X-Ray computed tomography meets robust chemometric latent space modeling for lean meat percentage prediction in pig carcasses

Abstract

This study presents a case of processing X-ray computed tomography (CT) data for pork scans using chemometric latent space modeling. The distribution of voxel intensities is shown to exemplify a multivariate, multi-collinear signal mixture. While this concept is not novel, it is revisited here from a chemometric perspective. To extract meaningful information from such multivariate signals, latent space modeling based on partial least squares (PLS) is an ideal solution. Furthermore, a robust PLS approach is even more effective for latent space modeling, as it can extract latent spaces unaffected by outliers, thereby enhancing predictive modeling. As an example, lean meat percentage is predicted using X-ray CT data and robust PLS regression. This method is applicable to X-ray CT quantification analysis, particularly in cases where unclear, erroneous, and outlying observations are suspected in the data.