Analysis of defects in coffee beans compared to biplots for simultaneous tables

Brighenti, Carla Regina Guimarães; Cirillo, Marcelo Ângelo

doi:10.5935/1806-6690.20180007

Cited by 7 publications

(8 citation statements)

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“…Therefore, when considering the effects of association and difference between harvests, it was found that all biplots corrected by the predictions of the GEE models led to identify the grain defect (black, green, and burnt) associated with the percentage of sieve grains between 20 and 30%, which is consistent with existing literature (Brighenti and Cirillo 2018;Costa et al 2018). In the case of the component S2014-S2015, the biplots presented different interpretations, so the results were not conclusive.…”

Section: Discussionsupporting

confidence: 87%

“…The database for application of the proposed methodology was obtained from a study by Brighenti and Cirillo (2018), in which the processed samples of coffees from Catuaí cultivar were harvested by rural producers in the Southern region of Minas Gerais (Brazil). The proportion of defective beans or impurities contained in a 300g sample of processed coffee was obtained for each type of defect (Table 1), according to the COB table.…”

Section: Methodsmentioning

confidence: 99%

“…In a case study, where a new methodological approach to study the relationship among defects considering information about the crops was proposed, Brighenti and Cirillo (2018) addressed the analysis of this database using a data structure organized in a double entry table relating the counts of the types of defects as a function of the sieve percentage, for each block effect represented by the harvests (S2014 and S2015). Based on the above, the combined biplots technique was applied, following the decomposing singular values methodology enhanced by Greenacre (2003), with the advantage of filtering eigenvalues corresponding to the construction of biplots which are specific to the additive effects of the blocks (S2014 + S2015) and the difference between them (S2014-S2015).…”

Section: Gee-logit Model Corrected Biplots With Harvest Effects On Coffee Beans Gradingmentioning

confidence: 99%

“…The procedure to be used to build the graphs consists of setting constants for the scale parameter δ, respectively at values 0, 1, and 0.5. Such aspects were not addressed in the statistical analysis of the granulometric classification given in Brighenti and Cirillo (2018), as well as in the construction of biplots described by Greenacre (2003).…”

Section: Gee-logit Model Corrected Biplots With Harvest Effects On Coffee Beans Gradingmentioning

confidence: 99%

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Gee-logit model corrected biplots with harvest effects on coffee beans grading

et al. 2021

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In a granulometric analysis of coffee beans with different categories of defects, the data can be organized in contingency tables, and when considering the discrimination by harvest, they may have a structure that suggest a more complex model, by means of the counting of defective coffee beans compared to different crops interacting with the classification of defects and percentages of sieve grains, which characterizes a block design with multivariate responses. However, due to the techniques based on the analysis of variance, considering the uniform correlation structure for all plots, it becomes feasible to propose a model that allows contemplating different structures between the plots, associating the effects of the crops to the defects in the granulometric procedure applied to the coffee beans. Thus, the hypothesis of incorporating the effects of crops associated with defects arises using the biplot multivariate technique. This work aims to propose the use of corrected biplots by predictions obtained trhough the fit to the Generalized Linear Model in the coffee grain size classification, broken down by components of the effect of the harvests. In conclusion, the use of GEE models with the corrected biplot technique by the predictions is feasible for application to be applied to the granulometric analysis of defective coffee beans, presenting discrimination regarding the effects of harvests.

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Section: Discussionsupporting

confidence: 87%

Section: Methodsmentioning

confidence: 99%

Section: Gee-logit Model Corrected Biplots With Harvest Effects On Coffee Beans Gradingmentioning

confidence: 99%

Section: Gee-logit Model Corrected Biplots With Harvest Effects On Coffee Beans Gradingmentioning

confidence: 99%

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Gee-logit model corrected biplots with harvest effects on coffee beans grading

et al. 2021

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“…For this example, the database contains information on the intrinsic defects which are those contained in the coffee bean, caused by improper use of agricultural and industrial processes and modifications of physiological or genetic origin, such as black beans, flamed grain, brocade grain, green grain, among others (Bandeira, Toci, Trugo, & Farah, 2009). It is noticed that many coffee farmers do not care about the defect percentage at the time of preparation of coffee for commercialization, which may impact on the prices charged by the market, and the price is one of the decisive factors in the coffee industry (Wachholz & Poyer, 2014;Brighenti & Cirillo, 2018).…”

Section: Application Examplementioning

confidence: 99%

<b>A new approach to simple correspondence analysis with emphasis on the violation of the independence assumption of the levels of categorical variables

2018

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The main hypothesis of correspondence analysis is given by the independence between the levels of categorical variables. Due to violation of this hypothesis, this study aims to improve the technique of correspondence analysis, providing a new approach for the calculation of the coordinates through the residual incorporation by tables in which categories have different levels of correlation. For this purpose, the simulation was made using Monte Carlo to generate frequencies from the correlated binomial distribution BC (n, π, ρ). It was concluded that in all evaluated scenarios the approach is promising in the sense that the objects were better discriminated against conventional approach. Moreover, the proposed procedure for obtaining the coordinates is likely to be used on real data as shown in the application example.

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