eFood

Volume 1, Issue 2, April 2020, Pages 173 - 180

Data Fusion Approach Improves the Prediction of Single Phenolic Compounds in Honey: A Study of NIR and Raman Spectroscopies

Authors
Haroon Elrasheid Tahir1, Muhammad Arslan1, Gustav Komla Mahunu2, Jiyong Shi1, *, Xiaobo Zou1, *, Mohammed Abdalbasit Ahmed Gasmalla1, 3, Abdalbasit Adam Mariod4
1School of Food and Biological Engineering, Jiangsu University, 301 Xuefu Rd., Zhenjiang, Jiangsu 212013, China
2Department of Food Science and Technology, Faculty of Agriculture, University for Development Studies, Tamale, Ghana
3Department of Nutrition and Food Technology, Omdurman Islamic University, Omdurman, Sudan
4College of Sciences and Arts-Alkamil, University of Jeddah, P.O. Box 110, Alkamil 2193, Saudi Arabia
*Corresponding authors. Email: shi_jiyong@ujs.edu.cn; zou_xiaobo@ujs.edu.cn
Corresponding Author
Jiyong Shi
Received 17 May 2019, Accepted 10 July 2019, Available Online 2 November 2019.
DOI
https://doi.org/10.2991/efood.k.191018.001How to use a DOI?
Keywords
Honey, phenolic compounds, data fusion, spectroscopy, partial least square (PLS)
Abstract

The combination of Near-Infrared Spectroscopy (NIR) and Raman Spectroscopy (RS) of 100 honey samples collected from different countries were used to develop the calibration model for determination of single phenolic compound. In high-performance liquid chromatography with diode-array detection analysis, 16 phenolic compounds were identified with p-hydroxybenzoic acid being the major compound detected in all honey samples. Thus, p-hydroxybenzoic acid was used for developing prediction models. Spectral data were modeled individually and using data fusion methodologies. The performance of the model based on RS spectra [ Rp2 = 0.9500, Root Mean Standard Error of Prediction (RMSEP) = 6.83] was higher than that based on the NIR spectra ( Rp2 = 0.8147, RMSEP = 13.80). The application of both low-level ( Rp2 = 0.9553, RMSEP = 6.59) and mid-level ( Rp2 = 0.9563, RMSEP = 7.95) data fusion together with Partial Least Squares (PLS) had effectively improved the prediction models of NIR but did not enhance prediction models based on RS technique. The results demonstrated that the NIR, RS, and data fusion approaches together with the PLS model could be used as alternative quantitative methods for determination of p-hydroxybenzoic acid in honey samples.

Copyright
© 2019 International Association of Dietetic Nutrition and Safety. Publishing services by Atlantis Press International B.V.
Open Access
This is an open access article distributed under the CC BY-NC 4.0 license (http://creativecommons.org/licenses/by-nc/4.0/).

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Journal
eFood
Volume-Issue
1 - 2
Pages
173 - 180
Publication Date
2019/11
ISSN (Online)
2666-3066
DOI
https://doi.org/10.2991/efood.k.191018.001How to use a DOI?
Copyright
© 2019 International Association of Dietetic Nutrition and Safety. Publishing services by Atlantis Press International B.V.
Open Access
This is an open access article distributed under the CC BY-NC 4.0 license (http://creativecommons.org/licenses/by-nc/4.0/).

Cite this article

TY  - JOUR
AU  - Haroon Elrasheid Tahir
AU  - Muhammad Arslan
AU  - Gustav Komla Mahunu
AU  - Jiyong Shi
AU  - Xiaobo Zou
AU  - Mohammed Abdalbasit Ahmed Gasmalla
AU  - Abdalbasit Adam Mariod
PY  - 2019
DA  - 2019/11
TI  - Data Fusion Approach Improves the Prediction of Single Phenolic Compounds in Honey: A Study of NIR and Raman Spectroscopies
JO  - eFood
SP  - 173
EP  - 180
VL  - 1
IS  - 2
SN  - 2666-3066
UR  - https://doi.org/10.2991/efood.k.191018.001
DO  - https://doi.org/10.2991/efood.k.191018.001
ID  - Tahir2019
ER  -