Search Results (557)

Huangshui (HS), fermented grains (FG), and pit mud (PM) are closely connected during the fermentation of Nongxiangxing baijiu (NXB); however, the contribution of HS to the differences in flavor profiles across PM, FG, and distilled baijiu has not been fully elucidated. Herein, instrumental techniques of electronic nose (E-nose), headspace gas chromatography–ion mobility spectrometry (HS-GC-IMS), and headspace solid-phase microextraction coupled with gas chromatography–mass spectrometry (HS-SPME-GC-MS) were used to compare the volatile profiles of HS, FG, PM, and two quality grades of NXB. E-nose analysis revealed that PM exhibited stronger signals related to sulfur-containing, organic, and terpene-associated compounds. Gas chromatography indicated that special-grade baijiu had a more favorable ethyl caproate/ethyl lactate ratio than superior-grade baijiu. Furthermore, HS-GC-IMS revealed different volatile profiles of the samples. Up to 183 volatile organic compounds (VOCs) were identified via HS-SPME-GC-MS, with esters and acids being the dominant classes. Multivariate analysis showed close relationships among HS, FG, and PM, and 35 VOCs were identified as important discriminatory compounds. These results suggest the differences and similarities in VOCs among the HS, FG, PM, and NXB samples, indicating that HS is chemically associated with both FG and PM. This study provides useful insights into HS utilization and quality regulation of NXB through the effective management of HS. Full article

Accurate classification of volatile organic compounds (VOCs) is important for environmental monitoring and industrial safety via electronic nose (E-nose) systems. However, extracting discriminative features from dynamic one-dimensional sensor responses remains challenging, especially when the recognition model is expected to maintain low computational complexity. This study introduces MSD-GasNet, a lightweight multi-scale depthwise convolutional network combined with Gramian Angular Summation Field (GASF) encoding, for VOC classification using E-nose response signals. The gas-sensing response curves are first transformed into two-dimensional GASF images to preserve temporal correlation information and provide structured inputs for convolutional feature learning. MSD-GasNet further adopts parallel 3 × 3 and 5 × 5 depthwise convolutional branches with feature fusion to capture local response details and broader morphology-related patterns while reducing parameter redundancy. Evaluated on Dataset 1, which contains five representative VOC categories including 1-butanol, acetone, benzaldehyde, butyl acetate, and dimethylbenzene, MSD-GasNet achieves an accuracy of 96.80 ± 0.78%, with 796.6 K parameters and 2.54 ms inference time per sample. Compared with traditional machine learning classifiers, conventional CNN baselines, recent lightweight networks, and a single-scale ablation model, MSD-GasNet shows better classification performance under the current five-class setting. An additional independent validation on Dataset 2 achieves an accuracy of 95.12 ± 1.11% under a chronological train/test split, further supporting the generalization potential of the proposed method. This work provides a GASF-based lightweight multi-scale framework with potential for efficient VOC recognition in portable or resource-limited E-nose applications. Full article

Quince (Cydonia oblonga Mill.) is an important fruit crop, yet a systematic quality evaluation framework is lacking. This study comprehensively characterized multiple germplasms from distinct production areas. An integrated strategy combining physicochemical analysis, FT-MIR (Fourier transform mid-infrared spectroscopy), electronic nose (E-nose), headspace solid-phase microextraction–gas chromatography–mass spectrometry (HS-SPME-GC-MS), and chemometrics was employed. Significant variations were observed among accessions: certain varieties were observed to exhibit the highest pectin (1.86%) and total phenolic content (143.40 mg/100 g), while others showed superior firmness and titratable acidity. β-Damascenone in one accession was found to have an exceptionally high odor activity value (OAV) of 265.05. Multivariate analysis, including PLS-DA and OPLS-DA, effectively discriminated among quince accessions, with PLS-DA achieving 100% classification accuracy, and tentatively identified 18 key markers (VIP > 1) for accession discrimination. Loading scatter plot analysis further validated the contribution of these markers to the separation between accessions. However, due to the limited sample size (n = 18) and partial confounding between cultivar and origin, these findings should be considered exploratory and require validation in larger independent studies. This work provides preliminary insights into the diversity and geographical patterns of quality and flavor traits in quince germplasm, offering a preliminary foundation for germplasm evaluation and targeted utilization. Full article

