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Interval ridge regression (iRR) as a fast and robust method for quantitative prediction and variable selection applied to edible oil adulteration

A novel quantitative prediction and variable selection method called interval ridge regression (iRR) is studied in this work. The method is performed on six data sets of FTIR, two data sets of UV-Vis and one data set of DSC. The obtained results show that models built with ridge regression on optimal variables selected with iRR significantly outperfom models built with ridge regression on all variables in both calibration (6 out of 9 cases) and validation (2 out of 9 cases). In this study, iRR is also compared with interval partial least squares regression (iPLS). iRR outperfomed iPLS in validation (insignificantly in 6 out of 9 cases and significantly in one out of 9 cases for p < 0.05). Also, iRR can be a fast alternative to iPLS, especially in case of unknown degree of complexity of analyzed system, i.e. if upper limit of number of latent variables is not easily estimated for iPLS. Adulteration of hempseed (H) oil, a well known health beneficial nutrient, is studied in this work by mixing it with cheap and widely used oils such as soybean (So) oil, rapeseed (R) oil and sunflower (Su) oil. Binary mixture sets of hempseed oil with these three oils (HSo, HR and HSu) and a ternary mixture set of H oil, R oil and Su oil (HRSu) were considered. The obtained accuracy indicates that using iRR on FTIR and UV-Vis data, each particular oil can be very successfully quantified (in all 8 cases RMSEP < 1.2%). This means that FTIR-ATR coupled with iRR can very rapidly and effectively determine the level of adulteration in the adulterated hempseed oil (R2 > 0.99).

Ridge regression; Partial-least squares regression; Variable selection; Hempseed oil; Adulteration

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