Journal
FOOD AND BIOPROCESS TECHNOLOGY
Volume 10, Issue 1, Pages 213-221Publisher
SPRINGER
DOI: 10.1007/s11947-016-1809-8
Keywords
Coffee; Random frog; Prediction map; Partial least squares regression
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Funding
- 863 National High-Tech Research and Development Plan [2012AA101903]
- Zhejiang Provincial Public Welfare Technology Research Projects [2014C32103]
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Hyperspectral imaging covering the spectral range of 874-1734 nm was used to determine caffeine content of coffee beans. Spectral data of 958.24-1628.89 nm were extracted and preprocessed. Partial least squares regression (PLSR) model on the preprocessed full spectra obtained good performance with coefficient of determination of prediction (R (2) (p) ) of 0.843 and root mean square error of prediction (RMSEP) of 131.904 mu g/g. In addition, 10 variable selection methods were applied to select the best optimal wavelengths. The PLSR models on the different optimal wavelengths obtained satisfactory results. The PLSR model on the wavelengths selected by random frog (RF) performed the best, with R (2) (p) of 0.878 and RMSEP of 116.327 mu g/g. The RF wavelength selection combined with the PLSR model also achieved satisfactory visualization of caffeine content between different coffee beans. The overall results indicated that optimal wavelength selection was an efficient method for spectral data preprocessing, and hyperspectral imaging was illustrated as a potential technique for real-time online determination for caffeine content of coffee beans.
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