Applications of Fluorescence Spectroscopy, RGB- and MultiSpectral Imaging for Quality Determinations of White Meat: A Review

Abstract

Fluorescence spectroscopy, color imaging and multispectral imaging (MSI) have emerged as effective analytical methods for the non-destructive detection of quality attributes of various white meat products such as fish, shrimp, chicken, duck and goose. Based on machine learning and convolutional neural network, these techniques can not only be used to determine the freshness and category of white meat through imaging and analysis, but can also be used to detect various harmful substances in meat products to prevent stale and spoiled meat from entering the market and causing harm to consumer health and even the ecosystem. The development of quality inspection systems based on such techniques to measure and classify white meat quality parameters will help improve the productivity and economic efficiency of the meat industry, as well as the health of consumers. Herein, a comprehensive review and discussion of the literature on fluorescence spectroscopy, color imaging and MSI is presented. The principles of these three techniques, the quality analysis models selected and the research results of non-destructive determinations of white meat quality over the last decade or so are analyzed and summarized. The review is conducted in this highly practical research field in order to provide information for future research directions. The conclusions detail how these efficient and convenient imaging and analytical techniques can be used for non-destructive quality evaluation of white meat in the laboratory and in industry.

Keywords: convolutional neural network; fluorescence spectroscopy; multispectral imaging; quality detection; white meat.

Figure 1

Jablonski diagram of the electron energy levels and transitions of fluorophores [29].

Figure 2

Diagram of the RGB vision system used to obtain color images of pure and contaminated meat samples [33].

Figure 3

The MSI system consists of a light source (HL-2000-FHSA; Ocean Optics, Dunedin, FL, USA) and focusable lens (Nikon, Tokyo, Japan) plus a multi-channel spectral camera (miniCAM5; QHY-CCD, China) [38].

Figure 4

The system consists of a snapshot spectral imaging system and a mini computer system similar to the NVIDIA Jetson [3].

Figure 5

Plot of actual versus predicted values of SM2 and OFL residues in duck meat from predicted samples based on the peak height algorithm [98].

Figure 6

Schematic diagram of a multispectral fluorescence imaging system (a) and a real-time multispectral fluorescence imaging system (b) for the detection of fecal material on the surface of chickens [71].

Figure 7

Fluorescence properties of CdSe quantum dots: (a) Fluorescence quantum yields of CdSe quantum dots. (b) Fluorescence lifetime of CdSe quantum dots [99].