High-precision discrimination of maize silage based on olfactory visualization technology integrated with chemometrics analysis

Abstract

Rapid, reliable and non-destructive detection of the quality of maize silage is essential to high-efficiency animal husbandry and food safety. In this study, the colorimetric sensor array (CSA) integrated with chemometric methods is innovatively proposed for qualitative discrimination of maize silage. First, 12 color-sensitive dyes were selected to fabricate colorimetric sensor arrays to be used as artificial olfactory sensors for obtaining odor fingerprints of maize silage. Machine vision algorithms were utilized to extract the color features, and principal component analysis was applied to reduce the dimensionality of the obtained data. Finally, the PCA results were input variables to develop different qualitative discrimination models. These models involve support vector machines (SVM), extreme learning machine (ELM), and random forest (RF). The analysis results show the 100% correct identification rate for independent samples. The general results sufficiently reveal that olfactory visualization technology integrated with chemometrics analysis has promising applications for high-precision discrimination of maize silage.