Thermal processing significantly impacts the quality and characteristics of shrimp meat. In this study, we employed essential physicochemical composition determination, texture analysis, sensory evaluation, an electronic tongue assay, and UHPLC-Q Exactive-MS/MS metabolomics to investigate changes in the edible quality and metabolites of the meat of Metapenaeus ensis meat subjected to different thermal processing procedures (air frying, boiling, frying, drying). A total of 79 key differentially abundant metabolites affecting the eating quality of shrimp were identified. The different metabolites were significantly correlated with the color, meat quality, pH value, and nutritional components of the shrimp (p < 0.05). The primary metabolites affecting shrimp flavour were AMP, D-pyroglutamic acid, succinic acid, and benzoic acid. The flavour richness and sensory evaluation scores of shrimp e-tongues subjected to air frying were the highest. Purines and heterocyclic amines produced in the boiling treatment group are the least harmful and healthiest heat treatment methods. Drying treatment is the most nutritious heat treatment method for ensuring flavour. This study clarified the molecular mechanism of the quality change in shrimp meat after heat treatment. This study provides a theoretical basis for optimizing and selecting Metapenaeus ensis processing technology. Graphical Abstract

Moisture content is a key quality attribute in dried areca nuts, affecting subsequent processing performance and storage stability, yet routine measurement by oven-drying is time-consuming and destructive. This study developed a rapid and non-destructive method for determining moisture content in dried areca nuts by integrating near-infrared spectroscopy with chemometric and machine learning-assisted methodologies. Various spectral preprocessing methods, feature wavelength selection algorithms, and modeling approaches were compared. The results indicated that Multiplicative Scatter Correction (MSC) most effectively eliminated physical scattering interference. The Partial Least Squares Regression (PLSR) model established using full-wavelength spectra demonstrated optimal predictive performance. It achieved a coefficient of determination for the prediction set (Rp²), root mean square error of prediction (RMSEP), and residual predictive deviation (RPD) of 0.9639, 0.1960, and 10.3461, respectively, indicating excellent predictive accuracy and robustness. Feature wavelength selection did not enhance model performance in this study, which can be attributed to the broad absorption bands of water in the near-infrared spectrum and its complex interactions with the sample matrix where the full spectrum data retains essential information more comprehensively. This research provides a reliable and practical technical means for moisture management in areca nuts, offering important support for quality assurance and standardized production practices within the areca industry.