摘要:
应用近红外光谱技术结合不同的定量分析方法建立面粉4种组分的快速定量模型。国标法测定68种面粉样品的水分、脂肪、碳水化合物和蛋白质的含量,并采集其近红外漫反射光谱图。选取58个校正集和10个验证集样品,通过马氏距离法剔除异常样品后,对比17种光谱预处理方式所建立的基于全光谱的偏最小二乘法(partial least squares,PLS)定量模型效果,在最佳预处理方法的基础上,采用向后区间偏最小二乘法(Backward interval PLS,BiPLS)筛选特征光谱,进一步得到最佳定量模型。结果表明,所建立的模型校正集相关系数Rcv均大于0.9650,内部交叉验证均方根误差均小于0.328;验证集相关系数均大于0.9926,预测均方根误差均低于0.383。因此,模型具有较好的准确性和稳定性,能应用于面粉的多指标快速检测。
Abstract:
A rapid quantitative model of 4 kinds of flour components was established by using near-infrared spectroscopy combined with different quantitative analysis methods. The content of water, fat, carbohydrate and protein in 68 flour samples was determined by GB method, and its near-infrared diffuse reflectance spectra were collected. After selecting 58 correction sets and 10 verification set samples, the quantitative model effect of partial least squares method (partial least squares, PLS) based on full spectrum was compared with 17 kinds of spectral pretreatments after eliminating abnormal samples by Mahalanobis distance method, and the optimal quantitative model was obtained by screening the characteristic spectra by using the backward interval partial least squares method (Backward interval PLS, BiPLS) on the basis of the best preprocessing method. The results showed that the correlation coefficient Rcv of the model correction set was greater than 0.9650, the mean square root error of the internal cross verification was less than 0.328, the correlation coefficient of the verification set was greater than 0.9926, and the prediction mean square root error is less than 0.383.Therefore, the model would have good accuracy and stability, and it could be applied to the multi-index rapid detection of flour.
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