Mid-infrared reflectance spectroscopy as a tool for forage feed composition prediction

Abstract

Attenuated Total Reflectance Fourier Transform Infrared (ATR-FTIR) spectroscopy, in the Mid-infrared (MIR) region, has been evaluated for the prediction of chemical components in forage feeds using a modified Partial Least Squares Regression (PLSR) model. Regression regression models have been developed that predict the chemical composition from a MIR spectrum of a given forage feed sample. Data collection was carried out on 140 herbage samples consisting of 84 ryegrass - white clover samples and 56 herb mix samples containing different combinations of chicory, plantain, white clover and red clover. Several spectral data pre-treatments were explored, the best of which combined Standard Normal Variant scaling (SNV) with a first-order Savitzky-Golay (SG) spectral derivative and smoothing filter. Several of the resulting models illustrated high quality predictions (for hemicellulose, 156. 9 g / kg with a standard error of prediction (SEPc) 19.8 g / kg, R2 = 0.92, Relative Performance Deviation (RPD) = 3.54; for neutral detergent fibre, 382.8 g / kg with SEPc = 43.5 g / kg, R2 = 0.86, RPD = 2.60), at least on par with, or superior to, current near-infrared (NIR) methods. The SNV and SG pre-treatment almost completely reduces the contribution of strong water-based signals to the regression model, allowing the possibility of in situ prediction of forage feed composition with minimal sample preparation. ATR-FTIR spectrometers are available in a hand-held form, and the results of this research suggest that in situ forage quality analysis could be performed using MIR reflectance spectroscopy.