction of species-specific profiles. Clinical studies have, however, successfully discriminated LOS patients from healthy individuals using fecal VOC analysis with the highest predictive value for Gram-negative pathogens. This review discusses the current advancements in the development of a non-invasive fecal VOC-based POCT for early diagnosis of LOS, which may potentially provide opportunities for early intervention and targeted treatment and could improve clinical neonatal outcomes. Identification of confounding variables impacting VOC synthesis, selection of an optimal detection device, and development of standardized sampling protocols will allow for the development of a novel POCT in the near future.

Discrimination between raw and ginger juice processed Fructus Gardeniae based on UHPLC-Q-TOF-MS and Heracles NEO ultra-fast gas phase electronic nose.

INTRODUCTION: Fructus Gardeniae (ZZ), a traditional Chinese herb, has been used in treating patients with jaundice, inflammation, etc. When mixed with ginger juice and stir-baked, ginger juice-processed Fructus Gardeniae (JZZ) is produced, and the chemical compositions in ZZ would be changed by adding the ginger juice. OBJECTIVE: To illuminate the differential components between ZZ and JZZ. METHODS: HPLC, UHPLC-Q-TOF-MS, and Heracles NEO ultra-fast gas phase electronic nose were applied to identify the differential components between ZZ and JZZ. RESULTS: HPLC fingerprints of ZZ and JZZ were established, and 24 common peaks were found. The content determination results showed that the contents of shanzhiside, geniposidic acid, genipin-1-ß-D-gentiobioside and geniposide increased, while the contents of crocin I and crocin II decreased in JZZ. By UHPLC-Q-TOF-MS, twenty-six possible common components were inferred, among which 11 components were different. In further investigation, eight components were identified as the possible distinctive non-volatile compounds between ZZ and JZZ. By Heracles NEO ultra-fast gas phase electronic nose, four substances were inferred as the possible distinctive volatile compounds in JZZ. CONCLUSION: Shanzhiside, caffeic acid, genipin-1-ß-D-gentiobioside, geniposide, rutin, crocin I, crocin II, and 4-Sinapoyl-5-caffeoylquinic acid were identified as the possible differential non-volatile components between ZZ and JZZ. Aniline, 3-methyl-3-sulfanylbutanol-1-ol, E-3-octen-2-one, and decyl propaonate were inferred as the possible distinctive volatile compounds in JZZ. This experiment explored a simple approach with objective and stable results, which would provide new ideas for studying decoction pieces with similar morphological appearance, especially those with different odors.

Optimization of Temperature Modulation for Gas Classification Based on Bayesian Optimization.

This study proposes an optimization method for temperature modulation in chemiresistor-type gas sensors based on Bayesian optimization (BO), and its applicability was investigated. As voltage for a sensor heater, our previously proposed waveform was employed, and the parameters determining the voltage range were optimized. Employing the Bouldin-Davies index (DBI) as an objective function (OBJ), BO was utilized to minimize the DBI calculated from a feature matrix built from the collected data followed by pre-processing. The sensor responses were measured using five test gases with five concentrations, amounting to 2500 data points per parameter set. After seven trials with four initial parameter sets (ten parameter sets were tested in total), the DBI was successfully reduced from 2.1 to 1.5. The classification accuracy for the test gases based on the support vector machine tends to increase with decreasing the DBI, indicating that the DBI acts as a good OBJ. Additionally, the accuracy itself increased from 85.4% to 93.2% through optimization. The deviation from the tendency that the accuracy increases with decreasing the DBI for some parameter sets was also discussed. Consequently, it was demonstrated that the proposed optimization method based on BO is promising for temperature modulation.

Early Identification of Rotten Potatoes Using an Electronic Nose Based on Feature Discretization and Ensemble Convolutional Neural Network.

The early identification of rotten potatoes is one of the most important challenges in a storage facility because of the inconspicuous symptoms of rot, the high density of storage, and environmental factors (such as temperature, humidity, and ambient gases). An electronic nose system based on an ensemble convolutional neural network (ECNN, a powerful feature extraction method) was developed to detect potatoes with different degrees of rot. Three types of potatoes were detected: normal samples, slightly rotten samples, and totally rotten samples. A feature discretization method was proposed to optimize the impact of ambient gases on electronic nose signals by eliminating redundant information from the features. The ECNN based on original features presented good results for the prediction of rotten potatoes in both laboratory and storage environments, and the accuracy of the prediction results was 94.70% and 90.76%, respectively. Moreover, the application of the feature discretization method significantly improved the prediction results, and the accuracy of prediction results improved by 1.59% and 3.73%, respectively. Above all, the electronic nose system performed well in the identification of three types of potatoes by using the ECNN, and the proposed feature discretization method was helpful in reducing the interference of ambient gases.

Effect of Starter Culture and Temperature on the Flavor and Sensory Characteristics of Dry-Cured Ham.

This study focused on understanding the effects of yeast and mold on the sensory properties of dry-cured ham aged at 20°C and 25°C. Debaryomyces hansenii isolated from Doenjang and fermented sausages, and Penicillium nalgiovense isolated from fermented sausages were utilized. The CIE a* tended to increase in all treatments as the aging period increased. At 6 weeks of aging, DFD25 showed a significantly higher CIE a* value than other treatments. The shear force tended to increase in all treatments as the aging period increased. At 6 weeks of aging, among the treatments aged at 25°C, DFD25 showed a low tendency to shear force. The PC1 of the electronic nose was 42.872%. At 25°C, the hexane content was higher and levels of ethanol, propan-2-one, 2,4,5-trimethylthiazole, and limonene were lower than that at 20°C. DFD25 showed significantly higher hexane content and significantly lower limonene content than other treatments. The PC1 of the electronic tongue was 84.529%. All treatments, except for the C starter, exhibited higher salt and lower sour levels at 25°C compared to 20°C when the same starter was used. The DFD25 showed the lowest sour taste and a higher tendency of umami than the other treatments. Sensory evaluation revealed that DFD25 had significantly higher scores for texture than C25, whereas no significant differences were observed in other aspects. Therefore, the used starters are considered suitable for aging at 25°C; among them, the DFD starter demonstrates superior qualities and enhanced commercial potential compared to the control.

