Chemometrics as a Green Analytical Tool

Chemometrics is a very important technique for green analytical chemistry. In this chapter, after describing briefly the history of chemometrics in green chemistry and its application to green analytical chemistry, the complexity of datasets, the design of experiments (DOE) and pre-processing methods, which provide basic knowledge for chemometrics, are outlined. Various methodologies of chemometrics are then introduced, which are classified into unsupervised pattern recognition, such as hierarchical cluster analysis (HCA) and principal component analysis (PCA), and supervised pattern recognition, such as multiple linear regression (MLR), principal component regression (PCR) and partial least-squares regression (PLSR). Finally, some examples of applications of spectroscopy–chemometrics research are described, such as the application of moving window partial least-squares regression (MWPLSR) to in vivo non-invasive monitoring of blood glucose by near-infrared diffuse reflectance spectroscopy and a Raman imaging study of the aggregation of lycopene in vivo in tomato.