Machine learning techniques in food processing

Ana M. Jiménez-Carvelo, Carlos M. Cruz, Luis Cuadros-Rodríguez, Anastasios Koidis

Research output: Chapter in Book/Report/Conference proceedingChapter

2 Citations (Scopus)

Abstract

Currently, the food society is in the transition to the age of information dubbed as “Industry 4.0” toward automation and data-driven decisions. Recent advances in computer technology and instrumentation engineering for industrial food processing have led to the use of multi/megavariate data analysis by using machine learning methods to manage the large amount of data generated. For this, to effectively carry out multivariate analysis of the data, machine learning methods play an important role in the food industry since from which building of multivariate models that can make predictions on data in order to make a decision about both food processes and foodstuffs. The aim of this chapter is to provide an overview of processing control and product quality control in the food industry in which machine learning/data mining methods are used. In addition, recent applications developed in this field are described, and finally, future perspectives are critically discussed.

Original languageEnglish
Title of host publicationCurrent developments in biotechnology and bioengineering: advances in food engineering
EditorsAyon Tarafdar, Ashok Pandey, Ranjna Sirohi, Claude-Gilles Dussap, Carlos Ricardo Soccol
PublisherElsevier
Chapter12
Pages333-349
ISBN (Electronic)9780323984843
ISBN (Print)9780323911580
DOIs
Publication statusPublished - 26 Aug 2022

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