Low risk of category misdiagnosis of rice syrup adulteration in three botanical origin honey by ATR-FTIR and general model.

This study is about the rice syrup adulteration determination in different botanical origin honey in the food product. Due to time-consuming and large risk of misdiagnosis, it is essential to establish a general model for adulteration detection regardless of the original category of honey. In this paper, infrared (IR) spectra combined with four supervised pattern recognition methods were employed to establish the general model for rice syrup adulteration detection in acacia, linden and jujube honey samples simultaneously. Moreover, Monte-Carlo sampling technology was executed to evaluate the models via the average accuracy, sensitivity and specificity. The first derivative-least squares support vector machines (Der-LS-SVM) gave an outstanding performance with higher accuracy (97.09%), higher sensitivity (96.64%), higher specificity (97.58%) and lower standard deviations after fifty trials. In addition, this study makes further efforts to control the quality of the honey product in the market on rice syrup adulteration.