Fish oil is becoming increasingly popular as a dietary supplement as well as for its use in animal feed, which is mainly due to its high contents of the health promoting omega-3 fatty acids. However, these polyunsaturated fatty acids are highly susceptible to oxidation, which results in a decrease of the fish oil quality. This study investigated the potential of ¹H NMR, FT-MIR, and FT-NIR spectroscopy in the quality assessment of fish oils. A total of 84 different fish oils, of which 22 were subjected to accelerated storage with varying temperature and light exposure, were used to develop models for predicting the peroxide value (PV), the anisidine value (AnV), and the acid value (AV). Predictions were based on comprehensive spectroscopic data in combination with Artificial Neural Networks (ANN) as well as Partial Least Squares Regression (PLSR). The best ANN model for PV was obtained from NMR data, with a predictive coefficient of determination (Q²) of 0.961 and a Root Mean Square Error of Prediction (RMSEP) of 1.5 meq O₂ kg^− 1. The combined MIR/NIR data provided the most reliable ANN model for AnV (Q² = 0.993; RMSEP = 0.74). For AV, the ANN model based on the MIR data yielded a Q² of 0.988 and an RMSEP of 0.43 mg NaOH g^− 1. In most cases, the accuracy of the ANN models was superior to the respective PLSR models. Variable selection and data dimensionality reduction turned out to improve the performance of the ANN models in some cases. The application of ¹H NMR, FT-MIR, and FT-NIR spectroscopy in combination with ANN can be considered very promising for a rapid, reliable, and sustainable assessment of fish oil quality.

鱼油作为膳食补充剂及其在动物饲料中的用途正变得越来越流行，这主要是由于鱼油中富含促进健康的omega-3脂肪酸。但是，这些多不饱和脂肪酸极易被氧化，导致鱼油品质下降。这项研究调查了¹ H NMR，FT-MIR和FT-NIR光谱在鱼油质量评估中的潜力。总共使用84种不同的鱼油，其中22种在不同的温度和曝光条件下进行了加速储存，用于开发预测过氧化物值（PV），茴香胺值（AnV）和酸值（AV）的模型）。预测基于综合光谱数据结合人工神经网络（ANN）以及偏最小二乘回归（PLSR）。从NMR数据获得最佳的PV神经网络模型，预测的预测系数（Q ²）为0.961 ，预测的均方根误差（RMSEP）为1.5 meq O ₂  kg ^-1。MIR / NIR组合数据为AnV提供了最可靠的ANN模型（Q ²  = 0.993； RMSEP = 0.74）。对于AV，​​基于MIR数据的ANN模型得出的Q ²为0.988，RMSEP为0.43 mg NaOH g ^-1。在大多数情况下，人工神经网络模型的准确性优于各自的PLSR模型。事实证明，在某些情况下，变量选择和数据降维可改善ANN模型的性能。的应用¹与ANN组合H NMR，FT-MIR和FT-NIR光谱可以被认为是鱼油质量快速，可靠，可持续的评价十分看好。