Rapid Assessment of Quality Changes in French Fries during Deep-frying Based on FTIR Spectroscopy Combined with Artificial Neural Network.

Abstract

Fourier transform infrared (FTIR) spectroscopy combined with backpropagation artificial neural network (BP-ANN) were utilized for rapid and simultaneous assessment of the lipid oxidation indices in French fries. The conventional indexes (i.e. total polar compounds, oxidized triacylglycerol polymerized products, oxidized triacylglycerol monomers, triacylglycerol hydrolysis products, and acid value), and FTIR absorbance intensity in French fries were determined during the deep-frying process, and the results showed the French fries had better quality in palm oil, followed by sunflower oil, rapeseed oil and soybean oil. The FTIR spectra of oil extracted from French fries were correlated to the reference oxidation indexes determined by AOCS standard methods. The results of BP-ANN prediction showed that the model based on FTIR fitted well (R2 > 0.926, RMSEC < 0.481) compared with partial least-squares model (R2 > 0.876). This facile strategy with excellent performance has great potential for rapid characterization quality of French fries during frying.