Prediction of fatty acids in rice storage based on odor characteristics by gas chromatography-ion mobility spectrometry

Abstract

In order to develop a quick method for predicting fatty acid in rice storage, gas chromatography-ion mobility spectrometry (GC-IMS) was applied to detect and analyze volatile organic compounds (VOCs) at different rice storage stages, and partial least squares regression (PLSR) algorithm was used to establish a linear regression model between fatty acid values and characteristic VOCs. The results showed that rice fatty acid values increased gradually with extension of storage time. Odor components of rice mainly included alcohols and aldehydes. Except for 1-octene-3-alcohol, the content of other VOCs showed an overall downward trend during the storage period. After variable optimization using two different algorithms, the correlation coefficient of the PLSR cross validation model could reach 0.9544, and the corresponding root mean square error was 2.4093. In conclusion, fatty acid values of rice with different storage periods could be accurately predicted by using characteristic VOCs variables and chemometric tools, which would provide a rapid and nondestructive detection method for rice quality during storage based on odor information.

Keywords:
rice; odor characteristics; fatty acids; partial squares regression; gas chromatography-ion mobility spectrometry