Liu Fei, He Yong
College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou 310029, China.
J Agric Food Chem. 2007 Oct 31;55(22):8883-8. doi: 10.1021/jf072057b. Epub 2007 Oct 24.
Visible and near infrared (VIS/NIR) transmission spectroscopy and chemometric methods were utilized for the fast determination of soluble solids content (SSC) and pH of cola beverage. A total of 180 samples were used for the calibration set, whereas 60 samples were used for the validation set. Some preprocessing methods were applied before developing the calibration models. Several PLS factors, extracted by partial least squares (PLS) analysis, were used as the inputs of least squares-support vector machine (LS-SVM) model according to their accumulative reliabilities. The correlation coefficient (r), root mean square error of prediction (rmsEP), bias, and RPD were 0.959, 1.136, -0.185, and 3.5 for SSC, whereas 0.973, 0.053, 0.017, and 4.1 for pH, respectively. An excellent prediction precision was achieved by LS-SVM compared with PLS. The results indicated that VIS/NIR spectroscopy combined with LS-SVM could be applied as a rapid and alternative way for the fast determination of SSC and pH of cola beverage.
可见-近红外(VIS/NIR)透射光谱法和化学计量学方法被用于快速测定可乐饮料的可溶性固形物含量(SSC)和pH值。总共180个样品用于校准集,而60个样品用于验证集。在建立校准模型之前应用了一些预处理方法。通过偏最小二乘法(PLS)分析提取的几个PLS因子,根据其累积可靠性用作最小二乘支持向量机(LS-SVM)模型的输入。对于SSC,相关系数(r)、预测均方根误差(rmsEP)、偏差和RPD分别为0.959、1.136、-0.185和3.5;对于pH,分别为0.973、0.053、0.017和4.1。与PLS相比,LS-SVM实现了优异的预测精度。结果表明,VIS/NIR光谱法结合LS-SVM可作为快速测定可乐饮料SSC和pH值的一种快速且替代的方法。