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Rural Industries Research & Development Corporation
by G. McL. Dryden
July 2003
RIRDC Publication No W03/007 RIRDC Project No UQ-109A
Executive Summary
Near infrared reflectance (NIR) spectroscopy has been
used over the last forty years to analyse accurately protein, fibre, and
other organic components in animal foods. NIR spectroscopy is a rapid,
non-destructive, and non-polluting technology.
NIR information can not be used to determine analyte concentrations directly because of the way in which near infrared radiation passes into, through, and is reflected from, the sample.
We have to predict the concentrations of the constituent we wish to measure from relationships which have been developed between reflectance and reference data, i.e. we have to use prediction equations. Robust prediction equations are based on calibration data sets which encompass the range of sample characteristics which we expect to encounter when the equation is used. It is also important to apply appropriate mathematical techniques (e.g.
smoothing and derivatisation) to the NIR data, and to make sure that the samples which we analyse are uniform in particle size and water content.
“Universal” equations have been developed to predict the nutrient composition of a wide range of foods of that type. There are several examples of European universal equations for grains and forages, and an equation for Australian mixed temperate pasture. It may be necessary to calculate “local corrections” before universal equations are used in any new context.
When properly calibrated, NIR spectroscopy predicts protein contents with great accuracy.
We can predict other constituents less precisely, although with precisions which are similar to those of conventional laboratory determinations. NIR spectroscopy is used successfully with both concentrate and forage foods. NIR information is obtained from the interactions of near infrared radiation with chemical bonds between non-mineral elements and so does not always accurately predict food mineral contents. NIR methods predict in vitro digestibility accurately and precisely, and can predict in vivo digestibility at least as well as conventional “wet chemistry” methods such as in vitro digestion or the pepsin-cellulase method, and much more rapidly. The DM intake of animals can also be predicted, although with less precision than chemical composition or digestibility.
Faecal indices, i.e. the concentrations of certain constituents in faeces, have been used to monitor the nutritional status of grazing animals, including wild deer. Faecal indices determined by wet chemistry have given mixed success, but substantially better results have been obtained with NIR spectroscopy. NIR spectroscopy may measure characteristics of faeces which integrate several different aspects of faecal chemistry, while wet chemical analyses focus on single entities.
NIR technology has been used to routinely monitor (through analysis of faecal samples) the nutritional status of cattle, and appears to have potential for identifying tick infestation, pregnancy, gender and animal species. Nutritional status data obtained by NIR analysis of grazing cattle faeces is used as an input to the NUTBAL Pro expert system for North American ranchers. The combination of NIR analysis and nutritional profiling with the NUTBAL Pro program has improved yearly economic returns to American cattle ranchers by up to USD26.50 per cow mated. These results, the preliminary evidence from similar attempts in northern Australia, and preliminary results of a NIR-based nutritional profiling program for deer in Texas, suggest that a similar technology could be developed to monitor the nutritional status of deer herds and predict the performance of farmed deer.
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