|Table of Contents|

Rapid Determination of Crude Protein and Crude Oil Content of Soybean Based on Near Infrared Diffuse Reflectance Spectroscopy(PDF)

《大豆科学》[ISSN:1000-9841/CN:23-1227/S]

Issue:
2019年02期
Page:
280-285
Research Field:
Publishing date:

Info

Title:
Rapid Determination of Crude Protein and Crude Oil Content of Soybean Based on Near Infrared Diffuse Reflectance Spectroscopy
Author(s):
WANG Li-ping; CHEN Wen-jie; ZHAO Xing-zhong; ZHANG Xin
(Hybrid Rapeseed Research Center of Shaanxi Province / Shaanxi Branch of National Oil Crop Improvement Center, Yangling 712100, China)
Keywords:
Soybean;  Near infrared model;  Rapid determination;  Protein;  Crude oil
PACS:
-
DOI:
10.11861/j.issn.1000-9841.2019.02.0280
Abstract:
In order to meet the need of quick determination of soybean quality, the feasibility of rapid determination of crude protein and crude fat content in soybean by near infrared diffuse reflectance spectroscopy was discussed in detail.The chemical values of crude protein and crude fat content of 120 soybeans were assayed by Kjedahl and Soxhlet methods separately. Meanwhile, near-infrared spectra of samples in the two states of whole seed and powder were collected. Finally, the correlation models between soybean spectra and chemical values were built by the partial least square(PLS) method in chemometrics. The determination coefficient (R2) of calibration model of crude protein content was 0.978 7 and the root mean square error of cross validation (RMSECV) was 0.003 8 with soybean samples in the state of powder. When 24 test samples were predicted, the root meansquare error of prediction (RMSEP) was 0.002 84. The R2 and RMSECV of crude oil content model was 0.934 1 and 0.003 69 respectively, and RMSEP was 0.003 53. The R2 and RMSECV of protein model set up by soybean whole seed samples was 0.872 4 and 0.009 07 respectively, and RMSEP was 0.007 49. The R2 and RMSECV of oil content model was 0.876 5 and 0.005 08 respectively, and RMSEP was 0.004 66. It was found that the state of calibration samples had a significant effect on the prediction ability of the model. The results indicated that the performance of crude protein and crude oil content models built by powder samples was better. On the other hand, the R2 of all models that were built by soybean whole seed samples were more than 0.87, so the models could be used to measure soybean quality roughly when the sample was not enough to grind. The results were of great importance in early screening of soybean breeding.

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Last Update: 2019-04-01