Nicholas Watson

Contact

• workRoom B14 Coates
  University Park
  Nottingham
  NG7 2RD
  UK
• work01157484848
• Nicholas.Watson@nottingham.ac.uk

Biography

Nik is a Chartered Engineer with a MEng in Mechanical Engineering (University of Hull, 2006) and PhD in Chemical Engineering (University of Leeds, 2010). Nik Joined the University of Nottingham in 2014 and is an Associate Professor of Chemical Engineering. Since joining the University of Nottingham Nik has published over 35 journal articles and led projects funded by Innovate UK, EPSRC, STFC and the Royal Academy of Engineering. Nik is currently the deputy director of the Horizon Centre for Doctoral Training and on the Food Standards Agency register of Experts.

Nik is a member of the Food, Water, Waste (FWW) Research Group and associate member of the Advanced Manufacturing Technologies (AMT) Research Group.

Nik is a member of the Food, Water, Waste (FWW) Research Group and associate member of the Advanced Manufacturing Technologies (AMT) Research Group.

Teaching Summary

Module Convener:

·CHEE4061: Food Processing

Research Summary

Nik's research is primarily focussed on data-driven in-process sensing to deliver sustainable, safe and productive current and future food manufacturing systems. Data-driven sensing combines… read more

Recent Publications

• OZTURK, S., BOWLER, A., RADY, A. and WATSON, N. J., 2023. Near-infrared spectroscopy and machine learning for classification of food powders during a continuous process: Journal of Food Engineering Journal of Food Engineering. 341,
• BOWLER, A., OZTURK, S., DI BARI, V., GLOVER, Z. J. and WATSON, N. J., 2023. Machine learning and domain adaptation to monitor yoghurt fermentation using ultrasonic measurements: Food Control Food Control. 147,
• SOBANWA, M., FOSTER, T. J., YAKUBOV, G. and WATSON, N. J., 2022. How hydrocolloids can control the viscoelastic properties of acid-swollen collagen pastes: Food Hydrocolloids Food Hydrocolloids. 126,
• MALLIAROUDAKI, M. I., WATSON, N. J., FERRARI, R., NCHARI, L. N. and GOMES, R. L., 2022. Energy management for a net zero dairy supply chain under climate change: Trends in Food Science and Technology Trends in Food Science and Technology.

• View all publications

Data Driven Resource Optimisation in the Barley Supply Chain: BARIToNE Project 03 - Data driven resource optimisation in the barley supply chain at University of Dundee on FindAPhD.com

Sensor Fusion for Sustainable Process Technologies (Industry Partner Schlumberger): https://jobs.nottingham.ac.uk/ENG1533

Horizon Centre for Doctoral Training: Apply : Horizon CDT

Midlands Centre for Data Driven Metrology: Index (mcddm.ac.uk)

Current Research

Nik's research is primarily focussed on data-driven in-process sensing to deliver sustainable, safe and productive current and future food manufacturing systems. Data-driven sensing combines cost-effective in-process sensors (e.g. optical and ultrasonic) with machine learning techniques and overcomes many of the challenges associated with utilising sensors to produce actionable information to monitor processes (e.g. mixing, cleaning and fermentation) and materials (e.g. online quality and safety inspection) within challenging manufacturing environments. Nik has broader expertise and research interests in Digital Manufacturing within the food and drink sector with projects exploring the use of data and digital technologies including robotics and the Industrial Internet of Things.

