Gao Huiyu, Wang Guodong, Wang Zhu
Key Laboratory of Trace Element Nutrition of National Health Commission, National Institute for Nutrition and Health, Chinese Center for Disease Control and Prevention, Beijing 100050, China.
Wei Sheng Yan Jiu. 2021 May;50(3):495-500. doi: 10.19813/j.cnki.weishengyanjiu.2021.03.025.
Near-infrared(NIR) spectroscopy combined with partial least squares(PLS) were applied to establish a rapid method for green direct determination of mineral elements(calcium, phosphorus and potassium) in wheat flour samples.
NIR spectra and analytical measurements of calcium, phosphorus and potassium were collected from 117 wheat flour samples with different processing levels(whole grain wheat, special grade No. 1 wheat and wheat core flour). Principal components analysis(PCA) was developed to assign 81 wheat flour samples to build models and 36 samples as the validation set to evaluate the performance of the developed models. The influence of wavelength range and spectral preprocessing method on the predictive ability of the model were discussed, and the best models were selected.
For calcium, the best NIR model showed a good prediction performance(r2=0. 7907, RMSEP=5. 35, RPD=2. 19); the best NIR model for phosphorus gave an excellent prediction performance(r2=0. 9777, RMSEP=15. 3, RPD=6. 71); the best model for potassium also gave an excellent prediction performance(r~2=0. 9777, RMSEP=18. 9, RPD=6. 84).
NIR spectroscopy can realize the rapid prediction of mineral elements(calcium, phosphorus and potassium) in wheat flour. By selecting the wavelength range and spectral preprocessing method, the prediction ability of the NIR model can be significantly improved.
应用近红外(NIR)光谱结合偏最小二乘法(PLS)建立快速绿色直接测定小麦粉样品中矿物质元素(钙、磷和钾)的方法。
采集了117个不同加工水平(全麦粉、特制一等粉和小麦芯粉)小麦粉样品的近红外光谱以及钙、磷和钾的分析测量值。采用主成分分析(PCA)将81个小麦粉样品用于建立模型,36个样品作为验证集来评估所建立模型的性能。讨论了波长范围和光谱预处理方法对模型预测能力的影响,并选择了最佳模型。
对于钙,最佳近红外模型显示出良好的预测性能(r² = 0.7907,RMSEP = 5.35,RPD = 2.19);磷的最佳近红外模型具有优异的预测性能(r² = 0.9777,RMSEP = 15.3,RPD = 6.71);钾的最佳模型也具有优异的预测性能(r² = 0.9777,RMSEP = 18.9,RPD = 6.84)。
近红外光谱可实现小麦粉中矿物质元素(钙、磷和钾)的快速预测。通过选择波长范围和光谱预处理方法,可显著提高近红外模型的预测能力。