Determination of soybean routine quality parameters using near‐infrared spectroscopy. Issue 4 (17th April 2018)

• Record Type: Journal Article
• Authors: Zhu, Zhenying; Chen, Shangbing; Wu, Xueyou; Xing, Changrui; Yuan, Jian
• Is Part Of: Food science & nutrition. Volume 6:Issue 4(2018)
• Page Start: 1109
• Page End: 1118
• Publication Date: 2018-04-17
• Subjects: mathematical model -- near‐infrared spectroscopy -- soybean; Food industry and trade -- Periodicals; Food -- Periodicals; Nutrition -- Periodicals; 664
• DOI: 10.1002/fsn3.652
• Languages: English
• ISSNs: 2048-7177

Abstract

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. Abstract : 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 predictive decision coefficients ( R 2 ) were .966, .958, and .958. NIRS performed well as a rapid method for the determination of routine quality parameters.