Current research projects include:

EATS (Enhancing Agri-Food Transparent Sustainability) (EPSRC) EATS (ukri.org)

Horizon: Trusted Data-Driven Products (EPSRC) Horizon: Trusted Data-Driven Products (ukri.org)

Horizon Centre for Doctoral Training: Creating our Lives in Data (EPSRC) Horizon Centre for Doctoral Training : Horizon

Connected Everything 2: Accelerating Digital Manufacturing Research Collaboration and Innovation (EPSRC) Connected Everything II: Accelerating Digital Manufacturing Research Collaboration and Innovation

Trustworthy Human Robot Teams (Trustworthy Autonomous Systems Hub) Trustworthy Human-Robot Teams - UKRI Trustworthy Autonomous Systems Hub (tas.ac.uk)

Future Research

I welcome enquires from potential PhD candidates from Home, EU and international countries who are interested in the following research areas: Digital Food and Drink Manufacturing, Machine Learning, Sensor Fusion, Food Safety and Quality, Sustainable Food Systems.

• OZTURK, S., BOWLER, A., RADY, A. and WATSON, N. J., 2023. Near-infrared spectroscopy and machine learning for classification of food powders during a continuous process: Journal of Food Engineering Journal of Food Engineering. 341,
• BOWLER, A., OZTURK, S., DI BARI, V., GLOVER, Z. J. and WATSON, N. J., 2023. Machine learning and domain adaptation to monitor yoghurt fermentation using ultrasonic measurements: Food Control Food Control. 147,
• SOBANWA, M., FOSTER, T. J., YAKUBOV, G. and WATSON, N. J., 2022. How hydrocolloids can control the viscoelastic properties of acid-swollen collagen pastes: Food Hydrocolloids Food Hydrocolloids. 126,
• MALLIAROUDAKI, M. I., WATSON, N. J., FERRARI, R., NCHARI, L. N. and GOMES, R. L., 2022. Energy management for a net zero dairy supply chain under climate change: Trends in Food Science and Technology Trends in Food Science and Technology.
• FISHER, O. J., WATSON, N. J., PORCU, L., BACON, D., RIGLEY, M. and GOMES, R. L., 2022. Data-driven modelling for resource recovery: Data volume, variability, and visualisation for an industrial bioprocess: Biochemical Engineering Journal Biochemical Engineering Journal. 185,
• PRICE, R., GRAY, D., WATSON, N., VIEIRA, J. and WOLF, B., 2022. Linking the yield stress functionality of polyglycerol polyricinoleate in a highly filled suspension to its molecular properties: LWT LWT. 165,
• RADY, A. and WATSON, N. J., 2022. Detection and quantification of peanut contamination in garlic powder using NIR sensors and machine learning: Journal of Food Composition and Analysis Journal of Food Composition and Analysis. 114,
• BOWLER, A. L., OZTURK, S., RADY, A. and WATSON, N., 2022. Domain Adaptation for In-Line Allergen Classification of Agri-Food Powders Using Near-Infrared Spectroscopy: Sensors Sensors. 22(19),
• KAHAR, A. A., PHENG, O. S., WATSON, N. J. and LIM, L. C., 2022. Effect of storage conditions and temperature on dried lemon myrtle leaves quality: International Journal of Postharvest Technology and Innovation International Journal of Postharvest Technology and Innovation. 8(4), 292-312
• FISHER, O. J., RADY, A., EL-BANNA, A. A. A., WATSON, N. J. and EMAISH, H. H., 2022. An image processing and machine learning solution to automate Egyptian cotton lint grading: Textile Research Journal Textile Research Journal.
