Introduction: Polyphenols are the foremost measure of phytochemicals in Chinese dates due to their many potential health benefits such as averting cancers, reducing the risk of coronary artery disease, diuretic activity, myocardial stimulant, coronary dilator and muscle relaxant.
Objective: To quantitate the polyphenols in Chinese dates using a data fusion approach with near-infrared (NIR) and mid-infrared (MIR) spectroscopy.
Material and methods: A total of 80 Chinese dates samples were used for data acquisition from both NIR and MIR spectroscopy. The efficient spectral intervals were extracted by the synergy interval partial least square (Si-PLS) algorithm as input variables for NIR-MIR fusion model. A genetic algorithm (GA) was used to construct the model based on NIR-MIR fusion. The performance of the developed models was evaluated using correlation coefficients of calibration (R2 ) and prediction (r2 ), root mean square error of prediction (RMSEP), bias and residual prediction deviation (RPD).
Results: The data fusion model based on the GA was superior compared to NIR and MIR build model. The optimal GA-fusion model yielded R2 = 0.9621, r2 = 0.9451, RPD = 2.44, calibration set bias = 0.004 and prediction set bias = 0.061, computing only 15 variables.
Conclusion: These findings reveal that integration of NIR and MIR is possible for the prediction of total polyphenol content in Chinese dates.
Keywords: genetic algorithms; polyphenols; principal component analysis; spectral interval selection; spectroscopy techniques.
© 2019 John Wiley & Sons, Ltd.