Prediction of Edible-oil Iodine Values Based on Raman and near Infrared Spectroscopy
Abstract
Based on Raman and near infrared spectroscopy (NIR), the modeling of edible-oil iodine value was conducted following chemometrics and support vector machine regression (SVR). The Raman and NIR spectral data of 44 oil samples were collected and preprocessed. The preprocessed spectral data at characteristic wavelengths were extracted with CARS and iPLS methods. Parameter optimization was performed following the grid search algorithm (GS), for the SVR iodine value prediction models. The results show that all the models built could predict the iodine values to some extent. An NIR-MSC-iPLS-SVR model exhibited the greatest stability and its correlation coefficient R of the prediction set reached 97.69%. The results show that NIR has more advantages in the fast prediction of iodine values and can be utilized to manufacture portable spectral instruments for edible-oil iodine-value determination. Vegetable oils can afford human body unsaturated fatty acids, vitamins, and other nutrients [1]. Wherein, the unsaturated fatty acids can ensure the physiological function of cells, reduce cholesterol contents, and perfect blood circulation [2]. The unsaturated fatty acid content can be indicated by unsaturation degree and iodine value (iodine number). The iodine value refers to the grams of iodine consumed in the addition reaction of 100 g of oil. The higher the iodine value is, the greater the unsaturation degree is, and the higher the unsaturated fatty acid content is. The iodine value determination methods include Wijs method (national standard), high-performance liquid chromatography (HPLC) method, and so forth [3-4]. Wherein, the Wijs method is precise and commonly used. Nevertheless, because the sample and titrant solution are immiscible, at the end point of titration, the color change is too slow. And, the method is tedious and requires a large amount of hazardous organic solvents [5-6]. Compared with the Wijs method, the spectral method is fast, efficient, and free of sample pretreatment. Hence, it has been increasingly used in oil and food industries [7]. Yu Yanbo et al. [8] used near infrared spectroscopy (NIR) technology to build a prediction model for the prediction of contents of 4 fatty acids in vegetable oils. MarÃa Ã. Carmona et al. [9] identified oils and determined iodine values based on Raman spectroscopy.
Keywords
Raman, Near infrared, Chemometrics, Edible oil, Iodine value
DOI
10.12783/dtcse/cmsam2017/16379
10.12783/dtcse/cmsam2017/16379
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