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Organisationseinheit der BAM
A lateral flow immunoassay (LFIA) using latex particles labeled with antibody to BSAclarithromycin (CLA) was developed for the rapid simultaneous group determination of six macrolide antibiotics. Optimization of antigen spotting on the membrane and latex probe loading allowed improving visual detectability (vLOD) 100 times, which was 1, 1, 10, 10, 50, and 1000 ng/mL for CLA, roxithromycin, erythromycin, dirithromycin, azithromycin, and oleandomycin in buffer, respectively. The calculated limits of instrumental detection (cLOD) were respectively 0.12, 0.15, 1.4, 2.1, 2.4, and 3.3 ng/mL. To avoid a strong influence of breast milk of a very diverse and variable composition, a sample pretreatment is proposed. The six macrolides mentioned can be visually detected in breast milk after 20 minutes pretreatment at concentrations of 10-1000 ng / ml or instrumentally with cLOD of 4.0, 2.5, 30, 42, 42 and 180 ng/mL. The recovery rate from the spiked samples carried out using a strip scanner device ranged from 71% to 110%, and precision expressed as relative standard deviation was between 3-14%. The described rapid on-site diagnostic assay format can be useful for monitoring the content of antibiotics in breast milk during macrolide treatment to ensure safe breastfeeding of infants.
Performance assessment for GuidedWave (GW)-based Structural Health Monitoring (SHM) systems is of major importance for industrial deployment.
With conventional feature extraction methods like damage indices, pathbased probability of detection (POD) analysis can be realized. To achieve reliability quantification enough data needs to be available, which is rarely the case.
Alternatives like methods for performance assessment on system level are still in development and in a discussion phase. In this contribution, POD results using an Artificial Intelligence (AI)-based data analysis are compared with those delivered by conventional data analysis. Using an open-access dataset from Open Guided Wave platform, the possibility of performance assessment for GW-based SHM systems using AI-based data analysis is shown in detail. An artificial neural network (ANN) classifier is trained to detect artificial damage in a stiffened CFRP plate. As input for the ANN, classical damage indicators are used. The ANN is tested to detect damage at another position, whose inspection data were not previously used in training. The findings show very high detection capabilities without sorting any specific path but only having a global view of current damage metrics. The systematic evaluation of the ANN predictions with respect to specific damage sizes allows to compute a probability of correct identification versus flaw dimension, somehow equivalent to and compared with the results achieved through classic path-based POD analysis. Also, sensitive paths are detected by ANN predictions allowing for evaluation of maximal distances between path and damage position. Finally, it is shown that the prediction performance of the ANN can be improved significantly by combining different damage indicators as inputs.
Reliability assessment of Structural Health Monitoring (SHM) systems
poses new challenges pushing the research community to address many questions which are still open. For guided wave-based SHM it is not possible to evaluate the system performance without taking into account the target structure and applied system parameters. This range of variables would result in countless measurements.
Factors like environmental conditions, structural dependencies and wave characteristics demand novel solutions for performance analysis of SHM systems compared to those relying on classical non-destructive evaluation. Such novel approaches typically require model-assisted investigations which may not only help to explain and understand performance assessment results but also enable complete studies without costly experiments. Within this contribution, a multi input multi output approach using a sparse transducer array permanently installed on a composite structure to excite and sense guided waves is considered. Firstly, the method and the analysis of path-based performance assessment are presented considering an open-access dataset from the Open Guided Wave platform. Then, a performance analysis of a guided wave-based SHM system using Probability of Detection is presented. To explain some unexpected results, the model-assisted investigations are used to understand the physical phenomena of wave propagation in the test specimen including the interaction with damage. Finally, issues and future steps in SHM systems’ performance assessment and their development are discussed.
Performance Assessment for a Guided Wave-Based SHM System Applied to a Stiffened Composite Structure
(2022)
To assess the ability of structural health monitoring (SHM) systems, a variety of prerequisites and contributing factors have to be taken into account. Within this publication, this variety is analyzed for actively introduced guided wave-based SHM systems. For these systems, it is not possible to analyze their performance without taking into account their structure and their applied system parameters. Therefore, interdependencies of performance assessment are displayed in an SHM pyramid based on the structure and its monitoring requirements. Factors influencing the quality, capability and reliability of the monitoring system are given and put into relation with state-of-the-art performance analysis in a non-destructive evaluation. While some aspects are similar and can be treated in similar ways, others, such as location, environmental condition and structural dependency, demand novel solutions. Using an open-access data set from the Open Guided Waves platform, a detailed method description and analysis of path-based performance assessment is presented.The adopted approach clearly begs the question about the decision framework, as the threshold affects the reliability of the system. In addition, the findings show the effect of the propagation path according to the damage position. Indeed, the distance of damage directly affects the system performance. Otherwise, the propagation direction does not alter the potentiality of the detection approach despite the anisotropy of composites. Nonetheless, the finite waveguide makes it necessary to look at the whole paths, as singular phenomena associated with the reflections may appear. Numerical investigation helps to clarify the centrality of wave mechanics and the necessity to take sensor position into account as an influencing factor. Starting from the findings achieved, all the issues are discussed, and potential future steps are outlined.