• FISHER, O. J., WATSON, N. J., PORCU, L., BACON, D., RIGLEY, M. and GOMES, R. L., 2021. Multiple target data-driven models to enable sustainable process manufacturing: An industrial bioprocess case study: Journal of Cleaner Production Journal of Cleaner Production. 296,
• BOWLER, A. L. and WATSON, N. J., 2021. Transfer learning for process monitoring using reflection-mode ultrasonic sensing: Ultrasonics Ultrasonics. 115,
• BOWLER, A. L., POUND, M. P. and WATSON, N. J., 2021. Domain adaptation and federated learning for ultrasonic monitoring of beer fermentation: Fermentation Fermentation. 7(4),
• WATSON, N. J., BOWLER, A. L., RADY, A., FISHER, O. J., SIMEONE, A., ESCRIG, J., WOOLLEY, E. and ADEDEJI, A. A., 2021. Intelligent Sensors for Sustainable Food and Drink Manufacturing: Frontiers in Sustainable Food Systems Frontiers in Sustainable Food Systems. 5,
• BOWLER, A. L., BAKALIS, S. and WATSON, N. J., 2020. A review of in-line and on-line measurement techniques to monitor industrial mixing processes: Chemical Engineering Research and Design Chemical Engineering Research and Design. 153, 463-495
• SOBANWA M, FOSTER TJ and WATSON N, 2020. The effect of cellulose and starch on the viscoelastic and thermal properties of acid-swollen collagen paste Food Hydrocolloids. 101, 105460
• FISHER OJ, WATSON NJ, ESCRIG JE and GOMES RL, 2020. Intelligent resource use to deliver waste valorisation and process resilience in manufacturing environments: Moving towards sustainable process manufacturing. Johnson Matthey Technology Review. 64, 93-99
• BOWLER, A. L., BAKALIS, S. and WATSON, N. J., 2020. Monitoring mixing processes using ultrasonic sensors and machine learning: Sensors (Switzerland) 20(7),
• ESCRIG, J., WOOLLEY, E., SIMEONE, A. and WATSON, N. J., 2020. Monitoring the cleaning of food fouling in pipes using ultrasonic measurements and machine learning: Food Control Food Control. 116,
• MALIK, N., MUTTAKIN, S., LOPEZ-QUIROGA, E., WATSON, N. J., FRYER, P. J., BAKALIS, S. and GOUSETI, O., 2020. Microstructure and reconstitution of freeze-dried gum Arabic at a range of concentrations and primary drying temperatures FOOD HYDROCOLLOIDS. 104,
• RADY, AHMED, FISCHER, JOEL, REEVES, STUART, LOGAN, BRIAN and WATSON, NICHOLAS JAMES, 2020. The Effect of Light Intensity, Sensor Height, and Spectral Pre-Processing Methods When Using NIR Spectroscopy to Identify Different Allergen-Containing Powdered Foods SENSORS. 20(1),
• FISHER OJ, WATSON N, ESCRIG JE, WITT R, PORCU L, BACON D, RIGLEY M and GOMES RL, 2020. Considerations, challenges and opportunities when developing data-driven models for process manufacturing systems Computers & Chemical Engineering. (In Press.)
• ESCRIG, J. E., SIMEONE, A., WOOLLEY, E., RANGAPPA, S., RADY, A. and WATSON, N. J., 2020. Ultrasonic measurements and machine learning for monitoring the removal of surface fouling during clean-in-place processes: Food and Bioproducts Processing Food and Bioproducts Processing. 123, 1-13
• SIMEONE, A., WOOLLEY, E., ESCRIG, J. and WATSON, N. J., 2020. Intelligent industrial cleaning: A multi-sensor approach utilising machine learning-based regression: Sensors (Switzerland) 20(13), 1-22
• GOUSETI, O., WATSON, N. J. and PACEK, A. W., 2020. A rheo-optic study of liquid/liquid systems with varying phases, volume fractions and viscosity ratios: Food and Bioproducts Processing Food and Bioproducts Processing. 123, 251-261
