Future trends in organic flour milling: the role of AI

Author:

Parrenin Loïc123,Danjou Christophe123,Agard Bruno123,Beauchemin Robert4

Affiliation:

1. Laboratoire en Intelligence des Données (LID), Montréal, QC, Canada

2. Laboratoire Poly-Industrie 4.0, Montréal, QC, Canada

3. Département de Mathématiques et génie industriel, École Polytechnique de Montréal, CP 6079, succursale Centre-Ville, Montréal, QC, Canada

4. La Meunerie Milanaise, Saint-Jean-sur-Richelieu, QC J2X5V5, Canada

Abstract

<abstract> <p>The milling of wheat flour is a process that has existed since ancient times. In the course of history, the techniques have improved, the equipment modernized. The interest of the miller in charge of the mill is still to ensure that a mill is functional and profitable, as well as to provide a consistent quality of flour. The production of organic flour means that methods of adding chemicals and unnatural agents are not possible. In organic flour production, it is necessary to work with the raw material. A grain of wheat is a living material, and its quality varies according to a multitude of factors. Challenges are therefore present at each stage of the value chain. The use of artificial intelligence techniques offers solutions and new perspectives to meet the different objectives of the miller. A literature review of artificial intelligence techniques developed at each stage of the value chain surrounding the issues of quality and yield is conducted. An analysis of a large number of variables, including process factors, process parameters and wheat grain quality from data collected on the value chain enables the development and training of artificial intelligence models. From these models, it is possible to develop decision support tools and optimize the wheat flour milling process. Several major research directions, other than constant quality, are to be studied to optimize the process and move towards a smart mill. This includes energy savings, resource optimization and mill performance.</p> </abstract>

Publisher

American Institute of Mathematical Sciences (AIMS)

Subject

Agricultural and Biological Sciences (miscellaneous),Food Science

Reference80 articles.

Cited by 3 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. A Decision Support Tool to Analyze Food Properties from Near Infrared Spectroscopy*;2023 15th IEEE International Conference on Industry Applications (INDUSCON);2023-11-22

2. Quality and its Improvement in the Context of Agility in Polish Organic Food Processing;Management Systems in Production Engineering;2023-08-01

3. A decision support tool for the first stage of the tempering process of organic wheat grains in a mill;International Journal of Food Science & Technology;2023-04-23

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