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为了探索一种快速测定完整藜麦(Chenopodium quinoa Willd)子粒脂肪含量的方法,采集100个藜麦样品的近红外光谱,运用近红外光谱法建立数学模型并进行预测。结果表明,在10 000~4 000 cm-1波长范围内,运用一阶导数+矢量归一化光谱方法进行预处理,结合化学方法所得数据建立藜麦粗脂肪近红外光谱定量模型,校正和预测效果最佳,所得的粗脂肪近红外定量模型的交叉验证决定系数(R2cv)为0.939 3,外部验证决定系数(R2val)为0.9235。建立的脂肪近红外光谱模型,可以用于藜麦脂肪含量的快速检测。
In order to explore a method for rapid determination of the fat content in whole grains of Chenopodium quinoa Willd, the near infrared spectra of 100 quinoa samples were collected and mathematically modeled by near infrared spectroscopy. The results showed that the first derivative and vector normalized spectra were used for pretreatment in the wavelength range from 10 000 to 4 000 cm-1, and the quantitative model of crude lipid in quinoa was established based on the data obtained from the chemical methods. The calibration and prediction The results showed that R2cv was 0.9393 and R2val was 0.9235. The established model of fat near infrared spectroscopy can be used for the rapid detection of the quinoa fat content.