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Keywords = colorgrams

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12 pages, 2632 KB  
Article
Development of a Non-Destructive Tool Based on E-Eye and Agro-Morphological Descriptors for the Characterization and Classification of Different Brassicaceae Landraces
by Alessandra Biancolillo, Rossella Ferretti, Claudia Scappaticci, Martina Foschi, Angelo Antonio D’Archivio, Marco Di Santo and Luciano Di Martino
Appl. Sci. 2023, 13(11), 6591; https://doi.org/10.3390/app13116591 - 29 May 2023
Cited by 3 | Viewed by 1558
Abstract
In recent years, Brassicaceae have piqued the interest of researchers due to their extremely rich chemical composition, particularly the abundance of antioxidants and anti-inflammatory compounds, as well as because of their antimutagenic and potential anticarcinogenic activity. Vegetables in this family can be found [...] Read more.
In recent years, Brassicaceae have piqued the interest of researchers due to their extremely rich chemical composition, particularly the abundance of antioxidants and anti-inflammatory compounds, as well as because of their antimutagenic and potential anticarcinogenic activity. Vegetables in this family can be found practically everywhere on the planet. In Italy, numerous varieties of Brassicaceae, as well as a diverse pool of local variants, are regularly cultivated. These landraces, which have a variety of peculiar features, have recently sparked increased interest, and the need to safeguard them to preserve genetic biodiversity has become a relevant topic. In the present study, eight distinct Brassicaceae folk varieties were studied using non-destructive tools (Multivariate Image analysis and agro-morphological descriptors). Eventually, the data were handled using explorative analysis (EA) and Soft Independent Modeling by Class Analogy (SIMCA). EA pointed out similarities/dissimilarities among the diverse investigated populations. SIMCA led to high sensitivity (>70%) in prediction (on the external test set) for seven (over eight) investigated classes. Although the investigated plants belong to different landraces, they bear strong similarities. This is mainly linked to the ability of Brassicaceae to hybridize. Despite this, the combination of colorgrams and SIMCA allowed for classifying samples with excellent accuracy. Full article
(This article belongs to the Special Issue Innovative Technologies in Food Detection)
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10 pages, 2018 KB  
Article
E-Eye Solution for the Discrimination of Common and Niche Celery Ecotypes
by Alessandra Biancolillo, Martina Foschi and Angelo Antonio D’Archivio
AppliedChem 2023, 3(1), 1-10; https://doi.org/10.3390/appliedchem3010001 - 22 Dec 2022
Cited by 3 | Viewed by 1926
Abstract
Celery (Apium graveolens L.) is a well- known plant and at the basis of the culinary tradition of different populations. In Italy, several celery ecotypes, presenting unique peculiarities, are grown by small local producers, and they need to be characterized, in order [...] Read more.
Celery (Apium graveolens L.) is a well- known plant and at the basis of the culinary tradition of different populations. In Italy, several celery ecotypes, presenting unique peculiarities, are grown by small local producers, and they need to be characterized, in order to be protected and safeguarded. The present work aims at developing a fast and non-destructive method for the discrimination of a common celery (the "Elne" celery) from a typical celery of Abruzzo (Central Italy). The proposed strategy is based on the use of an e-eye tool which allows the collection of images used to infer colorgrams. Initially, a principal component analysis model was used to investigate the trends and outliers in the data. Then, the classification between the common celery (Elne class) and celery from Torricella Peligna (Torricella class) was achieved by a discriminant analysis, conducted by sequential preprocessing through orthogonalization (SPORT) and sequential and orthogonalized covariance selection (SO-CovSel) and by a class-modelling method called soft independent modelling of class analogies (SIMCAs). Among these, the highest accuracy was provided by the strategies, based on the discriminant classifiers, both of which provided a total accuracy of 82% in the external validation. Full article
(This article belongs to the Special Issue Feature Papers in AppliedChem)
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