Visual modeling of laser-induced dough browning

Chen, Peter Yichen; Blutinger, Jonathan David; Meijers, Yorán; Zheng, Changxi; Grinspun, Eitan; Lipson, Hod

doi:10.1016/j.jfoodeng.2018.08.022

Cited by 21 publications

(7 citation statements)

References 16 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Sun et al [175] used a CNN to identify and predict the effectiveness of laser cleaning of rusty iron through analysis of images of the samples. In food science, Chen et al [176] used a CNN to predict the visual appearance of dough when irradiated by a CO 2 laser for different laser and dough parameters.…”

Section: Deep Learningmentioning

confidence: 99%

See 1 more Smart Citation

Lasers that learn: The interface of laser machining and machine learning

Mills

Grant-Jacob

2021

IET Optoelectronics

View full text Add to dashboard Cite

Laser machining is a highly flexible non-contact fabrication method used extensively across academia and industry. Whilst simulations based on fundamental understanding offer some insight into the processes, both the highly non-linear interactions between laser light and matter and the variety of materials involved mean that theoretical modelling is not particularly applicable to practical experimentation. However, recent breakthroughs in machine learning have resulted in neural networks that are capable of accurate and rapid modelling of laser machining at a scale, speed, and precision well beyond those of existing theoretical approaches with applications including 3-D surface visualisation and real-time error correction. A perspective at the intersection of laser machining and machine learning is presented, followed by a discussion of future milestones and challenges for this field.10 steps each, that corresponds to a total of 10^5 experimental trials. Because of the often highly non-linear relationships amongst parameters, the standard practice is generally the systematic collection of laser-machining data for all parameter combinations to identify the optimal combination. However, this process is both time-consuming and unfocussed and can take days or weeks, hence costing unnecessary effort, time, and money. Even when the optimal parameters have been determined, small changes during manufacturing, for example in laser power or beam shape, can result in final product quality that is below the required standard, again with associated costs in time and money. What is needed, therefore, is a set of modelling methodologies for identifying optimal parameters and providing real-time monitoring and error correction during manufacturing.However, the processes that describe laser machining, such as the light-matter interaction, heat conduction, phase change of material, and material removal, are particularly complex and hence challenging to model precisely and directly from a theoretical standpoint. The exact details of these processes depend on many factors, including laser parameters (e.g. wavelength, fluence, and pulse length), associated diffraction effects, and sample properties (e.g. absorption, reflectivity, melting temperature, and ablation threshold). In addition, different interaction effects become dominant as the temporal interaction length varies from continuous wave to ultrafast (i.e. femtosecondThis is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

show abstract

Section: Deep Learningmentioning

confidence: 99%

“…In food science, Chen et al. [176] used a CNN to predict the visual appearance of dough when irradiated by a CO 2 laser for different laser and dough parameters.…”

Section: Machine Learning and Laser Machiningmentioning

confidence: 99%

Lasers that learn: The interface of laser machining and machine learning

Mills

Grant-Jacob

2021

IET Optoelectronics

View full text Add to dashboard Cite

show abstract

“…Different foods require varying time and temperature exposures for optimal cooking. To address the challenge of precision cooking, lasers are under investigation as a viable cooking technology and have shown to be effective at palatably cooking various food products 5,6,[56][57][58] .…”

Section: Acceptance Of 3d-printed Foodmentioning

confidence: 99%

The future of software-controlled cooking

et al. 2023

Self Cite

View full text Add to dashboard Cite

To date, analog methods of cooking such as by grills, cooktops, stoves and microwaves have remained the world’s predominant cooking modalities. With the continual evolution of digital technologies, however, laser cooking and 3D food printing may present nutritious, convenient and cost-effective cooking opportunities. Food printing is an application of additive manufacturing that utilizes user-generated models to construct 3D shapes from edible food inks and laser cooking uses high-energy targeted light for high-resolution tailored heating. Using software to combine and cook ingredients allows a chef to more easily control the nutrient content of a meal, which could lead to healthier and more customized meals. With more emphasis on food safety following COVID-19, food prepared with less human handling may lower the risk of foodborne illness and disease transmission. Digital cooking technologies allow an end consumer to take more control of the macro and micro nutrients that they consume on a per meal basis and due to the rapid growth and potential benefits of 3D technology advancements, a 3D printer may become a staple home and industrial cooking device.

show abstract

“…Recent developments in deep learning also paved the way for generative models able to produce images or text. In an elegant application to food processes, Chen et al (2019) predicted the appearance of laser-browned dough for cookies.…”

Section: Techniques Used and Application Areasmentioning

confidence: 99%

A primer on predictive techniques for food and bioresources transformation processes

Sicard

Barbe²,

Boutrou

et al. 2023

J Food Process Engineering

View full text Add to dashboard Cite

To meet current societal demand for more sustainable transformation processes and bioresources, these processes must be optimized and new ones developed. The evolution of various systems (raw material, food, or process attributes) can be predicted to optimize the uses of biomass for better quality, safety, economic benefit, and sustainability. Predictive modeling can guide the necessary changes and influence industrials, governmental policies and consumers decision-making. However, achieving good predictive capability requires reflection on the models and model validation, which can be difficult. This review aims to help scientists begin to predict by presenting the techniques currently used in predictive science for food and related bioproducts. First, a guideline helps readers initiate a prediction process along with final tips and a warning about the risks involved, with a particular focus on the crucial validation step. Three broad categories of techniques are then presented: empirical, mechanistic, and artificial intelligence (or "data-driven"). For each category, the advantages and limitations of current techniques for prediction are explained in light of their current domains of applications, illustrated with literature studies and a detailed example. Thus this article provides engineering researchers information about predictive modeling which is a recent relevant development in optimization of both food and nonfood bioresources processes. Practical applicationsPredictive modeling is a recent development of much relevance in the optimization of both food and nonfood bioresources processes. The goal of this article is to guide those in research or industry who would like to start predicting. Therefore, the article is intended as a primer on prediction concepts and predictive techniques for food and non-food bioresources processing. Three categories of techniques commonly used in these fields are illustrated by various examples of current applications and a more detailed example helps to understand the implementation process. An increased ability of the global scientific body to predict the outcome of various decisions, often linked or sequential, will open new avenues for designing food products with circularity in mind: maintaining value and not creating waste in the process.

show abstract

Visual modeling of laser-induced dough browning

Cited by 21 publications

References 16 publications

Lasers that learn: The interface of laser machining and machine learning

Lasers that learn: The interface of laser machining and machine learning

The future of software-controlled cooking

A primer on predictive techniques for food and bioresources transformation processes

Contact Info

Product

Resources

About