Janik L J, Cozzolino D, Dambergs R, Cynkar W, Gishen M
The Australian Wine Research Institute, P.O. Box 197, Glen Osmond, Adelaide 5064, SA, Australia.
Anal Chim Acta. 2007 Jun 26;594(1):107-18. doi: 10.1016/j.aca.2007.05.019. Epub 2007 May 21.
This study compares the performance of partial least squares (PLS) regression analysis and artificial neural networks (ANN) for the prediction of total anthocyanin concentration in red-grape homogenates from their visible-near-infrared (Vis-NIR) spectra. The PLS prediction of anthocyanin concentrations for new-season samples from Vis-NIR spectra was characterised by regression non-linearity and prediction bias. In practice, this usually requires the inclusion of some samples from the new vintage to improve the prediction. The use of WinISI LOCAL partly alleviated these problems but still resulted in increased error at high and low extremes of the anthocyanin concentration range. Artificial neural networks regression was investigated as an alternative method to PLS, due to the inherent advantages of ANN for modelling non-linear systems. The method proposed here combines the advantages of the data reduction capabilities of PLS regression with the non-linear modelling capabilities of ANN. With the use of PLS scores as inputs for ANN regression, the model was shown to be quicker and easier to train than using raw full-spectrum data. The ANN calibration for prediction of new vintage grape data, using PLS scores as inputs, was more linear and accurate than global and LOCAL PLS models and appears to reduce the need for refreshing the calibration with new-season samples. ANN with PLS scores required fewer inputs and was less prone to overfitting than using PCA scores. A variation of the ANN method, using carefully selected spectral frequencies as inputs, resulted in prediction accuracy comparable to those using PLS scores but, as for PCA inputs, was also prone to overfitting with redundant wavelengths.
本研究比较了偏最小二乘法（PLS）回归分析和人工神经网络（ANN）在根据红葡萄匀浆的可见-近红外（Vis-NIR）光谱预测总花青素浓度方面的性能。从Vis-NIR光谱对新季样品的花青素浓度进行PLS预测具有回归非线性和预测偏差的特点。在实际应用中，这通常需要纳入一些新年份的样品以提高预测效果。使用WinISI LOCAL部分缓解了这些问题，但在花青素浓度范围的高低两端仍会导致误差增加。由于ANN在非线性系统建模方面具有固有优势，因此研究了将人工神经网络回归作为PLS的替代方法。本文提出的方法结合了PLS回归的数据降维能力和ANN的非线性建模能力的优点。将PLS得分用作ANN回归的输入时，该模型比使用原始全光谱数据训练起来更快、更容易。使用PLS得分作为输入对新年份葡萄数据进行预测的ANN校准比全局和局部PLS模型更线性、更准确，并且似乎减少了用新季样品更新校准的需求。与使用主成分分析（PCA）得分相比，具有PLS得分输入的ANN所需的输入更少，且不易出现过拟合。ANN方法的一种变体，使用精心选择的光谱频率作为输入，其预测准确性与使用PLS得分的情况相当，但与PCA输入一样，对于冗余波长也容易出现过拟合。
