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

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 conventional...

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Autores principales:	Elliot K. Anyidoho, Ernest Teye, Robert Agbemafle
Formato:	article
Lenguaje:	EN
Publicado:	Hindawi Limited 2021
Materias:	Nutrition. Foods and food supply TX341-641 Food processing and manufacture TP368-456
Acceso en línea:	https://doaj.org/article/d6a3039cde8e4325add2adc750aa3a4e
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spelling	oai:doaj.org-article:d6a3039cde8e4325add2adc750aa3a4e2021-11-29T00:56:15ZDifferentiation of Organic Cocoa Beans and Conventional Ones by Using Handheld NIR Spectroscopy and Multivariate Classification Techniques2314-576510.1155/2021/1844675https://doaj.org/article/d6a3039cde8e4325add2adc750aa3a4e2021-01-01T00:00:00Zhttp://dx.doi.org/10.1155/2021/1844675https://doaj.org/toc/2314-5765The 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.Elliot K. AnyidohoErnest TeyeRobert AgbemafleHindawi LimitedarticleNutrition. Foods and food supplyTX341-641Food processing and manufactureTP368-456ENInternational Journal of Food Science, Vol 2021 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Nutrition. Foods and food supply TX341-641 Food processing and manufacture TP368-456
spellingShingle	Nutrition. Foods and food supply TX341-641 Food processing and manufacture TP368-456 Elliot K. Anyidoho Ernest Teye Robert Agbemafle Differentiation of Organic Cocoa Beans and Conventional Ones by Using Handheld NIR Spectroscopy and Multivariate Classification Techniques
description	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.
format	article
author	Elliot K. Anyidoho Ernest Teye Robert Agbemafle
author_facet	Elliot K. Anyidoho Ernest Teye Robert Agbemafle
author_sort	Elliot K. Anyidoho
title	Differentiation of Organic Cocoa Beans and Conventional Ones by Using Handheld NIR Spectroscopy and Multivariate Classification Techniques
title_short	Differentiation of Organic Cocoa Beans and Conventional Ones by Using Handheld NIR Spectroscopy and Multivariate Classification Techniques
title_full	Differentiation of Organic Cocoa Beans and Conventional Ones by Using Handheld NIR Spectroscopy and Multivariate Classification Techniques
title_fullStr	Differentiation of Organic Cocoa Beans and Conventional Ones by Using Handheld NIR Spectroscopy and Multivariate Classification Techniques
title_full_unstemmed	Differentiation of Organic Cocoa Beans and Conventional Ones by Using Handheld NIR Spectroscopy and Multivariate Classification Techniques
title_sort	differentiation of organic cocoa beans and conventional ones by using handheld nir spectroscopy and multivariate classification techniques
publisher	Hindawi Limited
publishDate	2021
url	https://doaj.org/article/d6a3039cde8e4325add2adc750aa3a4e
work_keys_str_mv	AT elliotkanyidoho differentiationoforganiccocoabeansandconventionalonesbyusinghandheldnirspectroscopyandmultivariateclassificationtechniques AT ernestteye differentiationoforganiccocoabeansandconventionalonesbyusinghandheldnirspectroscopyandmultivariateclassificationtechniques AT robertagbemafle differentiationoforganiccocoabeansandconventionalonesbyusinghandheldnirspectroscopyandmultivariateclassificationtechniques
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Differentiation of Organic Cocoa Beans and Conventional Ones by Using Handheld NIR Spectroscopy and Multivariate Classification Techniques

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