Jump to main content
Jump to site search

Issue 37, 2017
Previous Article Next Article

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

Author affiliations

Abstract

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

Back to tab navigation

Supplementary files

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
  •   Request permissions

    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

Search articles by author

Spotlight

Advertisements