TY - JOUR
T1 - Inversion of true protein content in milk based on hyperspectral data
AU - Zhang, Qian Qian
AU - Tan, Kun
N1 - Publisher Copyright:
© 2015, Science Press. All right reserved.
PY - 2015/12/1
Y1 - 2015/12/1
N2 - As an indispensable drink of people's daily life, milk's quality has been also increasingly concerned by consumers. Rapid and accurate detection of milk and its products is the indispensable step for improving the quality of milk and daily products in production. However, traditional methods cannot meet the need. In this paper, rapid quantitative detection of true protein in pure milk was studied by using visible/near-infrared (VIS/NIR) reflectance spectroscopy (350~2500 nm). The spectral data and the protein content data of the pure milk samples were collected by ASD spectrometer and CEM rapid protein analyzer, respectively. Based on the analysis and comparison of different spectrum preprocessing methods and band selection methods, the feature bands were determined. Finally, using the Principle Component Regression (PCR) and Least Squares Support Vector Machine (LS-SVM) model, the regression models between the reflectance spectroscopy and the protein content in milk were presented for pure milk samples and the predictive ability was also analyzed. In this way, the optimal inversion model for true protein content in milk was established. The results were shown as follows: (1) In the process of spectral pretreatment, the combination of multiple scatter correction and second derivative achieved a better result; (2) Compared with the modeling of whole spectral, appropriate variable optimization models had the ability to improve the accuracy of the inversion results and reduce the modeling time; (3) The analysis results between PCR model and LS-SVM model demonstrated that the prediction accuracy of LS-SVM model was better than PCR model. The coefficient of determination (RP2) of PCR and LS-SVM were 0.9522 and 0.9580 respectively, and the root mean square error of prediction (RMSEP) of PCR and LS-SVM were 0.0487 and 0.0482 respectively. The result of this research is expected to provide a novel method for nondestructive and rapid detection of true protein in milk.
AB - As an indispensable drink of people's daily life, milk's quality has been also increasingly concerned by consumers. Rapid and accurate detection of milk and its products is the indispensable step for improving the quality of milk and daily products in production. However, traditional methods cannot meet the need. In this paper, rapid quantitative detection of true protein in pure milk was studied by using visible/near-infrared (VIS/NIR) reflectance spectroscopy (350~2500 nm). The spectral data and the protein content data of the pure milk samples were collected by ASD spectrometer and CEM rapid protein analyzer, respectively. Based on the analysis and comparison of different spectrum preprocessing methods and band selection methods, the feature bands were determined. Finally, using the Principle Component Regression (PCR) and Least Squares Support Vector Machine (LS-SVM) model, the regression models between the reflectance spectroscopy and the protein content in milk were presented for pure milk samples and the predictive ability was also analyzed. In this way, the optimal inversion model for true protein content in milk was established. The results were shown as follows: (1) In the process of spectral pretreatment, the combination of multiple scatter correction and second derivative achieved a better result; (2) Compared with the modeling of whole spectral, appropriate variable optimization models had the ability to improve the accuracy of the inversion results and reduce the modeling time; (3) The analysis results between PCR model and LS-SVM model demonstrated that the prediction accuracy of LS-SVM model was better than PCR model. The coefficient of determination (RP2) of PCR and LS-SVM were 0.9522 and 0.9580 respectively, and the root mean square error of prediction (RMSEP) of PCR and LS-SVM were 0.0487 and 0.0482 respectively. The result of this research is expected to provide a novel method for nondestructive and rapid detection of true protein in milk.
KW - Hyperspectral data
KW - Milk
KW - Protein
KW - Quantitative inversion model
UR - https://www.scopus.com/pages/publications/84952782565
U2 - 10.3964/j.issn.1000-0593(2015)12-3436-04
DO - 10.3964/j.issn.1000-0593(2015)12-3436-04
M3 - 文章
C2 - 26964225
AN - SCOPUS:84952782565
SN - 1000-0593
VL - 35
SP - 3436
EP - 3439
JO - Guang Pu Xue Yu Guang Pu Fen Xi/Spectroscopy and Spectral Analysis
JF - Guang Pu Xue Yu Guang Pu Fen Xi/Spectroscopy and Spectral Analysis
IS - 12
ER -