Centre for Integrative Digital Health, School of Nursing, The Hong Kong Polytechnic University, Hong Kong.
Med Eng Phys. 2013 Feb;35(2):172-7. doi: 10.1016/j.medengphy.2012.04.012. Epub 2012 May 29.
Partial least squares discriminant analysis (PLS-DA) is widely used in multivariate calibration method. Very often, only one single quantitative model is constructed to predict the relationship between the response and the independent variables. This approach can easily misidentify, under or over estimate the important features contained in the independent variables. The results obtained by a single prediction model are thus unstable or correlated to spurious spectral variance, particularly when the training set for PLS-DA is relatively small. A new algorithm developed by applying the Monte Carlo method to PLS-DA, namely MC-PLS-DA, is proposed to classify spectral data obtained from near-infrared blood glucose measurement. Noise in the data is removed by randomly selecting different subsets from the whole training dataset to generate a large number of models. The mean sensitivity and specificity of these models are then calculated to determine the model with the best classification rate. The results show that the MC-PLS-DA method gives more accurate prediction results when compared with other classification methods used for classifying near infrared spectroscopic data of blood glucose. Also, the stability of the PLS-DA model is enhanced.
偏最小二乘判别分析（PLS-DA）广泛应用于多元校准方法。通常，仅构建一个单一的定量模型来预测响应与自变量之间的关系。这种方法很容易错误识别、低估或高估自变量中包含的重要特征。因此，单个预测模型得到的结果不稳定或与虚假光谱方差相关，尤其是当 PLS-DA 的训练集相对较小时。提出了一种通过将蒙特卡罗方法应用于 PLS-DA 而开发的新算法，即 MC-PLS-DA，用于对近红外血糖测量获得的光谱数据进行分类。通过从整个训练数据集随机选择不同的子集来去除数据中的噪声，从而生成大量模型。然后计算这些模型的平均灵敏度和特异性，以确定具有最佳分类率的模型。结果表明，与用于对血糖近红外光谱数据进行分类的其他分类方法相比，MC-PLS-DA 方法给出了更准确的预测结果。此外，PLS-DA 模型的稳定性也得到了增强。
