Machine learning prediction of dual and dose-response effects of flavone carbon and oxygen glycosides on acrylamide formation.

ABSTRACT:

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

SUBMITTER: Wang L 

PROVIDER: S-EPMC9748078 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature