From Classical Regression to AI and Beyond: The Chronicles of Calibration in Spectroscopy: Part I

The analysis of chemical data has undergone a profound transformation, from early basic statistical methods to the modern era of machine learning (ML) and artificial intelligence (AI). This progression is particularly evident in the field of spectroscopy, where multivariate analysis techniques, such as classical regression, principal component analysis (PCA), and partial least squares (PLS), laid the foundation for today’s more advanced or automated ML calibration modeling techniques. This “Chemometrics in Spectroscopy” column traces the historical and technical development of these methods, emphasizing their application in calibrating spectrophotometers for predicting measured sample chemical or physical properties—particularly in near-infrared (NIR), infrared (IR), Raman, and atomic spectroscopy—and explores how AI and deep learning are reshaping the spectroscopic landscape. In this two-part series, we take a look back into the history of chemometrics (Part I) then peer into the future for an estimation of where chemometrics might be going (Part II).