Rapid assessment of quality changes in French fries during deep-frying
based on FTIR spectroscopy combined with artificial neural network
Fourier transform infrared (FTIR) spectroscopy combined with back
propagation 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, oxidation products, and
triacylglycerol hydrolysis products, acid values and peroxide values),
and FTIR absorbance intensity in French fries were determined during
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 method.
The results of BP-ANN prediction showed that the model based on FTIR
fitted well (R2 > 0.926, RMSEC < 0.614,
RMSEP< 0.550) compared with partial least-squares model (R2
> 0.876, RMSEC < 1.144, RMSEP< 1.257).
This facile strategy with excellent performance has great potential for
rapid characterization quality of French fries during frying.