High-throughput prediction of eucalypt lignin syringyl/guaiacyl content using multivariate analysis: a comparison between mid-infrared, near-infrared, and Raman spectroscopies for model development

BACKGROUND: In order to rapidly and efficiently screen potential biofuel feedstock candidates for quintessential traits, robust high-throughput analytical techniques must be developed and honed. The traditional methods of measuring lignin syringyl/guaiacyl (S/G) ratio can be laborious, involve hazardous reagents, and/or be destructive. Vibrational spectroscopy can furnish high-throughput instrumentation without the limitations of the traditional techniques. Spectral data from mid-infrared, near-infrared, and Raman spectroscopies was combined with S/G ratios, obtained using pyrolysis molecular beam mass spectrometry, from 245 different eucalypt and Acacia trees across 17 species. Iterations of spectral processing allowed the assembly of robust predictive models using partial least squares (PLS). RESULTS: The PLS models were rigorously evaluated using three different randomly generated calibration and validation sets for each spectral processing approach. Root mean standard errors of prediction for validation sets were lowest for models comprised of Raman (0.13 to 0.16) and mid-infrared (0.13 to 0.15) spectral data, while near-infrared spectroscopy led to more erroneous predictions (0.18 to 0.21). Correlation coefficients (r) for the validation sets followed a similar pattern: Raman (0.89 to 0.91), mid-infrared (0.87 to 0.91), and near-infrared (0.79 to 0.82). These statistics signify that Raman and mid-infrared spectroscopy led to the most accurate predictions of S/G ratio in a diverse consortium of feedstocks. CONCLUSION: Eucalypts present an attractive option for biofuel and biochemical production. Given the assortment of over 900 different species of Eucalyptus and Corymbia, in addition to various species of Acacia, it is necessary to isolate those possessing ideal biofuel traits. This research has demonstrated the validity of vibrational spectroscopy to efficiently partition different potential biofuel feedstocks according to lignin S/G ratio, significantly reducing experiment and analysis time and expense while providing non-destructive, accurate, global, predictive models encompassing a diverse array of feedstocks.

Phytochemicals in Japanese plums: impact of maturity and bioaccessibility

In recent years there has been increasing consumer interest in the potential health benefits of dietary derived phytochemicals such as polyphenols (including anthocyanins and flavonols) and carotenoids. A new variety of Japanese plum (Prunus salicina Lindl.), named Queen Garnet (QG), was developed as a high anthocyanin plum in a Queensland (Australia) Government breeding program and may be attractive to consumers, but knowledge of other phytochemical content, and bioaccessibility, is currently limited. As a result, the present study examined (1) the impact of harvest date on anthocyanins, quercetin glycosides and carotenoids in Queen Garnet and another red fleshed commercial Japanese plum variety, Black Diamond (BD), (2) the content of bound phenolics in plum fruit and (3) the in vitro bioaccessibility and release of these phytochemicals as an initial measure to predict their potential bioavailability. For both QG and BD, the last harvest resulted in the highest anthocyanin content in peel, flesh and whole fruit, whereas no significant effects could be observed for quercetin glycosides, and total carotenoids decreased over time. The highest content of bound phenolics (30% of total amount) could be found in BD flesh. Between 53% and 59% of quercetin glycosides and anthocyanins were released from QG after the gastric and small intestinal digestion procedure, whereas the release of carotenoids ranged between 4–6%. A relative high release of anthocyanins and quercetin glycosides could be observed from QG which may result in a higher gastro-intestinal absorption rate of these compounds. However, follow-up studies (clinical trials) are warranted to investigate the in vivo bioavailability and subsequently biological activity of QG.

