This study introduces a novel deep learning framework for lung health evaluation using exhaled gas. The framework synergistically integrates pyramid pooling and a dual-encoder network, leveraging SHapley Additive exPlanations (SHAP) derived feature importance to enhance its predictive capability. The framework is specifically designed to effectively distinguish between smokers, individuals with chronic obstructive pulmonary disease (COPD), and control subjects. The pyramid pooling structure aggregates multilevel global information by pooling features at four scales. SHAP assesses feature importance from the eight sensors. Two encoder architectures handle different feature sets based on their importance, optimizing performance. Besides, the model\'s robustness is enhanced using the sliding window technique and white noise augmentation on the original data. In 5-fold cross-validation, the model achieved an average accuracy of 96.40%, surpassing that of a single encoder pyramid pooling model by 10.77%. Further optimization of filters in the transformer convolutional layer and pooling size in the pyramid module increased the accuracy to 98.46%. This study offers an efficient tool for identifying the effects of smoking and COPD, as well as a novel approach to utilizing deep learning technology to address complex biomedical issues.

Although odor is an important indicator of herbal medicine quality, an objective odor evaluation method remains undiscovered. Quantitative measurement using previous methods is complicated as Citrus Unshiu Peel (Chimpi) emits an odor when broken. To establish odor evaluation methods for herbal medicines using chimpi as an example, we developed a reproducible method for breaking samples and an objective odor evaluation method using an electronic nose (e-nose). First, an odor-emitting device (OED) was fabricated by modifying a pill cutter, which suppressed the spread of odor components into the room air while cutting the samples. The odor was emitted from chimpi with an OED and measured using an e-nose. The cut length was then measured. The sensor intensity was positively correlated with the cut length (r = 0.840-0.927) in the same sample, and the intensity per unit length (INPULTH) calculated from the sensor intensity and cut length enables the comparison of the sensor intensity among different samples. In addition, average d-limonene emission level measured by GC-MS was positively correlated with average INPULTH (r = 0.999), which suggests that this OED and e-nose method enables the comparison of the sensor intensity and d-limonene emissions. INPULTH also positively correlated with other seven monoterpenes such as p-cymene, β-myrcene, β-phellandrene, α-pinene, β-pinene, γ-terpinene, and α-terpinolene (r = 0.701-0.865). Therefore, monoterpene content can be evaluated by measuring the odor in the same way as d-limonene. In conclusion, we developed a simple odor intensity evaluation method optimized for chimpi to establish an odor evaluation method for herbal medicines.

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.

Microbial metabolism is important for the unique flavor formation of Mei yu, a kind of traditional Chinese fermented fish pieces. However, the interactive relationship between microorganisms and flavor components during fermentation is still unclear. In this study, electronic nose and headspace-solid-phase microextraction-gas chromatography-mass spectrometry analysis were performed to identify flavor components in Mei yu during the fermentation, and the absolute microbial quantification was conducted to identify the diversity and succession of microbial communities. During fermentation, there was an increase in the types of volatile compounds. Alcohols, aldehydes, aromatics and esters were the main flavor compounds and significantly increased in Mei yu, while hydrocarbon and aldehydes significantly decreased. The absolute abundances of Lactobacillus, Lactococcus and Weissella increased significantly after 3 days\' fermentation, which were closely associated with the productions of 1-nonanol, 2-methoxy-4-vinylphenol, guaiacol, ethyl palmitate and ethyl caprylate that might though pathways related to fatty acid biosynthesis and amino acid metabolism. However, these genera were negatively correlated with the production of indole. Additionally, the total volatile basic nitrogen (TVB-N) levels of Mei yu fermented during 3 days were within the limits of 25 mg TVB-N/100 g fish, with the contents of free amino acids and lipoxygenase activities were significant lower than that of 4 days\' fermentation. In view of food safety and flavor, it suggested that the natural fermented Mei yu at room temperature should be controlled within 3 days. This study highlights the application of absolute quantification to microbiome analysis in traditional fermented Mei yu and provides new insights into the roles of microorganisms in flavor formation during fermentation.

OBJECTIVE: 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.
METHODS: 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.

Changes in the flavor and taste profiles of Paddy Field Carp after deodorization with perilla juice (PJ), cooking wine (CW) and a mixture of the two (PJ-CW) were analyzed using the E-nose, E-tongue, gas chromatography-ion mobility spectrometry (GC-IMS), free amino acid analysis and taste nucleotide analysis. The E-nose and E-tongue revealed that deodorization reduced the content of sulfur-containing compounds, enhanced umami, bitterness, sourness and astringency, and decreased saltiness. PCA and OPLS-DA analysis successfully distinguished between the effects of the treatments. Free amino acids increased from 8777.67 to 11,125.98 mg/100 g and umami amino acids increased from 128.24 to 150.37 mg/100 g after PJ-CW deodorization (p < 0.05). Equivalent umami concentration (EUC) comparisons showed that PJ-CW treatment produced the greatest synergistic umami enhancement (to 3.15 g MSG equiv./100 g). GC-IMS detected 52 aroma compounds; PJ treatment produced the greatest diversity of aldehydes, including heptanal, nonanal, hexanal, 3-methylbutanal, (E)-2-heptenal and (E,E)-2,4-heptadienal. The total content of volatile flavor compounds was the highest after PJ-CW treatment, and the content of many characteristic flavor substances (3-hydroxy-2-butanone, benzaldehyde, 5-methyl-2(3H)-furanone) increased. These findings provided a theoretical basis for the further development of deodorization methods for Paddy Field Carp.

