• Spectroscopy and Spectral Analysis
  • Vol. 41, Issue 11, 3385 (2021)
Yong HAO1、1;, Jiao-jun DU1、1;, Shu-min ZHANG2、2;, and Qi-ming WANG1、1;
Author Affiliations
  • 11. School of Mechatronics & Vehicle Engineering, East China Jiaotong University, Nanchang 330013, China
  • 22. Nanchang Customs Technology Center, Nanchang 330013, China
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    DOI: 10.3964/j.issn.1000-0593(2021)11-3385-07 Cite this Article
    Yong HAO, Jiao-jun DU, Shu-min ZHANG, Qi-ming WANG. Research on Construction of Visible-Near Infrared Spectroscopy Analysis Model for Soluble Solid Content in Different Colors of Jujube[J]. Spectroscopy and Spectral Analysis, 2021, 41(11): 3385 Copy Citation Text show less


    The quality of jujube is susceptible to factors such as the environment, causing changes in its post-harvest redness index, leading to large differences in fruit color, which affects the analysis accuracy of its soluble solids content (SSC) detection model. Visible-near infrared spectroscopy (Vis-NIRs) combined with spectral preprocessing methods including Norris-Williams smoothing (NWS), continuous wavelet derivative (CWD), multiplicative scattering correction (MSC), standard normal variate (SNV) and NWS-MSC were used to build the partial least squares (PLS), quantitative analysis models of the SSC of jujube, with different colors (red and green-MJ, green-GJ and red-RJ). Five independent sample sets, including MJ, GJ, RJ, MJ-GJ and MJ-GJ-RJ, were used to establish the quantitative analysis models of SSC for jujube, and test set samples MJ-GJ-RJ were used for model evaluation. The correlation coefficient of calibration set (Rc) and the root mean square error of cross-validation (RMSECV) were used to evaluate model accuracy. The correlation coefficients of prediction (Rp) and the root mean square error for prediction (RMSEP) were used to evaluate model prediction accuracy. The research results showed that when the independent sample sets of MJ, GJ and RJ were used for modeling, the models only achieved a better prediction for the SSC of jujube samples with the same color, respectively. When adding GJ and GJ-RJ samples to the MJ samples to construct the quantitative model of the two mixed sample sets, including MJ-GJ and MJ-GJ-RJ. The MJ-GJ model had better prediction results of SSC for MJ and GJ jujube samples, the model’s RMSECV, Rc, RMSEP, and Rp were 1.108, 0.698, 0.980, 0.724 and 1.108, 0.698, 0.983, 0.822, respectively, but the effect of RJ samples was relatively larger, the model’s RMSECV, Rc, RMSEP, Rp were 1.108, 0.698, 1.928, 0.597. The MJ-GJ-RJ model obtained good prediction results of SSC for the three colors jujube: for the SSC model of MJ, the RMSECV, Rc, RMSEP, Rp of the MJ-GJ-RJ model were 1.158, 0.796, 1.077, 0.668; for the SSC model of GJ, the model’s RMSECV, Rc, RMSEP, Rp were 1.158, 0.796, 0.881, 0.861; for the SSC model of RJ, the model’s RMSECV, Rc, RMSEP, Rp were 1.158, 0.796, 1.140, 0.841. After using the Monte Carlo uninformative variable elimination (MCUVE) method to optimize the variables of the MJ-GJ-RJ model further, the Rc and Rp were increased from 0.796 and 0.864 to 0.884 and 0.922, respectively. The RMSECV and RMSEP were reduced from 1.158 and 0.946 to 0.886 and 0.721, respectively. The model has better analysis accuracy. When the SSC of different color jujube was analyzed using near-infrared spectroscopy, similar sample set properties for calibration and prediction or modeling variables are required to construct universality models.