Rapid prediction of single green coffee bean moisture and lipid content by hyperspectral imaging.

Abstract

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.

Plant phenotyping relevance

単一の緑色コーヒー豆（種子）における水分・脂質という植物器官形質を、ハイパースペクトル画像と定量予測モデルで非破壊推定する方法の開発・性能評価が中心である。

Code and data availability

The article describes hyperspectral imaging of single green coffee beans with PLSR/MLR models for moisture and lipid prediction, but no blocks contain any data availability statement, public dataset deposit, hyperspectral image release, or author code/model repository. The only URLs present are the CC BY license notice

No evidence-backed public reproduction asset is currently recorded.