近红外光谱法快速测定藜麦籽粒粗蛋白含量

目的:为了满足高蛋白质藜麦的选育、栽培和农业实践所需,实现藜麦籽粒粗蛋白含量快速、无损检测。方法:本研究应用近红外光谱技术对藜麦籽粒粗蛋白含量的快速检测进行系统研究,选用具有代表性的122份藜麦品种为试材,以其中94份为建模集,28份为验证集,扫描得到藜麦建模集的近红外原始光谱,用Unscrambler 10.4软件进行光谱预处理并使用偏最小二乘法(PLS)建立藜麦籽粒粗蛋白含量的定量预测模型。结果:经滤波拟合法(Savitzky-Golay,SG)+标准正态变量(Standard Normal Variate,SNV)预处理建立的模型预测值决定系数(R²)为0.9380,被测组分浓度分析误差(RMSE)为0.4823,表现最佳。用此模型对验证集28份样品进行预测,相关分析表明,预测值与国标法实测值决定系数为0.9416;单因素方差分析表明,国标法实测值和模型预测值之间无显著差异(P>0.05),建立的模型具有很高的准确性,预测效果良好。结论:近红外光谱法作为一种简单快速无损的检测手段,能够用于藜麦籽粒粗蛋白含量的检测,可以为优质藜麦育种、栽培和农业实践提供技术支持。

Rapid Determination of Quinoa Seeds Crude Protein Content Using Near Infrared Spectroscopy

Objective:In order to meet the requirements of breeding, cultivation and agricultural practice of high protein quinoa, and determine a quickly and nondestructive measurement of quinoa grain crude protein content method. Method:In this study, the rapid detection of the crude protein content in quinoa grains was systematically studied by using near-infrared spectroscopy. 122 representative quinoa varieties were selected as the test materials, among which 94 were used as the modeling set and 28 were used as the verification set. The Unscrambler 10.4 software was used to preprocess the original near-infrared spectra data after scanning, and established the quantitative prediction model of quinoa kernel crude protein content by partial least square method (PLS). Results:Combing the filter fitting method (savitzky-golay, SG) and Standard Normal Variate (SNV), obtained the best result, with the model predictive value determination coefficient (R2) of 0.9380, and the component concentration analysis error (RMSE) of 0.4823.The correlation analysis of the 28 samples in the verification set showed that the determination coefficient between the predicted value and the measured value of the national standard method was 0.9416. One-way anova showed that there was no significant difference between the measured value of GB method and the predicted value of the model (P>0.05), and indicating that the established model had high accuracy and good prediction effect. Conclusion:As a simple, this method can be used as a rapid and non-destructive method for the detection of crude protein content of the quinoa seeds, and can provide technical support for the breeding, cultivation and agricultural practice of high-quality quinoa.