RESUMO
Potential of front-face fluorescence spectroscopy was evaluated to classify muscles according to their chemical and rheological characteristics. Seven bovine muscles (Semitendinosus, Semimembranosus, Tensor fasciae latae, Rectus abdominis, Longissimus thoracis et lumborum, Triceps branchii and Infraspinatus) were taken from 14 animals of the Charolais breed. Chemical characteristics and rheological properties of the meat were determined including dry matter, fat, collagen, protein, peak load, energy required to rupture and cooking loss. Emission spectra in the 305-400nm, 340-540nm and 410-700nm ranges were recorded using front-face fluorescence spectroscopy by fixing the excitation wavelengths at 290, 322 and 382nm, respectively. Analysis of variance (ANOVA) applied on chemical and rheological parameters showed that these muscles were significantly different (P<0.01) from each other. Chemical and rheological data were divided into low, medium and high range groups for each variable. The results of PLSDA showed that 305-400nm spectra were responsible for 67% (calibration), 53% (validation), 96% (calibration) and 55% (validation) of good classification for protein and cooking loss, respectively, while 340-540nm spectra allowed 75% of good classification (validation samples) for fat content.
RESUMO
Quantitative analysis of textile blends and textile fabrics is currently of particular interest in the industrial context. In this frame, this work investigates whether the use of Fourier transform (FT) near-infrared (NIR) spectroscopy and chemometrics is powerful for rapid and accurate quantitative analysis of cotton-polyester content in blend products. As samples of the same composition have many sources of variability that affect NIR spectra, indirect prediction is particularly challenging and a large sample population is required to design robust calibration models. Thus, a total of more than three-hundred cotton-polyester samples were selected covering the range from the 0% to 100% cotton and the corresponding NIR reflectance spectra were measured on raw fabrics. The data set obtained was used to develop multivariate models for quantitative prediction from reference measurements. A successful approach was found to rely on partial least squares (PLS) regression combined with genetic algorithms (GAs) for wavelength selection. It involved evaluating a set of calibration models considering different spectral regions. The results obtained considering 27.5% of the original variables yielded a prediction error (RMSEP) of 2.3 in percent cotton content. It demonstrates that FT-NIR spectroscopy has the potential to be used in the textile industry for the prediction of the composition of cotton-polyester blends. As a further consequence, it was observed that the spectral preprocessing and the complexity of the model are simplified compared to the full-spectrum approach. Also, the relevancy of the spectral intervals retained after variable selection can be discussed.