Examinando por Materia "Análisis de datos"

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Ciencia de datos con R aplicado a estudios e investigaciones agrarias
(Instituto Nacional de Innovación Agraria (INIA), 2026-07-09) Saravia Navarro, David
El presente documento es una presentación del curso-taller "Ciencia de Datos con R aplicada a Estudios e Investigaciones Agrarios", organizado por el Instituto Nacional de Innovación Agraria (INIA) del Ministerio de Desarrollo Agrario y Riego (MIDAGRI) del Perú. El curso aborda el uso del software R y su interfaz RStudio para el análisis estadístico y visualización de datos en contextos agrarios. Se presenta R como un ambiente de programación formado por un conjunto de herramientas muy flexibles que pueden ampliarse fácilmente mediante paquetes, librerías o la definición de nuevas funciones. Es gratuito y de código abierto, parte del proyecto GNU. El contenido incluye: introducción a la plataforma R, lectura de datos, resumen estadístico (summary), estructura de datos (str), gráficos de caja (boxplot), histogramas, tablas de frecuencia, gráficos de pastel (pie), diagramas de dispersión (scatter plots), gráficos 3D con scatterplot3d y rgl, gráficos con ggplot2, matrices de correlación con corrplot y pairs.panels (paquete psych), combinación de gráficas, gráficos de burbujas (symbols), y análisis exploratorio de datos. Se utilizan conjuntos de datos clásicos como iris (flores) y USArrests (datos de arrestos en Estados Unidos) como ejemplos didácticos.
Matrix-assisted laser desorption ionization time-of-flight mass spectrometry combined with chemometrics for protein profiling and classification of boiled and extruded quinoa from conventional and organic crops
(MDPI, 2024-06-17) Galindo Luján, Rocío; Pont, Laura; Quispe Jacobo, Fredy Enrique; Sanz Nebot, Victoria; Benavente, Fernando
Quinoa is an Andean crop that stands out as a high-quality protein-rich and gluten-free food. However, its increasing popularity exposes quinoa products to the potential risk of adulteration with cheaper cereals. Consequently, there is a need for novel methodologies to accurately characterize the composition of quinoa, which is influenced not only by the variety type but also by the farming and processing conditions. In this study, we present a rapid and straightforward method based on matrix-assisted laser desorption ionization time-of-flight mass spectrometry (MALDI-TOF-MS) to generate global fingerprints of quinoa proteins from white quinoa varieties, which were cultivated under conventional and organic farming and processed through boiling and extrusion. The mass spectra of the different protein extracts were processed using the MALDIquant software (version 1.19.3), detecting 49 proteins (with 31 tentatively identified). Intensity values from these proteins were then considered protein fingerprints for multivariate data analysis. Our results revealed reliable partial least squares-discriminant analysis (PLS-DA) classification models for distinguishing between farming and processing conditions, and the detected proteins that were critical for differentiation. They confirm the effectiveness of tracing the agricultural origins and technological treatments of quinoa grains through protein fingerprinting by MALDI-TOF-MS and chemometrics. This untargeted approach offers promising applications in food control and the food-processing industry.