Comparison of Partial Least Squares Regression (PLSR) and Principal Components Regression (PCR) Methods for Protein and Hardness Predictions using the Near-Infrared (NIR) Hyperspectral Images of Bulk Samples of Canadian Wheat

Food and Bioprocess Technology

Volumn 8, Issue 1, 2015, Pages 31-40

  Mahesh, S ^a   Jayas, D S ^a   Paliwal, J ^a   White, N D G ^b  

^a UNIVERSITY OF MANITOBA   (Canada)

^b Cereal Research Centre   (Canada)

Author keywords

Correlation coefficient; Hardness; Hyperspectral imaging; Mean square errors of prediction; Protein; Standard error of cross validation; Wheat

Indexed keywords

AGRICULTURAL ROBOTS; CROPS; ERRORS; FORECASTING; HARDNESS; HYPERSPECTRAL IMAGING; INFRARED DEVICES; MEAN SQUARE ERROR; PROTEINS; REGRESSION ANALYSIS; SPECTROSCOPY; SPRINGS (COMPONENTS);

CORRELATION COEFFICIENT; CROSS VALIDATION; HARDNESS PREDICTION; HYPERSPECTRAL IMAGE CUBES; PARTIAL LEAST SQUARES REGRESSIONS (PLSR); PREDICTION PERFORMANCE; PRINCIPAL COMPONENTS; WHEAT;

LEAST SQUARES APPROXIMATIONS;

EID: 84920711335     PISSN: 19355130     EISSN: 19355149     Source Type: Journal    
DOI: 10.1007/s11947-014-1381-z     Document Type: Article

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