Freshness Detection of Lamb Based on AW-OPS Hyperspectral Wavelength Selection Method

Hyperspectral data contain not only critical information but also some interference information and invalid information, and using these data to build the model will reduce the reliability and accuracy of the relational model. Extracting feature wavelengths from full-band data is an effective way to improve the accuracy of prediction models. Ordered Predictive Selection (OPS) is a feature wavelength extraction algorithm that selects effective wavelength variables based on the information vector, and has shown good performance in feature wavelength variable screening. However, the model was built without removing the less important variables, resulting in too many invalid variables being involved in the model and reducing the model's accuracy. The paper proposes an improved feature wavelength variable selection method based on an information vector and exponential decay function of ordered predictive selection method (AW-OPS) for lamb freshness detection, using lamb hyperspectral data as the research object. The algorithm calculates the information vector and ranks the wavelength variables by the relationship between the spectral data and the physicochemical value data. The exponential decay function (EDF) is used to remove some wavelength variables with relatively low absolute values of information vectors by multiple iterations. Finally, a multiple regression model was established by gradually adding wavelength points to the obtained effective wavelength variables, and the subset of wavelength variables with the lowest value of root mean square error (RMSECV) was selected as the characteristic wavelength variables. For the experiments, the partial least squares (PLS) relational models of lamb TVB-N were constructed by the OPS -and AW-OPS methods after selecting the characteristic wavelengths, respectively, and compared with the effects of FULL-PLS models. The results showed that the OPS algorithm took an average of 175. 9 s to run the program, preferentially selected 370 characteristic wavelength variables, with an average OPS-PLS model correlation coefficient(Rp)of 0. 963 1 and an average root mean square error(RMSEP) of 0. 727. while the improved ordered prediction selection method( AW-OPS)runs the program in an average time of 57. 6 s, preferentially selects 275 characteristic wavelength variables, and the AW-OPS-PLS model Rp improves to 0. 973 1 on average, and RMSEP reduces to 0. 572 8 on average. The number of full-spectrum wavelengths was 1 414 wavelength variables, and the average Rp of its PLS model was 0. 920 8, and the average RMSEP was 1. 048 3. The AWOPS-PLS model improved the test accuracy by 21. 2% compared to the OPS-PLS model and 45% compared to the full-spectrumPLS model, proving that the improved AW-OPS is an effective feature wavelength variable screening method that improves the accuracy of the OPS model and the efficiency of the program operation and reduces the complexity of the model.