Study on Brand Discrimination of Fish Oil Based on Multiple Spectroscopy Techniques
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Abstract:
In this paper, multiple spectroscopy techniques were used to distinguish different brands of fish oil in a rapid and non-invasive manner. Spectral characteristics of seven brands of fish oil, collected by visible spectroscopy, short wave near infrared spectroscopy (SNIR), long-wave near infrared spectroscopy (LNIR), mid-infrared spectroscopy (MIR), and nuclear magnetic resonance (NMR) spectroscopy, were set as inputs in partial least squares discrimination analysis (PLS-DA) and a least-squares support vector machine (LS-SVM) to establish the discrimination models. The discrimination results of the PLS-DA and LS-SVM models were subsequently compared. The results showed that LNIR achieved the highest discriminant accuracy, and the accuracies of modeling set and prediction set were up to 100%. The LS-SVM model using MIR and NMR spectroscopy also yielded a discriminant accuracy of 100%. On the other hand, the discriminant accuracies of those based on visible spectroscopy and SNIR were poor. In addition, LS-SVM was more suitable than PLS-DA to build identification models for fish oil brands using spectroscopic data. The results indicated that LNIR spectroscopy technique could effectively distinguished fish oil brands, providing the technical support and theoretical basis for developing portable instruments for the analysis of fish oil quality in the future.
