Rootstock’s and Cover-Crops’ Influence on Grape: A NIR-Based ANN Classification Model
In this study, a multivariate analysis combined with near-infrared (NIR) spectroscopy was employed to classify intact grape berries based on the rootstock x cover crops combination. NIR spectra were collected in diffuse reflection mode using a TANGO FT-NIR spectrometer (Bruker, Germany) with 8 cm<...
| Auteurs principaux: | , , |
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| Format: | Article |
| Langue: | English |
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MDPI AG
2022-12-01
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| Collection: | Agriculture |
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| Accès en ligne: | https://www.mdpi.com/2077-0472/13/1/5 |
| _version_ | 1827629600117620736 |
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| author | Teodora Basile Antonio Maria Amendolagine Luigi Tarricone |
| author_facet | Teodora Basile Antonio Maria Amendolagine Luigi Tarricone |
| author_sort | Teodora Basile |
| collection | DOAJ |
| description | In this study, a multivariate analysis combined with near-infrared (NIR) spectroscopy was employed to classify intact grape berries based on the rootstock x cover crops combination. NIR spectra were collected in diffuse reflection mode using a TANGO FT-NIR spectrometer (Bruker, Germany) with 8 cm<sup>−1</sup> resolution and 64 scans in the wave number range of 4000–10,000 cm<sup>−1</sup>. The chemometric analyses were performed with the statistical software R version 4.2.0 (2022-04-22). Elimination of uninformative variables was accomplished with a PCA and a genetic algorithm (GA). The discrimination performance of a linear discriminant analysis (LDA) model was not enhanced with either a PCA- or a GA-based selection. A multiclass classification model was built with an artificial neural network (ANN). The best fit multiclass classification model on test data was obtained with the GA-ANN model that gave a classification accuracy of close to 80% for samples belonging to the four classes. These results demonstrate that NIR spectroscopy could be used as a rapid method for the classification of berries based on their rootstock x cover-crops combination. |
| first_indexed | 2024-03-09T13:54:42Z |
| format | Article |
| id | doaj.art-0f68746cab724d0e8a6ea451d9f838d9 |
| institution | Directory Open Access Journal |
| issn | 2077-0472 |
| language | English |
| last_indexed | 2024-03-09T13:54:42Z |
| publishDate | 2022-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Agriculture |
| spelling | doaj.art-0f68746cab724d0e8a6ea451d9f838d92023-11-30T20:44:17ZengMDPI AGAgriculture2077-04722022-12-01131510.3390/agriculture13010005Rootstock’s and Cover-Crops’ Influence on Grape: A NIR-Based ANN Classification ModelTeodora Basile0Antonio Maria Amendolagine1Luigi Tarricone2Consiglio per la Ricerca in Agricoltura e L’analisi dell’Economia Agraria—Centro di Ricerca Viticoltura ed Enologia (CREA—VE), 70010 Turi, BA, ItalyConsiglio per la Ricerca in Agricoltura e L’analisi dell’Economia Agraria—Centro di Ricerca Viticoltura ed Enologia (CREA—VE), 70010 Turi, BA, ItalyConsiglio per la Ricerca in Agricoltura e L’analisi dell’Economia Agraria—Centro di Ricerca Viticoltura ed Enologia (CREA—VE), 70010 Turi, BA, ItalyIn this study, a multivariate analysis combined with near-infrared (NIR) spectroscopy was employed to classify intact grape berries based on the rootstock x cover crops combination. NIR spectra were collected in diffuse reflection mode using a TANGO FT-NIR spectrometer (Bruker, Germany) with 8 cm<sup>−1</sup> resolution and 64 scans in the wave number range of 4000–10,000 cm<sup>−1</sup>. The chemometric analyses were performed with the statistical software R version 4.2.0 (2022-04-22). Elimination of uninformative variables was accomplished with a PCA and a genetic algorithm (GA). The discrimination performance of a linear discriminant analysis (LDA) model was not enhanced with either a PCA- or a GA-based selection. A multiclass classification model was built with an artificial neural network (ANN). The best fit multiclass classification model on test data was obtained with the GA-ANN model that gave a classification accuracy of close to 80% for samples belonging to the four classes. These results demonstrate that NIR spectroscopy could be used as a rapid method for the classification of berries based on their rootstock x cover-crops combination.https://www.mdpi.com/2077-0472/13/1/5NIRgenetic algorithmLDAPCAANNgrape |
| spellingShingle | Teodora Basile Antonio Maria Amendolagine Luigi Tarricone Rootstock’s and Cover-Crops’ Influence on Grape: A NIR-Based ANN Classification Model Agriculture NIR genetic algorithm LDA PCA ANN grape |
| title | Rootstock’s and Cover-Crops’ Influence on Grape: A NIR-Based ANN Classification Model |
| title_full | Rootstock’s and Cover-Crops’ Influence on Grape: A NIR-Based ANN Classification Model |
| title_fullStr | Rootstock’s and Cover-Crops’ Influence on Grape: A NIR-Based ANN Classification Model |
| title_full_unstemmed | Rootstock’s and Cover-Crops’ Influence on Grape: A NIR-Based ANN Classification Model |
| title_short | Rootstock’s and Cover-Crops’ Influence on Grape: A NIR-Based ANN Classification Model |
| title_sort | rootstock s and cover crops influence on grape a nir based ann classification model |
| topic | NIR genetic algorithm LDA PCA ANN grape |
| url | https://www.mdpi.com/2077-0472/13/1/5 |
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