This study aims to classify plant communities by applying discriminant analysis based on principal components (DAPC) on near-infrared spectra (FT-NIRS) starting from fresh herbage samples. Grassland samples (n~156) belonged to (i) recent alfalfa pure crops (CAA), (ii) recent grass–legume mixtures (GLM), (iii) permanent meadows derived from old alfalfa stands that were re-colonized (PMA), and iv) permanent meadows originated from old grass–legume mixtures (PLM). Samples were scanned using FT-NIRS, and a multivariate exploration of the original spectra was performed using DAPC. The following two scenarios were proposed: (i) cross-validation, where all data were used for model training, and (ii) semi-external validation, where the group assignment was performed without samples of the training set. The first two components explained 98% of the total variability. The DAPC model resulted in an overall assignment success rate of 77%, and, from cross-validation, it emerged that it was possible to assign the CAA and PMA to their group with more than of 80% of success, which were different in botanical and chemical composition. In comparison, GLM and PLM obtained lower success of assignment (~52%). External validation suggested similarity between PLM and GLM groups (93%) and between GLM and PLM (77%). However, a dataset increase could improve group differentiation.
Discriminant Analysis as a Tool to Classify Grasslands Based on Near-Infrared Spectra / Parrini, Silvia; Fabbri, Maria Chiara; Argenti, Giovanni; Staglianò, Nicolina; Pugliese, Carolina; Bozzi, Riccardo. - In: ANIMALS. - ISSN 2076-2615. - ELETTRONICO. - 14:(2024), pp. 0-0. [10.3390/ani14182646]
Discriminant Analysis as a Tool to Classify Grasslands Based on Near-Infrared Spectra
Parrini, Silvia
Membro del Collaboration Group
;Fabbri, Maria ChiaraSoftware
;Argenti, GiovanniSupervision
;Staglianò, NicolinaMembro del Collaboration Group
;Pugliese, CarolinaMembro del Collaboration Group
;Bozzi, RiccardoSupervision
2024
Abstract
This study aims to classify plant communities by applying discriminant analysis based on principal components (DAPC) on near-infrared spectra (FT-NIRS) starting from fresh herbage samples. Grassland samples (n~156) belonged to (i) recent alfalfa pure crops (CAA), (ii) recent grass–legume mixtures (GLM), (iii) permanent meadows derived from old alfalfa stands that were re-colonized (PMA), and iv) permanent meadows originated from old grass–legume mixtures (PLM). Samples were scanned using FT-NIRS, and a multivariate exploration of the original spectra was performed using DAPC. The following two scenarios were proposed: (i) cross-validation, where all data were used for model training, and (ii) semi-external validation, where the group assignment was performed without samples of the training set. The first two components explained 98% of the total variability. The DAPC model resulted in an overall assignment success rate of 77%, and, from cross-validation, it emerged that it was possible to assign the CAA and PMA to their group with more than of 80% of success, which were different in botanical and chemical composition. In comparison, GLM and PLM obtained lower success of assignment (~52%). External validation suggested similarity between PLM and GLM groups (93%) and between GLM and PLM (77%). However, a dataset increase could improve group differentiation.File | Dimensione | Formato | |
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