Fusion-based strategy of CSA and mobile NIR for the quantification of free fatty acid in wheat varieties coupled with chemometrics

Muhammad Zareef; Muhammad Arslan; Md Mehedi Hassan; Waqas Ahmad; Huanhuan Li; Suleiman A Haruna; Malik Muhammad Hashim; Qin Ouyang; Quansheng Chen

doi:10.1016/j.saa.2023.122798

Fusion-based strategy of CSA and mobile NIR for the quantification of free fatty acid in wheat varieties coupled with chemometrics

Spectrochim Acta A Mol Biomol Spectrosc. 2023 Oct 5:298:122798. doi: 10.1016/j.saa.2023.122798. Epub 2023 May 2.

Authors

Muhammad Zareef¹, Muhammad Arslan¹, Md Mehedi Hassan¹, Waqas Ahmad¹, Huanhuan Li¹, Suleiman A Haruna¹, Malik Muhammad Hashim², Qin Ouyang³, Quansheng Chen⁴

Affiliations

¹ School of Food and Biological Engineering, Jiangsu University, Zhenjiang 213013, PR China.
² Institute of Food Science and Nutrition, Gomal University D.I Khan, Pakistan.
³ School of Food and Biological Engineering, Jiangsu University, Zhenjiang 213013, PR China. Electronic address: oyqyf@ujs.edu.cn.
⁴ School of Food and Biological Engineering, Jiangsu University, Zhenjiang 213013, PR China; College of Food and Biological Engineering, Jimei University, Xiamen 361021, PR China. Electronic address: qschen@ujs.edu.cn.

PMID: 37172420
DOI: 10.1016/j.saa.2023.122798

Abstract

The use of sensor fusion, a novel method of combining artificial senses, has become increasingly popular in the assessment of food quality. This study employed a combination of the colorimetric sensor array (CSA) and mobile near-infrared (NIR) spectroscopy to predict free fatty acids in wheat flour. In conjunction with a partial least squares model, Low- and mid-level fusion strategies were used for quantification. Accordingly, performance of the built model was evaluated based on higher correlation coefficients between calibration and prediction (R_C and R_P), lower root mean square error of prediction (RMSEP), and a higher residual predictive deviation (RPD). The mid-level fusion coupled PLS model produced superior data fusion findings, with R_C = 0.8793, RMSECV = 7.91 mg/100 g, R_P = 0.8747, RMSEP = 6.99 mg/100 g, and RPD = 2.27. The findings of the study suggest that the NIR-CSA fusion approach could be effectively applied to the prediction of free fatty acids in wheat flour.

Keywords: CSA; Chemometrics, Si-PLS; Free fatty acid; Fusion of sensors; Mobile NIR.

MeSH terms

Chemometrics
Colorimetry
Fatty Acids, Nonesterified*
Flour
Least-Squares Analysis
Triticum*

Substances

Fatty Acids, Nonesterified