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Detection of decomposition in mahi-mahi, croaker, red snapper, and weakfish using an electronic-nose sensor and chemometric modeling.

This study evaluated an electronic-nose (e-nose) sensor in combination with support vector machine (SVM) modeling for predicting the decomposition state of four types of fish fillets: mahi-mahi, croaker, red snapper, and weakfish. The National Seafood Sensory Expert scored fillets were thawed, 10-g portions were weighed into glass jars which were then sealed, and the jars were held at approximately 30°C to allow volatile components to be trapped and available for analysis. The measurement of the sample vial headspace was performed with an e-nose device consisting of nanocomposite, metal oxide semiconductor (MOS), electrochemical, and photoionization sensors. Classification models were then trained based on the sensory grade of each fillet, and the e-nose companion chemometric software identified that eight MOS were the most informative for determining a sensory pass from sensory fail sample. For SVM, the cross-validation (CV) correct classification rates for mahi-mahi, croaker, red snapper, and weakfish were 100%, 100%, 97%, and 97%, respectively. When the SVM prediction performances of the eight MOS were evaluated using a calibration-independent test set of samples, correct classification rates of 93-100% were observed. Based on these results, the e-nose measurements coupled with SVM models were found to be potentially promising for predicting the spoilage of these four fish species. PRACTICAL APPLICATION: This report describes the application of an electronic-nose sensor as a potential rapid and low-cost screening method for fish spoilage. It could provide regulators and stakeholders with a practical tool to rapidly and accurately assess fish decomposition.