Atractylodis Rhizoma is an important traditional Chinese medicinal material derived from two botanical origins, Maocangzhu (MCZ) and Beicangzhu (BCZ), which are difficult to distinguish by conventional morphological identification because of their similar appearance. However, differences in botanical origin may lead to variations in odor, taste, volatile constituents, and non-volatile metabolites, thereby affecting quality evaluation and clinical application. This study aimed to systematically characterize the sensory and chemical differences between MCZ and BCZ and to identify potential markers for their discrimination. A multi-dimensional analytical strategy combining electronic nose, electronic tongue, headspace gas chromatography–ion mobility spectrometry (HS-GC-IMS), and ultra-high-performance liquid chromatography–Orbitrap high-resolution mass spectrometry (UPLC-Orbitrap MS) was established. Electronic nose and electronic tongue were used to digitize odor and taste characteristics, HS-GC-IMS was employed to profile volatile organic compounds, and UPLC-Orbitrap MS was applied to characterize non-volatile metabolites. Principal component analysis (PCA), orthogonal partial least squares discriminant analysis (OPLS-DA), variable importance in projection (VIP) screening, permutation tests, and correlation analysis were further used to evaluate discrimination performance and screen characteristic markers. The electronic nose results showed that MCZ and BCZ exhibited distinct odor profiles, with W5S, W1W, and W1S identified as the main differential sensors, suggesting that nitrogen oxides, terpenoids, inorganic sulfides, and short-chain alkanes contributed to the odor differences between the two origins. Electronic tongue analysis further demonstrated clear taste discrimination, with sourness and richness identified as the key taste indicators. HS-GC-IMS detected 108 volatile organic compounds, and 24 volatile markers with VIP > 1.2 were screened as important contributors to the differentiation of MCZ and BCZ. Among them, propionic acid and 5-methyl-2-furancarboxaldehyde were mainly distributed in MCZ, whereas (E)-caryophyllene was present only or at higher levels in BCZ, indicating its potential as a characteristic volatile marker of BCZ. UPLC-Orbitrap MS detected 78 non-volatile constituents, and OPLS-DA screened 17 key non-volatile differential metabolites with VIP > 1.2. These results indicated that MCZ and BCZ could be clearly separated not only by sensory signals but also by volatile and non-volatile chemical profiles. This study revealed that the differences between MCZ and BCZ are mainly reflected in odor-active volatile compounds, key taste indicators, and non-volatile differential metabolites. The integration of electronic nose, electronic tongue, HS-GC-IMS, and UPLC-Orbitrap MS provides a comprehensive and reliable strategy for distinguishing the two botanical origins of Atractylodis Rhizoma. These findings provide valuable insights into the material basis underlying the sensory and chemical differences between MCZ and BCZ and offer scientific support for accurate authentication, quality evaluation, and rational clinical application of Atractylodis Rhizoma. Full article

This study compares the efficacy of ozonated sunflower oil (OSO) and ozonated olive oil (OOO) in terms of antioxidant properties, modulation of high-density lipoprotein (HDL) functionality, and protective effects against carboxymethyllysine (CML)-mediated stress in zebrafish embryos and adults. The spectral and electronic nose (e-nose) analyses revealed that OSO and OOO possessed markedly distinct physicochemical characteristics and volatile and olfactory constituents compared with non-ozonated sunflower (SO) and olive oil (OO). The fluorescence spectrum analysis of HDL treated with OOO and OSO exhibited a red shift (2.6~3.3 nm) in the wavelength maximum fluorescence (WMF), accompanied by pronounced quenching of tryptophan fluorescence. Additionally, a significant increase in HDL-associated paraoxonase (PON) and ferric ion reduction (FRA) activity was observed in the OSO- and OOO-treated HDL. However, compared to OOO, significantly higher PON and FRA activities were observed in HDL treated with OSO. Also, compared to OOO, OSO effectively reverses CML-induced oxidative stress, altered heart rate, and reduced embryo survival. Similarly, in adult zebrafish, CML-compromised survival, swimming impairment, and disturbed antioxidant parameters were prevented by treatment with OOO and OSO. Nonetheless, OSO showed significantly higher efficacy than OOO. Consistently, OSO substantially reduced the CML-elevated blood glucose, total cholesterol (TC), triglycerides (TG), and low-density lipoprotein cholesterol (LDL-C) levels with a marked increase in high-density lipoprotein cholesterol (HDL-C) levels. Notably, no significant effect of OOO was observed on the reduction in and augmentation of LDL-C and HDL-C, respectively. Both OOO and OSO significantly protect against CML-triggered liver and kidney damage. However, compared with OOO, OSO significantly reduced neutrophil infiltration, interleukin-6 (IL-6) production, liver steatosis, ROS generation, and cellular senescence in the kidneys. The study concludes that OSO exerts significantly higher beneficial effects than OOO on HDL functionality and antioxidant defense, thereby attenuating CML-induced inflammatory and oxidative damage. Full article

