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

•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.