Ricerc@Sapienza

Data generated by analytical instruments, such as spectrometers, may contain unwanted variation due to measurement mode, sample state and other external physical, chemical and environmental factors. Preprocessing is required so that the property of interest can be predicted correctly. Different correction methods may remove specific types of artefacts while still leaving some effects behind. Using multiple preprocessing in a complementary way can remove the artefacts that would be left behind by using only one technique.

In this chapter, a survey of the chemometric (data analytical) methods most used for the characterization of plant varieties and cultivars based on spectroscopic measurements is presented. After an introductory section, illustrating the basics of data representation, the main tools for exploratory (descriptive) data analysis and predictive modeling are discussed. In particular, how to predict quantitative responses by multivariate calibration methods and how to assess qualitative attributes by means of classification techniques are addressed.