Multi-Layer Biosensor for Pre-Symptomatic Detection of Puccinia strifformis, the Causal Agent of Yellow Rust.

The yellow rust of wheat (caused by Puccinia striiformis f. sp. tritici) is a devastating fungal infection that is responsible for significant wheat yield losses. The main challenge with the detection of this disease is that it can only be visually detected on the leaf surface between 7 and 10 days after infection, and by this point, counter measures such as the use of fungicides are generally less effective. The hypothesis of this study is to develop and use a compact electrochemical-based biosensor for the early detection of P. striiformis, thus enabling fast countermeasures to be taken. The biosensor that was developed consists of three layers. The first layer mimics the wheat leaf surface morphology. The second layer consists of a sucrose/agar mixture that acts as a substrate and contains a wheat-derived terpene volatile organic compound that stimulates the germination and growth of the spores of the yellow rust pathogen P. s. f. sp. tritici. The third layer consists of a nonenzymatic glucose sensor that produces a signal once invertase is produced by P. striiformis, which comes into contact with the second layer, thereby converting sucrose to glucose. The results show the proof that this innovative biosensor can enable the detection of yellow rust spores in 72 h.

Early detection of plant virus infection using multispectral imaging and spatial-spectral machine learning.

Cassava brown streak disease (CBSD) is an emerging viral disease that can greatly reduce cassava productivity, while causing only mild aerial symptoms that develop late in infection. Early detection of CBSD enables better crop management and intervention. Current techniques require laboratory equipment and are labour intensive and often inaccurate. We have developed a handheld active multispectral imaging (A-MSI) device combined with machine learning for early detection of CBSD in real-time. The principal benefits of A-MSI over passive MSI and conventional camera systems are improved spectral signal-to-noise ratio and temporal repeatability. Information fusion techniques further combine spectral and spatial information to reliably identify features that distinguish healthy cassava from plants with CBSD as early as 28 days post inoculation on a susceptible and a tolerant cultivar. Application of the device has the potential to increase farmers' access to healthy planting materials and reduce losses due to CBSD in Africa. It can also be adapted for sensing other biotic and abiotic stresses in real-world situations where plants are exposed to multiple pest, pathogen and environmental stresses.

Recent Advances in Enzymatic and Non-Enzymatic Electrochemical Glucose Sensing.

The detection of glucose is crucial in the management of diabetes and other medical conditions but also crucial in a wide range of industries such as food and beverages. The development of glucose sensors in the past century has allowed diabetic patients to effectively manage their disease and has saved lives. First-generation glucose sensors have considerable limitations in sensitivity and selectivity which has spurred the development of more advanced approaches for both the medical and industrial sectors. The wide range of application areas has resulted in a range of materials and fabrication techniques to produce novel glucose sensors that have higher sensitivity and selectivity, lower cost, and are simpler to use. A major focus has been on the development of enzymatic electrochemical sensors, typically using glucose oxidase. However, non-enzymatic approaches using direct electrochemistry of glucose on noble metals are now a viable approach in glucose biosensor design. This review discusses the mechanisms of electrochemical glucose sensing with a focus on the different generations of enzymatic-based sensors, their recent advances, and provides an overview of the next generation of non-enzymatic sensors. Advancements in manufacturing techniques and materials are key in propelling the field of glucose sensing, however, significant limitations remain which are highlighted in this review and requires addressing to obtain a more stable, sensitive, selective, cost efficient, and real-time glucose sensor.

The Potential of Polyethylene Terephthalate Glycol as Biomaterial for Bone Tissue Engineering.

The search for materials with improved mechanical and biological properties is a major challenge in tissue engineering. This paper investigates, for the first time, the use of Polyethylene Terephthalate Glycol (PETG), a glycol-modified class of Polyethylene Terephthalate (PET), as a potential material for the fabrication of bone scaffolds. PETG scaffolds with a 0/90 lay-dawn pattern and different pore sizes (300, 350 and 450 µm) were produced using a filament-based extrusion additive manufacturing system and mechanically and biologically characterized. The performance of PETG scaffolds with 300 µm of pore size was compared with polycaprolactone (PCL). Results show that PETG scaffolds present significantly higher mechanical properties than PCL scaffolds, providing a biomechanical environment that promotes high cell attachment and proliferation.

High Speed Crop and Weed Identification in Lettuce Fields for Precision Weeding.

