Prediction of pH in konjac using fluorescence spectroscopy

Abstract

In the quality control of konjac products, pH 11.0 or higher is required according to the standards set by the Ministry of Health, Japanese Labor and Welfare standards. However, the current destructive testing method faces challenges including difficulties in conducting comprehensive inspections and food waste due to testing procedures. This study aimed to develop a non-destructive prediction method for konjac pH using excitation-emission matrices (EEM). EEM measurements were performed on 54 konjac samples prepared under different pH condition, and prediction models were constructed using partial least squares regression (PLSR), transfer learning, and convolutional neural networks (CNN). EEM measurements revealed two characteristic fluorescence peaks: Ex. 280-300 nm / Em. 340-360 nm (Peak A) and Ex. 320- 330 nm / Em. 400-360 nm (Peak B). Peak A intensity significantly decreased with increasing pH, which was attributed to increased light scattering due to coagulation progression with rising pH. Among the prediction models compared, transfer learning using ResNet-50 with EEM data as input showed the highest accuracy, achieving a coefficient of determination R² = 0.893 and RMSE = 0.252. These results demonstrated the potential of non-destructive pH evaluation of konjac using fluorescence spectroscopy.