Data fusion strategy in quantitative analysis of spectroscopy relevant to olive oil adulteration

Abstract

Olive oil adulteration with various less expensive edible oils represents a great danger for consumers. Spectrometry has been used to detect olive oil adulteration with other oil, but we need more robust and accurate model. Therefore, this work investigated the combination of infrared (NIR) and mid infrared (MIR) spectroscopy for the quantification of rapeseed oil in olive oil blends. Furthermore, a partial least squares (PLS) model was established to predict the concentration of the adulterant. Models constructed using baseline correction by combination of standard normal variate (SNV), SG smoothing and vector normalization pretreatments, respectively. Three data fusion strategies (low, mid and high-level) have been applied to take advantage of the synergistic effect of the information obtained from NIR and MIR. We chose algorithm (SPA) to extract spectral features for mid-level data fusion. Binary linear regression used in high-level data fusion. We selected the best pretreatment for final evaluation according to the evaluation parameters (R2 of calibration and validation, RMSECV and RMSEP). NIR, MIR and data fusion models were evaluated by comparing the R2 of validation and RMSEP (root mean square error of prediction). The RMSEP of low-level (3.44), high-level (2.86) data fusion were better than NIR(7.09), MIR(4.04), mid-level(6.09)and the validation coefficient of determination R2 of low-level data fusion (0.975) and high-level data fusion (0.988) are better than the NIR (0.896) and MIR (0.966). Results showed that：(1) NIR and MIR are fast and non-destructive testing tools to detect the extra-virgin olive oil adulteration with rapeseed oil. (2) Low-level data fusion can effectively improve model prediction accuracy. (3) SPA reduced the number of variables, but it did not improved the results. (4) High-level data fusion strategy can be used as a reliable tool for quantitative analysis.