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-Ydiscrim; IPLS-Ygrid; IPLS-Ytotal. A systematic image regression typology is briefly introduced. e 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-Ydiscrim) and a food product (fruit) storage stability image analytical monitoring (IPLS-Ygrid). 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. esent 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.