The analysis of chemical data has undergone a profound transformation, from early basic statistical methods into 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 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 prediction of 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 the previous installment of this two-part series we have taken a look back into the history of chemometrics. In this second part of this series we will peer into the future for an estimation of where chemometrics might be going.
Workman et al. (Mon,) studied this question.