Prediction of fatty acids composition in the rainbow trout Oncorhynchus mykiss by using Raman micro-spectroscopy

The importance of poly-unsaturated fatty acids (PUFAs) in food is crucial for the animal and human development and health. As a complementary strategy to nutrition approaches, genetic selection has been suggested to improve fatty acids (FAs) composition in farmed fish. Gas chromatography (GC) is used as a reference method for the quantification of FAs; nevertheless, the high cost prevents large scale phenotyping as needed in breeding programs. Therefore, a calibration by means of Raman scattering spectrometry has been established in order to predict FA composition of visceral adipose tissue in rainbow trout Onchorhynchus mykiss. FA composition was analyzed by both GC and Raman microspectrometry techniques on 268 individuals fed with three different feeds, which have different FA compositions. Among the possible regression methods, the ridge regression method, was found to be efficient to establish calibration models from the GC and spectral data. The best cross-validated R-2 values were obtained for total PUFAs, omega-6 (Omega-6) and omega-3 (Omega-3) PUFA (0.79, 0.83 and 0.66, respectively). For individual Omega-3 PUFAs, alpha-linolenic acid (ALA, C18:3), eicosapentaenoic acid (EPA, C20:5) and docosahexenoic acid (DHA, C22:6) were found to have the best R-2 values (0.82, 0.76 and 0.81, respectively). This study demonstrates that Raman spectroscopy could be used to predict PUFAs with good correlation coefficients on adipocytes, for future on adipocytes physiology or for large scale and high throughput phenotyping in rainbow trout. (C) 2021 Elsevier B.V. All rights reserved.

Automated summary

The presence of poly-unsaturated fatty acids (PUFAs) in food plays a crucial role in animal and human development and health. This study demonstrates that Raman spectroscopy could be used to accurately predict the composition of PUFAs in visceral adipose tissue of rainbow trout. The method showed good correlation and could be applied for large-scale phenotyping in rainbow trout.