Physicochemical and flavor profile characterization of sesame oils from different processing methods: Implications for quality control and market regulation.

Continuous-flow enzymatic reactor for medium- and long-chain triglycerides production: From process intensification to flavor retention.

A novel hybrid sensor array with enhanced sensitivity and selectivity for biomarker detection of multiple respiratory diseases.

An electronic nose (e-nose) prototype equipped with nine commercial metal oxide semiconductor (MOS) sensors was used as a rapid, non-invasive tool to evaluate olive oil quality. To assess its performance, extra virgin olive oils (EVOO), with medium or intense fruitiness, were blended with rancid oil at different proportions (0%, 2%, 4%, 8%, 16%, 32%, 100%). According to physicochemical parameters (free acidity, peroxide value, UV extinction), oils containing up to 32% rancid oil were classified as EVOO or virgin olive oil (VOO), while rancid oil alone was categorized as lampante (LOO). Sensory evaluation confirmed progressive quality deterioration, assigning blends as EVOO (0% and 2%), VOO (4% and 8%), and LOO (16% and 32%) based on rancid intensity perception by trained panelists. When coupled with linear discriminant analysis, the e-nose accurately distinguished oils by commercial category, achieving average sensitivities of 92% and 95% (repeated K-fold cross-validation), respectively, for rancid blends with medium or intense defect-free oils. Additionally, e-nose signal profiles allowed establishing simple linear regressions (Signal = slope × Rancid + intercept), enabling quantitative prediction of rancid defect intensity on a 0–10 scale. For medium or intense fruitiness levels, the regression models showed satisfactory fitting (R 2 : 0.9851-0.9948), low error (RMSE: 0.23-0.43), high predictive potential (RPD: 3.5-16.6) and low detection and quantification limits (LOD: 0.5-1.0; LOQ: 1.5-2.9). These results demonstrate that the prototype accurately classifies oil grades and quantifies rancidity, offering a practical, user-friendly tool for olive oil quality screening, though further validation with more samples and standard reference methods is needed. • An E-nose with MOS sensors classified olive oils by commercial category • The device detected rancidity close to the human odor perception threshold • Simple regression models quantified rancidity across intensity ranges • The E-nose enabled rapid rancidity screening for efficient quality control • Non-invasive monitoring supported storage, classification, and decision-making

Pomelo peel waste was valorized through an ultrasound-assisted integrated strategy to simultaneously recover pectin and essential oil for the fabrication of active biodegradable packaging films. Ultrasonic cavitation significantly promoted cell wall disruption and mass transfer, enabling the efficient co-extraction of pectin and essential oil in a single process. The essential oil was further converted into a stable nanoemulsion via ultrasonic emulsification and incorporated into a pectin/polyvinyl alcohol/carboxymethyl cellulose composite film. The physicochemical, structural, mechanical, and aroma-related properties of the films were systematically characterized by FTIR, XRD, tensile analysis, and electronic nose. The optimized film exhibited balanced mechanical performance with a tensile strength of 6.27MPa, an elongation at break of 5.52%, and a light transmittance of 43.52%, indicating good flexibility and optical properties. Sensors responsive to nitrogen- and sulfur-containing compounds showed higher signals, likely due to oxidative transformation products rather than native sulfur volatiles. The preservation performance was evaluated using fresh strawberries as a model system. Compared with the control group, the ultrasound-enabled essential oil film effectively reduced weight loss, delayed microbial spoilage, and extended the storage life by nearly five days at room temperature. This study demonstrates that ultrasound not only intensifies the sustainable extraction of functional biopolymers but also regulates the structural assembly and release behavior of active packaging films. The proposed strategy provides a green and scalable route for converting Citrus processing waste into high-value antimicrobial packaging materials.

The content- and odor-advantaged volatile compounds and the visualized characteristic odor profile of n-3 PUFA-rich fried pork meat obtained from finishing pigs fed a diet enriched with linseed oil and selenomethionine.

Ultrasound-assisted sodium alginate impregnation enhances chlorophyll stability in Undaria pinnatifida.

Breath-based esophageal cancer diagnosis using an electronic nose with multimodal sensor array and machine learning

Integrated analysis of time-driven sensory deterioration and microbial dynamics in not-from-concentrate (NFC) pitaya juice during storage.

