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Abstract：
The δ2H and δ18O of 105 salmonids cultured in freshwater and seawater and from different regions were combined with linear discriminant analysis (LDA), k-nearest neighbor (KNN), and random forest (RF) to create discrimination models. To assess the stability of the discrimination models, seasonal variation in δ2H and δ18O in salmonids cultured in different systems was studied. δ2H and δ18O were significantly different between salmonids cultured in freshwater and seawater and from different geographical origins. δ2H and δ18O of salmonids cultured in an open system were vulnerable to seasonal effects. The KNN model had 100% accuracy for identifying the production methods of salmonids and was less affected by seasonal variation. The RF model had the highest accuracy for identifying the geographical origins of salmonids with an accuracy of over 80%. Thus, δ2H and δ18O were more effective for identifying the production methods of salmonids than their geographical origins.
摘要：
用线性判别分析（LDA）结合在淡水和海水中以及来自不同地区的105只鲑鱼的δ2H和δ18O，k-最近邻(KNN)，和随机森林(RF)来创建判别模型。为了评估判别模型的稳定性，研究了在不同系统中培养的鲑鱼中δ2H和δ18O的季节性变化。在淡水和海水中培养的鲑鱼和来自不同地理起源的鲑鱼之间，δ2H和δ18O显着不同。在开放系统中培养的鲑鱼的δ2H和δ18O容易受到季节性影响。KNN模型在识别鲑鱼的生产方法方面具有100％的准确性，并且受季节变化的影响较小。RF模型在识别鲑鱼的地理起源方面具有最高的准确性，准确率超过80％。因此，δ2H和δ18O比其地理起源更有效地确定了鲑鱼的生产方法。
