Differentiation of Organic Cocoa Beans and Conventional Ones by Using Handheld NIR Spectroscopy and Multivariate Classification Techniques

Elliot K Anyidoho; Ernest Teye; Robert Agbemafle

doi:10.1155/2021/1844675

Differentiation of Organic Cocoa Beans and Conventional Ones by Using Handheld NIR Spectroscopy and Multivariate Classification Techniques

Int J Food Sci. 2021 Nov 20:2021:1844675. doi: 10.1155/2021/1844675. eCollection 2021.

Authors

Elliot K Anyidoho^{1

2}, Ernest Teye^{1

3}, Robert Agbemafle⁴

Affiliations

¹ University of Cape Coast, College of Agriculture and Natural Sciences, School of Agriculture, Department of Agricultural Engineering, Cape Coast, Ghana.
² Ghana Cocoa Board, Cocoa Health and Extension Division, Elubo, Ghana.
³ University of Cape Coast, Africa Centre of Excellence for Food Fraud and Safety Food, AfriFoodinTegrity Centre, Cape Coast, Ghana.
⁴ University of Cape Coast, College of Agriculture and Natural Sciences, School of Physical Sciences, Department of Laboratory Technology, Cape Coast, Ghana.

Abstract

The global market for organic cocoa beans continues to show sturdy growth. A low-cost handheld NIR spectrometer (900-1700 nm) combined with multivariate classification algorithms was used for rapid differentiation analysis of organic cocoa beans' integrity. In this research, organic and conventionally cultivated cocoa beans were collected from different locations in Ghana and scanned nondestructively with a handheld spectrometer. Different preprocessing treatments were employed. Principal component analysis (PCA) and classification analysis, RF (random forest), KNN (K-nearest neighbours), LDA (linear discriminant analysis), and PLS-DA (partial least squares-discriminant analysis) were performed comparatively to build classification models. The performance of the models was evaluated by accuracy, specificity, sensitivity, and efficiency. Second derivative preprocessing together with PLS-DA algorithm was superior to the rest of the algorithms with a classification accuracy of 100.00% in both the calibration set and prediction set. Second derivative algorithm was found to be the best preprocessing tool. The identification rates for the calibration set and prediction set were 96.15% and 98.08%, respectively, for RF, 91.35% and 92.31% for KNN, and 90.38% and 98.08% for LDA. Generally, the results showed that a handheld NIR spectrometer coupled with an appropriate multivariate algorithm could be used in situ for the differentiation of organic cocoa beans from conventional ones to ensure food integrity along the cocoa bean value chain.