Rapid classification of the freshness grades of sea bass (Lateolabrax japonicus) fillets using a portable Raman spectrometer with machine learning method

Wang, Kaiqiang; Yue, Zilin; Lin, Hong; Wang, Qi; Wang, Lei; Tian, Ye; Ren, Lihui

doi:10.1016/j.microc.2023.108948

Cited by 5 publications

(1 citation statement)

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“…The portable fluorescence technology studied has not yet been applied to the device, and the price of the whole system is in the range of 50K to 100K RMB. Wang et al [ 38 ] used a portable Raman spectrometer to classify the freshness of fish through a CNN model, with a detection accuracy of 90.60%. The device is easy to carry, and the price of the device is 100–140K RMB.…”

Section: Resultsmentioning

confidence: 99%

Developing a Portable Fluorescence Imaging Device for Fish Freshness Detection

Pan,

Huang,

Zhu

et al. 2024

Sensors

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Rapid detection of fish freshness is of vital importance to ensuring the safety of aquatic product consumption. Currently, the widely used optical detecting methods of fish freshness are faced with multiple challenges, including low detecting efficiency, high cost, large size and low integration of detecting equipment. This research aims to address these issues by developing a low-cost portable fluorescence imaging device for rapid fish freshness detection. The developed device employs ultraviolet-light-emitting diode (UV-LED) lamp beads (365 nm, 10 W) as excitation light sources, and a low-cost field programmable gate array (FPGA) board (model: ZYNQ XC7Z020) as the master control unit. The fluorescence images captured by a complementary metal oxide semiconductor (CMOS) camera are processed by the YOLOv4-Tiny model embedded in FPGA to obtain the ultimate results of fish freshness. The circuit for the YOLOv4-Tiny model is optimized to make full use of FPGA resources and to increase computing efficiency. The performance of the device is evaluated by using grass carp fillets as the research object. The average accuracy of freshness detection reaches up to 97.10%. Moreover, the detection time of below 1 s per sample and the overall power consumption of 47.1 W (including 42.4 W light source power consumption) indicate that the device has good real-time performance and low power consumption. The research provides a potential tool for fish freshness evaluation in a low-cost and rapid manner.

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Section: Resultsmentioning

confidence: 99%