Automation Of Analytical Processes Research Articles

Automatic detection and quantification of floating marine macro-litter in aerial images: Introducing a novel deep learning approach connected to a web application in R.

The threats posed by floating marine macro-litter (FMML) of anthropogenic origin to the marine fauna, and marine ecosystems in general, are universally recognized. Dedicated monitoring programmes and mitigation measures are in place to address this issue worldwide, with the increasing support of new technologies and the automation of analytical processes. In the current study, we developed algorithms capable of detecting and quantifying FMML in aerial images, and a web-oriented application that allows users to identify FMML within images of the sea surface. The proposed algorithm is based on a deep learning approach that uses convolutional neural networks (CNNs) capable of learning from unstructured or unlabelled data. The CNN-based deep learning model was trained and tested using 3723 aerial images (50% containing FMML, 50% without FMML) taken by drones and aircraft over the waters of the NW Mediterranean Sea. The accuracies of image classification (performed using all the images for training and testing the model) and cross-validation (performed using 90% of images for training and 10% for testing) were 0.85 and 0.81, respectively. The Shiny package of R was then used to develop a user-friendly application to identify and quantify FMML within the aerial images. The implementation of this, and similar algorithms, allows streamlining substantially the detection and quantification of FMML, providing support to the monitoring and assessment of this environmental threat. However, the automated monitoring of FMML in the open sea still represents a technological challenge, and further research is needed to improve the accuracy of current algorithms.

Trigger-based concurrent control system for automating analytical processes

We present a system for the design and the automation of analytical processes based on concurrent actions developed by the equipment involved. The system enables the control of any hardware that acts during a given process. The control of the process is based on time and state parameters; thus, the continuous monitoring of these parameters is necessary in order to achieve this goal. To take decisions without human intervention is possible because a sub-system is in charge of intelligent control of the analytical process. The overall design of the analytical process is divided into two steps: the time-based process; and, the state-based control (triggers). The latter also manages the exceptions and the alerts activated during each experiment. Automation is carried out by a procedure constructed from the data design. Computer technologies, such as UML, Java and XML, have been used for program construction.

Electroanalytical Flow Measurements–Recent Advances

AbstractAs general trends in current development of chemical analysis, including electroanalysis, one can indicate a search for methods fast and multianalyte, for miniaturized measuring devices, mechanization and automation of analytical processes. In all these trends a significant role is played by measurements in flow conditions. The major advantage of flow electroanalysis is a possibility of utilizing a kinetic discrimination in potentiometric measurements and enhancement of mass transport in voltammetric techniques. Flow injection techniques provide shortening of time of a single analytical determination due to reproducible use of transient signal from the detector without need of obtaining a steady‐state equilibrium signal. Electrochemical detection in HPLC gives often improved selectivity and detection limit for electroactive solutes, whereas in capillary electrophoresis it allows a convenient design of portable, integrated chips for field application. This review presents state‐of‐the ‐art of flow electroanalysis based on 226 cited literature references

Automation of analytical processes

Abstract