The aim of this study was to investigate the use of near infrared reflectance (NIRS) spectroscopy to predict the nutritive value of silages from pastures and to assess the effect of silage structure type (e.g. bunker and bag silos) on the NIRS predictions. Samples (n = 120) were sourced from commercial farms and analyzed in a NIRS monochromator instrument (NIR Systems, Silver Spring, Maryland, USA) using wavelengths between 400 and 2500 nm in reflectance. Calibration models were developed between chemical and NIRS spectral data using partial least squares (PLS) regression. The coefficients of determination in calibration (R 2) and the standard error in cross validation (SECV) were 0.73 (SECV: 1.2%), 0.81 (SECV: 2.0%), 0.75 (SECV: 6.6%), 0.80 (SECV: 6.7%), 0.80 (SECV: 4.0%), 0.60 (SECV: 3.6%) and 0.70 (SECV: 0.34) for ash, crude protein (CP), neutral detergent fiber (NDF), dry matter (DM), acid detergent fiber (ADF), in vitro dry matter digestibility (IVDMD) and pH, respectively. The results showed the potential of NIRS to analyze DM, ADF and CP in silage samples from pastures.
Near infrared reflectance (NIR) spectroscopy combined with multivariate data analysis was used to discriminate between the geographical origins of yerba mate (Ilex paraguayensis St. Hil.) samples. Samples were purchased from the local market and scanned in the NIR region (1100-2500 nm) in a monochromator instrument in reflectance. Principal component analysis (PCA), partial least squares discriminant analysis (PLS-DA) and linear discriminant analysis (LDA) were used to classify the samples based on their NIR spectra according to their geographical origin. Full cross validation was used as validation method when classification models were developed. The overall classification rates obtained were 76 and 100% using PLS-DA and LDA, respectively. The results demonstrated the usefulness of NIR spectra combined with multivariate data analysis as an objective and rapid method to classify yerba mate samples according to their geographical origin. Nevertheless, NIR spectroscopic might provide initial screening in the food chain and enable costly methods to be used more productively on suspect specimens.
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