Honey is a highly valuable food, and due to its composition and price is increasingly becoming the target of adulteration. In this work, the analysis of the physicochemical properties was made for 15 samples of the original honey, and adulterated honey with the different proportions of adulterants (glucose and corn syrup) and chemometric models (PLS and ANN) were developed for the description and prediction of the added adulterants and physicochemical properties of honey based on NIR spectra. The results of physicochemical properties indicate that the analyzed samples of original honey in complying with the Regulations about honey and that the addition of adulterants can easily be detected based on the properties of electrical conductivity. Based on the validation determination coefficient, RMSEP values, SEP and RPD values for developed PLS models for the prediction of adulterants ratio in honey, it can be concluded that the developed models are robust and reliable for use on samples that are not included in the model. The results also indicate that developed neuronal networks ensure a good correlation between experimental values and the model of predicted values at the learning, testing and validation level. For all 15 samples, the determination coefficients at all three levels (learning, validation testing) are higher than 0.9 and the model error is low (RMSE < 0.01) and can be used to predict the ratio of added adulterants and the description of the physicochemical properties of adulterated honey based on the NIR spectrum.