Precision weeding can significantly reduce or even eliminate the use of herbicides in farming. To achieve high-precision, individual targeting of weeds, high-speed, low-cost plant identification is essential. Our system using the red, green, and near-infrared reflectance, combined with a size differentiation method, is used to identify crops and weeds in lettuce fields. Illumination is provided by LED arrays at 525, 650, and 850 nm, and images are captured in a single-shot using a modified RGB camera. A kinematic stereo method is utilised to compensate for parallax error in images and provide accurate location data of plants. The system was verified in field trials across three lettuce fields at varying growth stages from 0.5 to 10 km/h. In-field results showed weed and crop identification rates of 56% and 69%, respectively. Post-trial processing resulted in average weed and crop identifications of 81% and 88%, respectively.

Electrical impedance tomography as a tool for phenotyping plant roots.

BACKGROUND: Plant roots are complex, three-dimensional structures that play a central role in anchorage, water and nutrient acquisition, storage and interaction with rhizosphere microbes. Studying the development of the plant root system architecture is inherently difficult as soil is not a transparent medium. RESULTS: This study uses electrical impedance tomography (EIT) to visualise oilseed rape root development in horticultural compost. The development of healthy, control plants and those infected with the gall-forming pathogen, Plasmodiophora brassicae-the causative agent of clubroot disease-were compared. EIT measurements were used to quantify the development of the root system and distinguish between control and infected plants at the onset of gall formation, approximately 20 days after inoculation. Although clear and stark differences between healthy and infected plants were obtained by careful (and hence laborious) packing of the growth medium in layers within the pots; clubroot identification is still possible without a laborious vessel filling protocol. CONCLUSIONS: These results demonstrate the utility of EIT as a low-cost, non-invasive, non-destructive method for characterising root system architecture and plant-pathogen interactions in opaque growth media. As such it offers advantages over other root characterisation techniques and has the potential to act as a low-cost tool for plant phenotyping.

Multispectral imaging for presymptomatic analysis of light leaf spot in oilseed rape.

BACKGROUND: The use of spectral imaging within the plant phenotyping and breeding community has been increasing due its utility as a non-invasive diagnostic tool. However, there is a lack of imaging systems targeted specifically at plant science duties, resulting in low precision for canopy-scale measurements. This study trials a prototype multispectral system designed specifically for plant studies and looks at its use as an early detection system for visually asymptomatic disease phases, in this case Pyrenopeziza brassicae in Brassica napus. The analysis takes advantage of machine learning in the form of feature selection and novelty detection to facilitate the classification. An initial study into recording the morphology of the samples is also included to allow for further improvement to the system performance. RESULTS: The proposed method was able to detect light leaf spot infection with 92% accuracy when imaging entire oilseed rape plants from above, 12 days after inoculation and 13 days before the appearance of visible symptoms. False colour mapping of spectral vegetation indices was used to quantify disease severity and its distribution within the plant canopy. In addition, the structure of the plant was recorded using photometric stereo, with the output influencing regions used for diagnosis. The shape of the plants was also recorded using photometric stereo, which allowed for reconstruction of the leaf angle and surface texture, although further work is needed to improve the fidelity due to uneven lighting distributions, to allow for reflectance compensation. CONCLUSIONS: The ability of active multispectral imaging has been demonstrated along with the improvement in time taken to detect light leaf spot at a high accuracy. The importance of capturing structural information is outlined, with its effect on reflectance and thus classification illustrated. The system could be used in plant breeding to enhance the selection of resistant cultivars, with its early and quantitative capability.

A method for real-time classification of insect vectors of mosaic and brown streak disease in cassava plants for future implementation within a low-cost, handheld, in-field multispectral imaging sensor.