The effects of air-impingement jet drying (AID) at temperature of 40–70 °C on red sea bream surimi powder were studied. Results showed that water activity was below 0.5 in all groups. Total volatile basic nitrogen (TVB-N) levels were significantly below 30 mg/100 g. The 40 °C treatment group showed the most uniform surface morphology after rehydration, with the highest water-holding capacity (WHC) and gel strength. Fourier transform infrared (FTIR) spectroscopy revealed changes in C–H, C = O, C = C, and CH2 groups. Lactic acid, citric acid, and succinic acid contributed significantly to the taste, while 5′-nucleotides contributed relatively less. Electronic tongue showed that T40 was more bitter and aftertaste, while the electronic nose revealed that T70 had higher levels of inorganic sulfides, nitrogen oxides, short-chain alkanes, and aromatic compounds. Overall, T50 represents the optimal conditions for AID-dried surimi powder. These findings provide a scientific foundation for optimizing the AID drying process.

In this study, 10 popular commercial oat milk samples were analyzed for sensory quality and flavor chemistry using the Ideal Profile Method (IPM), electronic nose (E-nose), and gas chromatography-mass spectrometry (GC-MS). Based on consumer cognitive mapping of ideal products, samples were classified into "Ideal-like" and "Ideal-exceeding" categories. Ideal-like products exhibited light white appearance, pronounced oatiness, moderate sweetness and burntness, and low graininess, presenting a balanced flavor profile, whereas Ideal-exceeding samples surpassed consumer expectations in sweetness or graininess intensity, delivering stronger sensory stimulation. Furthermore, sensory differentiation among categories primarily stemmed from synergistic effects of lipid oxidation levels (e.g., 3,5-octadien-2-one) and physical stability (fiber and protein content affecting particle size distribution). This classification framework reveals that ideal sensory quality can be achieved through diverse physicochemical pathways in commercial oat milk, providing theoretical guidance for product formulation optimization and quality standardization.

Atractylodes macrocephala Rhizoma (AMR) is a frequently used medicinal herb for treating gastrointestinal disorders, with its quality influenced by factors such as origin and cultivation duration. Traditional quality control methods for AMR are time-consuming and invasive, making the development of faster and more efficient alternatives urgently needed. This study aims to utilize electronic nose (E-nose) and electronic tongue (E-tongue) to achieve the acquisition of odor-taste two-dimensional information of AMR. Integrating this approach with machine learning (ML) enables intelligent transformation from "experience-driven" to "data-driven" quality assessment, thereby developing a rapid and cost-effective quality control strategy for AMR. Feature-extraction and feature-selection techniques were employed to optimize back-propagation neural network (BPNN) classification and regression models for eight key quality markers, selecting the optimal feature subset. Additionally, nine machine-learning algorithms were applied with the optimal feature subset to establish classification models for different AMR grades and quantitative regression models for eight components based on E-nose and E-tongue data. The results demonstrated that the E-tongue combined with the k-nearest neighbors (KNN) algorithm could achieve a rapid classification of AMR grades with an accuracy of 95.56%. It also successfully predicted the contents of the extract, volatile oil, polysaccharides, atractylenolide I, atractylenolide II, atractylenolide III, bis-atractylenolide, and atractylone, with the test set's coefficient of determination (R2) values of 0.8874, 0.8313, 0.9628, 0.8406, 0.8736, 0.8532, 0.7758, and 0.8101, respectively. In conclusion, this study provides a comprehensive and rapid solution for AMR grade classification and quality evaluation, significantly improving efficiency compared with traditional methods. This strategy holds substantial promise for real-world applications, as it enables a high-throughput, non-destructive screening of AMR in settings such as post-harvest processing and market quality surveillance, thereby supporting the sustainable and intelligent development of the herbal medicine industry.

This study evaluated the potential of electronic nose (E-nose) technology to discriminate Spanish-style green table olives spoiled by different bacterial strains. Microbial growth, physicochemical properties, sensory attributes, and volatile organic compounds (VOCs) profiles were analyzed to assess spoilage patterns. The results indicated strain-dependent microbial survival during incubation, with Bacillus cereus and Enterobacter cloacae showing the highest tolerance. Inoculated olives exhibited significant changes in color, texture, pH, phenolic content, and antioxidant activity compared to the Control. Sensory evaluation revealed a reduction in positive attributes and the emergence of defects such as cooked, rancid, and woody aromas, particularly in olives inoculated with B. cereus and Escherichia coli. VOC analysis confirmed these alterations, showing strain-specific increases in aldehydes, phenols, and esters, along with reductions in alcohols and acids. Principal component analysis (PCA) of E-nose data successfully distinguished two groups-spoiled and non-spoiled samples-explaining 84.8% of variance, while Partial Least Squares Discriminant Analysis (PLS-DA) achieved a classification accuracy of 90.4%. These findings highlight the E-nose as a rapid, non-destructive, and reliable tool for detecting bacterial spoilage in table olives, with potential applications in quality control and early spoilage detection.

