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| A Sequential Classification Strategy Applied to the Detection of Terrestrial Animal Lipid in Fish Oil by MID-Infrared Spectroscopy |
| GAO Bing, XU Shuai, HAN Lu-jia, LIU Xian* |
| College of Engineering, China Agricultural University, Beijing 100083, China |
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Abstract Compared with other animal fat, fish oil has the traits of the high demand of extraction technology, low oil yield and high nutritive value. Authenticity appraisal of fish oil is conducive to the normal operation of the feed market and the protection of consumer rights and interests. In the present study, a Sequential Classification Strategy (SCS) was proposed and applied to identify illegally adulterated terrestrial animal lipid in fish oil. A total of 50 animal fat samples (12 fish oil, 10 lard, 9 chicken oil, 10 tallow and 9 suet) were collected in this experiment, and 160 adulterated fish oil samples with terrestrial animal fat were prepared by homogeneous mixing method. Exploratory research based on the principal component analysis (PCA) method was used to identify the feasibility of identification analysis. The results showed that pure fish oil and adulterated fish oil were well differentiated. The species identification of terrestrial animal fat adulterants is potential. Based on partial least squares-discriminant analysis (PLS-DA) and one class-partial least squares (OC-PLS), the first step was to establish a one class screening model to detect the authenticity of fish oil. In the second step, the identification model of multi-class adulterations (two types of classification) of terrestrial animal lipid was established. Results show that the one-class screening model distinguished pure fish oil from adulterated fish oil, and the recognition rate and rejection rate of the one-class screening model were both 100%, the first multi-class model for the classification of adulterants (lard, chicken oil, suet, tallow) in pure fish oil performed well with the recognition rate and rejection rate higher than 80% and the mis-discrimination ratio lower than 15%, the second multi-class model for the classification of adulterants (ruminant animal lipid, non-ruminant animal lipid) performed better than the first multi-class model with the recognition rate and rejection rate higher than 90% and the mis-discrimination ratio lower than 7%. With the proposed SCS, infrared spectroscopy combined with chemometrics can be used to identify pure fish oil form adulterated fish oil. Furthermore, the species source of adulterants can be recognized effectively. Finally, we can suggest this type of application as a potential tool to assist the feed industry and regulatory organisms in food quality control, allowing detection in feeding fish oil through direct, fast and reliable screening analyses.
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Received: 2019-11-11
Accepted: 2020-04-28
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Corresponding Authors:
LIU Xian
E-mail: lx@cau.edu.cn
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