Near infrared system coupled chemometric algorithms for enumeration of total fungi count in cocoa beans neat solution

• Published in: Food chemistry Vol. 240; pp. 231 - 238
• Main Authors: Kutsanedzie, Felix Y.H., Chen, Quansheng, Hassan, Md Mehedi, Yang, Mingxiu, Sun, Hao, Rahman, Md Hafizur
• Format: Journal Article
• Language: English
• Published: England Elsevier Ltd 01.02.2018
• Subjects: Algorithms; Cacao - microbiology; Calibration; Chemometric algorithms; chemometrics; cocoa beans; correlation; Fungi; least squares; Least-Squares Analysis; monitoring; near-infrared spectroscopy; Prediction; Preprocessed spectra; Spectral interval; Spectroscopy, Near-Infrared; system optimization; Variable selection; Chemometric algorithms; Spectral interval; Preprocessed spectra; Prediction; Variable selection
• ISSN: 0308-8146; 1873-7072; 1873-7072
• DOI: 10.1016/j.foodchem.2017.07.117

•An in-situ technique was achieved for total fungi count quantification in cocoa beans.•Total fungi count quantified via beans’ near-infrared spectra variables selection.•Full bean spectra based prediction models for total fungi count had lower stability.•Total fungi count prediction models were improved with variable selection algorithms.•Near-infrared system coupled Si-GAPLS was most reliable for fungi count prediction. Total fungi count (TFC) is a quality indicator of cocoa beans when unmonitored leads to quality and safety problems. Fourier transform near infrared spectroscopy (FT-NIRS) combined with chemometric algorithms like partial least square (PLS); synergy interval-PLS (Si-PLS); synergy interval-genetic algorithm-PLS (Si-GAPLS); Ant colony optimization – PLS (ACO-PLS) and competitive-adaptive reweighted sampling-PLS (CARS-PLS) was employed to predict TFC in cocoa beans neat solution. Model results were evaluated using the correlation coefficients of the prediction (Rp) and calibration (Rc); root mean square error of prediction (RMSEP), and the ratio of sample standard deviation to RMSEP (RPD). The developed models performance yielded 0.951≤Rp≤0.975; and 3.15≤RPD≤4.32. The models’ prediction stability improved in the order of PLS<CARS-PLS<ACO-PLS<Si-PLS<Si-GAPLS. FT-NIRS combined with Si-GAPLS may be employed for in-situ and noninvasive quantification of TFC in cocoa beans for quality and safety monitoring.