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Classifying green teas with near infrared hyperspectral imaging

NIR news 2019年第1-2期31卷 20 - 23页

作者： Puneet Mishra Alison Nordon WestCHEM Department of Pure and Applied Chemistry and Centre for Process Analytics and Control Technology University of Strathclyde Glasgow UK

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Pocket-size near-IR spectrometers for rapid examination of contaminated textile fibres at the crime-scene

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Vibrational Spectroscopy 2022年 123卷

作者： Rashed, Hamad S. Parrott, Andrew J. Nordon, Alison Baker, Matthew J. Palmer, David S. Department of Pure and Applied Chemistry University of Strathclyde Technology and Innovation Centre 99 George Street GlasgowG1 1RD United Kingdom Trace Evidence Section General Department of Forensic Science and Criminology Dubai Police GHQ Dubai United Arab Emirates Department of Pure and Applied Chemistry and Centre for Process Analytics and Control Technology University of Strathclyde Royal College Building 204 George Street GlasgowG1 1XW United Kingdom Department of Pure and Applied Chemistry University of Strathclyde Thomas Graham Building 295 Cathedral Street GlasgowG1 1XL United Kingdom School of Medicine Faculty of Clinical and Biomedical Sciences University of Central Lancashire Fylde Rd PrestonPR1 2HE

In a typical forensic investigation, fabric analysis plays a vital role in solving different crimes. Several types of textile fibre materials (cotton, polyester, denim, polypropylene, polycotton, and viscose) were analysed in the presence of common contaminants (blood, rainwater, seawater, sand and gunshot-residue) to evaluate the performance of two NIR spectrometers for in situ analysis of different crime scene conditions. The spectrometers used were SCIO® by Consumer Physics and NIRscan Nano by Texas Instruments. The SCIO instrument covers the third overtone region (740–1070 nm) and NIRscan Nano instrument covers the first and second overtone regions (900–1700 nm). Spectra from both instruments were pre-processed using the PRFFECTv2 software to eliminate noise and smooth the data for classification model construction. The models showed high accuracy, sensitivity and specificity with a range of 69–100% for binary classification (one class versus others) and range of 76–100% for multi-class classification of fibre material. This study shows for the first time the capability of pocket-size spectrometers coupled with random forest models for classification of fibre material in the presence of common contaminants in a rapid and non-destructive manner, and so is suitable for investigation of crime scenes. © 2022 The Authors

关键词： Spectrometers

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Quality by digital design to accelerate sustainable medicines development

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International Journal of Pharmaceutics 2025年

作者： Chantal L. Mustoe Alice J. Turner Stephanie J. Urwin Ian Houson Helen Feilden Daniel Markl Mohammed M. Al Qaraghuli Magdalene W.S. Chong Murray Robertson Alison Nordon Blair F. Johnston Cameron J. Brown John Robertson Claire Adjiman Hannah Batchelor Brahim Benyahia Massimo Bresciani Christopher L. Burcham Javier Cardona Ciro Cottini Alastair J. Florence CMAC University of Strathclyde Glasgow G1 1RD United Kingdom Strathclyde Institute of Pharmacy & Biomedical Science University of Strathclyde Glasgow G4 0RE United Kingdom WestCHEM Department of Pure and Applied Chemistry and Centre for Process Analytics and Control Technology (CPACT) University of Strathclyde Glasgow G1 1XL United Kingdom Department of Chemical Engineering Sargent Centre for Process Systems Engineering Imperial College London London SW5 2AZ United Kingdom Department of Chemical Engineering Loughborough University Loughborough LE11 3TU United Kingdom Eli Lilly and Company Lilly Corporate Center Indianapolis IN 46285 USA Department of Chemical & Process Engineering University of Strathclyde Glasgow G1 1XJ United Kingdom Chiesi Farmaceutici S.p.A. Parma Italy

We present a shared industry-academic perspective on the principles and opportunities for Quality by Digital Design (QbDD) as a framework to accelerate medicines development and enable regulatory innovation for new medicines approvals. This approach exploits emerging capabilities in industrial digital technologies to achieve robust control strategies assuring product quality and patient safety whilst reducing development time/costs, improving research and development efficiency, embedding sustainability into new products and processes, and promoting supply chain resilience. Key QbDD drivers include the opportunity for new scientific understanding and advanced simulation and model-driven, automated experimental approaches. QbDD accelerates the identification and exploration of more robust design spaces. Opportunities to optimise multiple objectives emerge in route selection, manufacturability and sustainability whilst assuring product quality. Challenges to QbDD adoption include siloed data and information sources across development stages, gaps in predictive capabilities, and the current extensive reliance on empirical knowledge and judgement. These challenges can be addressed via QbDD workflows; model-driven experimental design to collect and structure findable, accessible, interoperable and reusable (FAIR) data; and chemistry, manufacturing and control ontologies for shareable and reusable knowledge. Additionally, improved product, process, and performance predictive tools must be developed and exploited to provide a holistic end-to-end development approach.

