Correction to: Chemometric tools for the authentication of cod liveroil based on nuclear magnetic resonance and infrared spectroscopy data.

The article Chemometric tools for the authentication of cod liver oil based on nuclear magnetic resonance and infrared spectroscopy data, written by Editha Giese, Sascha Rohn and Jan Fritsche.

Chemometric tools for the authentication of cod liver oil based on nuclear magnetic resonance and infrared spectroscopy data.

Cod liver oil is a popular dietary supplement marketed as a rich source of omega-3 fatty acids as well as vitamins A and D. Due to its high market price, cod liver oil is vulnerable to adulteration with lower priced vegetable oils. In this study, 1H and 13C nuclear magnetic resonance spectroscopy, Fourier transform infrared spectroscopy, and gas chromatography (coupled to a flame ionization detector) were used in combination with multivariate statistics to determine cod liver oil adulteration with common vegetable oils (sunflower and canola oils). Artificial neural networks (ANN) were able to differentiate adulteration levels based on infrared spectra with a detection limit of 0.22% and a root mean square error of prediction (RMSEP) of 0.86%. ANN models using 1H NMR and 13C NMR data yielded detection limits of 3.0% and 1.8% and RMSEPs of 2.7% and 1.1%, respectively. In comparison, the ANN model based on fatty acid profiles determined by gas chromatography achieved a detection limit of 0.81% and an RMSEP of 1.1%. The approach of using spectroscopic techniques in combination with multivariate statistics can be regarded as a promising tool for the authentication of cod liver oil and may pave the way for a holistic quality assessment of fish oils. Graphical abstract.

Determining quality parameters of fish oils by means of 1H nuclear magnetic resonance, mid-infrared, and near-infrared spectroscopy in combination with multivariate statistics.

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 1.5meqO2kg-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 0.43mgNaOHg-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.