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.

High-temperature processing of carbohydrate-rich biomass, including starch liquefaction and lignocellulose saccharification, can improve sugar yields, reduce contamination risks, and facilitate integration with thermochemical pretreatments. However, the operational stability of native carbohydrate-active enzymes (CAZymes) at 60–90 °C remains a major bottleneck for efficient and economically viable biorefineries, and existing reviews rarely integrate multiscale molecular mechanisms with process-relevant engineering strategies. This review systematically examines the multiscale basis of thermostability in CAZymes—covering core hydrophobic packing, electrostatic and metal-mediated networks, disulfide bonds, conformational dynamics, and modular architectures involving catalytic domains, linkers, and carbohydrate-binding modules—and directly links these features to enzyme longevity under high-solids, inhibitor-rich conditions. We then critically compare key engineering strategies, including structure-guided rational design, semi-rational directed evolution, consensus design, ancestral sequence reconstruction, and machine learning-assisted workflows embedded in design–build–test–learn (DBTL) cycles, highlighting their respective strengths, limitations, and complementarity. Representative case studies demonstrate that engineered thermophilic α-amylases and cellulases achieving ΔT_m improvements of approximately 10–20 °C can sustain >80–85% residual activity after prolonged exposure at process-relevant temperatures, translating into tangible benefits such as 15–30% reductions in enzyme dosing and measurable decreases in steam consumption, alongside higher sugar titers. By bridging molecular determinants, engineering paradigms, and quantitative process performance, this review provides a pragmatic roadmap for deploying thermostable CAZymes as robust biocatalysts in sustainable high-temperature biomass conversion.