Intra-class variability in diffuse reflectance spectroscopy: application to porcine adipose tissue

Abstract

Optical diffuse reflectance spectroscopy (DRS) has great potential in the study, diagnosis, and discrimination of biological tissues. Discrimination is based on massive measurements that conform training sets. These sets are then used to classify tissues according to the biomedical application. Classification accuracy depends strongly on the training dataset, which typically comes from different samples of the same class, and from different points of the same sample. The variability of these measurements is not usually considered and is assumed to be purely random, although it could greatly influence the results. In this work, spectral variations within and between samples of different animals of ex-vivo porcine adipose tissue are evaluated. Algorithms for normalization, dimensionality reduction by principal component analysis, and variability control are applied. The PC analysis shows the dataset variability, even when a variability removal algorithm is applied. The projected data appear grouped by animal in the PC space. Mahalanobis distance is calculated for every group, and an ANOVA test is performed in order to estimate the variability. The results confirm that the variability is not random and is dependent at least on the anatomical location and the specific animal. The variability magnitude is significant, particularly if the classification accuracy is needed to be high. As a consequence, it should be taken generally into account in classification problems.