To further develop Liupao tea products and enhance their flavor, this study investigated the effects of different fermentation methods on the aroma quality of Liupao tea. The aroma quality of Liupao tea was comprehensively analyzed using HS-SPME in combination with GC-Q-TOF-MS, electronic nose, and sensory evaluations. Electronic nose detection showed that the aroma fingerprints of Liupao tea samples with different fermentation methods were different. Sulfides, alcohols, ketones, and methyls were the main aroma categories affecting the aroma of the four groups of Liupao tea samples. GC-Q-TOF-MS analysis revealed significant differences in the composition of aroma components among the four fermentation methods of Liupao tea (p < 0.05). Furthermore, the total amount of aroma compounds was found to be highest in the group subjected to hot fermentation combined with the inoculation of Monascus purpureus (DMl group). Based on the OPLS-DA model, candidate differential aroma components with VIP > 1 were identified, and characteristic aroma compounds were selected based on OAV > 10. The key characteristic aroma compounds shared by the four groups of samples were 1,2,3-Trimethoxybenzene with a stale aroma and nonanal with floral and fruity aromas. The best sensory evaluation results were obtained for the DMl group, and its key characteristic aroma compounds mainly included 1,2,3-Trimethoxybenzene, nonanal, and cedrol. The results of this study can guide the development of Liupao tea products and process optimization.

Consumers often face a lack of information regarding the quality of apples available in supermarkets. General appearance factors, such as color, mechanical damage, or microbial attack, influence consumer decisions on whether to purchase or reject the apples. Recently, devices known as electronic noses provide an easy-to-use and non-destructive assessment of ripening stages based on Volatile Organic Compounds (VOCs) emitted by the fruit. In this study, the \'Golden Delicious\' apples, stored and monitored at the ambient temperature, were analyzed in the years 2022 and 2023 to collect data from four Metal Oxide Semiconductor (MOS) sensors (MQ3, MQ135, MQ136, and MQ138). Three ripening stages (less ripe, ripe, and overripe) were identified using Principal Component Analysis (PCA) and the K-means clustering approach from various datasets based on sensor measurements in four experiments. After applying the K-Nearest Neighbors (KNN) model, the results showed successful classification of apples for specific datasets, achieving an accuracy higher than 75%. For the dataset with measurements from all experiments, an impressive accuracy of 100% was achieved on specific test sets and on the evaluation set from new, completely independent experiments. Additionally, correlation and PCA analysis showed that choosing two or three sensors can provide equally successful results. Overall, the e-nose results highlight the importance of analyzing data from several experiments performed over a longer period after the harvest of apples. There are similarities and differences in investigated VOC parameters (ethylene, esters, alcohols, and aldehydes) for less or more mature apples analyzed during autumn or spring, which can improve the determination of the ripening stage with higher predicting success for apples investigated in the spring.

Steamed bread is a traditional staple food in China, and it has gradually become loved by people all over the world because of its healthy production methods. With the improvement in people\'s living standards, the light flavor of steamed bread fermented by single yeast cannot meet people\'s needs. Multi-strain co-fermentation is a feasible way to improve the flavor of steamed bread. Here, the dynamic change profiles of volatile substances in steamed bread co-fermented by Saccharomyces cerevisiae SQJ20 and Wickerhamomyces anomalus GZJ2 were analyzed using the electronic nose (E-nose) and headspace solid-phase microextraction combined with gas chromatography-mass spectrometry (HS-SPME-GC-MS). The five detectors of the E-nose rapidly detected the changes in volatile substances in different dough or steamed bread with the highest response value in co-fermented dough. A total of 236 volatile substances were detected in all the samples using HS-SPME-GC-MS, and alcohols were the most variable component, especially Phenylethyl alcohol. Significantly, more alcohols and esters were upregulated in co-fermented dough, and the addition of W. anomalus GZJ2 improved the key volatile aroma compounds of steamed bread using the relative odor activity value method (ROAV), especially the aldehydes and alcohols. Moreover, these key volatile aroma compounds can be quickly distinguished using the W2S detector of the E-nose, which can be used for the rapid detection of aroma components in steamed bread.

The primary objective of this study was to investigate the impact of a phytogenic additive (PA) in broiler chickens\' diet on production, physiochemical parameters, and the profile of volatile organic compounds present in broiler chickens\' meat. The experiment was conducted in a commercial chicken house, where Ross 308 broiler chickens were divided into two groups, each consisting of 65,000 broilers. One group was fed a diet supplemented with 100 ppm of PA throughout the rearing period. The primary chemical composition of the meat and its physicochemical parameters were determined. A visual assessment of breast muscles for defects and volatile organic compounds were evaluated using an electronic nose system. No statistically significant differences were shown in the production performance of the chickens; while summarizing all production parameters, a higher EPEF index of 31 points in the experimental group was highlighted. Breast muscle quality showed differences in drip loss and WHC (p ≤ 0.01) in favor of the experimental group, and a lower cutting force value (p ≤ 0.05) was found for breast muscles from the experimental group. The group also had a lower proportion of muscles with a white striping defect, and the results of volatile organic compound profiling showed the most aroma units.