In Frontiers in nutrition
INTRODUCTION : The extensive occurrence of acrylamide in heat processing foods has continuously raised a potential health risk for the public in the recent 20 years. Machine learning emerging as a robust computational tool has been highlighted for predicting the generation and control of processing contaminants.
METHODS : We used the least squares support vector regression (LS-SVR) as a machine learning approach to investigate the effects of flavone carbon and oxygen glycosides on acrylamide formation under a low moisture condition. Acrylamide was prepared through oven heating via a potato-based model with equimolar doses of asparagine and reducing sugars.
RESULTS : Both inhibition and promotion effects were observed when the addition levels of flavonoids ranged 1-10,000 μmol/L. The formation of acrylamide could be effectively mitigated (37.6%-55.7%) when each kind of flavone carbon or oxygen glycoside (100 μmol/L) was added. The correlations between acrylamide content and trolox-equivalent antioxidant capacity (TEAC) within inhibitory range (R 2 = 0.85) had an advantage over that within promotion range (R 2 = 0.87) through multiple linear regression.
DISCUSSION : Taking ΔTEAC as a variable, a LS-SVR model was optimized as a predictive tool to estimate acrylamide content (R 2 inhibition = 0.87 and R 2 promotion = 0.91), which is pertinent for predicting the formation and elimination of acrylamide in the presence of exogenous antioxidants including flavonoids.
Wang Laizhao, Zhang Fan, Wang Jun, Wang Qiao, Chen Xinyu, Cheng Jun, Zhang Yu
2022
acrylamide formation, dual effect, flavone carbon and oxygen glycosides, flavonoid structure, least squares support vector regression
