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. The integration of sensor technologies into cold chain logistics enhances the ability to maintain the quality and safety of aquatic products. This review examines the advancements made in multifunctional sensor devices for the freshness assessment of aquatic products in cold chain logistics, as well as the application of pattern recognition algorithms for identification and classification. It begins by outlining the categories of freshness criteria, followed by an exploration of the development of four key sensor devices: electronic noses, electronic tongues, biosensors, and flexible sensors. Furthermore, the review discusses the implementation of advanced pattern recognition algorithms in sensor devices for freshness detection and evaluation. It highlights the current status and future potential of sensor technologies for aquatic products within the cold chain, while also addressing the significant challenges that remain to be overcome.

Evaluating the impact of different meteorological conditions on apple yield and predicting the future yield in Yantai City is essential for production. Furthermore, it provides a scientific basis for the increase in apple yield. In this study, first, a grey relational analysis (GRA) was used to determine the quantitative relationship between different meteorological factors and meteorological yield which is defined as affected only by meteorological conditions. Then, the comprehensive meteorological factors extracted by a principal component analysis (PCA) were used as inputs for multiple linear regression (MLR). The apple yield accuracy was compared with the lasso regression prediction. Trend analysis showed that the actual apple yield increased annually, but the meteorological yield decreased annually over a long time. Correlation ranking illustrated that the meteorological yield was significantly correlated with the frost-free period, the annual mean temperature, the accumulated temperature above 10 °C, etc. The good consistency between GRA and MLR–PCA showed that the accumulated temperature above 10 °C, the March–October mean temperature, and the June–August mean temperature are key meteorological factors. In addition, it was found that the principal components $F_2, F_4$, and $F_5$ were negatively correlated with meteorological yield, while the principal components $F_1$ and $F_3$ were positively correlated with meteorological yield. Moreover, the MLR–PCA model predicted the apple yield in 2020 as 47.256 t·ha⁻¹ with a 7.089% relative error. This work demonstrates that the principal component regression model can effectively extract information about different meteorological factors and improve the model’s accuracy for analyzing key meteorological factors and predicting apple yield.

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. Real-time electronic nose measurements were taken at various storage temperatures (4 °C, 12 °C, 20 °C, 28 °C) to thoroughly investigate quality changes under different storage conditions. Principal component analysis was utilized to reduce the 10-dimensional vectors to 3-dimensional vectors, enabling the clustering of samples into fresh, sub-fresh, and decayed categories. A GA-BP neural network model based on these three classes achieved a test data accuracy rate exceeding 93%. Expert input was solicited for performance analysis and optimization suggestions enhanced the efficiency and applicability of the established prediction system. The results demonstrate that combining an electronic nose with quality indices is an effective approach for diagnosing oyster spoilage and mitigating quality and safety risks in the oyster industry.

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. Notably, the relative and absolute contents of Cis-2-(2-Pentenyl) furan and other heterocyclic compounds exhibited an upward trend. This observation suggests the potential for developing a simpler test for oyster freshness based on these compounds. Linear Discriminant Analysis (LDA) demonstrated superior performance compared to Principal Component Analysis (PCA) in differentiating oyster samples at various storage times. At 4 °C, the classification accuracy of the optimal support vector machine (SVM) and random forest (RF) models exceeded 90%. At 12 °C, 20 °C, and 28 °C, the classification accuracy of the best SVM and RF models surpassed 95%. Pearson correlation analysis of the concentrations of various volatile compounds and characteristic markers with the sensor response values indicated that the selected sensors were more aligned with the volatiles emitted by oysters. Consequently, the volatile compounds in oysters during storage can be predicted based on the response information from the sensors in the detection system. This study also demonstrates that the detection system serves as a viable alternative to GC-MS for evaluating oysters of varying freshness grades.

The Yantai region in China, despite its ideal viticultural conditions, faces a challenge in producing white wines with distinct regional typicity. This study explores the potential of indigenous yeast strains—specifically, the Hanseniaspora uvarum H30 and Saccharomyces cerevisiae YT13, both isolated and identified from the Yantai vineyard environment—to enhance the aroma profiles and overall quality of white wines from this region. Italian Riesling and Petit Manseng grapes were fermented with two commercial Saccharomyces cerevisiae strains (EC1118 and Tropical/X16) and a sequential inoculation of the locally sourced Hanseniaspora uvarum H30 (with high β-glucosidase activity) followed by S. cerevisiae YT13. The results showed that sequential inoculation with H30 + YT13 significantly enhanced the concentration of key ester compounds and uniquely increased terpene levels. Notably, valuable terpenes such as farnesol and nerolidol were exclusively detected in the H30 + YT13 group for Italian Riesling. Sensory analysis confirmed that these chemical changes translated into higher scores for floral and fruity attributes in the sequentially inoculated wines. These findings demonstrate that sequential inoculation with indigenous yeasts effectively enhances the aromatic complexity and varietal typicity of white wines from Yantai, providing a promising strategy for developing wines with stronger regional identity.

Real-time quality monitoring during oyster cold chain transportation is a critical component in ensuring food safety. Addressing the issues of high redundancy and insufficient environmental adaptability in existing electronic nose systems, this study proposes a multi-algorithm collaborative optimization strategy for sensor array optimization. The system integrates ten gas sensors (TGS series, MQ series), employing Random Forest (RFA), Simulated Annealing (SA), and Genetic Quantum Particle Swarm Optimization (GA-QPSO) for sensor selection. KNN combined with K-means analysis validates the optimization outcomes. Under cold chain environments at 4 °C, 12 °C, 20 °C, and 28 °C, a multidimensional dataset was constructed by extracting global variables using feature correlation functions. Experiments demonstrate that the optimized sensor count decreases from 10 to 5–6 units while maintaining recognition accuracy above 95%, with redundancy decreased by over 40%. This multi-algorithm collaborative optimization effectively balances sensor array recognition precision, resource efficiency, and environmental adaptability, providing an intelligent, high-precision technical solution for oyster cold chain monitoring.