Application of PLS-DA in multivariate image analysis
Application of PLS-DA in multivariate image analysis
复制标题
DOI：
10.1002/cem.994
复制
发表时间：
2006-05-01
影响因子：
2.4
通讯作者：
Courcoux, Philippe
中科院分区：
文献类型：
--
作者：
Chevallier, Sylvie;Bertrand, Dominique;Courcoux, Philippe
为了检测食品原料的非均匀性，研制了一种简单的多变量图像采集系统。本工作的目标是，首先，证明该收购系统的能力，以区分不同性质的食品。其次，我们的目标是将偏最小二乘回归应用于这些多元图像，并评估各种分类策略的兴趣。一个数据集包含24个图像（702 × 524）在不同的波长采集的四种食品进行了分析。在建立了用于预测指标变量的PLS 2模型后，对观测值分类的四种策略进行了测试。第一种分类是通过选择指标变量的最大组成部分来进行的。其他的是基于测量的质量组的重心的距离。计算的距离可以是欧几里得距离或马氏距离。除基于分数的欧氏距离策略外，其他策略相当，在预测指标上略优于欧氏距离。通过在多变量图像上使用线性判别分析（LDA）解决的另一种可能性是将定性组表示为人工图像。最大的混乱出现在这两种谷物产品之间，而其他产品则被很好地分类。版权所有（C）2006约翰威利父子有限公司
A simple imaging system has been developed for acquiring multivariate images in order to characterise the heterogeneity of food materials. The objective of the present work is, first, to demonstrate the capability of this acquisition system to discriminate food products of different natures. Secondly, our goal is to apply Partial Least Squares regression on these multivariate images and to evaluate the interest of various strategies of classification. A data set containing 24 images (702 X 524) acquired at different wavelengths for four food products is analysed. After the establishment of the PLS2 models employed for predicting the indicator variables, four strategies of classification of observations are tested. The first classification is done by selecting the largest component of the indicator variables. The others are based on the measurement of distances to the barycentres of the qualitative groups. Distances calculated can be either Euclidian distances or Mahalonobis distances. Except the strategy based on the Euclidian distance on scores, the strategies are rather equivalent, with a slight advantage to the Euclidian distance on predicted indicators. Another possibility addressed by the use of linear discriminant analysis (LDA) on multivariate images is to represent the qualitative groups as artificial images. The largest confusion appears between both cereal products while others are well classified. Copyright (C) 2006 John Wiley & Sons, Ltd.