Application of Spectral Analysis Technology in Detection of Meat Products

Not long ago, "glue steak" became a hot food safety issue, causing public concern. “Recombination” steak is a kind of conditioning meat. It can add a series of additives such as carrageenan and TG enzyme to shape and enhance the taste. It has no effect on human health, but it is prone to microbial bacterial contamination inside. It needs to be cooked thoroughly and cooked. Although the splicing of meat is not illegal, the author visited the market and found that many “recombinant meats” were sold on the product packaging with the eye-catching labels of “original cut sirloin steak” and “original cut mignon steak”. Experts said that this kind of reorganization and processing is a "original cut" method that misleads consumers and is suspected of commercial fraud. At present, relevant domestic industry standards are being actively prepared. The inclusion of unmarked or falsely labeled meat varieties in meat products has gradually become a global problem.

At present, many meat traceability and adulteration identification techniques involve biochemistry, immunology, and molecular science. For example, polymerase chain reaction (PCR) as a molecular method can specifically identify specific DNA or RNA in a sample. However, these methods are not only time consuming, consumables, but also require pretreatment of the sample. Therefore, spectral analysis shows great advantages due to its fast and simple sample pretreatment characteristics. Near-infrared (NIR), mid-infrared (MIR), infrared (infrared, IR), Fourier-transform infrared (FTIR), ultraviolet-visible absorption (UV-VIS) spectroscopy And Raman spectroscopy can be applied to the detection of meat varieties in different processed meat products. These waves, by being reflected, transmitted or absorbed by the food sample, produce a specific spectrum that reflects the trait of the sample. These complex spectral data are processed using stoichiometry to maintain the accuracy of a particular spectrum. In addition, spectral analysis can also be used for quality analysis of fresh meat products.

Spectral Analysis Technology and Principles of Meat Species Identification
1 infrared spectrum
Meat products produced from different meat varieties have different compositions of moisture, protein and fatty acids, which result in differences in the spectrum at specific wavelengths. Among them, O-H, C-H, N-H, C=O and hydrogen bonds generate vibrational responses under infrared radiation and are recorded as infrared spectra. The infrared spectrum consists of two regions: near-infrared (12 500 to 4 000 cm-1) and mid-infrared (4,000 to 400 cm-1). O-H, C-H, N-H, C=O, and hydrogen bonds generate vibration and harmonics in the near-infrared region, while mid-infrared spectra reflect the bending, stretching, and rocking motion of these functional groups. Molecular more detailed information. When each atom in the molecule makes a simple harmonic motion near the equilibrium position at the same frequency and the same phase, the energy of the molecular vibration and the optical quantum energy of the infrared ray correspond exactly to generate an infrared spectrum. In short, when the vibration state of the molecule changes, the infrared spectrum can be emitted, or the infrared absorption spectrum can be generated by the vibration of the infrared radiation excitation molecule. The vibration and rotational energy of the molecule are not continuous but quantized. However, since the transitional transition is often accompanied by the vibrational transition of the molecule, the vibrational spectrum is banded. Therefore, each meat sample has a unique infrared absorption spectrum determined by its composition and structure, whereby the molecules can be structurally analyzed and identified.

In the mid-infrared region, 4 000 to 1 500 cm-1 is a functional group region, and 4 000 to 500 cm-1 is a fingerprint region. In the functional group region, O-H and N-H (3 700-2500 cm-1) and C-H (3 300-2800 cm-1) in the aldehydes (2 900-2 700 cm-1) can be detected. 1) Stretching. The three characteristic regions (C≡N, C≡C, C=C=C) have a characteristic region in the spectrum of 2 700 to 1 850 cm-1; double bonds (C=C, C=N, C=O) are 1 950 ~ 1 450 cm-1.

Compared with mid-infrared, near-infrared has a stronger ability to penetrate food; however, it requires some reference calibration in most NIR measurements, making its application selective and restrictive. In Fourier-transform (FT), the interferometer and Fourier transform separate the frequencies emitted by the source so that the energy value of each frequency passing through the sample can be measured. The use of an interferometer not only shortens the detection time and reduces noise, but also improves the sensitivity and accuracy of the detection. Since the signal quality of the wavenumber is proportional to the square root of the time it takes to measure the wavenumber, shortening the detection time also increases the signal to noise ratio (RS/N).

