163 research outputs found
An automated non-destructive prediction of peroxide value and free fatty acid level in mixed nut samples
This study aimed to develop an automated technique, which is rapid, non-destructive and inexpensive, to test for rancidity of nuts. A visible to near infrared benchtop hyperspectral camera was used to capture images from blanched canarium, unblanched canarium and macadamia samples. Support vector machine classification (SVC) and PLSR models were developed to segregate the pooled spectra of the nuts and predict their peroxide values (PV) and free fatty acid (FFA) concentrations. The SVC and PLSR models were then used in a hierarchical model to develop an automated system for predicting PV and FFA. The automated model was then tested using a test set providing classification accuracy of 87% and R2 between 0.60 and 0.76 and RPD between 1.6 and 2.7 for PV and FFA prediction. Overall, the automated system has the potential commercial application in nut processing to detect rancidity of mixed nut samples non-destructively and in real-time. It is suggested to train other machine learning models with more samples to improve the accuracy of predictions
Comparison of Hyperspectral Imaging and Near-Infrared Spectroscopy to Determine Nitrogen and Carbon Concentrations in Wheat
Hyperspectral imaging (HSI) is an emerging rapid and non-destructive technology that has promising application within feed mills and processing plants in poultry and other intensive animal industries. HSI may be advantageous over near infrared spectroscopy (NIRS) as it scans entire samples, which enables compositional gradients and sample heterogenicity to be visualised and analysed. This study was a preliminary investigation to compare the performance of HSI with that of NIRS for quality measurements of ground samples of Australian wheat and to identify the most important spectral regions for predicting carbon (C) and nitrogen (N) concentrations. In total, 69 samples were scanned using an NIRS (400–2500 nm), and two HSI cameras operated in 400–1000 nm (VNIR) and 1000–2500 nm (SWIR) spectral regions. Partial least square regression (PLSR) models were used to correlate C and N concentrations of 63 calibration samples with their spectral reflectance, with 6 additional samples used for testing the models. The accuracy of the HSI predictions (full spectra) were similar or slightly higher than those of NIRS (NIRS Rc2 for C = 0.90 and N = 0.96 vs. HSI Rc2 for C (VNIR) = 0.97 and N (SWIR) = 0.97). The most important spectral region for C prediction identified using HSI reflectance was 400–550 nm with R2 of 0.93 and RMSE of 0.17% in the calibration set and R2 of 0.86, RMSE of 0.21% and ratio of performance to deviation (RPD) of 2.03 in the test set. The most important spectral regions for predicting N concentrations in the feed samples included 1451–1600 nm, 1901–2050 nm and 2051–2200 nm, providing prediction with R2 ranging from 0.91 to 0.93, RMSE ranging from 0.06% to 0.07% in the calibration sets, R2 from 0.96 to 0.99, RMSE of 0.06% and RPD from 3.47 to 3.92 in the test sets. The prediction accuracy of HSI and NIRS were comparable possibly due to the larger statistical population (larger number of pixels) that HSI provided, despite the fact that HSI had smaller spectral range compared with that of NIRS. In addition, HSI enabled visualising the variability of C and N in the samples. Therefore, HSI is advantageous compared to NIRS as it is a multifunctional tool that poses many potential applications in data collection and quality assurance within feed mills and poultry processing plants. The ability to more accurately measure and visualise the properties of feed ingredients has potential economic benefits and therefore additional investigation and development of HSI in this application is warranted
A Performance Evaluation of Vis/NIR Hyperspectral Imaging to Predict Curcumin Concentration in Fresh Turmeric Rhizomes
Hyperspectral image (HSI) analysis has the potential to estimate organic compounds in plants and foods. Curcumin is an important compound used to treat a range of medical conditions. Therefore, a method to rapidly determine rhizomes with high curcumin content on-farm would be of significant advantage for farmers. Curcumin content of rhizomes varies within, and between varieties but current chemical analysis methods are expensive and time consuming. This study compared curcumin in three turmeric (Curcuma longa) varieties and examined the potential for laboratory-based HSI to rapidly predict curcumin using the visible–near infrared (400–1000 nm) spectrum. Hyperspectral images (n = 152) of the fresh rhizome outer-skin and flesh were captured, using three local varieties (yellow, orange, and red). Distribution of curcuminoids and total curcumin was analysed. Partial least squares regression (PLSR) models were developed to predict total curcumin concentrations. Total curcumin and the proportion of three curcuminoids differed significantly among all varieties. Red turmeric had the highest total curcumin concentration (0.83 ± 0.21%) compared with orange (0.37 ± 0.12%) and yellow (0.02 ± 0.02%). PLSR models predicted curcumin using raw spectra of rhizome flesh and pooled data for all three varieties (R2c = 0.83, R2p = 0.55, ratio of prediction to deviation (RPD) = 1.51) and was slightly improved by using images of a single variety (orange) only (R2c = 0.85, R2p = 0.62, RPD = 1.65). However, prediction of curcumin using outer-skin of rhizomes was poor (R2c = 0.64, R2p = 0.37, RPD = 1.28). These models can discriminate between ‘low’ and ‘high’ values and so may be adapted into a two-level grading system. HSI has the potential to help identify turmeric rhizomes with high curcumin concentrations and allow for more efficient refinement into curcumin for medicinal purposes
Statistical Telegram. MONTHLY STATISTICS OF REGISTERED UNEMPLOYMENT IN THE EUROPEAN COMMUNITY SEPTEMBER 1977. 1977.10
