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

Haroon Elrasheid Tahir; Muhammad Arslan; Gustav Komla Mahunu; Jiyong Shi; Xiaobo Zou; Mohammed Abdalbasit Ahmed Gasmalla; Abdalbasit Adam Mariod

doi:10.2991/efood.k.191018.001

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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 Authors

Jiyong Shi, Xiaobo Zou

Received 17 May 2019, Accepted 10 July 2019, Available Online 2 November 2019.

DOI: 10.2991/efood.k.191018.001 How 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 [ $R_{p}^{2}$ $Rp2$ = 0.9500, Root Mean Standard Error of Prediction (RMSEP) = 6.83] was higher than that based on the NIR spectra ( $R_{p}^{2}$ $Rp2$ = 0.8147, RMSEP = 13.80). The application of both low-level ( $R_{p}^{2}$ $Rp2$ = 0.9553, RMSEP = 6.59) and mid-level ( $R_{p}^{2}$ $Rp2$ = 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.
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: 2019/11/02
ISSN (Online): 2666-3066
DOI: 10.2991/efood.k.191018.001 How to use a DOI?
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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ris enw bib

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  - 2019/11/02
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  - 10.2991/efood.k.191018.001
ID  - Tahir2019
ER  -

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