Determination of beef tenderness based on airflow pressure combined with structural light three-dimensional (3D) vision technology

Luo, Xiuzhi; Xiong, Lijian; Gao, Xin; Hou, Yuxin; He, Min; Tang, Xiuying

doi:10.1016/j.meatsci.2023.109206

Cited by 5 publications

(5 citation statements)

References 22 publications

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“…Their findings indicated that a model based on the Extreme Learning Machine (ELM) algorithm exhibited a strong predictive capability for beef tenderness, with a R of 0.8356. Moreover, the ELM model achieved an impressive correct classification accuracy of 92.96% for tender beef [54].…”

Section: Airflow-optical Fusion Detection Technologymentioning

confidence: 96%

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Rapid Non-Destructive Detection Technology in the Field of Meat Tenderness: A Review

Li,

Wang,

Yang

et al. 2024

Foods

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Traditionally, tenderness has been assessed through shear force testing, which is inherently destructive, the accuracy is easily affected, and it results in considerable sample wastage. Although this technology has some drawbacks, it is still the most effective detection method currently available. In light of these drawbacks, non-destructive testing techniques have emerged as a preferred alternative, promising greater accuracy, efficiency, and convenience without compromising the integrity of the samples. This paper delves into applying five advanced non-destructive testing technologies in the realm of meat tenderness assessment. These include near-infrared spectroscopy, hyperspectral imaging, Raman spectroscopy, airflow optical fusion detection, and nuclear magnetic resonance detection. Each technology is scrutinized for its respective strengths and limitations, providing a comprehensive overview of their current utility and potential for future development. Moreover, the integration of these techniques with the latest advancements in artificial intelligence (AI) technology is explored. The fusion of AI with non-destructive testing offers a promising avenue for the development of more sophisticated, rapid, and intelligent systems for meat tenderness evaluation. This integration is anticipated to significantly enhance the efficiency and accuracy of the quality assessment in the meat industry, ensuring a higher standard of safety and nutritional value for consumers. The paper concludes with a set of technical recommendations to guide the future direction of non-destructive, AI-enhanced meat tenderness detection.

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Section: Airflow-optical Fusion Detection Technologymentioning

confidence: 96%

“…The detection model has good predictive performance, with a correlation coefficient of 0.8356, and a discrimination rate of 92.96% for tender beef [54].…”

Section: Beef Tenderness Elmmentioning

confidence: 98%

Rapid Non-Destructive Detection Technology in the Field of Meat Tenderness: A Review

Li,

Wang,

Yang

et al. 2024

Foods

View full text Add to dashboard Cite

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“…This comprehensive approach ensures the delivery of accurate invoice data through the utilization of AI visual intelligence and OCR+NLP review double insurance. Consequently, it enables an initial review of invoices with a remarkably high accuracy rate nearing 100% [1,2,3].…”

Section: Visual Positioning Guidancementioning

confidence: 99%

“…The height is 1/32 of the original, so it's 1, which is important; Through this step, we get 24 feature quantities, so the width is 24. In summary, we finally get the feature map of size [512, 1,24]. During prediction for a single instance, only the width needs to be adjusted to a multiple of 32.…”

Section: Height Measurement Algorithmmentioning

confidence: 99%

“…However, further adaptations are required to suit diverse business application scenarios such as in telecommunications and government enterprises. Future research can also be carried out from the following aspects: (1) Combining the latest industrial and information technology to further enhance the accuracy and efficiency of automating certificate making and ticket sticking. (2)With the aim of establishing a modern smart supply chain, exploring additional robots to replace complex and repetitive manual tasks, developing various intelligent and automated industrial robots, promoting customization for multiple scenarios in smart supply chains, applying automated robots, and enhancing the overall operational intelligence and automation level of supply chain scenes have become crucial steps for power enterprises to optimize their business environment.…”

Section: Limitations and Further Workmentioning

confidence: 99%

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Research on Automatic Unattended Bill Collection, Paste and Verifiction Integrated Robot Equipment and Control Platform based on Deep Convolutional Neural Network

Wang,

Chen,

Wang

2024

SCPE

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A new solution for fully automated and unmanned ticket pasting verification based on deep convolutional neural networks is designed to address the issues of low efficiency, error-proneness, and wastage of manpower in the supplier service hall. The technology makes full use of machine vision and image processing, AI precise positioning correction algorithm and other methods to build an automatic unattended bill collection, paste and verification platform. Through the technologies of high-speed identification of invoice information, 3D vision-guidance planning, control of the path of robotic arm , detection of invoice pasting and repeating based on ultrasonic sensors, and tidal temporary storage of paper invoices, and so on, the automatic high-speed identification and inspection of bills in the supplier service hall are realized, and the efficiency and accuracy of bill processing in the supplier hall are improved. Experiments show that this research method reinforces ability of identification calibration and order correlation, and improves the efficiency of Invoice filing.

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Beef marbling assessment by structured-illumination reflectance imaging with deep learning

Cai,

Lu,

Olaniyi

et al. 2024

Journal of Food Engineering

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Determination of beef tenderness based on airflow pressure combined with structural light three-dimensional (3D) vision technology

Cited by 5 publications

References 22 publications

Rapid Non-Destructive Detection Technology in the Field of Meat Tenderness: A Review

Rapid Non-Destructive Detection Technology in the Field of Meat Tenderness: A Review

Research on Automatic Unattended Bill Collection, Paste and Verifiction Integrated Robot Equipment and Control Platform based on Deep Convolutional Neural Network

Beef marbling assessment by structured-illumination reflectance imaging with deep learning

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