Identification of Transgenic Agricultural Products and Foods Using NIR Spectroscopy and Hyperspectral Imaging: A Review

Abstract

Spectroscopy and its imaging techniques are now popular methods for quantitative and qualitative analysis in fields such as agricultural products and foods, and combined with various chemometric methods. In fact, this is the application basis for spectroscopy and spectral imaging techniques in other fields such as genetics and transgenic monitoring. To date, there has been considerable research using spectroscopy and its imaging techniques (especially NIR spectroscopy, hyperspectral imaging) for the effective identification of agricultural products and foods. There have been few comprehensive reviews that cover the use of spectroscopic and imaging methods in the identification of genetically modified organisms. Therefore, this paper focuses on the application of NIR spectroscopy and its imaging techniques (including NIR spectroscopy and hyperspectral imaging techniques) in transgenic agricultural product and food detection and compares them with traditional detection methods. A large number of studies have shown that the application of NIR spectroscopy and imaging techniques in the detection of genetically modified foods is effective when compared to conventional approaches such as polymerase chain reaction and enzyme-linked immunosorbent assay.

Design and Experiment of a Portable Near-Infrared Spectroscopy Device for Convenient Prediction of Leaf Chlorophyll Content

“…The entire dataset was randomly divided into a 70:30 ratio for model training and prediction, respectively. Twelve regression algorithms were used to analyze the spectral data and SPAD values, including linear regression (LR) [25], K-nearest neighbor regression (KNN) [26], support vector regression (SVR) [27], ridge regression (RR) [28], Lasso regression (Lasso) [29], decision tree regression (DTR) [30], extremely randomized tree regression (ETR) [31], random forest regression (RFR) [32], AdaBoost regression (ABR) [33], gradient boosting regression (GBR) [34], bagging regression (BAR) [35], and partial least squares regression (PLSR) [36] (see Appendix C for details). For each collection band and all band combinations between 415 nm and 940 nm, the twelve regression algorithms were used for analysis and prediction.…”