Formulation and characterization of drug-loaded microparticles using distiller’s dried grain kafirin

Kafirin, a protein extracted from sorghum grain has been formulated into microparticles, and proposed for use as a delivery system due to the resistance of kafirin to upper gastrointestinal digestion. However, extracting kafirin from sorghum “distiller’s dried grains with solubles” (DDGS) may be more efficient as the carbohydrate component has been removed by fermentation. This study investigated the properties and use of kafirin extracted from DDGS to formulate microparticles. Prednisolone, an anti-inflammatory drug that could benefit from a delayed and targeted delivery system to the colon, was loaded into DDGS kafirin microparticles by phase separation using sodium chloride. Scanning electron micrographs revealed that the empty and prednisolone-loaded microparticles were round in shape and varied in size. Surface binding studies indicated prednisolone was loaded within the microparticles rather than being solely bound on the surface. These findings demonstrate DDGS kafirin can be formulated into microparticles and loaded with medication. Future studies could investigate the potential applications of DDGS kafirin microparticles as an orally administered targeted drug-delivery system.

Formulation and characterization of drug-loaded microparticles using distiller’s dried grain kafirin

Kafirin, a protein extracted from sorghum grain has been formulated into microparticles, and proposed for use as a delivery system due to the resistance of kafirin to upper gastrointestinal digestion. However, extracting kafirin from sorghum distillers dried grains with solubles (DDGS) may be more efficient as the carbohydrate component has been removed by fermentation. This study investigated the properties and use of kafirin extracted from DDGS to formulate microparticles. Prednisolone, an anti-inflammatory drug that could benefit from a delayed and targeted delivery system to the colon, was loaded into DDGS kafirin microparticles by phase separation using sodium chloride. Scanning electron micrographs revealed that the empty and prednisolone-loaded microparticles were round in shape and varied in size. Surface binding studies indicated prednisolone was loaded within the microparticles rather than being solely bound on the surface. These findings demonstrate DDGS kafirin can be formulated into microparticles and loaded with medication. Future studies could investigate the potential applications of DDGS kafirin microparticles as an orally administered targeted drug-delivery system.

Probiotic Bacillus amyloliquefaciens Strain H57 Improves the Performance of Pregnant and Lactating Ewes Fed a Diet Based on Palm Kernel Meal

