Nondestructive internal quality inspection of pear fruit by X-ray CT using machine learning

Looverbosch, Tim Van De; Bhuiyan, Md. Hafizur Rahman; Verboven, Pieter; Dierick, Manuel; Loo, Denis Van; Beenbouwer, Jan De; Sijbers, Jan; Nicolaı̈, Bart

doi:10.1016/j.foodcont.2020.107170

Cited by 63 publications

(21 citation statements)

References 49 publications

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“…Although the majority of previous work combining sensors and ML has focused on optical systems (imaging and spectroscopic), research has been performed using data from other sensor instruments. For example, X-ray measurements have been combined with ML for the internal inspection of fruit (Van De Looverbosch et al, 2020). Several articles reviewing ML research within food and drink are available which the reader may refer to Liakos et al (2018), Rehman et al (2019), Zhou et al (2019), andSharma et al (2020).…”

Section: Machine Learningmentioning

confidence: 99%

Intelligent Sensors for Sustainable Food and Drink Manufacturing

Watson

Bowler

Rady

et al. 2021

Front. Sustain. Food Syst.

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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.

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Section: Machine Learningmentioning

confidence: 99%

Intelligent Sensors for Sustainable Food and Drink Manufacturing

Watson

Bowler

Rady

et al. 2021

Front. Sustain. Food Syst.

View full text Add to dashboard Cite

show abstract

“…However, the detection rate is more important for most industry applications since the majority of the samples is expected to be without a defect. Such a study was performed, for example, in [ 20 ] for pear fruit inspection. Methods to distinguish between bones and no bones for different patches of fish images were proposed in [ 11 ].…”

Section: Related Workmentioning

confidence: 99%

Unsupervised Foreign Object Detection Based on Dual-Energy Absorptiometry in the Food Industry

et al. 2021

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X-ray imaging is a widely used technique for non-destructive inspection of agricultural food products. One application of X-ray imaging is the autonomous, in-line detection of foreign objects in food samples. Examples of such inclusions are bone fragments in meat products, plastic and metal debris in fish, and fruit infestations. This article presents a processing methodology for unsupervised foreign object detection based on dual-energy X-ray absorptiometry (DEXA). A novel thickness correction model is introduced as a pre-processing technique for DEXA data. The aim of the model is to homogenize regions in the image that belong to the food product and to enhance contrast where the foreign object is present. In this way, the segmentation of the foreign object is more robust to noise and lack of contrast. The proposed methodology was applied to a dataset of 488 samples of meat products acquired from a conveyor belt. Approximately 60% of the samples contain foreign objects of different types and sizes, while the rest of the samples are void of foreign objects. The results show that samples without foreign objects are correctly identified in 97% of cases and that the overall accuracy of foreign object detection reaches 95%.

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“…X-ray imaging was applied to detect both the internal disorder and external changes (firmness) of pears and kiwifruit during cold storage, respectively (Table 1) [11,79]. This technology involves the production of electromagnetic radiation by an X-ray tube when passed through a product to absorb part of X-ray beam energy [80].…”

Section: Advanced Technologies For Quality Assessment In Postharvest Supply Chain: State-of-the-artmentioning

confidence: 99%

Recent Advances in Reducing Food Losses in the Supply Chain of Fresh Agricultural Produce

et al. 2020

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Fruits and vegetables are highly nutritious agricultural produce with tremendous human health benefits. They are also highly perishable and as such are easily susceptible to spoilage, leading to a reduction in quality attributes and induced food loss. Cold chain technologies have over the years been employed to reduce the quality loss of fruits and vegetables from farm to fork. However, a high amount of losses (≈50%) still occur during the packaging, pre-cooling, transportation, and storage of these fresh agricultural produce. This study highlights the current state-of-the-art of various advanced tools employed to reducing the quality loss of fruits and vegetables during the packaging, storage, and transportation cold chain operations, including the application of imaging technology, spectroscopy, multi-sensors, electronic nose, radio frequency identification, printed sensors, acoustic impulse response, and mathematical models. It is shown that computer vision, hyperspectral imaging, multispectral imaging, spectroscopy, X-ray imaging, and mathematical models are well established in monitoring and optimizing process parameters that affect food quality attributes during cold chain operations. We also identified the Internet of Things (IoT) and virtual representation models of a particular fresh produce (digital twins) as emerging technologies that can help monitor and control the uncharted quality evolution during its postharvest life. These advances can help diagnose and take measures against potential problems affecting the quality of fresh produce in the supply chains. Plausible future pathways to further develop these emerging technologies and help in the significant reduction of food losses in the supply chain of fresh produce are discussed. Future research should be directed towards integrating IoT and digital twins for multiple shipments in order to intensify real-time monitoring of the cold chain environmental conditions, and the eventual optimization of the postharvest supply chains. This study gives promising insight towards the use of advanced technologies in reducing losses in the postharvest supply chain of fruits and vegetables.

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Nondestructive internal quality inspection of pear fruit by X-ray CT using machine learning

Cited by 63 publications

References 49 publications

Intelligent Sensors for Sustainable Food and Drink Manufacturing

Intelligent Sensors for Sustainable Food and Drink Manufacturing

Unsupervised Foreign Object Detection Based on Dual-Energy Absorptiometry in the Food Industry

Recent Advances in Reducing Food Losses in the Supply Chain of Fresh Agricultural Produce

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