<?xml version="1.0" encoding="UTF-8"?>
<rdf:RDF xmlns:rdf="http://www.w3.org/1999/02/22-rdf-syntax-ns#" xmlns="http://purl.org/rss/1.0/" xmlns:dc="http://purl.org/dc/elements/1.1/">
  <channel rdf:about="https://repositorio.ifg.edu.br/handle/prefix/100">
    <title>DSpace Coleção:</title>
    <link>https://repositorio.ifg.edu.br/handle/prefix/100</link>
    <description />
    <items>
      <rdf:Seq>
        <rdf:li rdf:resource="https://repositorio.ifg.edu.br/handle/prefix/2328" />
      </rdf:Seq>
    </items>
    <dc:date>2026-09-03T17:32:03Z</dc:date>
  </channel>
  <item rdf:about="https://repositorio.ifg.edu.br/handle/prefix/2328">
    <title>Análise multivariada de imagens digitais no controle de qualidade da torrefação na presença de adulterantes carbonáceos em amostras de café arábica</title>
    <link>https://repositorio.ifg.edu.br/handle/prefix/2328</link>
    <description>Título: Análise multivariada de imagens digitais no controle de qualidade da torrefação na presença de adulterantes carbonáceos em amostras de café arábica
Autor(es): Freitas, Sabrina Amaral de
Primeiro Orientador: Costa, Bruno Elias dos Santos
Abstract: This work proposes to monitor the quality of coffee in relation to the type of roasting&#xD;
and the undue addition of carbonaceous adulterants, which are generally made up of&#xD;
charred leaves and twigs. The type of roasting and carbonaceous adulterants affect the&#xD;
color of the coffee powder during the bean processing stage. Digital image analysis&#xD;
therefore becomes an accessible, economical and efficient alternative to be used as a&#xD;
resource for acquiring multivariate data based on the RGB color model for the&#xD;
development of analytical methods, aiming to determine the carbon content present in&#xD;
coffee samples. To this end, digital images of 101 samples made up of a mixture of&#xD;
coffee and charcoal in defined proportions (0-100% by mass of charcoal) were&#xD;
captured using a camera as an imaging device, and the distribution of image pixels&#xD;
was taken using RGB histograms and organized into a data matrix. The data was&#xD;
processed by chemometric tools, such as Principal Component Analysis (PCA) and&#xD;
through a multivariate calibration model based on Principal Component Regression&#xD;
(PCR) for quantitative predictions of the coal content in the samples. Two ways of&#xD;
arranging samples in separate containers were evaluated: Petri dish and plastic coffee&#xD;
cups. The PCA revealed the formation of two sample profiles: One with up to 40%&#xD;
coal and the other above 60% coal. The PCR calibration models provided satisfactory&#xD;
fits with R2 values ~0.97 and ~0.95 for samples in Petri dishes and cups, respectively.&#xD;
The samples arranged in the Petri Dish provided more satisfactory cross-validation&#xD;
results, with less interference from external lighting and shadows.
Editor: Insitituto Federal de Educação, Ciência e Tecnologia de Goiás
Tipo: Trabalho de Conclusão de Curso</description>
    <dc:date>2025-02-19T00:00:00Z</dc:date>
  </item>
</rdf:RDF>

