Data Fusion Approach Improves the Prediction of Single Phenolic Compounds in Honey: A Study of NIR and Raman Spectroscopies

Abstract

The combination of Near-Infrared Spectroscopy (NIR) and Raman Spectroscopy (RS) of 100 honey samples collected from different countries were used to develop the calibration model for determination of single phenolic compound. In high-performance liquid chromatography with diode-array detection analysis, 16 phenolic compounds were identified with p-hydroxybenzoic acid being the major compound detected in all honey samples. Thus, p-hydroxybenzoic acid was used for developing prediction models. Spectral data were modeled individually and using data fusion methodologies. The performance of the model based on RS spectra [(Formula presented.) = 0.9500, Root Mean Standard Error of Prediction (RMSEP) = 6.83] was higher than that based on the NIR spectra ((Formula presented.) = 0.8147, RMSEP = 13.80). The application of both low-level ((Formula presented.) = 0.9553, RMSEP = 6.59) and mid-level ((Formula presented.) = 0.9563, RMSEP = 7.95) data fusion together with Partial Least Squares (PLS) had effectively improved the prediction models of NIR but did not enhance prediction models based on RS technique. The results demonstrated that the NIR, RS, and data fusion approaches together with the PLS model could be used as alternative quantitative methods for determination of p-hydroxybenzoic acid in honey samples.