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Publicações

Publicações por Renan Tosin

2023

LIBS-Based Analysis of Elemental Composition in Skin, Pulp, and Seeds of White and Red Grape Cultivars

Autores
Tosin, R; Monteiro Silva, F; Martins, R; Cunha, M;

Publicação
CSAC 2023

Abstract

2023

Exploring the Impact of Water Stress on Grapevine Gene Expression and Polyphenol Production: Insights for Developing a Systems Biology Model †

Autores
Portis, I; Tosin, R; Oliveira Pinto, R; Pereira Dias, L; Santos, C; Martins, R; Cunha, M;

Publicação
Engineering Proceedings

Abstract
This scientific paper delves into the effects of water stress on grapevines, specifically focusing on gene expression and polyphenol production. We conducted a controlled greenhouse experiment with three hydric conditions and analyzed the expression of genes related to polyphenol biosynthesis. Our results revealed significant differences in the expression of ABCC1, a gene linked to anthocyanin metabolism, under different irrigation treatments. These findings highlight the importance of anthocyanins in grapevine responses to abiotic stresses. By integrating genomics, metabolomics, and systems biology, this study contributes to our understanding of grapevine physiology under water stress conditions and offers insights into developing sensor technologies for real-world applications in viticulture. © 2023 by the authors.

2023

Enhancing Kiwi Bacterial Canker Leaf Assessment: Integrating Hyperspectral-Based Vegetation Indexes in Predictive Modeling †

Autores
Reis Pereira, M; Tosin, R; Martins, C; Dos Santos, FN; Tavares, F; Cunha, M;

Publicação
Engineering Proceedings

Abstract
The potential of hyperspectral UV–VIS–NIR reflectance for the in-field, non-destructive discrimination of bacterial canker on kiwi leaves caused by Pseudomonas syringae pv. actinidiae (Psa) was analyzed. Spectral data (325–1075 nm) of twenty kiwi plants were obtained in vivo and in situ with a handheld spectroradiometer in two commercial kiwi orchards in northern Portugal over 15 weeks, resulting in 504 spectral measurements. The suitability of different vegetation indexes (VIs) and applied predictive models (based on supervised machine learning algorithms) for classifying non-symptomatic and symptomatic kiwi leaves was evaluated. Eight distinct types of VIs were identified as relevant for disease diagnosis, highlighting the relevance of the Green, Red, Red-Edge, and NIR spectral features. The class prediction was achieved with good model metrics, achieving an accuracy of 0.71, kappa of 0.42, sensitivity of 0.67, specificity of 0.75, and F1 of 0.67. Thus, the present findings demonstrated the potential of hyperspectral UV–VIS–NIR reflectance for the non-destructive discrimination of bacterial canker on kiwi leaves. © 2023 by the authors.

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