The ever-increasing demand for fish as a food, has led to the development of new handling and packaging technologies resulting in premium quality fish products. In order to avoid frauds reaching the market, fish quality assurance methods need to be developed.

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.

对鱼类作为食品的需求不断增长，导致了新的处理和包装技术的发展，从而产生了优质的鱼类产品。为了避免欺诈进入市场，需要开发鱼类质量保证方法。

在这项研究中，开发了两个在0和4°C下储存两周的大西洋鲑鱼中发生的生化过程的统计模型。这些模型被进一步用于检测鲑鱼的质量及其储存条件。使用核磁共振（NMR）光谱和主成分分析（PCA）监控生化过程。使用类比的软独立建模（SIMCA）方法来开发和评估模型。使用各种质量和存储参数的样本测试了模型的欺诈检测潜力。结果表明，所开发的模型能够区分所储存大西洋鲑的质量，时间和温度。