Probiotic supplements are single or mixed strain cultures of live microorganisms that benefit the host by improving the properties of the indigenous microflora (Seo et al 2010). In a pilot study at the University of Queensland, Norton et al (2008) found that Bacillus amyloliquefaciens Strain H57 (H57), primarily investigated as an inoculum to make high-quality hay, improved feed intake and nitrogen utilisation over several weeks in pregnant ewes. The purpose of the following study was to further challenge the potential of H57 -to show it survives the steam-pelleting process, and that it improves the performance of ewes fed pellets based on an agro-industrial by-product with a reputation for poor palatability, palm kernel meal (PKM), (McNeill 2013). Thirty-two first-parity White Dorper ewes (day 37 of pregnancy, mean liveweight = 47.3 kg, mean age = 15 months) were inducted into individual pens in the animal house at the University of Queensland, Gatton. They were adjusted onto PKM-based pellets (g/kg drymatter (DM): PKM, 408; sorghum, 430; chick pea hulls, 103; minerals and vitamins; Crude protein, 128; ME: 11.1MJ/kg DM) until day 89 of pregnancy and thereafter fed a predominately pelleted diet incorporating with or without H57 spores (10 9 colony forming units (cfu)/kg pellet, as fed), plus 100g/ewe/day oaten chaff, until day 7 of lactation. From day 7 to 20 of lactation the pelleted component of the diet was steadily reduced to be replaced by a 50:50 mix of lucerne: oaten chaff, fed ad libitum, plus 100g/ewe/day of ground sorghum grain with or without H57 (10 9 cfu/ewe/day). The period of adjustment in pregnancy (day 37-89) extended beyond expectations due to some evidence of mild ruminal acidosis after some initially high intakes that were followed by low intakes. During that time the diet was modified, in an attempt to improve palatability, by the addition of oaten chaff and the removal of an acidifying agent (NH4Cl) that was added initially to reduce the risk of urinary calculi. Eight ewes were removed due to inappetence, leaving 24 ewes to start the trial at day 90 of pregnancy. From day 90 of pregnancy until day 63 of lactation, liveweights of the ewes and their lambs were determined weekly and at parturition. Feed intakes of the ewes were determined weekly. Once lambing began, 1 ewe was removed as it gave birth to twin lambs (whereas the rest gave birth to a single lamb), 4 due to the loss of their lambs (2 to dystocia), and 1 due to copper toxicity. The PKM pellets were suspected to be the cause of the copper toxicity and so were removed in early lactation. Hence, the final statistical analysis using STATISTICA 8 (Repeated measures ANOVA for feed intake, One-way ANOVA for liveweight change and birth weight) was completed on 23 ewes for the pregnancy period (n = 11 fed H57; n = 12 control), and 18 ewes or lambs for the lactation period (n = 8 fed H57; n = 10 control). From day 90 of pregnancy until parturition the H57 supplemented ewes ate 17 more DM (g/day: 1041 vs 889, sed = 42.4, P = 0.04) and gained more liveweight (g/day: 193 vs 24.0, sed = 25.4, P = 0.0002), but produced lambs with a similar birthweight (kg: 4.18 vs 3.99, sed = 0.19, P = 0.54). Over the 63 days of lactation the H57 ewes ate similar amounts of DM but grew slower than the control ewes (g/day: 1.5 vs 97.0, sed = 21.7, P = 0.012). The lambs of the H57 ewes grew faster than those of the control ewes for the first 21 days of lactation (g/day: 356 vs 265, sed = 16.5, P = 0.006). These data support the findings of Norton et al (2008) and Kritas et al (2006) that certain Bacillus spp. supplements can improve the performance of pregnant and lactating ewes. In the current study we particularly highlighted the capacity of H57 to stimulate immature ewes to continue to grow maternal tissue through pregnancy, possibly through an enhanced appetite, which appeared then to stimulate a greater capacity to partition nutrients to their lambs through milk, at least for the first few weeks of lactation, a critical time for optimising lamb survival. To conclude, H57 can survive the steam pelleting process to improve feed intake and maternal liveweight gain in late pregnancy, and performance in early lactation, of first-parity ewes fed a diet based on PKM.

Automating Crop Damage Assessments with Unmanned Aerial Systems and Machine Learning

Agricultural pests are responsible for millions of dollars in crop losses and management costs every year. In order to implement optimal site-specific treatments and reduce control costs, new methods to accurately monitor and assess pest damage need to be investigated. In this paper we explore the combination of unmanned aerial vehicles (UAV), remote sensing and machine learning techniques as a promising methodology to address this challenge. The deployment of UAVs as a sensor platform is a rapidly growing field of study for biosecurity and precision agriculture applications. In this experiment, a data collection campaign is performed over a sorghum crop severely damaged by white grubs (Coleoptera: Scarabaeidae). The larvae of these scarab beetles feed on the roots of plants, which in turn impairs root exploration of the soil profile. In the field, crop health status could be classified according to three levels: bare soil where plants were decimated, transition zones of reduced plant density and healthy canopy areas. In this study, we describe the UAV platform deployed to collect high-resolution RGB imagery as well as the image processing pipeline implemented to create an orthoimage. An unsupervised machine learning approach is formulated in order to create a meaningful partition of the image into each of the crop levels. The aim of this approach is to simplify the image analysis step by minimizing user input requirements and avoiding the manual data labelling necessary in supervised learning approaches. The implemented algorithm is based on the K-means clustering algorithm. In order to control high-frequency components present in the feature space, a neighbourhood-oriented parameter is introduced by applying Gaussian convolution kernels prior to K-means clustering. The results show the algorithm delivers consistent decision boundaries that classify the field into three clusters, one for each crop health level as shown in Figure 1. The methodology presented in this paper represents a venue for further esearch towards automated crop damage assessments and biosecurity surveillance.