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    The role of Fourier-transform mid-infrared spectroscopy in improving the prediction of new and existing traits in New Zealand dairy cattle : a thesis presented in partial fulfilment of the requirements for the degree of Doctor of Philosophy in Animal Science at Massey University, AL Rae Centre, Hamilton, New Zealand
    (Massey University, 2022) Tiplady, Kathryn
    Bovine milk is a rich source of dietary nutrients that are important to human health. Market demand for bovine milk is driven by its nutritional value, price, processability, and consumer expectations and perceptions about food production systems. The ability to quantify traits associated with milk quality, processability, animal health and environmental impact is critical for selective breeding and thus highly valuable to the dairy industry. However, obtaining direct measurements of such traits can be difficult and expensive. Estimation of major milk components using Fourier-transform mid-infrared (FT-MIR) spectroscopy is common practice, and spectral-based predictions of these traits are already widely used in milk payment and animal evaluation systems. Applications using FT-MIR spectra to predict other traits have increased in popularity over the last decade, and are attractive alternatives to directly measuring phenotypes because the FT-MIR spectra are readily available as a by-product of routine milk testing. The objectives of this thesis were to improve understanding of the phenotypic and genetic characteristics of FT-MIR spectra, and assess the role that such data can play in predicting new traits or improving the prediction of existing traits in New Zealand dairy cattle. We assessed different strategies for improving the quality of spectral data and demonstrated that there are limitations in predicting traits such as pregnancy status, due to confounding effects such as stage of lactation. From a genetics perspective, we reviewed the evolving role of spectral data in the improvement of dairy cattle by selection and discussed opportunities for consolidating spectral datasets with other genomic and molecular data sources. We conducted GWAS on individual FT-MIR wavenumbers and demonstrated that the individual wavenumbers provided stronger association effects and improved power for identifying candidate causal variants, compared to conducting GWAS on FT-MIR predicted traits. We also demonstrated the potential utility of spectral data for predicting and incorporating fatty acids and protein traits into breeding programs, but showed that even when genetic correlations between directly measured and FT-MIR predicted traits were high, the detectable QTL underpinning these traits were not always the same. Although there are many potential applications for FT-MIR spectral datasets, there are still challenges to developing robust prediction equations and understanding the genetic relationships between traits of interest and their FT-MIR predictions. Addressing these challenges will provide opportunities to improve the prediction of new and existing traits in dairy cattle milk production systems and breeding programs into the future.
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    Physical and chemical attachment of pectins to substrates : methods, characterisation and application : thesis presented by Abdenor Fellah for the degree of Doctor of Philosophy, Massey University, New Zealand & Fonterra, New Zealand
    (Massey University, 2012) Fellah, Abdenor
    The plant cell wall is a complex biological matrix in which pectic polysaccharides play an instrumental role in regulating mechanical properties. Nanomechanical studies of single chains hold the promise of enabling the comprehension of fundamental aspects concerning the structural, mechanical and binding properties of pectin at an unprecedented level of molecular detail, using measured single polysaccharide force-extension behavior as a signature. However, before such promise can be fulfilled, a better understanding of the attachment of the polymer under study to the substrates between which it is stretched is required. Herein, chemoselective methodologies have been developed to covalently couple one end of a pectin chain onto a solid support. Prior to immobilization, pectin fine structure was investigated using accurate and non-invasive infrared spectroscopy. Comparison of experimental results with the predictions of quantum chemical calculations carried out using density functional theory confirmed this technique as an effective tool for the characterization of pectin fine structure. Subsequently, following appropriate functionalization of the support, pectin chains were anchored to polystyrene beads, specifically through their reducing end. These methods were shown to be efficient using IR spectroscopy, once more coupled with quantum chemical calculations, with the formation of specific newly introduced bonds being demonstrated. Finally, single-molecule force spectroscopy was used to stretch single pectin molecules covalently bonded to substrates using the previously described method applied to glass surfaces. Compared to physisorption, which was also extensively studied, tethering the pectin non-reducing end appeared to increase the average stretch length and improved significantly the probability of stretching a single chain to high forces.
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    ATR-FTIR chemometrics for biological samples : a thesis presented in partial fulfilment of the requirements for the degree of Master of Science in Nanoscience at Massey University, Manawatū, New Zealand
    (Massey University, 2018) Cleland, Josiah David
    This project has used the analytical infrared reflectance technique of Attenuated Total Reflectance Fourier Transform Infrared (ATR-FTIR) spectroscopy, for the prediction of chemical components in a range of biological samples. Data collection was carried out on 40 hyperaccumulator samples, 56 chicken feed samples, 54 lamb faecal samples and 188 forage feed samples. Predictions were made using several different regression methods including: Ridge, Least Absolute Shrinkage and Selection Operator (LASSO), Elastic Net, Principal Components (PCR) and Partial Least Squares (PLS). The best methods were identified as LASSO, Elastic Net and PLS. 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 (R2 > 0.8, Relative Performance Deviation (RPD) ≥ 2). 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 mid – infrared (MIR) reflectance spectroscopy.