The feasibility of using near infrared reflectance and near infrared transmission spectroscopy to evaluate the proximate composition and the content in fermentation end-products of fresh silage was investigated. In this study, the silage fermentation characteristics were predicted both by NIR reflectance (NIRSystems 6500 with a coarse sample cell) and by NIR transmittance (Tecator Infratec 1255). The silage was measured immediately on opening the silo with no sample preparation. The analysis of silage in its fresh state prevents volatilisation of fermentation end-products. The best results were obtained in the reflectance mode for all the constituents under investigation, using the full wavelength range. The best R2 values for pH, ammonia-nitrogen, lactic and acetic acids for validation samples were 0.90, 0.93, 0.86 and 0.85, respectively. The corresponding standard error values were 0.23 and 1.07 (% of total nitrogen), 8.35 and 1.65 (g kg−1 dry matter). It is concluded that silage fermentation characteristics can be predicted by NIR analysis on the silage in its fresh state. In this manner, the volatilisation of fermentation end-products is prevented.
The four most important regression methods are evaluated on very large data sets: Multiple Linear Regression (MLR), Partial Least Squares (PLS), Artificial Neural Network (ANN) and a new concept called "LOCAL" (PLS with selection of a calibration sample subset of the closest neighbours for each sample to predict). The Standard Errors of Prediction (SEPs) are statistically tested and the results show that the regression methods are almost equal and that the data matrices are more important than the fitting methods themselves. The types of pre-treatments (Multiplicative Scatter Correction, Detrend, Standard Normal Variate, derivative etc.) of the spectra are too numerous to be able to test all the combinations. For each test, the pre-treatment found as the best with the PLS method is fixed for the other ones. The second part of the paper emphasises the importance of the number of samples. If any agricultural commodity, and probably any kind of product measured by an NIR instrument, can be considered as a mixture of several constituents, the databases built by collecting actual samples bringing new information can reach hundreds, if not thousands, of samples.
NIR spectroscopy coupled with the multivariate calibration technique could be used to accurately measure the quality parameters of apples. In addition, in the case of breeding programs, NIR spectroscopy can be considered an interesting tool for sorting varieties according to a range of concentrations.
This paper investigates the effect of spectral data pre-treatment by using scatter correction techniques, detrending and derivatives on the standard error of NIR predictive models. It is shown that no particular spectral pre-treatment or no single derivative works best for the three constituents (protein, cellulose, organic matter digestibility) of the three forage databases which we investigated (grass-hay, tropical forages, maize whole plants). The best analytical results are obtained with SNVD, MSC or WMSC treatments. The best results are obtained with a first or second derivative with a segment and a gap of five data points. Local Regression was investigated for the prediction of forage quality. The standard errors of prediction were compared with those obtained with the best global calibration. Trial and error is the only way to fix the number of samples in the subset and the number of terms to retain in the model. Compared to the results for the traditional universal calibration method, the gain in SEP for protein, cellulose and digestibility in grass-hay, tropical forages or maize ranges between 5 and 11%.
1-mm particle sizes, and subjected to NIRS to measure quality parameters. Near-infrared spectroscopy can rap-Improving maize (Zea mays L.) forage yield and quality is a major idly measure multiple traits in food and agricultural goal for corn breeders in northern Europe. The objective of this research was to measure maize forage dry matter (DM) content and rope GmbH, Res. & Product Dev.,
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