Prediction of moisture content uniformity using hyperspectral imaging technology during the drying of maize kernel

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Abstract

Moisture content uniformity is one of critical parameters to evaluate the quality of dried products and the drying technique. The potential of the hyperspectral imaging technique for evaluating the moisture content uniformity of maize kernels during the drying process was investigated. Predicting models were established using the partial least squares regression method. Two methods, using the prediction value of moisture content to calculate the uniformity (indirect) and predicting the moisture content uniformity directly, were investigated. Better prediction results were achieved using the direct method (with correlation coefficients RP = 0.848 and root-mean-square error of prediction RMSEP = 2.73) than the indirect method (RP = 0.521 and RMSEP = 10.96). The hyperspectral imaging technique showed significant potential in evaluating moisture content uniformity of maize kernels during the drying process.

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International Agrophysics

The Journal of Institute of Agrophysics of Polish Academy of Sciences

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IMPACT FACTOR 2017: 1.242
5-year IMPACT FACTOR: 1.267

CiteScore 2017: 1.38

SCImago Journal Rank (SJR) 2017: 0.435
Source Normalized Impact per Paper (SNIP) 2017: 0.849

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