Discover Community Create You

Loading publication...

Noodle always counts identifier-free feature events for service operation. With your permission, Google Analytics can also estimate audience and engagement. Search text, publication identifiers, cookies, IP addresses and browser fingerprints are never included in Noodle's first-party event counters.

Privacy details

Article

Machine learning and statistics to qualify environments through multi-traits in Coffea arabica.

PloS one - 1 Jan 2021

Costa Weverton Gomes da, Barbosa Ivan de Paiva, de Souza Jacqueline Enequio, Cruz Cosme Damião, Nascimento Moysés, de Oliveira Antonio Carlos Baião

Abstract excerpt

Several factors such as genotype, environment, and post-harvest processing can affect the responses of important traits in the coffee production chain. Determining the influence of these factors is of great relevance, as they can be indicators of the characteristics of the coffee produced. The most efficient models choice to be applied should take into account the variety of information and the particularities of...

Read the complete abstract on PubMed

Topics

Share this publication in a Topic to start or enrich a Post.

Share to Brazil
Share to Coffea

Methodology Data sources Editorial policy Privacy About Contact Helena Bioinformatics

Share to Coffee

Share to Discriminant Analysis

Share to Food Handling

Share to Food Quality

Share to Machine Learning

Share to Principal Component Analysis

Source

Open in PubMed Open full text

Identifiers

PMID: 33434204
DOI: 10.1371/journal.pone.0245298
PMCID: PMC7802962

Provenance

PubMed metadata and Literature Corpus relationships. Corpus snapshot updated 5 Sept 2026.

Related research

Semantic proximity does not establish scientific evidence.

Research trail→

Click a neighbor to travelStep 1 · 12 closest

Machine learning and statistics to qualify environments through multi-traits in Coffea arabica.PMID 33434204Read ↗

Genotypic and environmental effects on coffee (Coffea arabica L.) bean fatty acid profile: impact on variety and origin chemometric determination.Semantic proximity Read ↗

Evaluation of genetic divergence of coffee genotypes using the volatile compounds and sensory attributes profile.Semantic proximity Read ↗

Adaptability and genotypic stability of Coffea arabica genotypes based on REML/BLUP analysis in Rio de Janeiro State, Brazil.Semantic proximity Read ↗

Discrimination of genotypes coffee by chemical composition of the beans: Potential markers in natural coffees.Semantic proximity Read ↗

AMMI-Bayesian perspective in the selection of pre-cultivars of carioca beans in Agreste-Sertão of Pernambuco, Brazil.Semantic proximity Read ↗

Chemical and sensory profile of new genotypes of Brazilian Coffea canephora.Semantic proximity Read ↗

Agro‐morphological characterization and diversity analysis of Coffea arabica germplasm collection from INIA, PeruSemantic proximity Read ↗

Chemometric Classification of Colombian Cacao Crops: Effects of Different Genotypes and Origins in Different Years of Harvest on Levels of Flavonoid and Methylxanthine Metabolites in Raw Cacao Beans.Semantic proximity Read ↗

Adaptability and stability of Coffea canephora to dynamic environments using the Bayesian approach.Semantic proximity Read ↗

Geographical and genotypic segmentation of arabica coffee using self-organizing mapsSemantic proximity Read ↗

Genomic selection for genotype performance and environmental stability in Coffea canephora.Semantic proximity Read ↗

Genetic diversity among 16 genotypes of Coffea arabica in the Brazilian cerrado.Semantic proximity Read ↗

⌁Select a neighboring publication to make it the new centre.