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.

With the growing availability of high throughput methodologies for food characterization and analysis, more and more data are being collected on food products that can be used for the authentication of their quality. In this context, the availability of different multi-block strategies, each with its own peculiarities and providing specific details on the investigated samples, allows to integrate the information from the different sources into a richer model with great flexibility.