Potencial da espectroscopia no infravermelho próximo para distinção de carvão de madeira de Eucalyptus e nativa

The objective of this study was to evaluate the potential of near infrared spectroscopy (NIRS) associated with multivariate statistics to distinguish coal produced from wood of planted and native forests. Timber forest species from the C errado (Cedrela sp., Aspidosperma sp., Jacaranda sp. and unknown species) and Eucalyptus clones from forestry companies (Vallourec and Cenibra) were carbonized in the final temperatures of 300, 500 and 700°C. In each heat treatment were carbonized 15 specimens of each vegetal material totaling 270 samples (3 treatments x 15 reps x 6 materials) produced in 18 carbonization (3 treatments x 6 materials). The acquisition of the spectra of coals in the near infrared using a spectrometer was performed. Principal Component Analysis (PCA) and Partial Least Squares Regression (PLS-R) were carried out in the spectra. NIR Spectroscopy associated with PCA was not able to differentiate charcoals produced from native and planted woods when utilizing all carbonized samples at different temperatures in the same analysis; The PCA of all charcoals was able to distinguish the samples depending on temperature in which they were carbonized. However, the separation of native and planted charcoal was possible when the samples were analyzed separately by final temperature. The prediction of native or planted classes by PLS-R presented better performance for samples carbonized at 300°C followed by those at 500°C, 700°C and for all together.

Componentes principais na elaboração de índices de provas de desempenho em bovinos

The principal component analysis assists the producers in making decision of which evaluated features must be maintained in performance tests indexes, according to the variation present in these animals evaluated. The objective in this study was to evaluate a set of characteristics measured in a performance test in semifeedlot cattle of the Simmental and Angus breeds, by means principal component analysis (PC), aim to identify the features that represent most of the phenotypic variation for preparation of indexes. It was used data from 39 Angus and 38 Simmental bulls from the Santa Éster farm, located in Silvianópolis - MG. The performance test period was from october 2014 to february 2015. The features evaluated in the test were: final weight (FW), average daily gain weight (GW), respiratory rate (RR), haircoat temperature (HT) and rectal (RT), hair number (HN), hair length (HL), hair thickness (HT), muscularity (MUSC), racial characteristics, angulation, reproductive and balance (BAL), height of the front and back, width and length of croup, body length, depth and heart girth, subcutaneous fat thickness and rump (FTR), loin eye area and marbling (MAR). It was used PRINCOMP from SAS program for procedure the PC analysis. It was found that of the 27 features evaluated, the first four PC for Simmental breed explained 74% total variation data. The four PC selected with the corresponding weighting coefficients formed the following index: (0.27 * FW) + (0.47 * MUSC) + (0.50 * HL) + (0.39 * HT). Since the characteristics related to the adaptability of great importance for the studied breed, it was decided to keep the index of evidence for the Angus breed, the feature hair number, because there is a feature that presented a great variability and occupied one of the first principal component. Thus, the Angus index was composed by five features, with 79% total variation data, resulting in the following formula: (0.26 * FW) + (0.33 * BAL) + (0.58 * MAR) - (0.43 * FTR) – (0.38 * HN). By the principal component analysis it was possible to minimize the features number to be evaluated on performance tests from that farm, making the animal selection rapidly and accurate.