BACKGROUND: The paper introduces a multispectral imaging system and data-processing approach for the identification and discrimination of morphologically indistinguishable cryptic species of the destructive crop pest, the whitefly Bemisia tabaci. This investigation and the corresponding system design, was undertaken in two phases under controlled laboratory conditions. The first exploited a prototype benchtop variant of the proposed sensor system to analyse four cryptic species of whitefly reared under similar conditions. The second phase, of the methodology development, employed a commercial high-precision laboratory hyperspectral imager to recover reference data from five cryptic species of whitefly, immobilized through flash freezing, and taken from across four feeding environments. RESULTS: The initial results, for the single feeding environment, showed that a correct species classification could be achieved in 85-95% of cases, utilising linear Partial Least Squares approaches. The robustness of the classification approach was then extended both in terms of the automated spatial extraction of the most pertinent insect body parts, to assist with the spectral classification model, as well as the incorporation of a non-linear Support Vector Classifier to maintain the overall classification accuracy at 88-98%, irrespective of the feeding and crop environment. CONCLUSION: This study demonstrates that through an integration of both the spatial data, associated with the multispectral images being used to separate different regions of the insect, and subsequent spectral analysis of those sub-regions, that B. tabaci viral vectors can be differentiated from other cryptic species, that appear morphologically indistinguishable to a human observer, with an accuracy of up to 98%. The implications for the engineering design for an in-field, handheld, sensor system is discussed with respect to the learning gained from this initial stage of the methodology development.

Photometric stereo for three-dimensional leaf venation extraction.

Leaf venation extraction studies have been strongly discouraged by considerable challenges posed by venation architectures that are complex, diverse and subtle. Additionally, unpredictable local leaf curvatures, undesirable ambient illuminations, and abnormal conditions of leaves may coexist with other complications. While leaf venation extraction has high potential for assisting with plant phenotyping, speciation and modelling, its investigations to date have been confined to colour image acquisition and processing which are commonly confounded by the aforementioned biotic and abiotic variations. To bridge the gaps in this area, we have designed a 3D imaging system for leaf venation extraction, which can overcome dark or bright ambient illumination and can allow for 3D data reconstruction in high resolution. We further propose a novel leaf venation extraction algorithm that can obtain illumination-independent surface normal features by performing Photometric Stereo reconstruction as well as local shape measures by fusing the decoupled shape index and curvedness features. In addition, this algorithm can determine venation polarity - whether veins are raised above or recessed into a leaf. Tests on both sides of different leaf species with varied venation architectures show that the proposed method is accurate in extracting the primary, secondary and even tertiary veins. It also proves to be robust against leaf diseases which can cause dramatic changes in colour. The effectiveness of this algorithm in determining venation polarity is verified by it correctly recognising raised or recessed veins in nine different experiments.

Towards Phosphate Detection in Hydroponics Using Molecularly Imprinted Polymer Sensors.

An interdigitated electrode sensor was designed and microfabricated for measuring the changes in the capacitance of three phosphate selective molecularly imprinted polymer (MIP) formulations, in order to provide hydroponics users with a portable nutrient sensing tool. The MIPs investigated were synthesised using different combinations of the functional monomers methacrylic acid (MAA) and N-allylthiourea, against the template molecules diphenyl phosphate, triethyl phosphate, and trimethyl phosphate. A cross-interference study between phosphate, nitrate, and sulfate was carried out for the MIP materials using an inductance, capacitance, and resistance (LCR) meter. Capacitance measurements were taken by applying an alternating current (AC) with a potential difference of 1 V root mean square (RMS) at a frequency of 1 kHz. The cross-interference study demonstrated a strong binding preference to phosphate over the other nutrient salts tested for each formulation. The size of template molecule and length of the functional monomer side groups also determined that a short chain functional monomer in combination with a template containing large R-groups produced the optimal binding site conditions when synthesising a phosphate selective MIP.

Supplemental blue LED lighting array to improve the signal quality in hyperspectral imaging of plants.

Hyperspectral imaging systems used in plant science or agriculture often have suboptimal signal-to-noise ratio in the blue region (400-500 nm) of the electromagnetic spectrum. Typically there are two principal reasons for this effect, the low sensitivity of the imaging sensor and the low amount of light available from the illuminating source. In plant science, the blue region contains relevant information about the physiology and the health status of a plant. We report on the improvement in sensitivity of a hyperspectral imaging system in the blue region of the spectrum by using supplemental illumination provided by an array of high brightness light emitting diodes (LEDs) with an emission peak at 470 nm.

Absorption spectroscopy in microfluidic flow cells using a metal clad leaky waveguide device with a porous gel waveguide layer.

Broadband absorption spectroscopy is advantageous because the full spectral profile of an analyte can permit identification of species. This work for the first time investigates the feasibility of a metal clad leaky waveguide (MCLW) device to obtain an absorption spectrum of an analyte of interest, methylene blue, using a white light source in a microfluidic flow cell. The MCLW device comprises a porous low refractive index gel, agarose, deposited on a titanium coated glass slide. The device was capable of detecting 2.3 µM of methylene blue at a wavelength of 650 nm. The corresponding minimum detectable absorbance is 1.6 × 10(-1) cm(-1). In comparison to commonly used detection devices the MCLW is simpler, robust, easier to fabricate and can be easily interfaced to microfluidic devices. It was also possible to store the MCLW devices dry for up to a year and rehydrate them in 30 s to a working condition.

