Quantitative detection of restructured steak adulteration based on hyperspectral technology combined with a wavelength selection algorithm cascade strategy

Liu, Xiaoyu; Sun, Zongbao; Zuo, Min; Zhang, Xiaobo; Wang, Tianzhen; Li, Junkui

doi:10.3136/fstr.27.859

Cited by 1 publication

(1 citation statement)

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“…Therefore, using hyperspectral technology for meat quality detection enables sensory evaluation based on surface physical features and provides insights into internal component content. The composition of a typical hyperspectral imaging system is illustrated in Figure 5 [31]. Non-destructive detection of hyperspectral systems is shown in Figure 6.…”

Section: Hyperspectral Technologymentioning

confidence: 99%

Research Progress on Quality Detection of Livestock and Poultry Meat Based on Machine Vision, Hyperspectral and Multi-Source Information Fusion Technologies

Xu,

Han,

Zhao

et al. 2024

Foods

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Presently, the traditional methods employed for detecting livestock and poultry meat predominantly involve sensory evaluation conducted by humans, chemical index detection, and microbial detection. While these methods demonstrate commendable accuracy in detection, their application becomes more challenging when applied to large-scale production by enterprises. Compared with traditional detection methods, machine vision and hyperspectral technology can realize real-time online detection of large throughput because of their advantages of high efficiency, accuracy, and non-contact measurement, so they have been widely concerned by researchers. Based on this, in order to further enhance the accuracy of online quality detection for livestock and poultry meat, this article presents a comprehensive overview of methods based on machine vision, hyperspectral, and multi-sensor information fusion technologies. This review encompasses an examination of the current research status and the latest advancements in these methodologies while also deliberating on potential future development trends. The ultimate objective is to provide pertinent information and serve as a valuable research resource for the non-destructive online quality detection of livestock and poultry meat.

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Section: Hyperspectral Technologymentioning

confidence: 99%