Rapid geographical origin discrimination of Tremella fuciformis is important for quality control and authenticity assessment; however, conventional analytical methods are often time-consuming and require complex sample preparation. In this study, a rapid discrimination approach was established by integrating electronic nose (E-nose) response fingerprints with machine learning. To capture temporal variation in the E-nose signals, fingerprint features were extracted from three response windows: the selected overall response window (0–69 s), the early response window (0–29 s), and the relatively stable response window (56–65 s). Random forest, partial least squares discriminant analysis (PLS-DA), Gaussian naive Bayes, nearest centroid, and decision tree were then constructed and evaluated. Classification performance varied among the temporal-window feature sets. Based on 100 repeated stratified random splits, PLS-DA model using the 56–65 s feature window achieved the best overall classification performance, with accuracy, balanced accuracy, F1-score (the harmonic mean of precision and recall), and ROC-AUC (the area under the receiver operating characteristic curve) values of 0.9933 ± 0.0255, 0.9928 ± 0.0256, 0.9919 ± 0.0293, 0.9991 ± 0.0085, respectively. These findings indicate that E-nose fingerprinting combined with PLS-DA may provide a rapid and effective method for geographical origin discrimination of T. fuciformis. Full article

Fragrant olive (Osmanthus fragrans var. aurantiacus (O. fragrans)) extract is known to influence neurophysiological responses through inhalation, yet research on concentration-dependent effects and sex-specific variations remains insufficient. This study utilized an electronic nose (E-nose), electroencephalography (EEG), and standardized low-resolution electromagnetic tomography (sLORETA) to characterize the volatile profiles and neurophysiological impacts of O. fragrans at 3% and 5% concentrations. E-nose analysis identified 48 volatile compounds, with chemometric modeling (PCA, HCA) showing clear discrimination between concentrations. EEG results demonstrated that inhalation induced significant concentration-dependent changes—specifically increasing sedation-related alpha waves and decreasing tension-related gamma waves—with 5% O. fragrans eliciting more widespread cortical responses than the 3% concentration. Notably, no significant sex-related differences were observed in general EEG patterns; however, sLORETA revealed that 5% inhalation specifically suppressed high beta and gamma activities in male participants within Brodmann areas 13, 21, 22, and 44, regions associated with emotional and multisensory processing. In conclusion, this study successfully quantified the relationship between volatile profiles and human brain responses using an integrated biomimetic and neurophysiological approach. These findings provide objective evidence that O. fragrans inhalation, particularly at 5%, modulates neural oscillations toward a relaxed state, offering valuable data for olfactory perception and potential applications as functional volatile compounds. Full article

This study evaluated the effects of partial replacement of fishmeal with 11% defatted Hermetia illucens meal (corresponding to approximately 35% replacement of the fishmeal-derived animal protein fraction) on growth performance, fillet proximate composition, and instrumental sensory responses of gilthead seabream (Sparus aurata) reared under commercial offshore farming conditions. A total of 60,000 fish were distributed into four sea cages and fed either a control diet (FM) or an insect-based diet (HIM) for 181 days. No significant differences were observed between dietary treatments in final body weight, weight gain, specific growth rate, feed conversion ratio, protein efficiency ratio, or somatic indices, indicating that insect meal inclusion did not impair productive performance under farm-scale conditions. Fillet proximate composition was largely preserved. Fillet sensory characteristics were assessed using an integrated artificial sensing platform including an electronic eye (E-eye), electronic nose (E-nose), and electronic tongue (E-tongue) coupled with multivariate analysis. E-eye and E-nose analyses showed no clear discrimination between dietary groups, indicating that dietary insect meal inclusion had limited effects on fillet visual appearance and volatile compound profiles. In contrast, E-tongue analysis revealed a clear separation between treatments, suggesting selective modulation of taste-related attributes associated with dietary inclusion of insect meal. Overall, the results demonstrate that defatted H. illucens meal can be incorporated into practical seabream diets under commercial farming conditions without compromising productive performance or major fillet quality traits. Furthermore, this study provides farm-scale evidence that artificial sensing technologies can effectively detect subtle diet-related changes in sensory characteristics, particularly those associated with taste perception. Full article

Efficient monitoring of pork freshness is essential to minimize spoilage-related losses in the meat industry. To address the limitations of existing detection technologies, namely high cost, poor timeliness and high environmental sensitivity, this study developed a novel electronic nose system integrating a hybrid sensor array with dynamic gas path control. By combining metal oxide semiconductor (MOS) and electrochemical sensors (e.g., MQ137, MQ136), the system exhibits high sensitivity to the key volatile organic compounds (VOCs) released during pork spoilage, achieving a detection accuracy of over 90% in identifying spoilage stages. Combined with a dual-mode gas circuit design (solenoid valve switching time: 0.85 s), the reliability of the system was further demonstrated. This technology offers an economical and efficient real-time monitoring solution for slaughterhouses and cold chain logistics, providing a new low-cost scientific approach for pork freshness assessment. Full article

