期刊
JOURNAL OF DAIRY SCIENCE
卷 105, 期 11, 页码 8638-8649出版社
ELSEVIER SCIENCE INC
DOI: 10.3168/jds.2022-21969
关键词
colostrum adulteration; near-infrared spectroscopy; dielectric spectroscopy; nonhomogeneity; qualitative analysis
资金
- National Natural Science Founda- tion of China
- Fundamental Research Funds for the Central Universi- ties (Yangling, China)
- [32172308]
- [2452021159]
This study compared the performance of near-infrared spectroscopy (NIRS) and dielectric spectroscopy (DS) in quantitatively predicting the content of mature milk as an adulterant in bovine colostrum. The results showed that DS had better identification performance, providing important insights for the quantitative prediction of nonhomogeneous liquid food.
The nonhomogeneity of bovine colostrum leads to strong scattering effects for electromagnetic waves, which affects the application of electromagnetic spec-troscopy in detecting colostrum. This work aimed to compare the performance of near-infrared spectroscopy (NIRS) and dielectric spectroscopy (DS) in quanti-tatively predicting the content of mature milk as an adulterant in colostrum. The near-infrared spectra in the range of 833 to 2,500 nm and the dielectric spectra in the range of 20 to 4,500 MHz of 150 adulterated colostrum samples containing 0 to 50% mature milk were analyzed. The different proportions of mature milk in colostrum significantly changed near-infrared and dielectric spectra. The principal component analy-sis score plot showed that both NIRS and DS could identify the proportion of mature milk in colostrum, but the 2 methods had different characteristics. Lin-ear partial least squares regression and nonlinear least squares support vector machine (LSSVM) models based on near-infrared and dielectric spectra were established to identify doping proportions. The results showed that DS had better identification performance with a root -mean-square error of prediction of 4.9% and a residual prediction deviation of 3.441 by successive projection algorithm LSSVM, whereas NIRS was relatively weak with a root-mean-square error of prediction of 7.3% and a residual prediction deviation of 2.301 by full-spectra LSSVM. This work provides important insights for the quantitative prediction of nonhomogeneous liquid food by DS.
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