Browsing Grassland Science by Subject "Near infrared reflectance spectroscopy"
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A note on the comparison of three near infrared reflectance spectroscopy calibration strategies for assessing herbage quality of ryegrassPerennial ryegrass (n = 1,836), Italian ryegrass (n = 137) and hybrid ryegrass (n = 103) herbage was taken from harvested plots from the Irish national variety evaluation scheme and analysed for in vitro dry matter digestibility, water soluble carbohydrate concentration, crude protein concentration and buffering capacity. Spectral data were obtained using near infrared reflectance spectroscopy and three calibration strategies (global, species-specific or local) were utilised to relate the reference values to the spectral data. The local strategy generally provided the poorest estimation of herbage composition, with global and species-specific calibration strategies producing similarly accurate estimates of each quality trait. The higher accuracy and easier maintenance of the global strategy make it the recommended calibration method for analysing quality of ryegrass.
Selection of calibration sub-sets to predict ryegrass quality using principle component analysis for near infrared spectroscopyNear infrared reflectance spectroscopy (NIRS) has become the routine method of assessing forage quality on grass evaluation and breeding programmes. NIRS requires predictive calibration models that relate spectral data to reference values developed using a calibration set (Burns et al. 2013). The samples that form the calibration set influence the accuracy and reliability of these models and need to be representative of samples that will likely be analysed (Shenk and Westerhaus, 1991; Burns et al. 2014). Analysing samples from the calibration set using reference techniques has a significant cost and time associated and needs to be considered in the context of the desired accuracy and robustness of calibration models. Calibration selection techniques can therefore maximise the accuracy and robustness of calibration models whilst reducing the number of samples requiring reference analysis. One such method is principal component analysis (PCA; Shenk and Westerhaus, 1991) whereby Shetty et al. (2012) reported that the number of samples could be reduced by up to 80% with a minimal loss in accuracy of calibration model. PCA selects representative calibration sub-sets through plotting all the samples in hyper-dimensional space, based on spectral data, and a sample is selected to represent a local neighbourhood cluster of samples for reference analysis. The aim of this research was to assess the accuracy of NIRS calibration models for buffering capacity, in vitro dry matter digestibility (DMD) and water soluble carbohydrate (WSC) content developed using calibration sub-sets selected by PCA.