For the antiepileptic drug and anthropogenic marker carbamazepine (CBZ), a fast and cost-effective immunoassay based on fluorescence polarization (FPIA) was developed. The required fluorophore conjugates were synthesized from different fluorescein and CBZ derivatives. The most suitable tracer was CBZ–triglycine–5-(aminoacetamido)fluorescein. Additionally, the applicability of the assay in tubes and on microtiter plates was tested. The first format can be performed in a portable instrument and therefore can be applied in field measurements. The measurement of an individual sample can be carried out within 4 min. This assay shows a measurement range of 2.5–1000 µg L-1 and a test midpoint (or IC50) of 36 µg L-1. The FPIA performed on microtiter plates is useful for the assay development and is suitable for a very high throughput (up to 24 samples in 20 min). The test midpoint of this assay is 13 µg L-1 and the measurement range is 1.5–300 µg L-1. Furthermore, this assay requires smaller sample volumes and less reagents, including the crucial amount of antibody. The applicability of both assays to spiked surface water samples was evaluated. The recovery rates vary between 66–110% on microtiter plates and 81–140% in tubes.
Introduction
Aflatoxin B1 (AFB1) is a toxic low‐molecular‐weight secondary metabolite of Aspergillus flavus and A. parasiticus. AFB1 was classified as a Group I carcinogen by the World Health Organisation for Research on Cancer in 1993. AFB1 is an unavoidable natural contaminant of some herbal medicine, able to cause serious health issues for humans consuming the related medicine.
Objective
Therefore, this study aimed to develop an efficient fluorescence polarisation immunoassay (FPIA) and a rapid, low‐cost, and easy‐to‐use membrane‐based flow‐through immunoassay (MBA) for determination of AFB1 in herbal medicine Origanum vulgare L., Rubus idaeus L., Urtica dioica L. and Sorbus aucuparia L.
Results
A cut‐off level of the developed MBA was 0.8 ppb. Validation of the developed test was performed with blank and spiked samples. Using three naturally contaminated or three artificially spiked samples. The FPIA showed a linear working range of 8.6 to 64 ppb, and a half maximal inhibitory concentration (IC50) of 24 ppb.
Conclusion
The results were in good correlation with the enzymelinked immunosorbent assay (ELISA) results (the IC50 0.1 ppb). Both the sample preparation and analysis are simple, cost‐effective and easy to perform on‐site in non‐laboratory environments. Liquid chromatography with tandem mass spectrometry (LC‐MS/MS) was used as a confirmatory technique.
BPA is a chemical commonly used in the production of polymer-based materials that can have detrimental effects on the thyroid gland and impact human reproductive health. Various expensive methods, such as liquid and gas chromatography, have been suggested for detecting BPA.
The fluorescence polarization immunoassay (FPIA) is an inexpensive and efficient homogeneous mix-and-read method that allows for high-throughput screening. FPIA offers high specificity and sensitivity and can be carried out in a single phase within a timeframe of 20–30 min. In this study, new tracer molecules were designed that linked the fluorescein fluorophore with and without a spacer to the bisphenol A moiety. To assess the influence of the C6 spacer on the sensitivity of an assay based on the respective antibody, hapten–protein conjugates were synthesized and assessed for performance in an ELISA setup, and this resulted in a highly sensitive assay with a detection limit of 0.05 µg/L. The lowest limit of detection was reached by employing the spacer derivate in the FPIA and was 1.0 µg/L, working range from 2 to 155 µg/L. The validation of the methods was conducted using actual samples compared to LC–MS/MS, which served as the reference method. The FPIA and ELISA both demonstrated satisfactory concordance.
A combination of TLC-bioautography, MALDI-TOF-MS and LC–MS/MS methods was used to identify flavonoids with anti-α-amylase activity in extracts of Lathyrus pratensis L. (herb), L. polyphillus L. (fruits), Thermopsis lanceolata R. Br. (herb) and S. japonica L. (buds). After the TLC-autobiography assay, substances with anti-amylase activity were identified by MALDI-TOF-MS followed by confirmation of the result by LC–MS/MS. Results of the study revealed that the flavonoids apigenin, luteolin, formononetin, genistein and kaempferol display marked anti-α-amylase activity. Formononetin showed the largest activity. Compared with LC–MS/MS, MALDI-TOF-MS is a quick and convenient method; results can be obtained within minutes; and only minor sample amounts are required which allows us to analyse mixtures of substances without preliminary separation. However, the inability to distinguish between isomers is the main limitation of the method.