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Proceedings Paper

Rapid and non-destructive assessment of polyunsaturated fatty acids contents in Salmon using near-infrared hyperspectral imaging
Author(s): Feifei Tao; Ogan Mba; Li Liu; Michael Ngadi
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Paper Abstract

Polyunsaturated fatty acids (PUFAs) are important nutrients present in Salmon. However, current methods for quantifying the fatty acids (FAs) contents in foods are generally based on gas chromatography (GC) technique, which is time-consuming, laborious and destructive to the tested samples. Therefore, the capability of near-infrared (NIR) hyperspectral imaging to predict the PUFAs contents of C20:2 n-6, C20:3 n-6, C20:5 n-3, C22:5 n-3 and C22:6 n-3 in Salmon fillets in a rapid and non-destructive way was investigated in this work. Mean reflectance spectra were first extracted from the region of interests (ROIs), and then the spectral pre-processing methods of 2nd derivative and Savitzky-Golay (SG) smoothing were performed on the original spectra. Based on the original and the pre-processed spectra, PLSR technique was employed to develop the quantitative models for predicting each PUFA content in Salmon fillets. The results showed that for all the studied PUFAs, the quantitative models developed using the pre-processed reflectance spectra by “2nd derivative + SG smoothing” could improve their modeling results. Good prediction results were achieved with RP and RMSEP of 0.91 and 0.75 mg/g dry weight, 0.86 and 1.44 mg/g dry weight, 0.82 and 3.01 mg/g dry weight for C20:3 n-6, C22:5 n-3 and C20:5 n-3, respectively after pre-processing by “2nd derivative + SG smoothing”. The work demonstrated that NIR hyperspectral imaging could be a useful tool for rapid and non-destructive determination of the PUFA contents in fish fillets.

Paper Details

Date Published: 28 April 2017
PDF: 7 pages
Proc. SPIE 10213, Hyperspectral Imaging Sensors: Innovative Applications and Sensor Standards 2017, 102130B (28 April 2017); doi: 10.1117/12.2262862
Show Author Affiliations
Feifei Tao, McGill Univ. (Canada)
Ogan Mba, McGill Univ. (Canada)
Li Liu, McGill Univ. (Canada)
Michael Ngadi, McGill Univ. (Canada)

Published in SPIE Proceedings Vol. 10213:
Hyperspectral Imaging Sensors: Innovative Applications and Sensor Standards 2017
David P. Bannon, Editor(s)

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