In this study (purchase required) the researchers build a classification model for differentiate freshwater from seawater shrimp (prawns), Litopenaeus vannamei, based on fatty acid (FA) profiling in muscle and hepatopancreas.
They built an untargeted model, using k-nearest neighbor (KNN) and random forest (RF), to identify discriminatory variables.
They then identified, using orthogonal partial least squares-discriminant analysis (OPLS-DA) specific FAs to create their classification model: six (C22:6n3, C20:3n3, C17:0, C18:3n3, C20:5n3, and C20:2) from the muscle and seven (C22:6n3, C16:0, C18:3n3, C18:2n6, C20:2, C20:1, and C18:1n9) from the hepatopancreas.
They report that, using FA profiles from the two tissues, both KNN and RF had initial and cross-validated classification rates >93%, while the predictive classification rates of the models based on muscle FA profiles were higher than that of the models based on hepatopancreas FA profiles. They conclude, therefore, that FA profiles in muscle were more effective than hepatopancreas FAs for this promising classification method.
Photo by Dan Dennis on Unsplash
