[1]王丽萍,陈文杰,赵兴忠,等.基于近红外漫反射光谱法的大豆粗蛋白和粗脂肪含量的快速检测[J].大豆科学,2019,38(02):280-285.[doi:10.11861/j.issn.1000-9841.2019.02.0280]
　WANG Li-ping,CHEN Wen-jie,ZHAO Xing-zhong,et al.Rapid Determination of Crude Protein and Crude Oil Content of Soybean Based on Near Infrared Diffuse Reflectance Spectroscopy[J].Soybean Science,2019,38(02):280-285.[doi:10.11861/j.issn.1000-9841.2019.02.0280]
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基于近红外漫反射光谱法的大豆粗蛋白和粗脂肪含量的快速检测

《大豆科学》[ISSN:1000-9841/CN:23-1227/S] 卷: 第38卷 期数: 2019年02期 页码: 280-285 栏目: 出版日期: 2019-03-20
Title:
Rapid Determination of Crude Protein and Crude Oil Content of Soybean Based on Near Infrared Diffuse Reflectance Spectroscopy
作者:
王丽萍; 陈文杰; 赵兴忠; 张新
（陕西省杂交油菜研究中心/国家油料作物改良中心陕西分中心，陕西 杨陵 712100）
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)
关键词:
大豆; 近红外模型; 快速检测; 粗蛋白; 粗脂肪
Keywords:
Soybean;  Near infrared model;  Rapid determination;  Protein;  Crude oil
DOI:
10.11861/j.issn.1000-9841.2019.02.0280
文献标志码:
A
摘要:
为满足大豆品质育种快速筛选的需求，本文详细探讨了利用近红外漫反射光谱法对大豆粗蛋白和粗脂肪含量实现快速测定的可行性。采用凯氏定氮法和索氏抽提法测定了120份大豆粗蛋白和粗脂肪的含量，分别采集大豆整粒和粉末两种状态的近红外光谱，然后运用化学计量学方法PLS建立近红外光谱与化学值之间的关系模型。其中粉末大豆样品建立的粗蛋白校正模型的决定系数R2为0.978 7，校正标准误差RMSECV为0.003 8，该模型对24份待测样品进行测定的预测标准误差RMSEP为0.002 84；粗脂肪校正模型的R2为0.934 1，RMSECV为0.003 69，RMSEP为0.003 53。整粒大豆建立的粗蛋白校正模型的R2为0.872 4，RMSECV为0.009 07，RMSEP为0.007 49；粗脂肪校正模型的R2为0.876 5，RMSECV为0.005 08，RMSEP为0.004 66。对比发现，建模样品的状态对近红外模型的预测性能有重要影响，样品在粉末状态下建立的粗蛋白和粗脂肪近红外模型的预测效果更好。另一方面，由于整粒样品建立的近红外模型的R2均在0.87以上，因此当样品量较少没有足够样品可用于粉碎时，该模型可以满足对整粒大豆品质进行粗测的需求。该结果对大豆育种早代筛选工作具有重要意义。
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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备注/Memo

收稿日期：2018-10-09

基金项目：陕西省现代农作物种业项目。
第一作者简介：王丽萍（1979-），女，硕士，助理研究员，主要从事品质分析检测工作。E-mail:421536061@qq.com。

更新日期/Last Update: 2019-04-01