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.

Soluble solids content (SSC) is one of the main quality indicators of apples, and it is important to improve the precision of online SSC detection of whole apple fruit. Therefore, the spectral pre-processing method of spectral-to-spectral ratio (S/S), as well as multiple characteristic wavelength member model fusion (MCMF) and characteristic wavelength and non-characteristic wavelength member model fusion (CNCMF) methods, were proposed for improving the detection performance of apple whole fruit SSC by diffuse reflection (DR), diffuse transmission (DT) and full transmission (FT) spectra. The modeling analysis showed that the S/S- partial least squares regression models for all three mode spectra had high prediction performance. After competitive adaptive reweighted sampling characteristic wavelength screening, the prediction performance of all three model spectra was improved. The particle swarm optimization–extreme learning machine models of MCMF and CNCMF had the most significant enhancement effect and could make all three mode spectra have high prediction performance. DR, DT, and FT spectra all had some prediction ability for apple whole fruit SSC, with FT spectra having the strongest prediction ability, followed by DT spectra. This study is of great significance and value for improving the accuracy of the online detection model of apple whole fruit SSC.