Fast and robust quality assessment of honeys using near infrared spectroscopy

In this present study, near infrared spectroscopy (NIRS) is employed to detect adulteration and predict soluble solids content (SSC) of raw honey samples based on reflectance spectral data. Diffuse reflectance near infrared data were acquired in wavelength range from 1000 to 2500 nm. Adulterated honey, made by mixing pure honey with commercial sugars, was detected and classified using principal component analysis (PCA). On the other hand, SSC content as predicted from pure honey samples using partial least square regression (PLSR). Standard normal variate (SNV) was applied as spectra correction method. The results showed that PCA based on spectra data, can accurately detect adulteration and classify honey samples with total explained variance from 2 principal components is 97%. Moreover, SSC of pure honey samples can be predicted with achieved correlation coefficient (r) of 0.96 and residual predictive deviation (RPD) index of 2.88 for raw un-corrected spectra, while r = 0.98 and RPD = 3.67 for corrected SNV spectra data. It may conclude that near infrared spectroscopy, can be used as fast and robust method to evaluate quality of raw honey samples.