TY - JOUR T1 - Rapid prediction of single green coffee bean moisture and lipid content by hyperspectral imaging. AU - Caporaso,Nicola, AU - Whitworth,Martin B, AU - Grebby,Stephen, AU - Fisk,Ian D, PY - 2018/6/5/entrez PY - 2018/6/5/pubmed PY - 2018/6/5/medline KW - Chemical imaging KW - Coffee fat KW - Coffee quality KW - Individual bean analysis KW - Machine vision technology KW - Near-infrared spectroscopy SP - 18 EP - 29 JF - Journal of food engineering JO - J Food Eng VL - 227 N2 - Hyperspectral imaging (1000-2500 nm) was used for rapid prediction of moisture and total lipid content in intact green coffee beans on a single bean basis. Arabica and Robusta samples from several growing locations were scanned using a "push-broom" system. Hypercubes were segmented to select single beans, and average spectra were measured for each bean. Partial Least Squares regression was used to build quantitative prediction models on single beans (n = 320-350). The models exhibited good performance and acceptable prediction errors of ∼0.28% for moisture and ∼0.89% for lipids. This study represents the first time that HSI-based quantitative prediction models have been developed for coffee, and specifically green coffee beans. In addition, this is the first attempt to build such models using single intact coffee beans. The composition variability between beans was studied, and fat and moisture distribution were visualized within individual coffee beans. This rapid, non-destructive approach could have important applications for research laboratories, breeding programmes, and for rapid screening for industry. SN - 0260-8774 UR - https://www.unboundmedicine.com/medline/citation/29861528/Rapid_prediction_of_single_green_coffee_bean_moisture_and_lipid_content_by_hyperspectral_imaging_ DB - PRIME DP - Unbound Medicine ER -