Cold chain maintenance evaluation of pre-cooked pasta by visible and short wave infrared spectroscopy

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.