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A colorimetric sensor array-based classification of coffees
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AI
DOI：10.1016/j.snb.2018.08.058.png)
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Abstract
En 中文
A method based on colorimetric sensor array (CSA) was used to classify different coffees. The array was fabricated by printing twelve sensing materials selective to key coffee aroma odorants on a hydrophobic membrane. The unique color profile for each coffee sample was obtained, resulting in facile differentiation by the naked eye without any statistical analysis. The color responses of CSA were evaluated by principal component analysis and hierarchical cluster analysis to identify classification patterns of ground roasted coffee, espresso coffee, and aeropress coffee samples, and the samples were successfully distinguished according to their categories such as variety and roast degree of coffee beans or brewing procedure. The results of hierarchical cluster analysis and linear discriminant analysis using the CSA data obtained from sixty coffee samples demonstrate that the CSA is a potentially promising methodology for rapid discrimination of different coffees.
Keywords:
Colorimetric artificial nose
Coffee roasting
Roast degree
Principal component analysis (PCA)
Hierarchical cluster analysis (HCA)
Linear discriminant analysis (LDA)
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7.7
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3.4W
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12.6W
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