Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

Analysis of Omega-3 (ω-3) Fatty Acid in Indonesia Fish Oils Using Infrared Spectroscopy and Multivariate Data Analysis

Version 1 : Received: 3 May 2023 / Approved: 4 May 2023 / Online: 4 May 2023 (04:17:19 CEST)

How to cite: Irnawati, I.; Windarsih, A.; Fadzilah, N.A.; Riswanto, F.D.O.; Rohman, A. Analysis of Omega-3 (ω-3) Fatty Acid in Indonesia Fish Oils Using Infrared Spectroscopy and Multivariate Data Analysis. Preprints 2023, 2023050185. https://doi.org/10.20944/preprints202305.0185.v1 Irnawati, I.; Windarsih, A.; Fadzilah, N.A.; Riswanto, F.D.O.; Rohman, A. Analysis of Omega-3 (ω-3) Fatty Acid in Indonesia Fish Oils Using Infrared Spectroscopy and Multivariate Data Analysis. Preprints 2023, 2023050185. https://doi.org/10.20944/preprints202305.0185.v1

Abstract

Omega-3 fatty acids (ω-3 FAs) are important fatty acids having the beneficial roles in human health including reducing blood pressure, lowering the risk of cardiovascular disease and exerting anti-inflammation activities. Omega-3 FAs were mainly found in fish oils, therefore, determination of these FAs is very important. This study highlighted the employment of FTIR spectroscopy combined with multivariate data analysis for determination of ω-3 FAs in fish oils. Fish oils were obtained from the extraction of corresponding fishes and subjected to purification. The oils were further subjected to FTIR spectroscopic measurement at mid infrared region (4000-450 cm-1). Fatty acid compositions of ω-3 FAs namely eicosapentaenoic acid (EPA) and docosahexaenoic acid (DHA) were determined using gas chromatography with flame ionization (GC-FID), and the results from GC-FID were used as actual values. Two multivariate regressions along with wavenumbers regions or their combinations were optimized and compared to provide the best condition for prediction of EPA and DHA in fish oils. The results showed that partial least square regression (PLSR) was suitable for prediction of DHA applying the variable of absorbance values of the second derivative spectra, with the values of coefficient of determination (R2) of 0.9916 and 0.9316 in calibration and validation models, respectively. The values of root mean square error of calibration (RMSEC) and root mean square error of prediction (RMSEP) obtained were 0.789 and 2.53. While, prediction of EPA was performed using principal component regression with R2 value of > 0.72 and low values of RMSEC and RMSEC. It can be concluded that the combination of FTIR spectra and multivariate regression provides the effective tools and alternative GC-FID method for the prediction of EPA and DHA in fish oils.

Keywords

EPA-DHA; marine oils; FTIR spectra; chemometrics; partial least square

Subject

Chemistry and Materials Science, Analytical Chemistry

Comments (0)

We encourage comments and feedback from a broad range of readers. See criteria for comments and our Diversity statement.

Leave a public comment
Send a private comment to the author(s)
* All users must log in before leaving a comment
Views 0
Downloads 0
Comments 0
Metrics 0


×
Alerts
Notify me about updates to this article or when a peer-reviewed version is published.
We use cookies on our website to ensure you get the best experience.
Read more about our cookies here.