• RADY, A. M., GUYER, D. E., DONIS-GONZÁLEZ, I. R., KIRK, W. and WATSON, N. J., 2020. A comparison of different optical instruments and machine learning techniques to identify sprouting activity in potatoes during storage: Journal of Food Measurement and Characterization Journal of Food Measurement and Characterization.
• RADY, A. M., GUYER, D. E. and WATSON, N. J., 2020. Near-infrared Spectroscopy and Hyperspectral Imaging for Sugar Content Evaluation in Potatoes over Multiple Growing Seasons: Food Analytical Methods Food Analytical Methods.
• FISHER, OLIVER J., WATSON, NICHOLAS J., ESCRIG, JOSEP E., WITT, ROB, PORCU, LAURA, BACON, DARREN, RIGLEY, MARTIN and GOMES, RACHEL L., 2020. Considerations, challenges and opportunities when developing data-driven models for process manufacturing systems COMPUTERS & CHEMICAL ENGINEERING. 140,
• MARTINEZ-RAMOS, TANIA, BENEDITO-FORT, JOSE, WATSON, NICHOLAS JAMES, RUIZ-LOPEZ, IRVING I., CHE-GALICIA, GAMALIEL and CORONA-JIMENEZ, EDITH, 2020. Effect of solvent composition and its interaction with ultrasonic energy on the ultrasound-assisted extraction of phenolic compounds from Mango peels (Mangifera indica L.) FOOD AND BIOPRODUCTS PROCESSING. 122, 41-54
• SOBANWA, M., FOSTER, T. J. and WATSON, N. J., 2020. The effect of cellulose and starch on the viscoelastic and thermal properties of acid-swollen collagen paste FOOD HYDROCOLLOIDS. 101,
• FISHER, OLIVER J., WATSON, NICHOLAS J., ESCRIG, JOSEP E. and GOMES, RACHEL L., 2020. Intelligent Resource Use to Deliver Waste Valorisation and Process Resilience in Manufacturing Environments Moving towards sustainable process manufacturing JOHNSON MATTHEY TECHNOLOGY REVIEW. 64(1), 93-99
• Y AL-AUFI, B HEWAKANDAMBY, G DIMITRAKIS, M HOLMES and N J WATSON, 2019. Thin film thickness measurements in two phase annular flows using ultrasonic pulse echo techniques Flow Measurement and Instrumentation. 66, 67-78
• J. ESCRIG, ESCRIG, E. WOOLLEY, S. RANGAPPA, A. SIMEONE and N.J. WATSON, 2019. Clean-in-place monitoring of different food fouling materials using ultrasonic measurements Food Control. 104, 358 - 366
• AL-LASHI, R. S., POVEY, M. J. W. and WATSON, N. J., 2018. Ultrasonic wave propagation in powders
• FISHER, O., WATSON, N., PORCU, L., BACON, D., RIGLEY, M. and GOMES, R. L., 2018. Cloud manufacturing as a sustainable process manufacturing route: Journal of Manufacturing Systems Journal of Manufacturing Systems. 47, 53-68
• SIMEONE, ALESSANDRO, DENG, BIN, WATSON, NICHOLAS and WOOLLEY, ELLIOT, 2018. Enhanced Clean-In-Place Monitoring Using Ultraviolet Induced Fluorescence and Neural Networks. Sensors (Basel, Switzerland). 18(11),
• N J WATSON, M J W POVEY and N G PARKER, 2017. Acoustic Microscopy. In: M VILLAMIEL and J V GARCIA-PEREZ, eds., Ultrasound: Food Applications Wiley.
• YOUSUF ALAUFI, NICHOLAS J WATSON, BARRY J. AZZOPARDI, BUDDHIKA N. HEWAKANDAMBY, GEORGIOS DIMITRAKIS, ABBAS HASAN and ALEXANDER N. KALASHNIKOV1, 2016. Thin Film Thickness Measurements using Ultrasonic pulse echo technique In: ICMF-2016 – 9th International Conference on Multiphase Flow.
• WATSON, N. J., POVEY, M. J. W., REYNOLDS, G. K., DING, Y. and XU, B. H., 2016. Development of a discrete element model with moving realistic geometry to simulate particle motion in a Mi-Pro granulator COMPUTERS & CHEMICAL ENGINEERING. 93, 234-247
