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Issue 37, 2017
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In situ cocoa beans quality grading by near-infrared-chemodyes systems

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Fermentation level is a key bean quality indicator in the cocoa industry. A colorimetric sensor e-nose (CS e-nose) and an innovatively designed near infrared chemo-intermediary-dyes spectra technique (NIR-CDS) combined with four chemometric algorithms – extreme machine learning (ELM), support vector machine (SVM), linear discriminant analysis (LDA) and k-nearest neighbors (k-NN) – were applied to classify 90 sampled cocoa beans into three quality grades – fully fermented, partially fermented and non-fermented. The CS e-nose (89% ≤ Rp ≤ 94%) and NIR-CDS (85% ≤ Rp ≤ 94%) achieved comparable classification rates, with the systems' data cluster analysis yielding cophenetic correlation coefficients of 0.85–0.89. Both systems combined with SVM and ELM achieved a high classification rate (Rp = 94%) and could be applied to cocoa bean quality classification on an in situ and nondestructive basis. This novel NIR-CDS technique proved a pragmatic approach for the selection of sensitive chemo-dyes used in the fabrication of e-nose colorimetric sensor arrays compared with the hitherto trial-and-error method, which is time-consuming and dye-wasteful. The technique could also be deployed in near-infrared systems for the detection of volatile (gaseous) compounds, which previously had been a limitation.

Graphical abstract: In situ cocoa beans quality grading by near-infrared-chemodyes systems

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Publication details

The article was received on 18 Jul 2017, accepted on 31 Aug 2017 and first published on 01 Sep 2017

Article type: Paper
DOI: 10.1039/C7AY01751K
Citation: Anal. Methods, 2017,9, 5455-5463
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    In situ cocoa beans quality grading by near-infrared-chemodyes systems

    F. Y. H. Kutsanedzie, Q. Chen, H. Sun and W. Cheng, Anal. Methods, 2017, 9, 5455
    DOI: 10.1039/C7AY01751K

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