Abstract
Large differences in quality existed between soybean samples. In order to rapidly detect soybean quality between samples from different areas, we have developed near‐infrared spectroscopy (NIRS) models for the moisture, crude fat, and protein content of soybeans, based on 360 soybean samples collected from different areas. Compared with whole kernels, soybean powder with particle sizes of 60 mesh was more suitable for modeling of moisture, crude fat, and protein content. To increase the reproducibility of the prediction model, uniform particle sizes of soybeans were prepared by grinding and sieving soybeans with different sizes and colors. Modeling analysis showed that the internal cross‐validation correlation coefficients (R cv) for the moisture, crude fat, and protein content of soybeans were .965, .941, and .949, respectively, and the determination coefficients (R 2) were .966, .958, and .958. NIRS performed well as a rapid method for the determination of routine quality parameters and provided reference data for the analysis of soybean quality using FT‐NIRS.
Highlights
China is a high consumption country of soybean which has been regarded as the health food (He & Chen, 2013)
Near-infrared spectroscopy (NIRS) is a rapid technique that can be used for the simultaneous detection and analysis of multiple components (Acquah, Via, Billor, Fasina, & Eckhardt, 2016; Baianu et al, 2012; Louw & Theron, 2010; Wehling, Pierce, & Froning, 1988; Williams, Norris, & Sobering, 1985)
The number of samples used for modeling was much higher than 50, which is the minimum sample size proposed for NIRS modeling (Williams et al, 1985)
Summary
China is a high consumption country of soybean which has been regarded as the health food (He & Chen, 2013). Soybeans are one of the main agricultural products of China and several hundred varieties are grown, with huge differences in composition that arise from the rich genetic diversity and regional planting (Lam et al, 2010). Traditional analyse methods are used to analyze soybean quality indices, which are moisture, crude fat, and protein content. These methods produce highly accurate results, but the analytical processes are time-consuming and laborious and the chemical reagents that are used contribute to environmental pollution (Liu, 1997). Alternative rapid and accurate analytical methods are, urgently needed (Baianu et al, 2012; Martin, 1992; Zhu et al, 2011).
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