An Unsupervised Learning Method for the Detection of Genetically Modified Crops Based on Terahertz Spectral Data Analysis

Abstract

Genetically modified crops have been planted commercially on a large scale since 1996. However, the food safety issue of genetically modified crops remains controversial. Conventional genetically modified crops’ detection methods require a plenty of detective time and complex operations that cannot rapidly identify. Previous reports show that combining terahertz time-domain spectroscopy and supervised learning has advanced to identify genetically modified crops, but supervised learning requires large data to train the model. To solve the above problem, we proposed an unsupervised learning method, PCA-mean shift, to identify genetically modified crops. Principal component analysis was employed to reduce the absorbance data dimensionality. After principal component analysis, the first three principal components were used as the input of mean shift. At last, our proposed method had 100% identification accuracy, and K-means had 98.75% identification accuracy. The comparison results demonstrated that PCA-mean shift outperforms K-means. Therefore, PCA-mean shift combined with terahertz time-domain spectroscopy is a potential identification tool for genetically modified crops’ identification.