Examinando por Materia "Multivariate analysis"

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Caracterización morfológica de cinco variedades de café (Coffea arabica L.) y su resistencia a la roya (Hemileia vastatrix), en el Valle del Alto Huallaga, Tingo María
(Dirección de Desarrollo Tecnológico Agrario. Instituto Nacional de Innovación Agraria, 2020-11-13) Cosme de la Cruz, Roberto Carlos; Buendía Molina, Marilyn; Adama Rojas, Enrique; Pocomucha Poma, Vicente
El objetivo fue caracterizar morfológicamente 13 caracteres cualitativos de cinco variedades de café de la especie Coffea arábica L. (Colombia, Catimor, Limani, Catuai y Caturra) y evaluar su grado de resistencia a la roya amarilla. La investigación se realizó, en el Centro Piloto de Innovación Tecnológica de café de la EEA Santa Ana, Tingo María. Se utilizó la lista de descriptores de café del International Resources Institute y se determinó los caracteres responsables de la mayor variabilidad en las variedades mediante el análisis multivariado de agrupamiento mediante software NTSYS 2.0. También, se evaluó la incidencia de la roya (Hemileia vastatrix) en las cinco variedades. De las 13 características cualitativas evaluadas en cinco variedades, nueve características fueron similares (hábito de ramificación: con muchas ramas primarias y secundarias, ángulo de inserción: semi erecto, forma de estípula: oval, forma de la hoja: lanceolada, forma de ápice: apiculada, color del peciolo: verde, forma de fruto: oblonga, color de semilla: amarillo y forma de semilla: obovada) y en las cuatro restantes se observó diferencias (forma de planta, color de hoja madura, color de brotes y color de fruto). También, la variedad caturra se mostró como la más susceptible a la roya, mientras las variedades Colombia, Catimor y Limani se mostraron resistentes a la roya.
Characterisation of volatile profiles in 50 native Peruvian chili pepper using solid phase microextraction–gas chromatography mass spectrometry (SPME–GCMS)
(El Sevier, 2016-08-26) Patel, Kirti; Ruíz, Candy; Calderón, Rosa; Marcelo Salvador, Mavel; Rojas, Rosario
The volatiles were characterised by headspace solid phase micro extraction (HS-SPME), gas chromatography mass spectrometry (GC-FID/MS). A total of 127 compounds were identified with terpenes (including mono terpenes and sesquiterpenes – a total of 45 compounds), esters (31 compounds) and hydrocarbons (20 compounds) were the predominant volatile compounds. Principal component analysis (PCA) of the volatile compounds yielded 2 significant PC's, which together accounted for 90.3% of the total variance in the data set and the scatter plot generated between PC1 and PC2 successfully segregated the 50 chili pepper samples into 7 groups. Clusters of hydrocarbons, esters, terpenes, aldehyde and ketones formed the major determinants of the difference.
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.