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

Prado, Enora; Eklouh‐Molinier, Christophe; Enez, Florian; Causeur, David; Blay, Carole; Dupont‐Nivet, Mathilde; Labbé, Laurent; Petit, Vincent; Moréac, Alain; Taupier, Grégory; Haffray, Pierrick; Bugeon, Jérôme; Corraze, Généviève; Nazabal, Virginie

doi:10.1016/j.aca.2021.339212

Cited by 5 publications

(4 citation statements)

References 44 publications

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“…In the Lintvedt study, the main variation in FA composition was related to different feeds, which can have contributed to the improved predictive performance. In another recent study on Rainbow trout ( Oncorhynchus Mykiss ), Raman micro-spectroscopy was used to predict EPA (R 2 = 0.76) and DHA (R 2 = 0.81) [ 9 ]. For this study, however, measurements were performed on adipose tissue and not in the muscle as such.…”

Section: Resultsmentioning

confidence: 99%

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Raman and near Infrared Spectroscopy for Quantification of Fatty Acids in Muscle Tissue—A Salmon Case Study

Afseth

Dankel

Andersen

et al. 2022

Foods

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The aim of the present study was to critically evaluate the potential of using NIR and Raman spectroscopy for prediction of fatty acid features and single fatty acids in salmon muscle. The study was based on 618 homogenized salmon muscle samples acquired from Atlantic salmon representing a one year-class nucleus, fed the same high fish oil feed. NIR and Raman spectra were used to make regression models for fatty acid features and single fatty acids measured by gas chromatography. The predictive performance of both NIR and Raman was good for most fatty acids, with R2 above 0.6. Overall, Raman performed marginally better than NIR, and since the Raman models generally required fewer components than respective NIR models to reach high and optimal performance, Raman is likely more robust for measuring fatty acids compared to NIR. The fatty acids of the salmon samples co-varied to a large extent, a feature that was exacerbated by the overlapping peaks in NIR and Raman spectra. Thus, the fatty acid related variation of the spectroscopic data of the present study can be explained by only a few independent principal components. For the Raman spectra, this variation was dominated by functional groups originating from long-chain polyunsaturated FAs like EPA and DHA. By exploring the independent EPA and DHA Raman models, spectral signatures similar to the respective pure fatty acids could be seen. This proves the potential of Raman spectroscopy for single fatty acid prediction in muscle tissue.

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Section: Resultsmentioning

confidence: 99%

“…For both techniques, however, lipid signals are readily available in the spectra. Thus, both techniques have been extensively studied for quantification of fatty acid features and single fatty acids in fats and oils [ 6 , 7 ] and lipid rich matrices such as adipose tissue [ 8 , 9 , 10 ].…”

Section: Introductionmentioning

confidence: 99%

Raman and near Infrared Spectroscopy for Quantification of Fatty Acids in Muscle Tissue—A Salmon Case Study

Afseth

Dankel

Andersen

et al. 2022

Foods

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“…For individual ω-3 FAs, ALA, EPA, and DHA were found to have good R 2 values (0.82, 0.76, and 0.81, respectively). This indicated that the combination of Raman and PLS-RRM is suitable for analysis of individual ω-3 FAs and total ω-6 FAs but is not accurate for prediction total ω-3 FAs [ 79 ].…”

Section: Molecular Spectroscopic Methods For Analysis Of ω-3 Fas and ...mentioning

confidence: 99%

Application of Chromatographic and Spectroscopic-Based Methods for Analysis of Omega-3 (ω-3 FAs) and Omega-6 (ω-6 FAs) Fatty Acids in Marine Natural Products

et al. 2023

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Omega-3 fatty acids v(ω-3 FAs) such as EPA (eicosapentaenoic acid) and DHA (docosahexaenoic acid) and omega-6 fatty acids (ω-6 FAs) such as linoleic acid and arachidonic acid are important fatty acids responsible for positive effects on human health. The main sources of ω-3 FAs and ω-6 FAs are marine-based products, especially fish oils. Some food, supplements, and pharmaceutical products would include fish oils as a source of ω-3 FAs and ω-6 FAs; therefore, the quality assurance of these products is highly required. Some analytical methods mainly based on spectroscopic and chromatographic techniques have been reported. Molecular spectroscopy such as Infrared and Raman parallel to chemometrics has been successfully applied for quantitative analysis of individual and total ω-3 FAs and ω-6 FAs. This spectroscopic technique is typically applied as the alternative method to official methods applying chromatographic methods. Due to the capability to provide the separation of ω-3 FAs and ω-6 FAs from other components in the products, gas and liquid chromatography along with sophisticated detectors such as mass spectrometers are ideal analytical methods offering sensitive and specific results that are suitable for routine quality control.

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“…Up to now, multivariate calibration and machine learning have been the two methods facilitating Raman spectroscopy analysis in a complicated matrix. Multivariate calibration techniques, including the typical multiple linear regression, principal components analysis, partial least-squares regression, ridge regression, , the recently emerging non-negative least-squares regression, , non-negative lasso regression, and non-negative elastic net, , have successfully realized the qualitative and quantitative analysis of mixtures (such as mixture analysis of methanol, ethanol, and acetonitrile) . However, due to the small computation of these algorithms, their performance is less than satisfactory in samples where strong molecular interaction was involved, which was partly solved with the introduction of machine learning.…”

Section: Introductionmentioning

confidence: 99%

Visualization of a Machine Learning Framework toward Highly Sensitive Qualitative Analysis by SERS

et al. 2022

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Surface-enhanced Raman spectroscopy (SERS), providing near-single-molecule-level fingerprint information, is a powerful tool for the trace analysis of a target in a complicated matrix and is especially facilitated by the development of modern machine learning algorithms. However, both the high demand of mass data and the low interpretability of the mysterious black-box operation significantly limit the well-trained model to real systems in practical applications. Aiming at these two issues, we constructed a novel machine learning algorithm-based framework (Vis-CAD), integrating visual random forest, characteristic amplifier, and data augmentation. The introduction of data augmentation significantly reduced the requirement of mass data, and the visualization of the random forest clearly presented the captured features, by which one was able to determine the reliability of the algorithm. Taking the trace analysis of individual polycyclic aromatic hydrocarbons in a mixture as an example, a trustworthy accuracy no less than 99% was realized under the optimized condition. The visualization of the algorithm framework distinctly demonstrated that the captured feature was well correlated to the characteristic Raman peaks of each individual. Furthermore, the sensitivity toward the trace individual could be improved by least 1 order of magnitude as compared to that with the naked eye. The proposed algorithm distinguished by the lesser demand of mass data and the visualization of the operation process offers a new way for the indestructible application of machine learning algorithms, which would bring push-to-the-limit sensitivity toward the qualitative and quantitative analysis of trace targets, not only in the field of SERS, but also in the much wider spectroscopy world. It is implemented in the Python programming language and is open-source at https://github.com/3331822w/Vis-CAD.

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

Cited by 5 publications

References 44 publications

Raman and near Infrared Spectroscopy for Quantification of Fatty Acids in Muscle Tissue—A Salmon Case Study

Raman and near Infrared Spectroscopy for Quantification of Fatty Acids in Muscle Tissue—A Salmon Case Study

Application of Chromatographic and Spectroscopic-Based Methods for Analysis of Omega-3 (ω-3 FAs) and Omega-6 (ω-6 FAs) Fatty Acids in Marine Natural Products

Visualization of a Machine Learning Framework toward Highly Sensitive Qualitative Analysis by SERS

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