Hamburg University of Applied Sciences, Faculty of Life Sciences/Food Science, Ulmenliet 20, 21033 Hamburg, Germany; University of Hamburg, Hamburg School of Food Science, Institute of Food Chemistry, Grindelallee 117, 20146 Hamburg, Germany; Max Rubner-Institut, Federal Research Institute of Nutrition and Food, Department of Safety and Quality of Milk and Fish Products, Hermann-Weigmann-Strasse 1, 24103 Kiel, Germany.
Eurofins Analytik GmbH, Neuländer Kamp 1, 21079 Hamburg, Germany.
Food Res Int. 2018 Apr;106:116-128. doi: 10.1016/j.foodres.2017.12.041. Epub 2017 Dec 16.
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 H 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 (Q) of 0.961 and a Root Mean Square Error of Prediction (RMSEP) of 1.5meqOkg. The combined MIR/NIR data provided the most reliable ANN model for AnV (Q=0.993; RMSEP=0.74). For AV, the ANN model based on the MIR data yielded a Q of 0.988 and an RMSEP of 0.43mgNaOHg. 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 H 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.
鱼油作为一种膳食补充剂越来越受欢迎，也被用于动物饲料，这主要是因为它含有丰富的对健康有益的ω-3 脂肪酸。然而，这些多不饱和脂肪酸极易氧化，导致鱼油质量下降。本研究探讨了 H NMR、FT-MIR 和 FT-NIR 光谱在鱼油质量评估中的应用潜力。共使用了 84 种不同的鱼油，其中 22 种进行了加速储存，温度和光照条件不同，用于建立预测过氧化物值（PV）、茴香胺值（AnV）和酸值（AV）的模型。预测是基于综合光谱数据，结合人工神经网络（ANN）和偏最小二乘回归（PLSR）。PV 的最佳 ANN 模型来自 NMR 数据，预测系数（Q）为 0.961，预测均方根误差（RMSEP）为 1.5meqOkg。MIR/NIR 数据的组合为 AnV 提供了最可靠的 ANN 模型（Q=0.993；RMSEP=0.74）。对于 AV，基于 MIR 数据的 ANN 模型的 Q 值为 0.988，RMSEP 为 0.43mgNaOHg。在大多数情况下，ANN 模型的准确性优于相应的 PLSR 模型。变量选择和数据降维在某些情况下提高了 ANN 模型的性能。H NMR、FT-MIR 和 FT-NIR 光谱与 ANN 的结合可以被认为是一种快速、可靠和可持续的鱼油质量评估方法。
