Rapid determination of total phenolic content and antioxidant capacity of maple syrup using Raman spectroscopy and deep learning.

Abstract

Total phenolic content (TPC) and antioxidant capacity of maple syrup were determined using Raman spectroscopy and deep learning. TPC was determined by Folin-Ciocalteu assay, while the antioxidant capacity was measured by 2,2-diphenyl-1picrylhydrazyl (DPPH) assay, oxygen radical absorbance capacity (ORAC) assay, and ferric reducing antioxidant power (FRAP) assay. A total of 360 spectra were collected from 36 maple syrup samples of different colours (dark, amber, light) by both benchtop and portable Raman spectrometers. These spectra were used to establish predictive models for assessing the antioxidant profiles of maple syrup. Deep learning models developed along with portable Raman spectroscopy exhibited comparable predictive performance to those developed along with benchtop Raman spectroscopy. Base on the spectral dataset collected using portable Raman spectroscopy, the developed deep learning models exhibited low RMSEs (root mean square errors, 7.2-17.9 % of mean reference values), low MAEs (mean absolute errors, 5.2-13.1 % of mean reference values) and high R² values (>0.88). The results showed a great goodness of fit and accuracy for predicting the antioxidant profiles of maple syrup, indicating the potential of using portable Raman spectrometer for on-site analysis of antioxidant profiles of maple syrup.