Simultaneous quantitation of free fatty acid in rice by synergetic data fusion of colorimetric sensor arrays, NIR, and MIR spectroscopy

Muhammad Arslan; Muhammad Zareef; Haroon Elrasheid Tahir; Zhai Xiaodong; Allah Rakha; Shujat Ali; Jiyong Shi; Zou Xiaobo

doi:10.1016/j.saa.2023.122359

Simultaneous quantitation of free fatty acid in rice by synergetic data fusion of colorimetric sensor arrays, NIR, and MIR spectroscopy

Spectrochim Acta A Mol Biomol Spectrosc. 2023 May 5:292:122359. doi: 10.1016/j.saa.2023.122359. Epub 2023 Jan 18.

Authors

Muhammad Arslan¹, Muhammad Zareef², Haroon Elrasheid Tahir², Zhai Xiaodong², Allah Rakha³, Shujat Ali⁴, Jiyong Shi², Zou Xiaobo⁵

Affiliations

¹ Agricultural Product Processing and Storage Lab, School of Food and Biological Engineering, Jiangsu University, 301 Xuefu Rd., 212013 Zhenjiang, Jiangsu, China; Yixing Institute of Food and Biotechnology, Yixing, Jiangsu, China.
² Agricultural Product Processing and Storage Lab, School of Food and Biological Engineering, Jiangsu University, 301 Xuefu Rd., 212013 Zhenjiang, Jiangsu, China.
³ National Institute of Food Science and Technology, University of Agriculture, Faisalabad 38000, Pakistan.
⁴ College of Electrical and Electronic Engineering, Wenzhou University, Wenzhou 325035, China.
⁵ Agricultural Product Processing and Storage Lab, School of Food and Biological Engineering, Jiangsu University, 301 Xuefu Rd., 212013 Zhenjiang, Jiangsu, China; Yixing Institute of Food and Biotechnology, Yixing, Jiangsu, China. Electronic address: zou_xiaobo@ujs.edu.cn.

PMID: 36736044
DOI: 10.1016/j.saa.2023.122359

Abstract

This study evaluated the feasibility of colorimetric sensor array (CSA), near-infrared (NIR) and mid-infrared (MIR) spectroscopy for quantitation of free fatty acids in rice using data fusion. Purposely, different data sets of low-level (CSA-NIR_LL, CSA-MIR_LL, and NIR-MIR_LL) and mid-level (CSA-NIR_ML, CSA-MIR_ML, and NIR-MIR_ML) fusion were adopted to enhance the statistical parameters. The model performance was evaluated using coefficient of determination for prediction, (R²p), root mean square error of prediction (RMSEP) and residual predictive deviation (RPD). Synergetic low-level and mid-level fusion model yielded 0.7707 ≤ R²p ≤ 0.8275, 14.4 ≤ RMSEP ≤ 16.3 and 2.19 ≤ RPD ≤ 2.48; and 0.7788 ≤ R²p ≤ 0.8571, 12.4 ≤ RMSEP ≤ 16.8 and 2.12 ≤ RPD ≤ 2.88, respectively. The CSA-NIR_ML model delivered an optimal performance for prediction of free fatty acid. The integration of CSA, NIR and MIR was feasible and could improve the prediction accuracy of free fatty acids in rice.

Keywords: Chemometrics; Colorimetric sensor arrays; Data fusion; Free fatty acid; Rice; Spectroscopy.

MeSH terms

Colorimetry
Fatty Acids, Nonesterified
Least-Squares Analysis
Oryza*
Spectrophotometry, Infrared / methods
Spectroscopy, Near-Infrared* / methods

Substances

Fatty Acids, Nonesterified