Near-infrared spectroscopy and multivariate calibration as an alternative to the Agtron to predict roasting degrees in coffee beans and ground coffees
[Display omitted] •A versatile and fast method was proposed to evaluate roasting degrees of coffee.•Chemometrics models were developed for Agtron value prediction.•Near-infrared spectroscopy and multivariate calibration were successfully combined.•Complete analytical validation proved the performanc...
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Veröffentlicht in: | Food chemistry 2021-12, Vol.365, p.130471-130471, Article 130471 |
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Sprache: | eng |
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•A versatile and fast method was proposed to evaluate roasting degrees of coffee.•Chemometrics models were developed for Agtron value prediction.•Near-infrared spectroscopy and multivariate calibration were successfully combined.•Complete analytical validation proved the performance of predictive models.•Final models can be extremely functional in the routine of coffee industries.
Agtron method is widely used in the industry to determine roasting degrees in whole and ground coffee but it suffers from some inconveniences associated with unavailability of equipment, high cost, and lack of reproductive results. This study investigates the feasibility to determine roasting degrees in coffee beans and ground specialty coffees using near-infrared (NIR) spectroscopy combined with multivariate calibration based on partial least squares (PLS) regression. Representative data sets were considered to cover all Agtron roasting profiles for whole and ground coffees. Proper development of models with outlier evaluation and complete validation using parameters of merit such as accuracy, adjust, residual prediction deviation, linearity, analytical sensitivity, and limits of detection and quantification are presented to prove their performance. The results indicated that predictive chemometric models, for intact coffee beans and ground coffee, could be used in the coffee industry as an alternative to Agtron, thus digitalizing the roasting quality control. |
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ISSN: | 0308-8146 1873-7072 |
DOI: | 10.1016/j.foodchem.2021.130471 |