Walnut oil oxidizes and becomes rancid during storage, that could be significantly affecting flavor and quality. This study aimed to monitor the volatile compounds present in walnut oil during storage, identify the characteristic markers of walnut oil at different oxidation levels, and establish a correlation network analysis based on the relationship between the olfactory analyzer and the characteristic markers to understand their correlation. The results indicated that the oxidation level of walnut oil had a positive correlation with the response of the olfactory analyzer. 219 volatile compounds were identified in walnut oil, with 89 identified as key volatile compounds (VIP >1). Among these, compounds such as (E, E)-2,4-decadienal (6.10%-23.04%)，(E, E)-2,4-heptadienal (2.23%-13.61%)，(E)-2-octenal (0.95%-11.71%)， hexanoic acid (1.63%-4.30%)，1-octen-3-ol (2.53%-19.01%)，(Z)-2-heptenal (5.95%-25.01%)，2,3-dihydro-furan (1.08%-3.20%)，2-pentyl-furan (0.13%-0.54%), pyrazine (0.33%-1.32%), hexanal (24.52%-1.33%)，3-hethylbutylacetate (12.44%-1.29%)， 2-methyl butyl acetate (7.74%-1.56%) and ethenyl hexanoate (4.39%-0.41%) were found to be characteristic volatile compounds in the oxidation process of walnut oil. Furthermore, the correlation network analysis revealed a strong correlation between the olfactory analyzer sensors and the characteristic volatile compounds. The findings of this study can provide valuable data for the development of rapid determination of the oxidation level of walnut oil.

Odor is an important characteristic of walnut oil; walnut oil aromas from different varieties smell differently. In order to compare the differences of volatile flavor characteristics in different varieties of walnut oil, the volatile organic compounds (VOCs) of walnut oil from five different walnut varieties in Northwest China were detected and analyzed using headspace gas chromatography–ion mobility spectrometry (HS–GC–IMS). The results showed that 41 VOCs in total were identified in walnut oil from five different varieties, including 14 aldehydes, 8 alcohols, 4 ketones, and 2 esters. Walnut oil (WO) extracted from the “Zha343” variety was most abundant in VOCs. The relative odor activity value (ROAV) analysis showed that aldehydes were the main aroma substances of walnut oil; specifically, hexanal, pentanal, and heptanal were the most abundant. Fingerprints and heat map analysis indicated that WO extracted from the “Xin2”, “185”, “Xin’guang”, and “Zha343” varieties, but not from the “Xinfeng” variety, had characteristic markers. The relative content differences of eight key VOCs in WO from five varieties can be directly compared by Kruskal–Wallis tests, among which the distribution four substances, hexanal (M), hexanal (D), pentanal (M), (E)-2-hexanal (M), presented extremely significant differences (P<0.01). According to the results of the principal component analysis (PCA), WO extracted from the “Zha343” variety was distinct from the other four varieties; in addition, WO extracted from the “Xin2” variety exhibited similarity to WO extracted from the “185” variety, and WO extracted from the “Xinfeng” variety showed similarity to WO extracted from the “Xin’guang” variety. These results reveal that there are certain differences in the VOCs extracted from five different WO varieties, making it feasible to distinguish different varieties of walnut oil or to rapidly detect walnut oil quality based on its volatile substances profile.

In order to further realize the resource reuse of walnut meal after oil extraction, walnut meal was used as raw material to prepare polypeptide, and its angiotensin-converting enzyme (ACE) inhibitory activity was investigated. The ACE inhibitory peptides were prepared from walnut meal protein by alkaline solution and acid precipitation. The hydrolysis degree and ACE inhibition rate were used as indexes to optimize the preparation process by single-factor experiment and response surface method. The components with the highest ACE activity were screened by ultrafiltration, and their antioxidant activities were evaluated in vitro. The effect of gastrointestinal digestion on the stability of walnut peptide was analyzed by measuring molecular weight and ACE inhibition rate. The results showed that the optimal extraction conditions were pH 9.10, hydrolysis temperature 54.50 °C, and hydrolysis time 136 min. The ACE inhibition rate of walnut meal hydrolysate (WMH) prepared under these conditions was 63.93% ± 0.43%. Under the above conditions, the fraction less than 3 kDa showed the highest ACE inhibitory activity among the ACE inhibitory peptides separated by ultrafiltration. The IC₅₀ value of scavenging ·OH free radical was 1.156 mg/mL, the IC₅₀ value of scavenging DPPH free radical was 0.25 mg/mL, and the IC₅₀ value of scavenging O₂⁻ was 3.026 mg/mL, showing a strong total reducing ability. After simulated gastrointestinal digestion in vitro, the ACE inhibitory rate of walnut peptide decreased significantly, but it still maintained over 90% ACE inhibitory activity. This study provides a reference for the application of low-molecular-weight walnut peptide as a potential antioxidant and ACE inhibitor.

To meet market demand for fresh ‘Dinosaur Egg’ Apricot plum and realize effective quality classification, this study developed a non-destructive quality evaluation method using near-infrared spectroscopy (NIRS) with cross-parameter feature fusion. Spectral data were preprocessed, and key bands were screened via Competitive Adaptive Reweighted Sampling (CARS) and Shuffled Frog Leaping Algorithm (SFLA). Partial Least Squares Regression (PLSR) models for soluble solids content (SSC), moisture content (MC), and fruit firmness (FF) were established. Chemical index features were fused with FF-related preliminary features, and SHapley Additive exPlanations (SHAP) optimized feature contribution. Final models showed high performance: SSC ($R_c^2$ = 0.9354, $R_p^2$ = 0.9302, $RMSE$ = 0.5212° Brix), MC ($R_c^2$ = 0.9367, $R_p^2$ = 0.9314, $RMSE$ = 5.037 × 10⁻⁵), and FF ($R_c^2$ = 0.8151, $R_p^2$ = 0.7986, $RMSE$ = 1.2710 N). This strategy improved the multi-quality detection accuracy, especially for FF, and provides technical support for intelligent fruit grading.