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A revised state space modelling approach and improved fault detection using combined index monitoring for dynamic processes

A revised state space modelling approach and improved fault ...

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作者： Stubbs, S. Zhang, J. Morris, J. Centre for Process Analytics and Control Technology Newcastle University Newcastle NE1 7RU United Kingdom

ISBN: (纸本)9783902661692

Larimore's state space model derivation and stochastic estimation algorithm, first published in 1983, have been the adopted standard for deriving the state variables and parameters of the five (5) matrices state space model representation which continues to be applied extensively in the literature for applications ranging from controls, system identification and process monitoring. This paper presents an alternate derivation and stochastic estimation algorithm. The paper also discusses how strategic classification of the process inputs may, for some applications, facilitate the use of a simplified stochastic estimation algorithm. The alternative state space modeling approaches demonstrated better fault monitoring statistic performance for specific types of faults simulated. The canonical variate based state space modeling approaches were evaluated on a simulate CSTR process - with recycle through a heat exchanger. The results demonstrates the potential benefits to be derived from using a combined monitoring index based upon monitoring statistics derived from independent state space models for improved overall fault detecting capabilities and reliability of the fault monitoring scheme. © 2009 IFAC.

关键词： Fault detection

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Towards the robust application of PAT in real time control

Towards the robust application of PAT in real time control

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作者： Morris, Julian Chen, Zengping Lovett, David Centre for Process Analytics and Control Technology Newcastle University United Kingdom State Key Laboratory of Chemo/Biosensing and Chemometrics Hunan University China Perceptive Engineering Daresbury Innovation Centre Daresbury Cheshire United Kingdom

ISBN: (纸本)9783902661708

process Analytical Technologies (PAT) are increasingly being explored and adopted by pharma-chem and bio-pharma companies for enhanced process understanding, Quality by Design (QbD) and Real-time-Release (RTR). To achieve these aspirations there is a critical need to extract the most information, and hence understanding, from complex and often very 'messy' spectroscopic data data. A number of new approaches will be shown to overcome the limitations of existing calibration/modelling methodologies and algorithms and their use in some industrial applications will be presented. © 2010 IFAC.

关键词： Calibration

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A Revised State Space Modelling Approach and Improved Fault Detection Using Combined Index Monitoring for Dynamic processes

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IFAC Proceedings Volumes 2010年第5期43卷 805-810页

作者： S. Stubbs J. Zhang J. Morris Centre for Process Analytics and Control Technology Newcastle University Newcastle NE1 7RU UK (Tel: +***)

Larimore's state space model derivation and stochastic estimation algorithm, first published in 1983, have been the adopted standard for deriving the state variables and parameters of the five (5) matrices state space model representation which continues to be applied extensively in the literature for applications ranging from controls, system identification and process monitoring. This paper presents an alternate derivation and stochastic estimation algorithm. The paper also discusses how strategic classification of the process inputs may, for some applications, facilitate the use of a simplified stochastic estimation algorithm. The alternative state space modeling approaches demonstrated better fault monitoring statistic performance for specific types of faults simulated. The canonical variate based state space modeling approaches were evaluated on a simulate CSTR process – with recycle through a heat exchanger. The results demonstrates the potential benefits to be derived from using a combined monitoring index based upon monitoring statistics derived from independent state space models for improved overall fault detecting capabilities and reliability of the fault monitoring scheme.

关键词： Dynamic modeling Stochastic modeling State space modeling Fault detection Canonical Variate Analysis Combined Index

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Progressive PCA modeling for enhanced fault diagnosis in a batch process

Progressive PCA modeling for enhanced fault diagnosis in a b...