Prediction of total volatile basic nitrogen (TVB-N) in fish meal using a metal-oxide semiconductor electronic nose based on the VMD-SSA-LSTM algorithm.

BACKGROUND: The total volatile basic nitrogen (TVB-N) is the main indicator for evaluating the freshness of fish meal, and accurate detection and monitoring of TVB-N is of great significance for the health of animals and humans. Here, to realize fast and accurate identification of TVB-N, in this article, a self-developed electronic nose (e-nose) was used, and the mapping relationship between the gas sensor response characteristic information and TVB-N value was established to complete the freshness detection. RESULTS: The TVB-N variation curve was decomposed into seven subsequences with different frequency scales by means of variational mode decomposition (VMD). Each subsequence was modelled using different long short-term memory (LSTM) models, and finally, the final TVB-N prediction result was obtained by adding the prediction results based on different frequency components. To improve the performance of the LSTM, the sparrow search algorithm (SSA) was used to optimize the number of hidden units, learning rate and regularization coefficient of LSTM. The prediction results indicated that the high accuracy was obtained by the VMD-LSTM model optimized by SSA in predicting TVB-N. The coefficient of determination (R2), the root-mean-squared error (RMSE) and relative standard deviation (RSD) between the predicted value and the actual value of TVBN were 0.91, 0.115 and 6.39%, respectively. CONCLUSIONS: This method improves the performance of e-nose in detecting the freshness of fish meal and provides a reference for the quality detection of e-nose in other materials. © 2024 Society of Chemical Industry.

Diagnostic performance of volatile organic compounds analysis and electronic noses for detecting colorectal cancer: a systematic review and meta-analysis.

Introduction: The detection of Volatile Organic Compounds (VOCs) could provide a potential diagnostic modality for the early detection and surveillance of colorectal cancers. However, the overall diagnostic accuracy of the proposed tests remains uncertain. Objective: This systematic review is to ascertain the diagnostic accuracy of using VOC analysis techniques and electronic noses (e-noses) as noninvasive diagnostic methods for colorectal cancer within the realm of clinical practice. Methods: A systematic search was undertaken on PubMed, EMBASE, Web of Science, and the Cochrane Library to scrutinize pertinent studies published from their inception to September 1, 2023. Only studies conducted on human subjects were included. Meta-analysis was performed using a bivariate model to obtain summary estimates of sensitivity, specificity, and positive and negative likelihood ratios. The Quality Assessment of Diagnostic Accuracy Studies 2 tool was deployed for quality assessment. The protocol for this systematic review was registered in PROSPERO, and PRISMA guidelines were used for the identification, screening, eligibility, and selection process. Results: This review encompassed 32 studies, 22 studies for VOC analysis and 9 studies for e-nose, one for both, with a total of 4688 subjects in the analysis. The pooled sensitivity and specificity of VOC analysis for CRC detection were 0.88 (95% CI, 0.83-0.92) and 0.85 (95% CI, 0.78-0.90), respectively. In the case of e-nose, the pooled sensitivity was 0.87 (95% CI, 0.83-0.90), and the pooled specificity was 0.78 (95% CI, 0.62-0.88). The area under the receiver operating characteristic analysis (ROC) curve for VOC analysis and e-noses were 0.93 (95% CI, 0.90-0.95) and 0.90 (95% CI, 0.87-0.92), respectively. Conclusion: The outcomes of this review substantiate the commendable accuracy of VOC analysis and e-nose technology in detecting CRC. VOC analysis has a higher specificity than e-nose for the diagnosis of CRC and a sensitivity comparable to that of e-nose. However, numerous limitations, including a modest sample size, absence of standardized collection methods, lack of external validation, and a notable risk of bias, were identified. Consequently, there exists an imperative need for expansive, multi-center clinical studies to elucidate the applicability and reproducibility of VOC analysis or e-nose in the noninvasive diagnosis of colorectal cancer. Systematic review registration: https://www.crd.york.ac.uk/prospero/#recordDetails, identifier CRD42023398465.

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.

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.

A fresh-cut papaya freshness prediction model based on partial least squares regression and support vector machine regression.

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.

Food for Thought: Optical Sensor Arrays and Machine Learning for the Food and Beverage Industry.

Arrays of cross-reactive sensors, combined with statistical or machine learning analysis of their multivariate outputs, have enabled the holistic analysis of complex samples in biomedicine, environmental science, and consumer products. Comparisons are frequently made to the mammalian nose or tongue and this perspective examines the role of sensing arrays in analyzing food and beverages for quality, veracity, and safety. I focus on optical sensor arrays as low-cost, easy-to-measure tools for use in the field, on the factory floor, or even by the consumer. Novel materials and approaches are highlighted and challenges in the research field are discussed, including sample processing/handling and access to significant sample sets to train and test arrays to tackle real issues in the industry. Finally, I examine whether the comparison of sensing arrays to noses and tongues is helpful in an industry defined by human taste.