Intelligent Evaluation and Dynamic Prediction of Oysters Freshness with Electronic Nose Non-Destructive Monitoring and Machine Learning.

Physiological and environmental fluctuations in the oyster cold chain can lead to quality deterioration, highlighting the importance of monitoring and evaluating oyster freshness. In this study, an electronic nose was developed using ten partially selective metal oxide-based gas sensors for rapid freshness assessment. Simultaneous analyses, including GC-MS, TVBN, microorganism, texture, and sensory evaluations, were conducted to assess the quality status of oysters.

A Review of Advanced Sensor Technologies for Aquatic Products Freshness Assessment in Cold Chain Logistics.

The evaluation of the upkeep and freshness of aquatic products within the cold chain is crucial due to their perishable nature, which can significantly impact both quality and safety. Conventional methods for assessing freshness in the cold chain have inherent limitations regarding specificity and accuracy, often requiring substantial time and effort. Recently, advanced sensor technologies have been developed for freshness assessment, enabling real-time and non-invasive monitoring via the detection of volatile organic compounds, biochemical markers, and physical properties.

Intelligent Evaluation and Dynamic Prediction of Oyster Freshness with Electronic Nose Based on the Distribution of Volatile Compounds Using GC-MS Analysis.

The quality of oysters is reflected by volatile organic components. To rapidly assess the freshness level of oysters and elucidate the changes in flavor substances during storage, the volatile compounds of oysters stored at 4, 12, 20, and 28 °C over varying durations were analyzed using GC-MS and an electronic nose. Data from both GC-MS and electronic nose analyses revealed that alcohols, acids, and aldehydes are the primary contributors to the rancidity of oysters.

Case report: Intraabdominal infection of in an immunocompetent patient confirmed by whole-genome sequencing.

Background: The taxonomic group of non-tuberculous mycobacteria (NTM) encompasses more than 190 species and subspecies, some of which can cause pulmonary and extrapulmonary diseases across various age groups in humans. However, different subspecies exhibit differential drug sensitivities, and traditional detection techniques struggle to accurately classify NTM. Therefore, clinicians need more effective detection methods to identify NTM subtypes, thus providing personalized medication for patients.

DFT calculation and analysis of the gas sensing mechanism of methoxy propanol on Ag decorated SnO (110) surface.

Methoxy propanol has been widely used in modern industry and consumer products. Inhalation or skin exposure to methoxy propanol for a long period would bring about safety challenges on human habitat and health. Ag decorated SnO mesoporous material has been synthesized and shown to exhibit high sensitivity and good selectivity to methoxy propanol among other interferential VOC gases.

A Novel Sparse Representation Classification Method for Gas Identification Using Self-Adapted Temperature Modulated Gas Sensors.

A novel sparse representation classification method (SRC), namly SRC based on Method of Optimal Directions (SRC_MOD), is proposed for electronic nose system in this paper. By finding both a synthesis dictionary and a corresponding coefficient vector, the -th class training samples are approximated as a linear combination of a few of the dictionary atoms. The optimal solutions of the synthesis dictionary and coefficient vector are found by MOD.

Temperature-dependent ultraviolet Raman scattering and anomalous Raman phenomenon of AlGaN/GaN heterostructure.

Temperature-dependent ultraviolet (UV) Raman scattering from AlGaN/GaN heterostructure is investigated. Compared to the visible Raman spectrum, four new peaks at 600, 700, 780, and 840 cm are observed in the UV Raman spectrum. The peak at 780 cm is from the AlGaN A(LO) mode.

Development of a LeNet-5 Gas Identification CNN Structure for Electronic Noses.

A new LeNet-5 gas identification convolutional neural network structure for electronic noses is proposed and developed in this paper. Inspired by the tremendous achievements made by convolutional neural networks in the field of computer vision, the LeNet-5 was adopted and improved for a 12-sensor array based electronic nose system. Response data of the electronic nose to different concentrations of CO, CH₄ and their mixtures were acquired by an automated gas distribution and test system.