Abstract Electronic noses, inspired by the mammalian olfactory system, have emerged as vital tools across various domains for gas detection and analysis. However, predicting the concentrations of mixed gases poses a significant challenge due to the nonlinear responses of gas sensors and the intricate nature of gas mixtures. To tackle this issue, this study proposes a novel bidirectional Mamba model that integrates state space modeling and bidirectional information fusion to improve the accuracy and reliability of mixed gas concentration prediction. Leveraging bidirectional time series modeling, the model effectively captures both past and future gas concentration dynamics, excelling in predicting methane, carbon monoxide, and ethylene concentrations with superior performance metrics R2 values of 0.9989, 0.9985, and 0.9905, respectively compared to traditional methods. Its bidirectional structure and residual learning mechanism adeptly handle cross-sensitivity, long-term dependencies, and nonlinearity in gas mixtures, enabling precise global information capture from sensor responses. This work provides innovative insights and methodologies for enhancing electronic nose applications in complex gas monitoring scenarios.

In vitro fecal inoculation coupled with gas chromatography-mass spectrometry (GC-MS) has been used for evaluating fecal deodorants. However, high cost and complex data interpretation limit its routine application. An electronic nose (eNose) offers a rapid, cost-effective alternative. This study aimed to evaluate the eNose as a screening tool for fecal odor compared with solid-phase microextraction gas chromatography-mass spectrometry (SPME GC-MS) and to examine the in vitro effects of fecal deodorant supplements on fecal odor profiles. Feces from ten healthy cats were serially diluted (1:1 to 1:8) and analyzed using both instruments. Four dietary supplements-Yucca schidigera extract (YSE), Quillaja saponaria extract (QSE), fructooligosaccharides (FOS), and oat beta-glucans (OBG)-were tested at concentrations of 0.0, 0.2, 0.4, and 0.8 g/100 mL. The eNose showed comparable performance to GC-MS in discriminating among sample dilutions. In vitro fermentation showed that FOS and OBG significantly increased volatile fatty acid (VFA)-related sensor responses while signals linked to ammonia and sulfur compounds were reduced. QSE had minimal effect, whereas YSE produced moderate changes. The total sensor response intensities did not differ between treatments. These findings indicate that prebiotic supplements exert stronger effects than saponin-based supplements and highlight the potential of eNoses with in vitro fermentation for rapid screening of fecal deodorants.

Ultrafine dark tea powder (UDTP) was prepared by superfine grinding and sieving through a 200-mesh sieve, and incorporated into cookies to improve their textural properties, sensory acceptability and flavor characteristics. Through single-factor experiments and orthogonal testing, the optimal formulation was determined. The quality of cookies was evaluated by texture analysis, sensory evaluation, electronic nose (E-nose), and gas chromatography-mass spectrometry (GC-MS). Results showed that cookies supplemented with 4 g UDTP per 80 g butter exhibited significantly lower hardness and comparable fracturability, along with higher sensory scores in texture, odor and taste compared to basic butter cookies. E-nose and GC-MS analyses revealed that UDTP enrichment promoted the formation of desirable volatile compounds, particularly aldehydes, ketones, and heterocyclic compounds, which contributed to floral, fruity, roasted nutty, and caramel aromas. This study demonstrates that UDTP can effectively improve both the textural and flavor properties of cookies, providing a viable approach for developing tea-fortified baked products with enriched sensory profiles.

This work presents an enhanced sensing framework for MEMS gas sensors based on tunable-amplitude periodic modulation, enabling multi-state excitation and feature enrichment without increasing the number of sensing elements. A multi-level periodic driving scheme is introduced to realize sensor virtualization, and the resulting multi-state responses are processed using a short-term baseline-tracking algorithm and a dislocated sparse-sampling strategy to improve feature discrimination. A lightweight multilayer perceptron (MLP) classifier is subsequently optimized and deployed on a field-programmable gate array (FPGA)-based accelerator to enable gas recognition under constrained hardware resources. Experimental results obtained from ternary mixtures of CH4, CO, and H2 demonstrate a classification accuracy of 98.5%, accompanied by a 60% reduction in model size and a fivefold improvement in computational speed on the FPGA accelerator.

Rapid Discrimination and Quantification of Adulterants in High-Value Yak Milk Using Electronic Nose and Machine Learning

Data fusion and multivariate analysis based on near infrared spectroscopy, electronic noses, and high resolution mass spectrometry: A synergetic approach to boost performance on the authenticity analysis of toxic herbs for Aconitum.

Hybridization enhances rabbit meat quality by modulating muscle Fiber traits, fatty acid profiles, and volatile/non-volatile Metabolomic features.