Application of terahertz spectrum and interval partial least squares method in the identification of genetically modified soybeans

• Published in: Spectrochimica acta. Part A, Molecular and biomolecular spectroscopy Molecular and biomolecular spectroscopy, 2020-09, Vol.238, p.118453, Article 118453
• Main Authors: Wei, Xiao, Zheng, Wanqin, Zhu, Shiping, Zhou, Shengling, Wu, Weiji, Xie, Zhiyong
• Format: Article
• Language: English
• Subjects: iPLS; PCA-BPNN; Soybean; SVM; THz; Transgenic identification
• ISSN: 1386-1425
• DOI: 10.1016/j.saa.2020.118453

Genetically modified soybeans are the world's most important genetically modified agricultural product. At present, the traditional methods for identifying genetically modified and non-transgenic soybeans are time-consuming, costly, and complicated to operate, which cannot meet the needs of practical applications. Therefore, it is necessary to discover a fast and accurate method for identifying transgenic soybeans. Terahertz (THz) time domain spectra were collected in sequence from 225 transgenic and non-transgenic soybean samples. Fourier transform was used to convert the terahertz time domain spectrum into a THz frequency domain spectrum with a frequency range of 0.1‐2.5 THz. Firstly, the interval partial least squares (iPLS) method was used to remove interference spectral bands and select appropriate spectral intervals. Secondly, 168 samples were selected as the calibration set. Discriminant partial least squares (DPLS), Grid Search support vector machine (Grid Search-SVM) and principal component analysis back propagation neural network (PCA-BPNN) were used to establish a qualitative identification model. Afterwards, 57 test set samples were predicted. By comparing the experimental results, it was found that iPLS could effectively screen and remove the interference THz band, which was more helpful to improve the efficiency and accuracy of the identification model. After the iPLS and mean center pre-treatment technology, the Grid Search-SVM identification model had the best identification effect, with a total accuracy rate of 98.25% (transgenic identification rate was 96.15%, non-transgenic identification rate was 100%). This study shows that after selecting spectra from iPLS, THz spectroscopy combined with chemometrics can more accurately, quickly, and efficiently identify transgenic and non-transgenic soybeans. [Display omitted] •Compare the discrimination effect of the THz spectrum after iPLS and 10 pre-treatment technologies•Analyze the discrimination effect of DPLS, Grid Search-SVM and PCA-BPNN discrimination models•The experimental results proved that iPLS could improve the accuracy of the identification model•After iPLS and mean center pre-treatment technology, the Grid Search-SVM model had the best identification effect