基于近红外光谱快速定量检测面粉中曲酸的方法建立

Using Near-Infrared(NIR) Spectroscopy for Rapid,Quantitative Detection of Kojic Acid in Wheat Flour

利用近红外光谱技术快速定量检测面粉中非法添加的褐变抑制剂曲酸。选取市场上常见3种基本类型的面粉(高、中、低筋面粉),分别制备曲酸质量分数为0.0%、0.5%、1.0%、3.0%、5.0%、10.0%的面粉样品,并采集其在1 000～2 400 nm波段下的光谱数据。对比不同预处理下高筋面粉样品数据所建偏最小二乘(partial least squares,PLS)回归模型效果,选取Savitzky-Golay一阶导数为最优预处理方法。采用区间偏最小二乘(interval partial least squares,i PLS)法选取1 088.8～1 153.5 nm为最佳光谱区间。结果表明,基于最佳光谱区间所建PLS回归模型预测效果优于基于全波段光谱数据所建模型。进一步,基于所选最优区间对中、低筋面粉和混合样品集分别建立PLS回归模型。高、中、低筋面粉及混合样品集基于最优区间的PLS模型的决定系数为0.949～0.972,标准误差为0.581%～0.830%,验证集标准偏差与预测标准偏差的比值为4.171～4.830。结果表明,基于最优区间的近红外光谱方法对不同类型面粉中曲酸质量分数为1.0%～10.0%的样品具有较好的预测结果,结合具有低检测限的化学检测方法,在对大批量样品的检测中可提高检测效率。

The viability of using near-infrared(NIR)spectroscopy to detect illegally added kojic acid(KA)as a browning inhibitor in wheat flour was studied.For this purpose,three common types of commercial flour,i.e.,high-gluten flour,plain flour and low-gluten flour,were added with different amounts of KA(0.0%,0.5%,1.0%,3.0%,5.0%,and 10.0%),respectively.NIR spectra of all samples were collected in the wavelength range of 1 000–2 400 nm.For high-gluten flour samples,three common spectral preprocessing methods were compared with each other,as well as with non-preprocessing using partial least squares(PLS)regression.Savitzky-Golay derivative(SGD)was found to be the best preprocessing method.Then interval partial least squares(iPLS)was adopted to obtain optimized spectral interval in the wavelength range from 1 088.8 to 1 153.5 nm.The PLS model based on the optimal spectral interval showed better performance than that in the full wavelength range.Moreover,a PLS model was developed based on the optimal spectral interval for plain flour,lowgluten flour and a mixture of all three types,respectively.The models for all three types of flour and their mixture showed a determination coefficient(R2)of 0.949–0.972,a root mean square error(RMSE)of 0.581%–0.830%,and a ratio of standard deviation of the validation set to standard error of prediction(RPD)of 4.171–4.830.The model exhibited a good prediction performance for wheat samples with KA contents of 1.0%–10.0%.These results indicated that NIR spectroscopy could be used as an auxiliary method for precise chemical detection to improve the detection efficiency for massive samples.