Since triglycerides of different fatty acids are a one-component system, spectral analysis has a clear advantage in the analysis of fats and fats, which allows fats and fats to be measured directly in a neat form. Therefore, in many meat species identification studies, fat is extracted from meat samples and subjected to actual spectral analysis to classify and quantify different meat species.

When measuring meat in infrared, the reflection mode has specular reflection and diffuse reflection. In specular reflection, since infrared radiation cannot penetrate the surface of the meat sample, the response is generated only by the surface of the sample without generating an absorption response. In diffuse reflection, infrared radiation penetrates the surface of the sample into the interior, interacts with the sample matrix and returns to the surface of the sample, producing an absorption response. However, when the particle size of the sample is much larger than the wavelength of the irradiated light, a scattering effect occurs.

2 Raman spectroscopy

At present, various analytical methods such as Fourier Raman spectroscopy, surface enhanced Raman spectroscopy, laser resonance Raman spectroscopy and microscopic confocal Raman spectroscopy have been formed, which are widely used in food quality evaluation and quality safety testing.

In the Raman spectrum, the molecule is originally in the basic electronic state, and when excited by the light energy, the electrons in the bond transition to the virtual state. Therefore, it reflects the transition of molecules between different vibration states. Raman scattering is a two-photon process in which one photon is absorbed while photons of another different energy level are released. Elastic scattering, also known as Rayleigh scattering, is not useful in obtaining molecular feature information because the state of electrons combined with chemical bonds does not change. Non-elastic scattering (only 0.001%) is necessary for the identification of functional groups. Compared with the infrared spectrum, because the Raman scattering of water is very weak, the Raman spectrum can ignore the influence of moisture on the spectrum, while the infrared spectrum is affected by the moisture content in the sample. In addition, Raman spectroscopy can capture detailed information on protein secondary structures (such as α-helix, β-sheet) and amino acid residues in the sample. The Raman spectral bands of different wavenumber intervals correspond to different functional groups such as amino acids or lipids, respectively. Studies have found 510 ~ 545 cm-1 (cysteine), 829 cm-1 (phenylalanine, leucine, valine), 856 cm-1 (glutamine, valine, Lai , 879 cm-1 (glutamate, lysine), 1 082 cm-1 and 1 126 cm-1 (lipid), 1 247 cm-1 and 1 304 cm-1 (protein beta, respectively) - Folding and α-helix) isospectral peaks [1].

3 laser induced breakdown spectroscopy
Laser-induced breakdown spectroscopy (LIBS) is an emerging technology that focuses laser energy on a sample in a short period of time, allowing the atom to release a characteristic spectrum, the LIBS spectrum. Therefore, this technique is very suitable for the qualitative and quantitative analysis of elements in meat products. Similar to other spectral analyses, applying chemometrics to extract valid information from complex spectra yields a characteristic spectrum of sample elemental composition.

Various spectral analysis techniques have been used for the identification of meat species. Some studies have revealed the importance of spectral analysis techniques in correctly identifying meat varieties in meat products. However, there are still some technical limitations to these methods. To provide a powerful solution for the meat industry, these technologies should be applied in combination with all the constraints and process variables.

Edit review
In recent years, near-infrared analysis technology has become more and more widely used in the meat industry as a new detection technology. It can replace detector tools that are time-consuming and expensive, and also endanger health or pollute the environment. Detection technology can also analyze the physical properties and sensory quality of meat. Through the spectral analysis technology, the meat industry can realize the rapid and non-destructive and on-line detection of meat. The application of near-spectral analysis technology in the meat industry will enable It is of great practical significance to improve the safety monitoring of the meat and meat products industry.

Other Pigment & Dye

Permanent Makeup Ink,Permanent Makeup Pigment,Pigment & Dye

Danyang Trading Co., Ltd. ,

Posted on