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

Abstract

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.

Keywords: meat; non-destructive testing; tenderness.

Figure 1

Working principle diagrams of detection device. This figure shows the main structure of the device. Note: 1. Spectral acquisition unit; 2. Distance measuring unit; 3. Sample placement table; 4. Sample transmission unit.

Figure 2

Original spectrum at different distances of the first band and second band. This figure shows the analysis process of the raw data. Note: (a) First band; (b) Second band.

Figure 3

Schematic diagram of hyperspectral imaging system. This system is used to collect hyperspectral scattering images. Note: 1. InGaAs camera; 2. spectrograph; 3. FOV of line scan camera offset 5 mm from center of incident beam of light; 4. steak sample; 5. automated vertical stage; 6. linear slide, which moved the sample under the line scan in the direction of travel; 7. incident fiber optic cable; 8. light source tungsten halogen lamp; 9. photoelectric switch.

Figure 4

Portable Raman system. This figure shows the composition of the portable Raman system. Note: Handheld Raman device (center), miniaturized spectrometer (right), and netbook (left).

Figure 5

Structure diagram of controlled laser–airflow detection system. This figure shows us in detail all the components of the detection system. Note: 1. Analog input output conversion module; 2. Laser signal amplifier; 3. Laser displacement sensor A/D conversion module; 4. Pressure regulating valve; 5. Two-stage air filtration; 6. Lifting table; 7. Stepper motor; 8. Stepper motor driver; 9. Electrical proportional valve; 10. Electromagnetic valve; 11. ARM embedded integrated machine; 12. Laser displacement sensor; 13. Quartz window; 14. Air chamber; 15. Nozzles; 16. Samples; 17. Electromagnetic force balance sensor; 18. Power switch.

Figure 6

Air-puff and structured-light system. This figure shows the overall structure of the detection system. Note: 1. Small air compressor; 2. Air storage tank; 3. SMC proportional valve; 4. Pneumatic solenoid valve; 5. Ventilation pipeline and nozzle; 6. DLP digital projector; 7. Camera; 8. Portable computer.