Abstract
Pasta is widely used in many cuisines all around the world for its important nutritional properties. The quality assurance and the maintenance of the cold chain of pre-cooked pasta products have a significant impact in economic terms on the manufacturing companies. For this reason, a fast, reliable, not-destructive and non-invasive method is needed to fulfill the above-mentioned goals. Visible and Near InfraRed spectroscopy, coupled with chemometric analysis, are powerful tools that can make the production and supply of pre-cooked pasta more transparent, also reducing food waste. In this study, a spectrophotoradiometer operating in the Visible - Short Wave InfraRed (Vis-SWIR) range (350-2500 nm) was used to acquire reflectance spectra on pre-cooked pasta samples, with two levels of saltiness, produced in Italy and intended for the US market. Partial Least Squares - Discriminant Analysis (PLS-DA) classification models were calibrated and validated to recognize the samples according to their salting and physical conditions (i.e. frozen/thawed), starting from their spectral signatures. Classification performances showed promising ability in characterizing samples according to the previously mentioned attributes.
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