Effect of micro-propagation on the health status of strawberry planting material for commercial production of strawberry runners for Queensland

Plant tissue culture has been used for a number of years to produce micropropagated strawberry plants for planting into runner growing beds in the Stanthorpe (Queensland) and Bothwell (Tasmania) regions. This process has allowed the rapid release of new cultivars from the LAWS (Late Autumn, Winter, Spring) breeding program into the current runner production system. Micro-propagation in vitro allows plants to be produced during the autumn and winter months, when mother plants would normally be in a fruit production phase in the field in Queensland. The plants produced are of a high health status when they are planted. The subsequent arrival and build up of various diseases in the runner fields are closely monitored. Using tissue culture for the first generation reduces the time the plants spend in the field by twelve months, reducing disease incidence. To date, any disease outbreak has been successfully managed using early detection and rapid response methods.

High-Throughput Prediction of Acacia and Eucalypt Lignin Syringyl/Guaiacyl Content Using FT-Raman Spectroscopy and Partial Least Squares Modeling

High-throughput techniques are necessary to efficiently screen potential lignocellulosic feedstocks for the production of renewable fuels, chemicals, and bio-based materials, thereby reducing experimental time and expense while supplanting tedious, destructive methods. The ratio of lignin syringyl (S) to guaiacyl (G) monomers has been routinely quantified as a way to probe biomass recalcitrance. Mid-infrared and Raman spectroscopy have been demonstrated to produce robust partial least squares models for the prediction of lignin S/G ratios in a diverse group of Acacia and eucalypt trees. The most accurate Raman model has now been used to predict the S/G ratio from 269 unknown Acacia and eucalypt feedstocks. This study demonstrates the application of a partial least squares model composed of Raman spectral data and lignin S/G ratios measured using pyrolysis/molecular beam mass spectrometry (pyMBMS) for the prediction of S/G ratios in an unknown data set. The predicted S/G ratios calculated by the model were averaged according to plant species, and the means were not found to differ from the pyMBMS ratios when evaluating the mean values of each method within the 95 % confidence interval. Pairwise comparisons within each data set were employed to assess statistical differences between each biomass species. While some pairwise appraisals failed to differentiate between species, Acacias, in both data sets, clearly display significant differences in their S/G composition which distinguish them from eucalypts. This research shows the power of using Raman spectroscopy to supplant tedious, destructive methods for the evaluation of the lignin S/G ratio of diverse plant biomass materials.