Zareef M, Arslan M, Hassan MM, Ahmad W, Li H, Haruna SA, Hashim MM, Ouyang Q, Chen Q. 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;
298:122798. [PMID:
37172420 DOI:
10.1016/j.saa.2023.122798]
[Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/31/2022] [Revised: 03/08/2023] [Accepted: 04/26/2023] [Indexed: 05/15/2023]
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 (RC and RP), 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 RC = 0.8793, RMSECV = 7.91 mg/100 g, RP = 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.
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