Precision in estimating the ripeness of fruits is critical in quality control and minimizing losses in supply chains of agricultural produce following harvesting. Conventional ripeness assessment techniques tend to be destructive, time-consuming and unsuited to monitoring in real-time. In order to avoid these drawbacks, this research suggests a cloud-integrated smart electronic nose (E-nose) system to predict fruit ripeness in a non-destructive and real-time manner. The system uses a low-priced, non-selective gas sensor array with an ESP8266-based Internet of Things (IoT) board to record volatile organic compound (VOC) signatures released at various maturation phases of fruits. The obtained sensor data will be sent to a cloud server to be preprocessed centrally and classified using machine learning, thus reducing the computational needs at the edge. There is a collection of 953 samples of the unripe, ripe, and rotten stages of banana under controlled conditions. Several supervised machine learning algorithms are tested, and methods of ensemble boosting proved to be more effective. The Light Gradient Boosting Machine (LightGBM) is the most accurate in terms of classification of 96.50% and weighted F1-score of 96.49%. The confusion matrix analysis shows that the majority of misclassifications are observed among the neighboring stages of ripeness, indicating the gradual biochemical changes. The system is practically applicable as visualization of the predicted ripeness levels occurs in real time via a mobile application. The suggested model provides a scalable, low-cost, and smart solution to precision agriculture, which can allow efficient, automated, and non-destructive measurement of fruit quality. Full article

This study aims to conduct a comparative analysis of the quality and flavor of meat from four categories of Mongolian horses (Wushen, Baicha, Barhu, and Ujimqin). Physicochemical indicators, electronic nose, electronic tongue, and lipidomics were used to characterize meat quality and flavor and to screen for differential markers. Results showed that Wushen Horses had the highest pH_45min, serine, glutamic acid, total free amino acids (∑FAA), total non-essential amino acids (∑NEAA), total amino acids (∑TAA), NEAA/TAA, W2S sensor response, umami and richness values, and had the lowest cooking loss, EAA/TAA, EAA/NEAA, sourness, bitterness and aftertaste B values (p < 0.01). In contrast, Barhu Horses had the highest b*_45min, C20:2 and saltiness values, and had the lowest W5S, W1S and W2W sensor responses (p < 0.01). Lipidomics identified 163 differential lipids (DELs) as potential markers, including LPC (18:2/0:0) and PC (16:0_16:0). Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis showed DELs were significantly enriched in glycerolipid, linoleic acid, arachidonic acid and α-linolenic acid metabolism pathways. Correlation analysis indicated 23 DELs (e.g., carnitine C20:4) correlated positively with umami, W2S and richness, but negatively with shear force and cooking loss. In summary, our data show that among the four categories of Mongolian horses, Wushen Horses exhibited the best meat quality and flavor, while Barhu Horses showed the poorest. The differences in meat quality and flavor were closely associated with changes in lipid composition. This study provides direct molecular evidence from lipids for the variation in meat quality among Mongolian horses. Full article

This study investigated the comprehensive effects of harvest timing and stir-frying on Ziziphi Spinosae Semen (ZSS) quality using chemical profiling, Caenorhabditis elegans bioassays, and intelligent sensory analysis (electronic nose (E-nose), electronic tongue (E-tongue), and gas chromatography-ion mobility spectrometry (GC-IMS)). Results indicated that delaying harvest to 15 September significantly promoted bioactive accumulation, with total saponins reaching 9.54 g kg⁻¹ at this stage. Stir-frying the optimal raw material further enhanced pharmacological efficacy; spinosin content increased 1.48-fold, and C. elegans motility cessation time significantly shortened from 240 s to 180 s, demonstrating superior sedative activity. Additionally, stir-frying improved the total sensory score from 53.8 to 80.4, characterized by a harmonized balance of bitterness and umami. GC-IMS analysis identified Maillard reaction products, specifically 2-methylpyrazine and 2-methylbutanal as key markers responsible for the distinctive roasted aroma. Consequently, harvesting the fruits of Ziziphus jujuba var. spinosa at physiological maturity, followed by the stir-frying of ZSS effectively enhances its sedative effects and flavor profile. Full article

