近红外法测定豆浆蛋白质、脂肪和可溶性固形物含量

利用傅里叶变换近红外光谱仪采用积分球漫反射方式对60个豆浆样品进行光谱的采集,结合常规分析结果分别建立了3种成分的近红外校正模型。结果表明:豆浆蛋白质、脂肪及可溶性固形物光谱分别经过消除常数偏移量、一阶导数和矢量归一化(SNV)预处理后建模效果最好。蛋白质、脂肪和可溶性固形物含量的校正模型决定系数(R2)分别为:0.966 4、0.950 0和0.950 7,交叉验证均方根差(RMSECV)依次为0.076 9、0.087 4和0.316;对模型进行外部验证,验证集化学值和模型预测值之间差异不显著,说明模型可以用于豆浆中蛋白质、脂肪和可溶性固形物含量的检测。

Determination of protein, fat and soluble solids content of soy milk by Near-Infrared Spectroscopy

The objective of this research was to realize the rapid detection of protein, fat and soluble solids content in soy milk. With the mode of integrating sphere diffuse, the spectra of 60 soy milk simples were obtained by the Fourier transform near-infrared spectrometer (FT-NIRS). Combined with the results of chemical analysis, the calibration models of the three components were established separately. The calibration models had a best prediction performance when the spectra of the protein, fat and soluble solids were preprocessed by constant offset elimination, first order derivative and standard normal variate transformation (SNV), respectively. The determination coefficients (R2) for the protein, fat and soluble solids content were 0.9664, 0.9500 and 0.9507, respectively, and the root mean square errors of cross-validation (RMSECV) were 0.0769, 0.0874 and 0.316, respectively. External validation of the model showed there was no significant difference between chemical values and model predictions, which indicated that the calibration models could be used to detect protein, fat and soluble solids content of soy milk. The results indicated that the near-infrared spectroscopy (NIRS) technology could realize the rapid detection of soy milk ingredients. This detection method can be used to support for an effective way to detect soy milk components of the enterprise.