Fast and sensitive aflatoxin B1 and total aflatoxins ELISAs for analysis of peanuts, maize and feed ingredients

Aflatoxins are a group of carcinogenic compounds produced by Aspergillus fungi that can grow on different agricultural crops. Both acute and chronic exposure to these mycotoxins can cause serious illness. Due to the high occurrence of aflatoxins in crops worldwide fast and cost-effective analytical methods are required for the identification of contaminated agricultural commodities before they are processed into final products and placed on the market. In order to provide new tools for aflatoxin screening two prototype fast ELISA methods: one for the detection of aflatoxin B₁ and the other for total aflatoxins were developed. Seven monoclonal antibodies with unique high sensitivity and at the same time good cross-reactivity profiles were produced. The monoclonal antibodies were characterized and two antibodies showing IC₅₀ of 0.037 ng/mL and 0.031 ng/mL for aflatoxin B₁ were applied in simple and fast direct competitive ELISA tests. The methods were validated for peanut matrix as this crop is one of the most affected by aflatoxin contamination. The detection capabilities of aflatoxin B₁ and total aflatoxins ELISAs were 0.4 μg/kg and 0.3 μg/kg for aflatoxin B₁, respectively, which are one of the lowest reported values. Total aflatoxins ELISA was also validated for the detection of aflatoxins B₂, G₁ and G₂. The application of the developed tests was demonstrated by screening 32 peanut samples collected from the UK retailers. Total aflatoxins ELISA was further applied to analyse naturally contaminated maize porridge and distiller's dried grain with solubles samples and the results were correlated with these obtained by UHPLC-MS/MS method.

Constructive Synthesis of Memory-Intensive Accelerators for FPGA From Nested Loop Kernels

Field-programmable gate arrays are ideal hosts to custom accelerators for signal, image, and data processing but de- mand manual register transfer level design if high performance and low cost are desired. High-level synthesis reduces this design burden but requires manual design of complex on-chip and off-chip memory architectures, a major limitation in applications such as video processing. This paper presents an approach to resolve this shortcoming. A constructive process is described that can derive such accelerators, including on- and off-chip memory storage from a C description such that a user-defined throughput constraint is met. By employing a novel statement-oriented approach, dataflow intermediate models are derived and used to support simple ap- proaches for on-/off-chip buffer partitioning, derivation of custom on-chip memory hierarchies and architecture transformation to ensure user-defined throughput constraints are met with minimum cost. When applied to accelerators for full search motion estima- tion, matrix multiplication, Sobel edge detection, and fast Fourier transform, it is shown how real-time performance up to an order of magnitude in advance of existing commercial HLS tools is enabled whilst including all requisite memory infrastructure. Further, op- timizations are presented that reduce the on-chip buffer capacity and physical resource cost by up to 96% and 75%, respectively, whilst maintaining real-time performance.

Continuous statistical modelling for rapid detection of adulteration of extra virgin olive oil using mid infrared and Raman spectroscopic data

The main objective of this work was to develop a novel dimensionality reduction technique as a part of an integrated pattern recognition solution capable of identifying adulterants such as hazelnut oil in extra virgin olive oil at low percentages based on spectroscopic chemical fingerprints. A novel Continuous Locality Preserving Projections (CLPP) technique is proposed which allows the modelling of the continuous nature of the produced in-house admixtures as data series instead of discrete points. The maintenance of the continuous structure of the data manifold enables the better visualisation of this examined classification problem and facilitates the more accurate utilisation of the manifold for detecting the adulterants. The performance of the proposed technique is validated with two different spectroscopic techniques (Raman and Fourier transform infrared, FT-IR). In all cases studied, CLPP accompanied by k-Nearest Neighbors (kNN) algorithm was found to outperform any other state-of-the-art pattern recognition techniques.