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Shallot Species and Subtypes Discrimination Based on Morphology Descriptors.

ABSTRACT: Shallots are an edible Alliaceous crop representing a group of genetically and morphologically different species. Shallot species determination is rather complex due to the high variability in phenotypes within a single species. Flower morphology has been successfully employed in shallot species determination; however, shallot florogenesis depends upon many genetic and environmental factors. There is a need for more accessible morphological descriptors used in shallot species determination, since flowering in shallot may not be consistent. In this study, we investigated the discriminating power of shallot vegetative and bulb morphology descriptors. European Cooperative Programme for Plant Genetic Resources morphology descriptors were used for describing 35 Croatian shallot accessions. The proposed methodology based on vegetative and bulb morphological descriptors could be used for shallot species discrimination. Additionally, two subtypes of A. cepa Aggregatum group were identified in this study: the first being the shallot type (1) and a potato onion type (2), which differed based on bulb morphology descriptors (bulb shape, bulb skin color, and a number of bulblets).

SUBMITTER: Perkovic J

PROVIDER: S-EPMC7823654 | biostudies-literature | 2020 Dec

REPOSITORIES: biostudies-literature

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Shallot Species and Subtypes Discrimination Based on Morphology Descriptors.

Perković Josipa J Major Nikola N Ban Dean D Cvitan Danko D Ban Smiljana Goreta SG

Plants (Basel, Switzerland) 20201229 1

Shallots are an edible <i>Alliaceous</i> crop representing a group of genetically and morphologically different species. Shallot species determination is rather complex due to the high variability in phenotypes within a single species. Flower morphology has been successfully employed in shallot species determination; however, shallot florogenesis depends upon many genetic and environmental factors. There is a need for more accessible morphological descriptors used in shallot species determinatio ...[more]

PMID: 33383969

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Project description:BackgroundThere are more than 300 species of capillariids that parasitize various vertebrate groups worldwide. Species identification is hindered because of the few taxonomically informative structures available, making the task laborious and genus definition controversial. Thus, its taxonomy is one of the most complex among Nematoda. Eggs are the parasitic structures most viewed in coprological analysis in both modern and ancient samples; consequently, their presence is indicative of positive diagnosis for infection. The structure of the egg could play a role in genera or species discrimination. Institutional biological collections are taxonomic repositories of specimens described and strictly identified by systematics specialists.MethodsThe present work aims to characterize eggs of capillariid species deposited in institutional helminth collections and to process the morphological, morphometric and ecological data using machine learning (ML) as a new approach for taxonomic identification. Specimens of 28 species and 8 genera deposited at Coleção Helmintológica do Instituto Oswaldo Cruz (CHIOC, IOC/FIOCRUZ/Brazil) and Collection de Nématodes Zooparasites du Muséum National d'Histoire Naturelle de Paris (MNHN/France) were examined under light microscopy. In the morphological and morphometric analyses (MM), the total length and width of eggs as well as plugs and shell thickness were considered. In addition, eggshell ornamentations and ecological parameters of the geographical location (GL) and host (H) were included.ResultsThe performance of the logistic model tree (LMT) algorithm showed the highest values in all metrics compared with the other algorithms. Algorithm J48 produced the most reliable decision tree for species identification alongside REPTree. The Majority Voting algorithm showed high metric values, but the combined classifiers did not attenuate the errors revealed in each algorithm alone. The statistical evaluation of the dataset indicated a significant difference between trees, with GL + H + MM and MM only with the best scores.ConclusionsThe present research proposed a novel procedure for taxonomic species identification, integrating data from centenary biological collections and the logic of artificial intelligence techniques. This study will support future research on taxonomic identification and diagnosis of both modern and archaeological capillariids.

Dataset Information

Shallot Species and Subtypes Discrimination Based on Morphology Descriptors.

Publications

Shallot Species and Subtypes Discrimination Based on Morphology Descriptors.

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