کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
5132459 1492052 2018 8 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
Near infrared system coupled chemometric algorithms for enumeration of total fungi count in cocoa beans neat solution
ترجمه فارسی عنوان
سیستم مادون قرمز، الگوریتم های شیمیایی همراه برای شمارش کل قارچ ها در محلول شسته نشده کاکائو
موضوعات مرتبط
مهندسی و علوم پایه شیمی شیمی آنالیزی یا شیمی تجزیه
چکیده انگلیسی


- 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.

ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Food Chemistry - Volume 240, 1 February 2018, Pages 231-238
نویسندگان
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