Agitation and mixing processes automation using current sensing and reinforcement learning

Aljaafreh, Ahmad

doi:10.1016/j.jfoodeng.2017.02.001

Cited by 15 publications

(7 citation statements)

References 6 publications

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“…For example, humans and animals learn to walk in the absence of a mentor whereas agents will acquire the learning by trial and error. RL is based on giving rewards, to agents when they complete the assigned work by themselves ( Aljaafreh, 2017 ). RL problems are mostly modeled as a Markov decision process (MDP).…”

Section: Machine Learning Techniquesmentioning

confidence: 99%

Machine learning techniques for analysis of hyperspectral images to determine quality of food products: A review

Saha

Manickavasagan

2021

Current Research in Food Science

231

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Non-destructive testing techniques have gained importance in monitoring food quality over the years. Hyperspectral imaging is one of the important non-destructive quality testing techniques which provides both spatial and spectral information. Advancement in machine learning techniques for rapid analysis with higher classification accuracy have improved the potential of using this technique for food applications. This paper provides an overview of the application of different machine learning techniques in analysis of hyperspectral images for determination of food quality. It covers the principle underlying hyperspectral imaging, the advantages, and the limitations of each machine learning technique. The machine learning techniques exhibited rapid analysis of hyperspectral images of food products with high accuracy thereby enabling robust classification or regression models. The selection of effective wavelengths from the hyperspectral data is of paramount importance since it greatly reduces the computational load and time which enhances the scope for real time applications. Due to the feature learning nature of deep learning, it is one of the most promising and powerful techniques for real time applications. However, the field of deep learning is relatively new and need further research for its full utilization. Similarly, lifelong machine learning paves the way for real time HSI applications but needs further research to incorporate the seasonal variations in food quality. Further, the research gaps in machine learning techniques for hyperspectral image analysis, and the prospects are discussed.

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Section: Machine Learning Techniquesmentioning

confidence: 99%

Machine learning techniques for analysis of hyperspectral images to determine quality of food products: A review

Saha

Manickavasagan

2021

Current Research in Food Science

231

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“…As highlighted for the milling process in Section 6, the kneading process needs innovations and improvement strategies that must never lose sight of the essential aspect of environmental sustainability. In this direction, the most interesting eco-friendly improvement strategies for the kneading phase are controlling the dough temperature during kneading using alternative, eco-sustainable, refrigerants [40][41][42]; use of organic acids, recovered from by-products, to improve dough rheology and bread characteristics [40,43]; correct management of the water addition during kneading [4,40]; and, finally, development of automatic and adaptive kneading machines able to optimize the kneading process [40,44].…”

Section: Innovations and Improvements In Dough Kneadingmentioning

confidence: 99%

“…Last but not least, the development of automatic and adaptive kneading machines, able to measure kneading progress in real time, could significantly improve either dough rheology or bread characteristics, reducing the energy demand and the environmental pressures [40,44]. The first step in this direction was taken by Aljaafreh, (2017) [44] who proposed a novel design for an intelligent process controller to automate the kneading process. The design is based on current sensing, and on-line learning through reinforcement, using operator input [40,44].…”

Section: Innovations and Improvements In Dough Kneadingmentioning

confidence: 99%

“…The first step in this direction was taken by Aljaafreh, (2017) [44] who proposed a novel design for an intelligent process controller to automate the kneading process. The design is based on current sensing, and on-line learning through reinforcement, using operator input [40,44]. The system developed by the author might represent a low-cost solution to automating production equipment that currently is managed manually.…”

Section: Innovations and Improvements In Dough Kneadingmentioning

confidence: 99%

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Challenges and Opportunities in Wheat Flour, Pasta, Bread, and Bakery Product Production Chains: A Systematic Review of Innovations and Improvement Strategies to Increase Sustainability, Productivity, and Product Quality

