Journal
FOOD ANALYTICAL METHODS
Volume 11, Issue 5, Pages 1501-1509Publisher
SPRINGER
DOI: 10.1007/s12161-017-1135-4
Keywords
FTIR spectroscopy; Adulteration; Salmon; Partial least squares discriminant analysis
Categories
Funding
- National Natural Science Funds for Young Scholar [61501531]
- Natural Science Foundation of Guangdong Province [2015A030313602]
- Collaborative Innovation Major Projects of Guangzhou [201508010013]
- National Key Research and Development Program of China [2017YFC160089]
- Science and Technology Plan of Guangdong province [2015A020209173, 2015A090905014, 2013B040500013, 201704030098]
Ask authors/readers for more resources
Norwegian salmon is often adulterated with Heilongjiang salmon at local fish markets. To promote fair price competition at fish markets and protect consumer rights, we developed a quick and accurate identification method to distinguish adulterated and non-adulterated Norwegian salmon using a Fourier transform infrared spectroscopy (FTIR). In this study, Norwegian and Heilongjiang salmon could be readily distinguished using partial least squares discriminant analysis (PLS-DA), but it failed to detect the accurate level of 20 to 80% of adulterated Norwegian salmon samples. In order to improve the PLS-DA model, several pre-processing methods, including standard normal variate (SNV), multiplicative scatter correction (MSC), and normalization, were used to evaluate individually to select the most appropriate correction method. Characteristics of the spectra within the waveband range covering 450 to 4000 cm(-1) were also analyzed to determine the optimum sub-waveband range to improve the accuracy of the model. The results of the study showed that using FTIR and the improved PLS-DA model established in this study, the adulterated and non-adulterated Norwegian salmon could be completely distinguished. The accuracy of the adulteration level and the prediction accuracy of the model were also significantly improved when normalization method was used at 450 to 1790 cm(-1) sub-wavebands. For the calibration and cross-validation sample sets, the determination coefficients of the improved PLS-DA model were at 0.99 and 0.98, respectively. The mean square errors were 2.3 and 4%, resulting in a 90% accuracy of validation sample sets. This technology should provide fish markets an easy and reliable way to distinguish the adulterated and non-adulterated salmon.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available