Estimating Hydroxymethyfurfural (HMF) Concentration Via Modified Seliwanoff Test Using Artificial Neural Network (ANN)

Abstract

Abstract Hydroxymethylfurfural (HMF) is a quality indicator, especially in foods where changes in protein-carbohydrate interactions are observed during the applied process. In this study absorbance and L*, a*, b* values of red color emerged due to the relationship between hydroxymethylfurfural (HMF) and resorcinol during the modified Seliwanoff test were used as input data artificial neural network (ANN) to determine the HMF concentration for the first time. A linear relationship, between HMF concentration and absorbance of red color, can be represented by equation absorbance = 0.0020 + 0.0012* concentration of HMF (mg L-1) with R2 = 99.6%, Fisher ratio: 0.18, p value of lack of fit: 0.975, correlation coefficient: 0.9960. Intra-day and inter-day precision expressed as relative standard deviation (RSD) %, were 2.35 - 3.65% and 3.16 - 4.73%, respectively. Recovery rates and RSDs were in the range of 99.34 - 100.47% and 1.58 - 3.68%. It showed high correlation compared to HPLC method used as reference method (0.998). The R2 values of ANN for estimation of HMF concentration were found 0.90 for training, 0.96 for validation, and 0.99 for testing and AARD was found 8.85%. Evaluation of the absorbance and L*, a*, b* values of the red color with artificial intelligence is a reliable way to determine the HMF concentration.

Keywords

• hydroxymethylfurfural (HMF)
• seliwanoff test
• artificial neural network (ANN)