Cappelli

Cini

2021

Sustainability

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Pasta, bread, and bakery products are considered worldwide as essential foods for human nutrition. In particular, ancient wheats and whole wheat flours, despite being able to provide health benefits via bioactive compounds, present significant technological problems related to poorer dough rheological properties and final product characteristics. Moreover, both the food industry and consumers are increasingly sensitive to environmental impacts, highlighting the urgent need for sustainable innovations and improvement strategies, from cradle to grave, for the entire production chains, thus motivating this review. The aim of this review is to provide technological innovations and improvement strategies to increase the sustainability, productivity, and quality of flours, pasta, bread, and bakery products. This review is focused on the main operations of the production chains (i.e., wheat cultivation, wheat milling, dough processing, and, finally, the manufacturing of pasta, bread, and bakery products). To achieve this goal, the use of life-cycle assessment (LCA) analysis proved to be an effective tool that can be used, from early stages, for the development of eco-friendly improvement strategies. The correct management of the wheat cultivation stage was found to be essential since it represents the most impacting phase for the environment. Successively, particular attention needs to be paid to the milling process, the kneading phase, to breadmaking, and, finally, to the manufacturing of pasta. In this review, several specifically developed solutions for these essential phases were suggested. In conclusion, despite further investigations being necessary, this review provided several innovations and improvement strategies, using an approach “from cradle to grave”, able to increase the sustainability, productivity, and final quality of flour, semolina, pasta, bread, and bakery products.

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“…Cappelli and Cini (2021) [ 4 ] highlighted that it is essential to correctly manage kneading time, dough temperature, mixing speed, dough aeration, and other key parameters, to guarantee optimal dough rheology and best bread characteristics. Regarding the improvement of the kneading process, control the dough temperature during kneading using alternative, eco-sustainable, refrigerants (like carbonic snow) [ 7 , 9 , 16 ], correctly manage the water addition during kneading [ 8 ], delay the addition of bran and middlings during kneading in whole wheat dough and bread production [ 17 ], and, finally, develop automatic, adaptive, and more usable kneading machines [ 8 , 18 , 19 , 20 ], seem to be the most interesting strategies.…”

Section: Introductionmentioning

confidence: 99%

The Effects of Storage Time and Environmental Storage Conditions on Flour Quality, Dough Rheology, and Biscuit Characteristics: The Case Study of a Traditional Italian Biscuit (Biscotto di Prato)

Cappelli

Bini

Cini

2022

Foods

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Many types of baked goods are firmly rooted in the food habits of many people in different countries. Although there have been great strides in improving milling, kneading, and baking, given the lack of essential studies, further steps forward need to be taken to understand the effects of storage time and environmental storage conditions, thus motivating this work. The aim of this study is to assess the effects of storage time, using one-way ANOVA, and environmental storage conditions (environmental temperature and humidity), using MOLS analysis, on flour composition, dough rheology, and biscuit characteristics. Seven levels of storage time were tested: T0 (control), T1, T2, T3, T4, T5, and T6. The results showed that flour storage time significantly increased dough tenacity (P) and curve configuration ratio (P/L), and decreased the biscuit volume (best at T0). However, 2–3 weeks of storage highlighted a significant increase in deformation energy (W), an essential alveograph parameter that is closely correlated to the technological success of leavened products. This optimum found for W might be considered as a great stride in understanding the effects of storage time, confirming that wheat flour can reach its optimal performance after two-three weeks of storage, in particular for W. Moreover, this information could be useful, not only for biscuits production, but also for bread and bakery products (and, thus, the entire bakery industry). MOLS analysis highlighted that dough rheology and biscuit characteristics are mainly affected by flour composition (primarily from starch content) rather than environmental storage parameters. In conclusion, to optimize the biscuit characteristics, it is necessary to use flours with a low content of damaged starch by selecting the most suitable milling technique and carefully managing the operative parameters.

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Agitation and mixing processes automation using current sensing and reinforcement learning

Cited by 15 publications

References 6 publications

Machine learning techniques for analysis of hyperspectral images to determine quality of food products: A review

Machine learning techniques for analysis of hyperspectral images to determine quality of food products: A review

Challenges and Opportunities in Wheat Flour, Pasta, Bread, and Bakery Product Production Chains: A Systematic Review of Innovations and Improvement Strategies to Increase Sustainability, Productivity, and Product Quality

The Effects of Storage Time and Environmental Storage Conditions on Flour Quality, Dough Rheology, and Biscuit Characteristics: The Case Study of a Traditional Italian Biscuit (Biscotto di Prato)

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