Evaluation of adulteration in distillate samples of Rosa damascena Mill using colorimetric sensor arrays, chemometric tools and dispersive liquid-liquid microextraction-GC-MS.

Abstract

Rosa damascena Mill distillate and its essential oil are widely used in cosmetics, perfumes and food industries. Therefore, the methods of detection for its authentication is an important issue. We suggest colorimetric sensor array and chemometric methods to discriminate natural Rosa distillate from synthetic adulterates. The colour responses of 20 indicators spotted on polyvinylidene fluoride (PVDF) substrate were monitored with a flatbed scanner; then their digital representation was analysed with principal component analysis (PCA), partial least squares-discriminant analysis (PLS-DA) and soft independent modelling of class analogy (SIMCA). Accurate discrimination of the diluted- and synthetic-mixture samples from the original ones was achieved by PLS-DA and SIMCA models with error rate of 0.01 and 0, specificity of 0.98 and 1, sensitivity of 1 and 1, and accuracy of 0.98 and 0.96, respectively. Discrimination of the synthetic adulterate from the original samples was achieved with error rate of 0.03 and 0.03, specificity of 0.94 and 0.93, sensitivity of 1 and 1, and accuracy of 0.93 and 0.71 with PLS-DA and SIMCA models, respectively. Moreover, the chemical constituents of the samples were analysed using dispersive liquid-liquid microextraction and gas chromatography-mass spectrometry (GC-MS). The main constituents of the distillate were geraniol, citronellol, and phenylethyl alcohol in different percentages, in both original and synthetic adulterate samples. These results point out the successful combination of colorimetric sensor array and PLS-DA and SIMCA as a fast, sensitive and inexpensive screening tool for discrimination of original samples of R. damascena Mill distillate from those prepared from synthetic Rosa essential oils.