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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.
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论文共 71 篇作者统计合作学者相似作者
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JOURNAL OF CLEANER PRODUCTION (2024): 140181
Chemical Engineering Journal (2023): 145734-145734
Food and Bioproducts Processing (2023): 23-35
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Food Control (2023): 109622
2023 32nd IEEE International Conference on Robot and Human Interactive Communication (RO-MAN)pp.2140-2147, (2023)
Sensorsno. 21 (2023): 8671-8671
International Food Research Journalno. 3 (2022): 572-581
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