Non-Destructive Near-Infrared Spectroscopy of Physical and Chemical
Indicator of Pork Meat
LIU Yu-ming1, 2, 3, WANG Qiao-hua1, 2, 3*, CHEN Yuan-zhe1, LIU Cheng-kang1, FAN Wei1, ZHU Zhi-hui1, LIU Shi-wei1
1. College of Engineering, Huazhong Agricultural University, Wuhan 430070, China
2. Shenzhen Institute of Nutrition and Health, Huazhong Agricultural University, Wuhan 430070, China
3. Shenzhen Branch, Guangdong Laboratory for Lingnan Modern Agriculture, Genome Analysis Laboratory of the Ministry of Agriculture, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen 518124, China
Abstract：China is the world's largest pork producer and consumer. The quality of pork affects the quality of protein intake for people. There needs to be more effective rapid non-destructive testing methods to cope with the huge testing needs. In order to rapidly determine volatile salt nitrogen (TVB-N), pH and moisture content of frozen pork and to propose a new method for pork quality testing, this paper uses near-infrared spectroscopy combined with chemometric methods to establish mathematical models for TVB-N, pH and moisture content of frozen pork. The NIR spectral data were collected and combined with chemometric tests to obtain the measured values of TVB-N, pH and moisture content. ~5 000 cm-1 region, while the absorption peaks around 8 600～8 450 cm-1 were significantly smaller than the other absorption peaks. The SPXY (sample set partitioning based on joint X-Y distances) algorithm was used to partition the data set into a training set and a test set in the ratio of 3∶1. The abnormal data were removed using Monte Carlo cross-validation (MCCV) and a partial least squares regression (PLSR) was used to establish The regression relationships of TVB-N, pH, water content and full-band spectral information were established by partial least squares regression (PLSR), and the raw spectra were pre-processed using data centering, Savitzky-Golay(S-G) first-order derivatization, S-G second-order derivatization, direct difference second-order derivatization and multiple scattering corrections to explore the appropriate pre-processing methods. The results show that the data centering, direct differential second-order derivation and second-order derivation achieve good experimental results, so the combination of competitive adaptive reweighted sampling (CARS), uninformative variables elimination (UVE), and multiplicative scatter correction (MSC) has been applied. The PLSR feature band model was developed and analysed by combining the competitive adaptive reweighted sampling (CARS), uninformative variables elimination (UVE) and successive projections algorithm (SPA). The results showed that the prediction models for TVB-N, pH and water content had excellent performance when the structures were data centered-CARS-PLSR, direct difference second-order derivative-CARS-PLSR and second-order derivative-CARS-PLSR, respectively, where the training set correlation coefficients RC were 0.947 1, 0.998 8 and 0.997 1, respectively, and the root mean square errors (RMSE) were 1.208 8, 0.008 7 and 0.001 5, respectively; the test set correlation coefficients RP were 0.927 5, 0.963 0 and 0.945 9, respectively, and the RMSE were 1.683 6, 0.051 7 and 0.005 6, respectively. In summary, it can be seen that the NIR band region is significantly correlated with pork TVB-N, pH and moisture content, and the use of NIR spectra can The NIR spectra can be used to predict TVB-N, pH and moisture content in pork accurately.
Key words：Pork; Near-infrared spectroscopy; Volatile salt-based nitrogen(TVB-N); pH; Water content
刘瑜明，王巧华，陈远哲，刘成康，范 维，祝志慧，刘世伟. 猪肉理化指标的近红外光谱无损检测[J]. 光谱学与光谱分析, 2024, 44(05): 1346-1353.
LIU Yu-ming, WANG Qiao-hua, CHEN Yuan-zhe, LIU Cheng-kang, FAN Wei, ZHU Zhi-hui, LIU Shi-wei. Non-Destructive Near-Infrared Spectroscopy of Physical and Chemical
Indicator of Pork Meat. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(05): 1346-1353.
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