7 resultados para principal components analysis (PCA) algorithm
em Repositório da Produção Científica e Intelectual da Unicamp
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Universidade Estadual de Campinas . Faculdade de Educação Física
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Inductively Coupled Plasma Optical Emission Spectrometry was used to determine Ca, Mg, Mn, Fe, Zn and Cu in samples of processed and natural coconut water. The sample preparation consisted in a filtration step followed by a dilution. The analysis was made employing optimized instrumental parameters and the results were evaluated using methods of Pattern Recognition. The data showed common concentration values for the analytes present in processed and natural samples. Principal Component Analysis (PCA) and Hierarchical Cluster Analysis (HCA) indicated that the samples of different kinds were statistically different when the concentrations of all the analytes were considered simultaneously.
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Flavanones (hesperidin, naringenin, naringin, and poncirin) in industrial, hand-squeezed orange juices and from fresh-in-squeeze machines orange juices were determined by HPLC/DAD analysis using a previously described liquid-liquid extraction method. Method validation including the accuracy was performed by using recovery tests. Samples (36) collected from different Brazilian locations and brands were analyzed. Concentrations were determined using an external standard curve. The limits of detection (LOD) and the limits of quantification (LOQ) calculated were 0.0037, 1.87, 0.0147, and 0.0066 mg 100 g(-1) and 0.0089, 7.84, 0.0302, and 0.0200 mg 100 g(-1) for naringin, hesperidin, poncirin, and naringenin, respectively. The results demonstrated that hesperidin was present at the highest concentration levels, especially in the industrial orange juices. Its average content and concentration range were 69.85 and 18.80-139.00 mg 100 g(-1). The other flavanones showed the lowest concentration levels. The average contents and concentration ranges found were 0.019, 0.01-0.30, and 0.12 and 0.1-0.17, 0.13, and 0.01-0.36 mg 100 g(-1), respectively. The results were also evaluated using the principal component analysis (PCA) multivariate analysis technique which showed that poncirin, naringenin, and naringin were the principal elements that contributed to the variability in the sample concentrations.
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This manuscript aims to show the basic concepts and practical application of Principal Component Analysis (PCA) as a tutorial, using Matlab or Octave computing environment for beginners, undergraduate and graduate students. As a practical example it is shown the exploratory analysis of edible vegetable oils by mid infrared spectroscopy.
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Descriptive terminology and sensory profile of three varieties of brazilian varietal white wines (cultivars Riesling, Gewürztraminer and Chardonnay) were developed by a methodology based on the Quantitative Descriptive Analysis (QDA). The sensory panel consensually defined the sensory descriptors, their respective reference materials and the descriptive evaluation ballot. Ten individuals were selected as judges based on their discrimination, reproducibility and individual consensus with the sensory panel. Twelve descriptors were generated showing similarities and differences among the wine samples. Each descriptor was evaluated using a nine-centimeters non-structured scale with the intensity terms anchored at its ends. The collected data were analysed by ANOVA, Tukey test and Principal Component Analysis (PCA). The results showed a great difference within the sensory profile of Riesling and Gewürztraminer wines, whereas Chardonnay wines showed a lesser variation. PCA separated samples into two groups: a first group formed by wines higher in sweetness and fruitty flavor and aroma; and a second group of wines higher in sourness, adstringency, bitterness, alcoholic and fermented flavors.
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Universidade Estadual de Campinas . Faculdade de Educação Física
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In this work, the volatile chromatographic profiles of roasted Arabica coffees, previously analyzed for their sensorial attributes, were explored by principal component analysis. The volatile extraction technique used was the solid phase microextraction. The correlation optimized warping algorithm was used to align the gas chromatographic profiles. Fifty four compounds were found to be related to the sensorial attributes investigated. The volatiles pyrrole, 1-methyl-pyrrole, cyclopentanone, dihydro-2-methyl-3-furanone, furfural, 2-ethyl-5-methyl-pyrazine, 2-etenyl-n-methyl-pyrazine, 5-methyl-2-propionyl-furan compounds were important for the differentiation of coffee beverage according to the flavour, cleanliness and overall quality. Two figures of merit, sensitivity and specificity (or selectivity), were used to interpret the sensory attributes studied.