Research on construction method and validity mechanism of robust analysis model in snow peach quality detection based...

Visible-near infrared (Vis-NIR) spectra analysis method is widely used in the quality grading of bulk fruits with its rapid, non-destructive, diverse detection modes and flexible modular integration scheme. However, during the online grading of fruits, the random mechanized way of dropping fruit onto the conveyor belt method and the open detection environment led to more spectral abnormal samples, which affect the accuracy of the detection. In this paper, the soluble solids content (SSC) of snow peach is quantitatively analyzed by static and online detection methods. Several spectral preprocessing methods including Norris-Williams Smoothing (NWS), Savitzky-Golay Smoothing (SGS), Continuous Wavelet Derivative (CWD), Multivariate Scattering Correction (MSC), and Variable Sorting for Normalization (VSN) are adopted to eliminate spectral rotation and translation errors and improve the signal-to-noise ratio. Monte Carlo Uninformative Variable Elimination (MCUVE) method is used for the selection of optimal characteristic modeling variables. Partial Least Squares Regression (PLSR) is used to model and analyze the preprocessed spectra and the spectral variables optimized by MCUVE, and the effectiveness of the method is evaluated. Sparse Partial Least Squares Regression (SPLSR) and Sparse Partial Robust M Regression (SPRMR) are used for the construction of robust models. The results showed that the SGS preprocessing method can effectively improve the analysis accuracy of static and online models. The MCUVE method can realize the extraction of stable characteristic variables. The SPRMR model based on SGS preprocessing method and the effective variables has the optimal analysis results. The analysis accuracy of snow peach static model is slightly better than that of online analytical model. Through the test results of the PLSR, SPLSR and SPRMR models by the artificially adding noise test method, it can be seen that the SPRMR method eliminates the influence of abnormal samples on the model during the modeling process, which can effectively improve the anti-noise ability and detection reliability.