Development of Calibration and Validation Models for the Determination of Acrylamide Levels in Pizza Samples Through Ft-nir Spectroscopy

Abstract

Analytical methods to quantitatively detect acrylamide (AA) in food are expensive, laborious, time consuming and require costly scientific instruments, as LC-MS/MS and GC-MS. Near-Infrared (NIR) spectroscopy is a reliable technique, easy to use and able to quantify chemical components, and therefore could represent a fast tool for acrylamide screening in cooked foods. The aim of this study was to develop a new and innovative method to predict AA content in pizza, using a non-destructive NIR spectroscopy. Specifically, NIR reflectance spectra (1000-2500 nm) of freeze-dried pizza samples, with a known acrylamide level, previously measured by UHPLC, were accurately captured. The recorded spectra were processed to design calibration, by chemometric methods for quantitative analysis as Partial Least Squares (PLS) regression, and validation models for the prediction of acrylamide in cooked pizza samples. Spectral range and the number of PLS factors were examined and the lowest Standard Error of Calibration (SEC) and highest Correlation Coefficient of Determination (R2) were selected. The optimized calibration was applied in scanning the NIR spectra of a new set of pizza samples to validate the created method. Results showed that NIR spectroscopy technique is a screening tool capable of rapidly predicting, with reasonable accuracy, the AA content in pizza. Overall, good linear correlation was found between the predicted acrylamide levels in solid matrix by NIR method, and the actual acrylamide values measured by UHPLC in extracted pizza samples. KEYWORDS: acrylamide, FT-NIR spectroscopy, PLS regression, pizza,