期刊
FOOD CHEMISTRY
卷 240, 期 -, 页码 231-238出版社
ELSEVIER SCI LTD
DOI: 10.1016/j.foodchem.2017.07.117
关键词
Chemometric algorithms; Prediction; Preprocessed spectra; Spectral interval; Variable selection
资金
- National Natural Science Foundation of China [31471646]
- Key R & D Program of Jiangsu Province [BE2017357]
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.
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