Capillary zone electrophoresis for the analysis of glycoforms of cellobiohydrolase.

Cellobiohydrolase (CBH) is an important enzyme for the conversion of lignocellulosic biomass to ethanol. This work separated the glycoforms of CBH possessing different numbers of neutral mannoses using capillary zone electrophoresis (CZE) in a 50 mM, pH 7.5 phosphate buffer. The method analysed CBH in an intact form using a polyacrylamide coated fused silica capillary without requiring additives or labelling of the enzyme. The migration time of the major peak was found to be 21.6±0.1 min (n=3) and the approach is suitable for testing of batch-to-batch consistency of CBH. Ease-of-use, automation and speed are the other benefits due to which the use of CZE for analysing glycoforms of CBH was concluded to be ideal.

A specific, robust, and automated method for routine at-line monitoring of the concentration of cellulases in genetically modified sugarcane plants.

Bagasse is one of the waste crop materials highlighted as commercially viable for cellulosic bio-ethanol production via enzymatic conversion to release fermentable sugars. Genetically modified sugarcane expressing cellobiohydrolases (CBH), endoglucanase (EG), and ß-glucosidases (BG) provide a more cost-effective route to cellulose breakdown compared to culturing these enzymes in microbial tanks. Hence, process monitoring of the concentration profile of these key cellulases in incoming batches of sugarcane is required for fiscal measures and bio-ethanol process control. The existing methods due to their non-specificity, requirement of trained analysts, low sample throughput, and low amenability to automation are unsuitable for this purpose. Therefore, this paper explores a membrane-based sample preparation method coupled to capillary zone electrophoresis (CZE) to quantify these enzymes. The maximum enzyme extraction efficiency was obtained by using a polyethersulfone membrane with molecular cut-off of 10 kDa. The use of 15 mM, pH 7.75, phosphate buffer resulted in CZE separation and quantification of CBH, EG, and BG within 10 min. Migration time reproducibility was between 0.56% and 0.7% and hence, suitable for use with automatic peak detection software. Therefore, the developed CZE method is suitable for at-line analysis of BG, CBH, and EG in every batch of harvested sugarcane.

Isotachophoresis-based sample preparation of cellulases in sugarcane juice using bovine serum albumin as a model protein.

The foremost requirement of quantification of cellulases expressed in genetically modified sugarcane is an efficient sample clean-up. This work investigates the feasibility of isotachophoresis for this purpose. An electrolyte system comprising a leading electrolyte of 10mM formic acid at pH 9.0 and a terminating electrolyte of 10mM ß-alanine was devised and used to perform isotachophoresis of cellulases. The use of a simple front cutting method removed a majority of interfering species in the juice, thereby resulting in the formation of a distinct zone of desired proteins. In comparison to techniques such as ultrafiltration and liming, the analysis time and loss of desired proteins was lower when the sample was prepared by using isotachophoresis. Hence, isotachophoresis was an ideal choice for purification of the proteins in question from the remaining components in the juice.

Toward a microfluidic-based rapid amylase assay system.

This article describes work into a prototype system for the assay of amylase, using microfludic technologies. The new system has a significantly shorter cycle time than the current laboratory methods, which generally use microtitre plates, yet is capable of generating significantly superior results. As such, we have shown that sensitivity is enhanced by a factor of 10 in the standard assay trials, and by a factor of 2 in the real-sample lab trials. In both assays, the use of a microreactor system reduced the reaction time by a factor of 6.2, from 20 min incubation to 3.2 min. Basing the conclusion on the Megazyme Cerealpha Standard Method, and using the Cerealpha units as a measure of assay efficiency, the typical response for the microfluidic assay was shown to be 1.0 x 10(-3) CU/mL (standard deviation [SD] 2.5 x 10(-4) CU/mL), compared to 2.56 x 10(-4) CU/mL (SD 5.94 x 10(-5) CU/mL) for the standard macroassay. It is believed that this improvement in the reaction schematics is due to the inherent advantages of microfluidic devices such as superior mixing, higher thermal efficiency, and enhanced reaction kinetics.