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Near‐Infrared Spectroscopy and Partial Least‐Squares Regression for Determination of Arachidonic Acid in Powdered Oil
Author(s) -
Yang Meiyan,
Nie Shaoping,
Li Jing,
Xie Mingyong,
Xiong Hua,
Deng Zeyuan,
Zheng Weiwan,
Li Lin,
Zhang Xiaoming
Publication year - 2010
Publication title -
lipids
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.601
H-Index - 120
eISSN - 1558-9307
pISSN - 0024-4201
DOI - 10.1007/s11745-010-3423-2
Subject(s) - partial least squares regression , arachidonic acid , correlation coefficient , chemistry , analytical chemistry (journal) , coefficient of determination , near infrared spectroscopy , chromatography , infrared spectroscopy , cross validation , linear regression , regression analysis , mathematics , statistics , biochemistry , organic chemistry , biology , neuroscience , enzyme
Near‐infrared (NIR) spectroscopy was evaluated as a rapid method of predicting arachidonic acid content in powdered oil without the need for oil extraction. NIR spectra of powdered oil samples were obtained with an NIR spectrometer and correlated with arachidonic acid content determined by a modification of the AOCS Method. Partial Least‐Squares regression was applied to calculate models for the prediction of arachidonic acid. The model developed with the raw spectra had the best performance in cross‐validation ( n = 72) and validation ( n = 21) with a correlation coefficient of 0.965, and the root mean square error of cross‐validation and prediction were both 0.50. The results show that NIR, a well‐established and widely applied technique, can be applied to determine the arachidonic acid content in powdered oil.