Intelligent Sensors for Sustainable Food and Drink Manufacturing

Food and drink is the largest manufacturing sector worldwide and has significant environmental impact in terms of resource use, emissions, and waste. However, food and drink manufacturers are restricted in addressing these issues due to the tight profit margins they operate within. The advances of t...

Descripción completa

Guardado en:

Detalles Bibliográficos
Autores principales:	Nicholas J. Watson, Alexander L. Bowler, Ahmed Rady, Oliver J. Fisher, Alessandro Simeone, Josep Escrig, Elliot Woolley, Akinbode A. Adedeji
Formato:	article
Lenguaje:	EN
Publicado:	Frontiers Media S.A. 2021
Materias:	digital manufacturing sensors machine learning food and drink manufacturing intelligent manufacturing industry 4.0 Nutrition. Foods and food supply TX341-641 Food processing and manufacture TP368-456
Acceso en línea:	https://doaj.org/article/d6792e3117824d9c8d6b7db5b025025a
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

id	oai:doaj.org-article:d6792e3117824d9c8d6b7db5b025025a
record_format	dspace
spelling	oai:doaj.org-article:d6792e3117824d9c8d6b7db5b025025a2021-11-05T17:42:54ZIntelligent Sensors for Sustainable Food and Drink Manufacturing2571-581X10.3389/fsufs.2021.642786https://doaj.org/article/d6792e3117824d9c8d6b7db5b025025a2021-11-01T00:00:00Zhttps://www.frontiersin.org/articles/10.3389/fsufs.2021.642786/fullhttps://doaj.org/toc/2571-581XFood and drink is the largest manufacturing sector worldwide and has significant environmental impact in terms of resource use, emissions, and waste. However, food and drink manufacturers are restricted in addressing these issues due to the tight profit margins they operate within. The advances of two industrial digital technologies, sensors and machine learning, present manufacturers with affordable methods to collect and analyse manufacturing data and enable enhanced, evidence-based decision making. These technologies will enable manufacturers to reduce their environmental impact by making processes more flexible and efficient in terms of how they manage their resources. In this article, a methodology is proposed that combines online sensors and machine learning to provide a unified framework for the development of intelligent sensors that work to improve food and drink manufacturers' resource efficiency problems. The methodology is then applied to four food and drink manufacturing case studies to demonstrate its capabilities for a diverse range of applications within the sector. The case studies included the monitoring of mixing, cleaning and fermentation processes in addition to predicting key quality parameter of crops. For all case studies, the methodology was successfully applied and predictive models with accuracies ranging from 95 to 100% were achieved. The case studies also highlight challenges and considerations which still remain when applying the methodology, including efficient data acquisition and labelling, feature engineering, and model selection. This paper concludes by discussing the future work necessary around the topics of new online sensors, infrastructure, data acquisition and trust to enable the widespread adoption of intelligent sensors within the food and drink sector.Nicholas J. WatsonAlexander L. BowlerAhmed RadyOliver J. FisherAlessandro SimeoneAlessandro SimeoneJosep EscrigElliot WoolleyAkinbode A. AdedejiFrontiers Media S.A.articledigital manufacturingsensorsmachine learningfood and drink manufacturingintelligent manufacturingindustry 4.0Nutrition. Foods and food supplyTX341-641Food processing and manufactureTP368-456ENFrontiers in Sustainable Food Systems, Vol 5 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	digital manufacturing sensors machine learning food and drink manufacturing intelligent manufacturing industry 4.0 Nutrition. Foods and food supply TX341-641 Food processing and manufacture TP368-456
spellingShingle	digital manufacturing sensors machine learning food and drink manufacturing intelligent manufacturing industry 4.0 Nutrition. Foods and food supply TX341-641 Food processing and manufacture TP368-456 Nicholas J. Watson Alexander L. Bowler Ahmed Rady Oliver J. Fisher Alessandro Simeone Alessandro Simeone Josep Escrig Elliot Woolley Akinbode A. Adedeji Intelligent Sensors for Sustainable Food and Drink Manufacturing
description	Food and drink is the largest manufacturing sector worldwide and has significant environmental impact in terms of resource use, emissions, and waste. However, food and drink manufacturers are restricted in addressing these issues due to the tight profit margins they operate within. The advances of two industrial digital technologies, sensors and machine learning, present manufacturers with affordable methods to collect and analyse manufacturing data and enable enhanced, evidence-based decision making. These technologies will enable manufacturers to reduce their environmental impact by making processes more flexible and efficient in terms of how they manage their resources. In this article, a methodology is proposed that combines online sensors and machine learning to provide a unified framework for the development of intelligent sensors that work to improve food and drink manufacturers' resource efficiency problems. The methodology is then applied to four food and drink manufacturing case studies to demonstrate its capabilities for a diverse range of applications within the sector. The case studies included the monitoring of mixing, cleaning and fermentation processes in addition to predicting key quality parameter of crops. For all case studies, the methodology was successfully applied and predictive models with accuracies ranging from 95 to 100% were achieved. The case studies also highlight challenges and considerations which still remain when applying the methodology, including efficient data acquisition and labelling, feature engineering, and model selection. This paper concludes by discussing the future work necessary around the topics of new online sensors, infrastructure, data acquisition and trust to enable the widespread adoption of intelligent sensors within the food and drink sector.
format	article
author	Nicholas J. Watson Alexander L. Bowler Ahmed Rady Oliver J. Fisher Alessandro Simeone Alessandro Simeone Josep Escrig Elliot Woolley Akinbode A. Adedeji
author_facet	Nicholas J. Watson Alexander L. Bowler Ahmed Rady Oliver J. Fisher Alessandro Simeone Alessandro Simeone Josep Escrig Elliot Woolley Akinbode A. Adedeji
author_sort	Nicholas J. Watson
title	Intelligent Sensors for Sustainable Food and Drink Manufacturing
title_short	Intelligent Sensors for Sustainable Food and Drink Manufacturing
title_full	Intelligent Sensors for Sustainable Food and Drink Manufacturing
title_fullStr	Intelligent Sensors for Sustainable Food and Drink Manufacturing
title_full_unstemmed	Intelligent Sensors for Sustainable Food and Drink Manufacturing
title_sort	intelligent sensors for sustainable food and drink manufacturing
publisher	Frontiers Media S.A.
publishDate	2021
url	https://doaj.org/article/d6792e3117824d9c8d6b7db5b025025a
work_keys_str_mv	AT nicholasjwatson intelligentsensorsforsustainablefoodanddrinkmanufacturing AT alexanderlbowler intelligentsensorsforsustainablefoodanddrinkmanufacturing AT ahmedrady intelligentsensorsforsustainablefoodanddrinkmanufacturing AT oliverjfisher intelligentsensorsforsustainablefoodanddrinkmanufacturing AT alessandrosimeone intelligentsensorsforsustainablefoodanddrinkmanufacturing AT alessandrosimeone intelligentsensorsforsustainablefoodanddrinkmanufacturing AT josepescrig intelligentsensorsforsustainablefoodanddrinkmanufacturing AT elliotwoolley intelligentsensorsforsustainablefoodanddrinkmanufacturing AT akinbodeaadedeji intelligentsensorsforsustainablefoodanddrinkmanufacturing
_version_	1718444130408857600

Intelligent Sensors for Sustainable Food and Drink Manufacturing

Ejemplares similares