Quantitative Prediction Model of the Crude Protein Content in the Chicken Freeze-dried Powder Based on the Optimizing Spectral Region of Near Infrared Spectroscopy
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Abstract:
This paper is trying to obtain the optimum spectra to build the calibration model by the spectral characteristics of the chicken protein in near infrared region, interval partial least-squares regression (iPLS) and geneti calgorithm (GA), and aims to improve the prediction accuracy and robustness of the near infrared spectroscopy quantitative prediction model of the crude protein content in the chicken freeze-dried powder.Taking the muscles powders of 260 freeze-dried legs as the research object, the proteins from the muscles powders of 100 freeze-dried legs were extracted, the near infrared reflectance spectra (NIRS) from the freeze-dried leg muscle powder samples and the protein samples of the freeze-dried leg muscle powder were scanned in the 999.7~2502.3 nm wavelength region. The differences between two NIRS were studied. The NIRS of the freeze-dried leg muscles powder was divided into 10 spectral regions based on spectral characteristic and principal component analysis (PCA). The partial least squares regression (PLS) was used to build the quantitative prediction model. First, the modeling results based on full-spectrum and combining spectral regions of the characteristic spectra were compared to select the optimum spectral regions. Next, the PLS (FiPLS) and backward interval PLS (BiPLS). It is the third time the modeling spectra were filtrated by GA and forward and backward regions and the optimum combining region based on the characteristic spectra by forward interval PLS (FiPLS) and backward interval PLS optimum spectral regions were extracted from the full-spectrum (BiPLS). Then, the modeling spectra were filtrated by GA and forward and backward interval partial least squares (FBiPLS). The result showed that the most optimum modeling spectral ranges were 1811.6~1794.0 nm、1756.2~1722.4 nm、1704.4~1688.9 nm、1594.4~1580.8 nm、1510.8~1485.7 nm、1472.1-1424.3 nm、1222.2~1057.6 nm and 1051.2~1008.7 nm by using FBiPLS. In conclusion, to ensure the accuracy and robustness of the calibration model, the selection method of optimum spectral regions was the combination of the spectral characteristics of sample composition and mathematical algorithm of wavelength selection.
