Rapid Determination For Quality Of Fish Oil Based On Multiple-band Spectroscopy Technology

Fish oil contains a large amount of Poly-unsaturated Fatty acids (PUFA), such as EPA, DHA and DPA. These PUFA are famous for its health care effects including reducing blood fat, lowering blood pressure, resisting arteriosclerosis and so on. More and more people are taking fish oil as supplement medicine. Nowadays, there are so many brands of fish oil, some of which are of poor quality and will destroy the health of people. However, it is always very difficult for consumers to tell the differences between the good and the bad only according to the appearance of fish oil. Traditional detecting methods such as gas chromatography (GC) always take a very long time to process and need a lot of labor work. Thus they are not suitable for rapid determination of fish oil's quality. Based on this situation, this paper aims at developing a rapid and thoroughly determination method for quality of fish oil. The major research contents and conclusions are as follows:1. Visible-short wave near infrared spectroscopy technology (Vis-SNIR), long-wave near infrared spectroscopy technology (LNIR), mid-infrared spectroscopy technology (MIR), nuclear magnetic resonance technology (NMR) were used separately to discriminate 7 different brands of fish oil. Vis-SNIR-PLS model and 2nd Der-LNIR-PLS model had the best performances, both with correction rates of 97.14%. MIR-PLS and NMR-PLS models had poor results.2. Studied the application prospect of Vis-NIR technology in the determination of adulterated oil in fish oil. Adulterated different contents of soybean oil or rapeseed oil in fish oil and collected the Vis-NIR spectra of all the samples to establish PLS models. For fish oil adulterated soybean oil, the best model had Rp of 0.9386. PLS model based on 11 specific wavelengths selected by SPA had Rp of 0.9412. For fish oil adulterated rapeseed oil, the best model was MSC-PLS model with Rp of 0.9593. PLS model based on 15 specific wavelengths selected by SPA had Rp of 0.9326.3. Collected the Vis-SNIR spectra, LNIR spectra, MIR spectra and NMR spectra of fish oil and detected the contents of EPA, DHA and DPA of fish oil samples by GC. Established PLS models and studied different spectrum preprocess methods. For EPA prediction, NMR-PLS model and NMR-SPA-PLS model had the best results with Rp of 0.9792 and 0.9768. For DHA prediction, SPA-PLS model had the best Rp of 0.9859. For DPA prediction,2nd Der-LNIR-PLS model had the best result with Rp of 0.9649. The SPA-PLS model had Rp of 0.9859.4. Studied the application prospect of Vis-NIR technology in determining the oxidation degree of fish oil. The results showed that if put 3 different brands of fish oil in 1 model, the correction rate was only 56.67%. If established PLS models separately for 3 brands of fish oil, the correction rates were 85.00%, 73.33% and 66.67%. After using the best preprocessing methods, the correction rates were improved to 93.33%,83.33% and 73.33%. The results illustrated that Vis-NIR technology combined with preprocessing algorithms can be used in determining the oxidation degree of fish oil. However the performances of the models needed to be further improved.