Accurate characterization of closely related Ilex taxa is essential for the conservation, documentation, and utilization of plant genetic resources. In this study, five Ilex taxa from eastern China (Ilex rotunda Thunb., Ilex chinensis, Ilex cornuta Lindl. & Paxt., Ilex cornuta ‘Fortunei’, and Ilex latifolia Thunb.) were evaluated using an integrated framework combining fruit morphometric traits, CIELAB color parameters, and electronic-nose (E-nose) volatile fingerprints. Fruit transverse diameter, longitudinal diameter, single-fruit weight, fruit shape index, and peel color traits (L*, a*, b*, and chroma, C*) differed significantly among taxa (one-way ANOVA, all p < 0.001). I. cornuta produced the largest and heaviest fruits, I. chinensis showed the most elongated fruit shape, and I. rotunda exhibited the highest redness and chroma values. Chemometric analyses of E-nose responses further improved taxon discrimination and revealed clear divergence in volatile-response patterns. Trait-space relationships were broadly consistent with the preset phylogenetic framework, with I. rotunda showing the greatest divergence and I. cornuta and I. cornuta ‘Fortunei’ showing the closest similarity. These findings indicate that integrated fruit phenotyping and rapid volatile profiling provide a practical approach for Ilex germplasm identification, comparative evaluation, and resource documentation, with potential value for conservation planning and horticultural utilization. Full article

Prostate cancer (PCa) is one of the leading causes of cancer-related morbidity and mortality in men worldwide, and early detection remains crucial for ensuring effective treatment and improving patient outcomes. In this context, the development of non-invasive, accurate, and cost-effective screening strategies is of paramount importance. One particularly promising and innovative approach is the analysis of volatile organic compounds (VOCs), a field known as volatolomics. VOCs, which are metabolic by products released by the body, reflect underlying biochemical processes and offer a valuable, non-invasive source of diagnostic information. Recent advances have highlighted the potential of VOC profiling in PCa detection. A variety of biological systems have demonstrated remarkable sensitivity and specificity in recognizing disease-associated VOC signatures. Notably, trained dogs, selected invertebrates, and artificial sensing platforms have all shown the ability to identify PCa-related olfactory patterns. Among technological approaches, electronic noses (eNoses), which combine chemical sensor arrays with pattern recognition algorithms such as neural networks, represent a rapidly evolving diagnostic tool. Together, these biologically inspired and technology-driven strategies are reshaping the landscape of cancer diagnostics. They offer a compelling foundation for the development of rapid, non-invasive, and clinically translatable methods for PCa detection. This narrative review summarizes recent advances in using VOCs for PCa diagnosis and evaluates the reproducibility and clinical robustness of these approaches, focusing on challenges such as standardizing sampling, storage, and analysis, small cohort sizes, and the need for external validation and regulatory integration. Full article

Classification of vehicle engines using the chemical composition of the exhaust from these engines can be used to identify the engine’s design and verify compliance with environmental regulations through the vehicle’s emissions. This paper describes a method to identify the type of vehicles using machine learning (ML), where low-cost MQ series sensors measure the gases and particle emissions from a vehicle exhaust system, while simultaneously collecting and measuring the vehicle’s temperature and humidity levels. A custom-designed multi-sensor exhaust sensing module is employed to capture real-time exhaust emissions prior to entering the atmosphere. Exhaust samples are collected from vehicles representing three major engine categories: petrol, diesel, and compressed natural gas (CNG). In addition, fresh air samples are collected as a baseline environmental reference for comparison. All exhaust measurements are collected under controlled and consistent engine operating conditions to ensure comparable emission profiling across vehicle classes. To ensure consistent combustion-based emission profiling, this study focuses on conventional fuel-powered vehicles. MQ-series gas sensors are sensitive to combustion by-products emitted during engine operation, such as carbon monoxide (CO), hydrocarbons (HC), while also exhibiting cross-sensitivity to other gaseous components present in exhaust mixtures. Nevertheless, the proposed system performs pattern-based classification using relative sensor response signatures. Standardization of data is achieved through z-score normalization. The best models developed (based on three separate experimental designs) are trained and validated using six supervised machine learning algorithms such as Logistic Regression, Support Vector Machine (RBF), k-Nearest Neighbors, Random Forest, Gradient Boosting Decision Tree, and XGBoost and are compared against one another. Evaluation of the tested algorithms using various evaluation metrics demonstrated that ensemble models outperformed all other algorithms, achieving the highest accuracy of 99.5%. Furthermore, noise analysis confirms that the proposed solution maintains high classification accuracy (more than 89%) even under substantial sensor perturbations mimicking the real-world deployment. The solution proposed below illustrates how using gas sensors and advanced algorithms can provide accurate exhaust identification and identify engines in real-time. Full article