Prediction of acrylamide formation in biscuits based on fingerprint data generated by ambient ionization mass spectrometry employing direct analysis in real time (DART) ion source

The objective of this study is the evaluation of the potential of high-throughput direct analysis in real time-high resolution mass spectrometry (DART-HRMS) fingerprinting and multivariate regression analysis in prediction of the extent of acrylamide formation in biscuit samples prepared by various recipes and baking conditions. Information-rich mass spectral fingerprints were obtained by analysis of biscuit extracts for preparation of which aqueous methanol was used. The principal component analysis (PCA) of the acquired data revealed an apparent clustering of samples according to the extent of heat-treatment applied during the baking of the biscuits. The regression model for prediction of acrylamide in biscuits was obtained by partial least square regression (PLSR) analysis of the data matrix representing combined positive and negative ionization mode fingerprints. The model provided a least root mean square error of cross validation (RMSECV) equal to an acrylamide concentration of 5.4 μg kg−1 and standard error of prediction (SEP) of 14.8 μg kg−1. The results obtained indicate that this strategy can be used to accurately predict the amounts of acrylamide formed during baking of biscuits. Such rapid estimation of acrylamide concentration can become a useful tool in evaluation of the effectivity of processes aiming at mitigation of this food processing contaminant. However, the robustness this approach with respect to variability in the chemical composition of ingredients used for preparation of biscuits should be tested further.