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International Conference on control, Automation and Systems ( ICCAS)

作者： Jeong Jin Hong Jie Zhang Centre for Process Analytics and Control Technology (CPACT) School of Chemical Engineering and Advanced Materials University of Newcastle Newcastle-upon-Tyne UK

The conventional process monitoring procedure using principal component analysis (PCA) can show which variable is highly related with the fault by looking at the contribution plots for the monitoring statistics, SPE (squared prediction errors) and T 2 . However, this procedure is not able to determine if the variable is just affected by the fault or the variable is the cause of the fault. In addition, it is not able to show fault propagation through the process variables during the process time. The proposed progressive PCA modeling procedure can identify all variables related to the fault through progressively removing the identified variables and PCA modeling with the remaining variables. It can also provide timing information of when abnormal behaviors are observed for the identified variables by using time series SPE plots with control limits estimated by weighted chi-squared distribution. Based on the timing information, it is able to build a flow chart showing the fault propagation paths. The proposed method is demonstrated on a benchmark fed-batch penicillin process simulator.

关键词： Principal component analysis Time series analysis Monitoring Substrates Data models Timing Feeds

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Towards the Robust Application of PAT in Real Time control

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IFAC Proceedings Volumes 2010年第6期43卷 1-6页

作者： Julian Morris Zengping Chen David Lovett Centre for Process Analytics & Control Technology Newcastle University. UK State Key Laboratory of Chemo/Biosensing & Chemometrics Hunan University China Perceptive Engineering Daresbury Innovation Centre Daresbury Cheshire UK

process Analytical Technologies (PAT) are increasingly being explored and adopted by pharma-chem and bio-pharma companies for enhanced process understanding, Quality by Design (QbD) and Real-time-Release (RTR). To achieve these aspirations there is a critical need to extract the most information, and hence understanding, from complex and often very ‘messy’ spectroscopic data. A number of new approaches will be shown to overcome the limitations of existing calibration/modelling methodologies and algorithms and their use in some industrial applications will be presented.

关键词： process Analytical Technologies Spectroscopic Data Chemometrics Calibration process control

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Theoretical analysis of chirp excitation of contrast agents

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Physics Procedia 2010年第1期3卷 743-747页

作者： Euan Barlow Anthony J. Mulholland Alison Nordon Anthony Gachagan Department of Mathematics University of Strathclyde 26 Richmond Street Glasgow G1 1XH UK WestCHEM Department of Pure and Applied Chemistry Centre for Process Analytics and Control Technology (CPACT) University of Strathclyde Glasgow G1 1XL UK Department of Electronic and Electrical Engineering The Centre for Ultrasonic Engineering (CUE) University of Strathclyde GlasgowG1 1XW UK

Analytic expressions are found for the amplitude of the first and second harmonics of the Ultrasound Contrast Agent’s (UCA’s) dynamics when excited by a chirp. The dependency of the second harmonic amplitude on the system parameters, the UCA shell parameters, and the insonifying signal parameters is then investigated. It is shown that optimal parameter values exist which give rise to a clear increase in the second harmonic component of the UCA’s motion.

关键词： Ultrasound contrast agent Chirp Second harmonic Theoretical Small parameter perturbation

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process analytical technology and compensating for nonlinear effects in process spectroscopic data for improved process monitoring and control

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Biotechnology Journal 2009年第5期4卷 610-619页

作者： Chen, Zengping Morris, Julian Centre for Process Analytics and Control Technology School of Chemical Engineering and Advanced Materials Newcastle University Newcastle upon Tyne NE1 7RU United Kingdom

Robust fit-for-purpose multivariate calibration models are of critical importance to on-line/in-line quantitative monitoring of bio-chemicals and pharmaceuticals using spectroscopic instruments. Unlike in off-line assays, the spectroscopic measurements in on-line/in-line real-time applications are almost inevitably subjected to variations in measurement conditions (e.g. temperature) and samples' physical properties (e.g. cell density, particle size, sample compactness), which can invalidate the assumption of a linear relationship between the spectroscopic measurements and the concentrations of the target chemical components. This Biotech Highlight discusses the effects of such variations on spectroscopic measurements, and presents an overview of recent work on modelling and correcting of the detrimental effects of variations in measurement conditions and samples' physical properties.A number of application studies to complex datasets and an industrial plant demonstrate the methodologies and algorithms discussed.

关键词： Multiplicative light scattering Multivariate linear calibration Near infrared spectroscopy Optical path length estimation and correction Temperature‐induced spectral variations

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