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A rapid and simple approach to identify different sunflower oil types by means of near‐infrared reflectance spectroscopy
Author(s) -
Velasco Leonardo,
PérezVich BegoɁa,
FernándezMartínez José M.
Publication year - 1998
Publication title -
journal of the american oil chemists' society
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.512
H-Index - 117
eISSN - 1558-9331
pISSN - 0003-021X
DOI - 10.1007/s11746-998-0345-8
Subject(s) - sunflower oil , oleic acid , chemistry , palmitic acid , reflectivity , fatty acid , sunflower , infrared spectroscopy , stearic acid , spectroscopy , near infrared reflectance spectroscopy , chromatography , degree of unsaturation , analytical chemistry (journal) , near infrared spectroscopy , food science , biochemistry , organic chemistry , biology , optics , horticulture , physics , quantum mechanics , neuroscience
The potential of near‐infrared reflectance spectroscopy (NIRS) to perform an easy and rapid classification of different sunflower oil types was investigated. A total of 118 oil samples showing large variation in their fatty acid compositions were analyzed by both NIRS and gas‐liquid chromatography (GLC). They were classified into five classes, characterized by (i) high palmitic acid content (>29%), (ii) high palmitic acid in high oleic acid background (>27 and >51%, respectively), (iii) high stearic acid content (>22%), (iv) high oleic acid content (>83%), and (v) standard oil type. Second‐derivative transformation and scatter corrections were applied to the original log (1/ R ) spectra, and the correlation coefficients between NIRS spectral information and GLC fatty acid values were studied to identify the wavelengths with the best discriminating ability. The use of the spectral data at 2134 nm permitted all the samles with high levels of total saturated fatty acids (>29%, classes i, ii, and iii) to be discriminated from the samples with standard levels (<22%, classes iv and v). The use of a second wavelength, 2192 nm, led to a further separation of the samples with high C 18:1 content within each group (classes ii and iv, respectively). Therefore, an accurate discrimination of four of the five sunflower oil types was achieved by using the spectral information at two wavelengths exclusively. The oil samples belonging to classes i and iii could not be separated with this approach, which was explained on the basis of the small spectral differences observed between the two classes.