Spectroscopy and Spectral Analysis, Volume. 45, Issue 2, 492(2025)

Establishment and Optimization of the Hyperspectral Detection Model for Soluble Solids Content in Fortunella Margarita

LI Wei-qi1, WANG Yi-fan1, YU Yue1, and LIU Jie1,2,3、*
Author Affiliations
  • 1College of Engineering, Huazhong Agricultural University, Wuhan 430070, China
  • 2Key Laboratory of Agricultural Equipment in Mid-Lower Yangtze River, Ministry of Agriculture and Rural Affairs, Wuhan 430070, China
  • 3Citrus Mechanization Research Base, Ministry of Agriculture and Rural Affairs, Wuhan 430070, China
  • show less

    To develop a rapid measurement method of SSC in Fortunella margarita, the detection models based on hyperspectral imaging data were established and optimized by employing various preprocess and regression algorithms, and the pseudo-color distribution of SSC with storage time was analyzed. The 307 whole citrus and 227 hemisected citrus samples were involved in hyperspectral data collection and the SSC values. The effects of preprocessing, including standard normal variate (SNV), multiplicative scatter correction (MSC), Savitzky-Golay (SG) filtering, normalization (NM), first derivative (FD), standardization (SD), and wavelet transformation (WT), on the performance of the partial least squares regression (PLSR) model were compared to select the appropriate preprocessing method. Then, the detection models were established by using PLSR, least absolute shrinkage and selection operator (LASSO) regression, support vector machine regression (SVR), artificial neural networks (ANN), decision trees (DT), random forest (RF) and light gradient boosting machine (Light GBM) algorithms. Furthermore, the models were optimized using genetic algorithms (GA) to select characteristic spectral wavelengths. The results indicated that for the whole citrus samples, the FD preprocessing could extract more features, and the LASSO regression model performed better than other models with 0.925 7 and 0.976 5 as the prediction determination coefficient (Rp2) and root mean square error of prediction (RMSEP), respectively. For the hemisected samples, the RF model based on the spectral after SD preprocessing had higher Rp2 at 0.896 3 and lower RMSEP at 1.063 0. The GA could remove 53.85% and 50.58% wavelength variables to reduce the computational complexity for the whole and hemisected sample spectral, of which the SVR model has Rp2 at 0.918 9. RMSEP at 1.017 3 RF model having Rp2 at 0.895 3 and RMSEP at 1.084 3 performed better than other models. The results provided a feasible solution for high-throughput, non-destructive detection of SSC of Fortunella margarita.

    Tools

    Get Citation

    Copy Citation Text

    LI Wei-qi, WANG Yi-fan, YU Yue, LIU Jie. Establishment and Optimization of the Hyperspectral Detection Model for Soluble Solids Content in Fortunella Margarita[J]. Spectroscopy and Spectral Analysis, 2025, 45(2): 492

    Download Citation

    EndNote(RIS)BibTexPlain Text
    Save article for my favorites
    Paper Information

    Received: Feb. 3, 2024

    Accepted: May. 21, 2025

    Published Online: May. 21, 2025

    The Author Email: LIU Jie (liujie@mail.hzau.edu.cn)

    DOI:10.3964/j.issn.1000-0593(2025)02-0492-09

    Topics