食品科学 ›› 2017, Vol. 38 ›› Issue (16): 140-144.doi: 10.7506/spkx1002-6630-201716022

• 成分分析 • 上一篇    下一篇

近红外漫反射光谱法快速检测谷子蛋白质和淀粉含量

田翔,,刘思辰,,王海岗,,秦慧彬,,乔治军   

  1. (1.山西省农业科学院农作物品种资源研究所,山西?太原 030031;2.农业部黄土高原作物基因与种质创制重点实验室,山西?太原 030031;3.杂粮种质资源发掘与遗传改良山西省重点实验室,山西?太原 030031)
  • 出版日期:2017-08-25 发布日期:2017-08-18
  • 基金资助:
    国家谷子高粱产业技术体系谷子糜子生理岗位项目(CARS-06-13.5-A16);国家农作物种质资源平台山西作物子平台项目(NICGR2016-02);山西省农作物种质资源鉴定评价研究项目(2017ZZCX-17);山西省省级财政支农专项(2016zyzx41)

Application of Near Infrared Diffuse Reflectance Spectroscopy in Rapid Detection of Crude Protein and Starch in Foxtail Millet

TIAN Xiang,, LIU Sichen,, WANG Haigang,, QIN Huibin,, QIAO Zhijun,   

  1. (1. Institute of Crop Germplasm Resources, Shanxi Academy of Agricultural Sciences, Taiyuan 030031, China; 2. Key Laboratory of Crop Gene Resources & Germplasm Enhancement on Loess Plateau, Ministry of Agriculture, Taiyuan 030031, China; 3. Shanxi Key Laboratory of Genetic Resources and Genetic Improvement of Minor Crops, Taiyuan 030031, China)
  • Online:2017-08-25 Published:2017-08-18

摘要: 建立近红外漫反射光谱法测定谷子中的蛋白质和淀粉含量,提供一种快速、简便、无损的分析方法进行 谷子品种资源鉴定和筛选。以191 份山西核心谷子种质为材料,采用近红外漫反射光谱法建立谷子蛋白质和淀粉含 量的快速检测模型。结果表明,采用一阶导数+矢量归一化光谱预处理,分别建立谷子蛋白质和淀粉含量的校正 模型,模型的校正决定系数(R2 cal)分别为0.977 0和0.907 3,交叉验证均方根误差分别为0.203%和0.466%,外部验 证决定系数(R2 val)分别为0.989 6和0.977 2,预测均方根误差分别为0.225%和0.368%。对于谷子蛋白质和淀粉的预 测,化学法和近红外仪器法测定间无显著差异,近红外测定结果是准确可靠的。说明采用近红外漫反射光谱分析技 术能够满足对谷子蛋白质和淀粉含量的检测。

关键词: 谷子, 近红外漫反射光谱, 蛋白质, 淀粉

Abstract: This study aimed to establish a near infrared (NIR) diffuse reflectance spectrometry method for measuring the contents of crude protein and starch in foxtail millet for providing a fast, simple and non-destructive analytical method for the identification and screening of foxtail millet germplasm resources. For this purpose, a total of 191 samples of a foxtail millet core collection in Shanxi province were measured using NIR diffuse reflectance spectroscopy. The results showed that with the spectral preprocessing method of first derivative + vector normalization, calibration models were established to predict the contents of crude protein and starch in foxtail millet. The corresponding coefficients of determination for calibration (R2cal) were 0.977 0 and 0.907 3, and the root mean square errors of cross-validation (RMSECV) were 0.203% and 0.466%, the coefficients of determination for external validation (R2val) were 0.989 6 and 0.977 2, and the root mean square errors of prediction (RMSEP) were 0.225% and 0.368%, respectively. For the detection of crude protein and starch foxtail millet, no significant differences were seen between chemical analysis and NIR measurement, but the results obtained with NIR measurement were more accurate and reliable. NIR diffuse reflectance spectrometry technology can be useful for the detection of crude protein and starch in foxtail millet.

Key words: foxtail millet, near infrared (NIR) diffuse reflectance spectrometry, crude protein, starch

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