توسعه یک مدل آماری برای شناسایی کیفیت و شرایط ذخیره سازی ماهی قزل آلا

In this study, two statistical models of biochemical processes that occur in Atlantic salmon during two weeks of storage at 0 and 4 °C were developed. These models were further used to detect salmon quality and its storage conditions. The biochemical processes were monitored using Nuclear Magnetic Resonance (NMR) spectroscopy and principal component analysis (PCA). The Soft Independent Modeling of Class Analogy (SIMCA) approach was applied to develop and evaluate the models. The fraud detection potential of the models was tested using samples of various quality and storage parameters. It was shown that the developed models are able to discriminate quality, time and temperature of stored Atlantic salmon.