Principles of MIR, multivariate image regression I: Regression typology and representative application studies

We present an introduction to Multivariate Image Regression (MIR) with a selection of illustrative application studies. Generalisation from two-way multivariate calibration to the three-way regimen leads to-at least-three alternative image regression cases depending on the nature of the available Y-data: IPLS-Y-discrim; IPLS-Y-grid; IPLS-Y-total. A systematic image regression typology is briefly introduced. We here present the core of the principles of applied MIR. Two major MIR application studies are worked through, a food mass product industrial inspection study (IPLS-Y-discrim) and a food product (fruit) storage stability image analytical monitoring (IPLS-Y-grid). These exemplifications are presented as archetypes, representing a much wider range of potential industrial/technological application areas. Based on simple three-channel imagery (in order to simulate many industrial systems), they nevertheless represent all higher-dimensional multivariate image cases as well, since the pertinent MIR principles and software are invariant w.r.t. any number of channels/variables employed. The present paper represents one major element of our work towards establishing a complete, stand-alone facility for Multivariate Image Regression (MIR); the second paper in this series deals with the development, implementation and extensive exemplifications of a complementary cross-validation facility. (C) 2001 Elsevier Science B.V. All rights reserved.