Classification of Gilthead Sea Bream (<i>Sparus aurata</i>) from ¹H NMR Lipid Profiling Combined with Principal Component and Linear Discriminant Analysis

The combination of 1H NMR fingerprinting of lipids from gilthead sea bream (Sparus aurata) with nonsupervised and supervised multivariate analysis was applied to differentiate wild and farmed fish and to classify farmed specimen according to their areas of production belonging to the Mediterranean basin. Principal component analysis (PCA) applied on processed 1H NMR profiles made a clear distinction between wild and farmed samples. Linear discriminant analysis (LDA) allowed classification of samples according to the geographic origin, as well as for the wild and farmed status using both PCA scores and NMR data as variables. Variable selection for LDA was achieved with forward selection (stepwise) with a predefined 5% error level. The methods allowed the classification of 100% of the samples according to their wild and farmed status and 85–97% to geographic origin. Probabilistic neural network (PNN) analyses provided complementary means for the successful discrimination among classes investigated.本研究将金头鲷（Sparus aurata）脂质的氢谱核磁共振指纹图谱（1H NMR fingerprinting）与无监督、有监督多变量分析相结合，用于区分野生与养殖鱼类，并根据养殖个体所属地中海流域的养殖产地对其进行分类。对经预处理的1H NMR图谱应用主成分分析（PCA），可清晰区分野生与养殖样本。线性判别分析（LDA）以PCA得分及NMR数据作为变量，能够根据样本的地理起源以及野生/养殖状态完成分类。LDA的变量筛选采用预先设定5%误差水平的前进式（逐步）选择法实现。该方法可100%准确区分样本的野生/养殖状态，对地理起源的分类准确率达85%~97%。概率神经网络（PNN）分析为所研究类别间的有效区分提供了补充手段。