Nondestructive determination of common indicators of beef for freshness assessment using airflow-three dimensional (3D) machine vision technique and machine learning

Luo, Xiuzhi; Sun, Qinming; Yang, Tianxi; He, Ke; Tang, Xiuying

doi:10.1016/j.jfoodeng.2022.111305

Cited by 12 publications

(9 citation statements)

References 24 publications

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“…Therefore, the external quality, such as color, texture information, material shape, and surface defects can be automatically identified by MV. Automatic recognition, which uses stoichiometry and machine learning technology to extract image features and interpret them without human intervention (Patrício & Rieder, 2018), has been widely used in food analysis fields such as meat classification (Luo et al., 2023), fruit classification (Khojastehnazhand & Ramezani, 2020), and grain quality and safety (Li et al., 2022a).…”

Section: Spectral Data Fusion Techniquesmentioning

confidence: 99%

“…For example, spectroscopic techniques could provide information on the molecular vibration or atomic composition of the sample. MV can reflect the external color and morphology of the sample (Luo et al., 2023). The E‐nose is based on the principle of mimicking biological physiological responses to volatile substances, using integrated sensors to contact volatile substances to generate electrical signals (Xu et al., 2019).…”

Section: Introductionmentioning

confidence: 99%

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Spectral data fusion in nondestructive detection of food products: Strategies, recent applications, and future perspectives

Guo,

Wang,

Lin

et al. 2024

Comp Rev Food Sci Food Safe

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In recent years, the food industry has shown a growing interest in the development of rapid and nondestructive analytical methods. However, the utilization of a solitary nondestructive detection technique offers only a constrained extent of physical or chemical insights regarding the sample under examination. To overcome this limitation, the amalgamation of spectroscopy with data fusion strategies has emerged as a promising approach. This comprehensive review delves into the fundamental principles and merits of low‐level, mid‐level, and high‐level data fusion strategies within the domain of food analysis. Various data fusion techniques encompassing spectra‐to‐spectra, spectra‐to‐machine vision, spectra‐to‐electronic nose, and spectra‐to‐nuclear magnetic resonance are summarized. Moreover, this review also provides an overview of the latest applications of spectral data fusion techniques (SDFTs) for classification, adulteration, quality evaluation, and contaminant detection within the purview of food safety analysis. It also addresses current challenges and future prospects associated with SDFTs in real‐world applications. Despite the extant technical intricacy, the ongoing evolution of online data fusion platforms and the emergence of smartphone‐based multi‐sensor fusion detection technology augur well for the pragmatic realization of SDFTs, endowing them with formidable capabilities for both qualitative and quantitative analysis in the realm of food analysis.

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Section: Spectral Data Fusion Techniquesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Spectral data fusion in nondestructive detection of food products: Strategies, recent applications, and future perspectives

Guo,

Wang,

Lin

et al. 2024

Comp Rev Food Sci Food Safe

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show abstract

“…When a test sample is positioned beneath the nozzle, the force exerted by the high-speed airflow can induce surface deformation. Concurrently, optical sensors are deployed to monitor the sample's deformation, yielding a series of test data that can be analyzed for nondestructive evaluation of meat attributes such as tenderness and freshness [49][50][51][52]. This technology has garnered extensive research and application, with airflow laser technology and airflow structured light three-dimensional (3D) vision technology being particularly noteworthy.…”

Section: Airflow -Optical Fusion Detection Technologymentioning

confidence: 99%

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

Li,

Wang,

Yang

et al. 2024

Preprint

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“…When a test sample is positioned beneath the nozzle, the force exerted by the high-speed airflow can induce surface deformation. Concurrently, optical sensors are deployed to monitor the sample's deformation, yielding a series of test data that can be analyzed for non-destructive evaluation of meat attributes such as its tenderness and freshness [49][50][51][52]. This technology has garnered extensive research and application, with airflow laser technology and airflow-structured light three-dimensional (3D) vision technology being particularly noteworthy.…”

Section: Airflow-optical Fusion Detection Technologymentioning

confidence: 99%

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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Nondestructive determination of common indicators of beef for freshness assessment using airflow-three dimensional (3D) machine vision technique and machine learning

Cited by 12 publications

References 24 publications

Spectral data fusion in nondestructive detection of food products: Strategies, recent applications, and future perspectives

Spectral data fusion in nondestructive detection of food products: Strategies, recent applications, and future perspectives

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

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