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首页> 外文会议>ITMS 2012 >Comparison of Multivariate Calibrations for the Determination of Soluble Solids Content of Tea Beverage Using UV-VIS-NIR Spectroscopy
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Comparison of Multivariate Calibrations for the Determination of Soluble Solids Content of Tea Beverage Using UV-VIS-NIR Spectroscopy

机译：使用UV-Vis-nir光谱法测定茶叶饮料可溶性固体含量的多元校准的比较

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Ultra-violet, visible and near infrared (UV-VIS-NIR) spectroscopy combined with chemometrics was investigated for fast determination of soluble solids content (SSC) of tea beverage. In this study, a total of 120 tea samples with SSC range of 4.0-9.5 °Brix were tested. Samples were randomly divided for calibration (n=90) and independent validation (n=30). Spectra were collected by a mobile fiber-type UV-VIS-NIR spectrophotometer in transmission mode with recorded wavelength range of 203.64-1128.05 nm. Various calibration approaches, i.e., principal components analysis (PCA), partial least squares (PLS) regression, least squares support vector machine (LSSVM) and back propagation artificial neural network (BPANN), were investigated. The combinations of PCA-BPANN, PCA-LSSVM, PLS-BPANN and PLS-LSSVM were also investigated to build calibration models. Validation results indicated that all these investigated models achieved high prediction accuracy. Especially, PLS-LSSVM achieved best performance with mean coefficient of determination (R~2) of 0.99, root-mean-square error of prediction (RMSEP) of 0.12 and residual prediction deviation (RPD) of 15.16. This experiment suggests that it is feasible to measure SSC of tea beverage using UV-VIS-NIR-spectroscopy coupled with appropriate multivariate calibration, which may allow using the proposed method for off-line and on-line quality supervision in the production of soft drink.
机译：紫外，可见和近红外（UV-VIS-NIR）光谱与化学计量学的快速测定的茶饮料的可溶性固形物含量（SSC）进行了研究相结合。在这项研究中，总共有4.0至9.5°白利糖度的SSC范围120个茶样品进行了测试。将样品随机分为用于校准（N = 90）和独立的验证（N = 30）。光谱通过在与记录波长范围内的波长203.64-1128.05传输模式下的移动光纤型UV-VIS-NIR分光光度计收集。各种校准方法，即，主成分分析（PCA），偏最小二乘（PLS）回归，最小二乘支持向量机（LSSVM）和反向传播神经网络（BPANN），进行了研究。 PCA-BPANN，PCA-LSSVM，PLS-BPANN和PLS-LSSVM的组合也进行了调查，以构建校准模型。验证结果表明，所有这些研究模型取得了较高的预测精度。特别是，PLS-LSSVM实现最佳性能与0.99 0.12测定的平均系数（R〜2），预测（RMSEP）的根均方误差和15.16残差预测偏差（RPD）。该实验表明，这是可行的使用UV-VIS-NIR光谱法加上适当的多变量校准以测量茶饮料的SSC，这可以允许使用用于离线和在线质量监督生产软饮料的所提出的方法。

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