• A SIMEONE, N WATSON, I STERRITT and E WOOLLEY, 2016. A multi-sensor approach for fouling level assessment in clean-in-place processes In: 5th CIRP Global Web Conference - Research and Innovation for Future Production (CIRPe 2016). 55. 134-139
• N J WATSON, 2015. Ultrasound Tomography. In: M WANG, ed., Industrial Tomography: Systems and applications Woodhead Publishing. 235-257
• WATSON, N. J., HAZLEHURST, T., POVEY, M. J. W., DRENNAN, A. and SEAMAN, P., 2015. COMSOL modelling of the acoustoelastic effect: Journal of Physics: Conference Series Journal of Physics: Conference Series. 581(1),
• WATSON, N. J., POVEY, M. J. W., REYNOLDS, G. K., XU, B. H. and DING, Y., 2014. Acoustic emission monitoring from a lab scale high shear granulator-A novel approach INTERNATIONAL JOURNAL OF PHARMACEUTICS. 465(1-2), 262-274
• WATSON, N., HAZLEHURST, T., POVEY, M., VIEIRA, J., SUNDARA, R. and SANDOZ, J-P, 2014. Can airborne ultrasound monitor bubble size in chocolate? 12TH ANGLO-FRENCH PHYSICAL ACOUSTICS CONFERENCE (AFPAC2013). 498,
• HOLMES, M. J., SOUTHWORTH, T., WATSON, N. G. and POVEY, M. J. W., 2014. Enzyme activity determination using ultrasound 12TH ANGLO-FRENCH PHYSICAL ACOUSTICS CONFERENCE (AFPAC2013). 498,
• WATSON, N. J., JOHAL, R. K., GLOVER, Z., REINWALD, Y., WHITE, L. J., GHAEMMAGHAMI, A. M., MORGAN, S. P., ROSE, F. R. A. J., POVEY, M. J. W. and PARKER, N. G., 2013. Post-processing of polymer foam tissue scaffolds with high power ultrasound: A route to increased pore interconnectivity, pore size and fluid transport MATERIALS SCIENCE & ENGINEERING C-MATERIALS FOR BIOLOGICAL APPLICATIONS. 33(8), 4825-4832
• CORONA, EDITH, GARCIA-PEREZ, JOSE V., GOMEZ ALVAREZ-ARENAS, TOMAS E., WATSON, NICHOLAS, POVEY, MALCOLM J. W. and BENEDITO, JOSE, 2013. Advances in the ultrasound characterization of dry-cured meat products JOURNAL OF FOOD ENGINEERING. 119(3), 464-470
• M.J.W.POVEY, N.J.WATSON and N.G.PARKER, 2013. Ultrasonic and acoustic microscopy: principles and applications to food microstructures. In: V.J.MORRIS, ed., Food Microstructures Microscopy, Measurement and Modelling Woodhead Publishing. 192-222
• N.J.WATSON, M.J.W.POVEY and M.J.HOLMES, 2013. Modelling and development of microwave meals using Comsol multiphysics In: 14th International Conference on Microwave and High Frequency Heating, Nottingham, UK.
• WATSON, N., POVEY, M., CORONA, E., BENEDITO, J. and PARKER, N., 2012. Acoustic microscopy in the food industry INTERNATIONAL SYMPOSIUM ON ULTRASOUND IN THE CONTROL OF INDUSTRIAL PROCESSES (UCIP 2012). 42,
• N.J.WATSON, B.H.XU, Y.DING, M.J.W.POVEY, G.K.REYNOLDS, M.CLAYBOURNE and S.WEIR, 2009. DEM simulation of particle motion is a MiPro granulator In: Particles and Grains, AIP Conf. Proc 1145. 679-682
• WATSON, N., XU, B. H., DING, Y., POVEY, M. J. W., REYNOLDS, G. K., CLAYBOURN, M. and WEIR, S., 2009. DEM simulation of particle motion in a mipro granulator
• ABBAS HASAN, B. AZZOPARDI, B. HEWAKANDAMBY, Y. ALAUFI, N. J. WATSON and S. K. MOHAMMED, Experimental characterization of interfacial wave structure of a falling liquid film in a vertical large pipe diameter In: ICMF -2016 - 9th - 9th International Conference on Multiphase flow.