Abstract Background Ascochyta blight, caused by the fungus Ascochyta lentis, is one of the most destructive lentil diseases worldwide, resulting in over $16 million AUD annual loss in Australia alone. The use of resistant cultivars is currently considered the most effective and environmentally sustainable strategy to control this disease. However, little is known about the genes and molecular mechanisms underlying lentil resistance against A. lentis. Results To uncover the genetic basis of lentil resistance to A. lentis, differentially expressed genes were profiled in lentil plants during the early stages of A. lentis infection. The resistant ‘ILL7537’ and susceptible ‘ILL6002’ lentil genotypes were examined at 2, 6, and 24 h post inoculation utilising high throughput RNA-Sequencing. Genotype and time-dependent differential expression analysis identified genes which play key roles in several functions of the defence response: fungal elicitors recognition and early signalling; structural response; biochemical response; transcription regulators; hypersensitive reaction and cell death; and systemic acquired resistance. Overall, the resistant genotype displayed an earlier and faster detection and signalling response to the A. lentis infection and demonstrated higher expression levels of structural defence-related genes. Conclusions This study presents a first-time defence-related transcriptome of lentil to A. lentis, including a comprehensive characterisation of the molecular mechanism through which defence against A. lentis is induced in the resistant lentil genotype
Transcriptome profiling of lentil (Lens culinaris) through the first 24 hours of Ascochyta lentis infection reveals key defence response genes
Eco-toxicological effects of the avermectin family with a focus on abamectin and ivermectin
Hosseini Bai, S ORCiD: 0000-0001-8646-6423Avermectin family members are categorised as highly effective but toxic natural products that are used as pharmaceuticals in both humans and animals and for crop protection. Abamectin and ivermectin are the two most commonly used compounds from this family with abamectin the only compound to be used for both crop protection and pharmaceutical purposes. Avermectins are produced by the soil dwelling actinomycetes Streptomyces avermitilis and despite having complex chemical structures, they are manufactured via synthesis in large scales for commercial use.Although the extent of the eco-toxicological effects of avermectins is not well documented, reports of eco-toxicity exist. Avermectins have short half-lives and their residues can be eliminated through different food processing methods. However, avermectins can persist in water, sediment, soil and food products and therefore management practices that reduce the potential risks associated with eco-toxicity of these highly toxic compounds need to be further developed. This manuscript provides a critical review of the eco-toxicological risks and the potential for food contamination associated with avermectin use. © 2016 Elsevier Ltd
Glyphosate: Environmental contamination, toxicity and potential risks to human health via food contamination
Hosseini Bai, S ORCiD: 0000-0001-8646-6423Glyphosate has been the most widely used herbicide during the past three decades. The US Environmental Protection Agency (EPA) classifies glyphosate as ‘practically non-toxic and not an irritant’ under the acute toxicity classification system. This classification is based primarily on toxicity data and due to its unique mode of action via a biochemical pathway that only exists in a small number of organisms that utilise the shikimic acid pathway to produce amino acids, most of which are green plants. This classification is supported by the majority of scientific literature on the toxic effects of glyphosate. However, in 2005, the Food and Agriculture Organisation (FAO) reported that glyphosate and its major metabolite, aminomethylphosphonic acid (AMPA), are of potential toxicological concern, mainly as a result of accumulation of residues in the food chain. The FAO further states that the dietary risk of glyphosate and AMPA is unlikely if the maximum daily intake of 1 mg kg−1body weight (bw) is not exceeded. Research has now established that glyphosate can persist in the environment, and therefore, assessments of the health risks associated with glyphosate are more complicated than suggested by acute toxicity data that relate primarily to accidental high-rate exposure. We have used recent literature to assess the possible risks associated with the presence of glyphosate residues in food and the environment. © 2016, Springer-Verlag Berlin Heidelberg
Eco-toxicological effects of the avermectin family with a focus on abamectin and ivermectin
Avermectin family members are categorised as highly effective but toxic natural products that are used as pharmaceuticals in both humans and animals and for crop protection. Abamectin and ivermectin are the two most commonly used compounds from this family with abamectin the only compound to be used for both crop protection and pharmaceutical purposes. Avermectins are produced by the soil dwelling actinomycetes Streptomyces avermitilis and despite having complex chemical structures, they are manufactured via synthesis in large scales for commercial use.Although the extent of the eco-toxicological effects of avermectins is not well documented, reports of eco-toxicity exist. Avermectins have short half-lives and their residues can be eliminated through different food processing methods. However, avermectins can persist in water, sediment, soil and food products and therefore management practices that reduce the potential risks associated with eco-toxicity of these highly toxic compounds need to be further developed. This manuscript provides a critical review of the eco-toxicological risks and the potential for food contamination associated with avermectin use. © 2016 Elsevier Ltd
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