Food Analytical Methods

, Volume 11, Issue 5, pp 1501–1509 | Cite as

Identification of Adulterated and Non-adulterated Norwegian Salmon Using FTIR and an Improved PLS-DA Method

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Abstract

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.

Keywords

FTIR spectroscopy Adulteration Salmon Partial least squares discriminant analysis 

Notes

Acknowledgements

The authors thank the anonymous reviewers for their critical and constructive comments and suggestions. This work was partially supported by the National Natural Science Funds for Young Scholar (Grant No. 61501531), Natural Science Foundation of Guangdong Province (Grant No. 2015A030313602), Collaborative Innovation Major Projects of Guangzhou (201508010013), the National Key Research and Development Program of China (2017YFC160089) and Science and Technology Plan of Guangdong province (Grant No. 2015A020209173, 2015A090905014, 2013B040500013, 201704030098).

Compliance with Ethical Standards

Conflict of Interest

Wu Ting declares that he has no conflict of interest. Yang Ling declares that he has no conflict of interest. Zhong Nan declares that he has no conflict of interest.

Ethical Approval

This article does not contain any studies with human participants or animals performed by any of the authors.

Informed Consent

Not applicable.

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© Springer Science+Business Media, LLC, part of Springer Nature 2017

Authors and Affiliations

  1. 1.College of EngineeringSouth China Agricultural UniversityGuangzhouChina
  2. 2.Key Laboratory of Key Technology on Agricultural Machine and EquipmentMinistry of EducationGuangzhouChina
  3. 3.Guangdong Provincial Key Laboratory of Food Quality and SafetySouth China Agricultural UniversityGuangzhouChina
  4. 4.School of Information Science and TechnologyZhongkai University of Agriculture and EngineeringGuangzhouChina

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