Abstract
The ability to determine the origin of soybeans is an important issue following the inclusion of this information in the labeling of agricultural food products becoming mandatory in South Korea in 2017. This study was carried out to construct a prediction model for discriminating Chinese and Korean soybeans using Fourier-transform infrared (FT-IR) spectroscopy and multivariate statistical analysis. The optimal prediction models for discriminating soybean samples were obtained by selecting appropriate scaling methods, normalization methods, variable influence on projection (VIP) cutoff values, and wave-number regions. The factors for constructing the optimal partial-least-squares regression (PLSR) prediction model were using second derivatives, vector normalization, unit variance scaling, and the 4000–400 cm–1 region (excluding water vapor and carbon dioxide). The PLSR model for discriminating Chinese and Korean soybean samples had the best predictability when a VIP cutoff value was not applied. When Chinese soybean samples were identified, a PLSR model that has the lowest root-mean-square error of the prediction value was obtained using a VIP cutoff value of 1.5. The optimal PLSR prediction model for discriminating Korean soybean samples was also obtained using a VIP cutoff value of 1.5. This is the first study that has combined FT-IR spectroscopy with normalization methods, VIP cutoff values, and selected wave-number regions for discriminating Chinese and Korean soybeans.
Highlights
The soybean (Glycine max) is a useful plant crop with high lipid and protein contents [1]
Korean soybeans were obtained from the National Agricultural Products Quality Management Service
Fourier-transform infrared (FT-IR) spectral data were obtained for each soybean sample
Summary
The soybean (Glycine max) is a useful plant crop with high lipid and protein contents [1]. Soybeans can be used to produce soybean oil, as a protein source, or as a good source of nutrients. They are pharmacologically active, with these effects originating from their constituent. Discrimination and prediction of the origin of soybeans using FT-IR with multivariate statistical analysis. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript
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