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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| Format: | Article |
| Language: | English |
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Wiley
2021-01-01
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| Series: | International Journal of Food Science |
| Online Access: | http://dx.doi.org/10.1155/2021/1844675 |
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| author | Elliot K. Anyidoho Ernest Teye Robert Agbemafle |
| author_facet | Elliot K. Anyidoho Ernest Teye Robert Agbemafle |
| author_sort | Elliot K. Anyidoho |
| collection | DOAJ |
| 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 |
| id | doaj-art-d6a3039cde8e4325add2adc750aa3a4e |
| institution | OA Journals |
| issn | 2314-5765 |
| language | English |
| publishDate | 2021-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | International Journal of Food Science |
| spelling | doaj-art-d6a3039cde8e4325add2adc750aa3a4e2025-08-20T02:19:34ZengWileyInternational Journal of Food Science2314-57652021-01-01202110.1155/2021/1844675Differentiation of Organic Cocoa Beans and Conventional Ones by Using Handheld NIR Spectroscopy and Multivariate Classification TechniquesElliot K. Anyidoho0Ernest Teye1Robert Agbemafle2University of Cape CoastUniversity of Cape CoastUniversity of Cape CoastThe 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.http://dx.doi.org/10.1155/2021/1844675 |
| spellingShingle | Elliot K. Anyidoho Ernest Teye Robert Agbemafle Differentiation of Organic Cocoa Beans and Conventional Ones by Using Handheld NIR Spectroscopy and Multivariate Classification Techniques International Journal of Food Science |
| title | 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_short | 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 |
| url | http://dx.doi.org/10.1155/2021/1844675 |
| work_keys_str_mv | AT elliotkanyidoho differentiationoforganiccocoabeansandconventionalonesbyusinghandheldnirspectroscopyandmultivariateclassificationtechniques AT ernestteye differentiationoforganiccocoabeansandconventionalonesbyusinghandheldnirspectroscopyandmultivariateclassificationtechniques AT robertagbemafle differentiationoforganiccocoabeansandconventionalonesbyusinghandheldnirspectroscopyandmultivariateclassificationtechniques |