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Nondestructive Testing Model of Mango Dry Matter Based on Fluorescence Hyperspectral Imaging Technology

Author

Listed:
  • Zhiliang Kang

    (College of Mechanical and Electrical Engineering, Sichuan Agriculture University, Ya’an 625000, China)

  • Jinping Geng

    (College of Mechanical and Electrical Engineering, Sichuan Agriculture University, Ya’an 625000, China)

  • Rongsheng Fan

    (College of Mechanical and Electrical Engineering, Sichuan Agriculture University, Ya’an 625000, China)

  • Yan Hu

    (College of Mechanical and Electrical Engineering, Sichuan Agriculture University, Ya’an 625000, China)

  • Jie Sun

    (College of Mechanical and Electrical Engineering, Sichuan Agriculture University, Ya’an 625000, China)

  • Youli Wu

    (College of Mechanical and Electrical Engineering, Sichuan Agriculture University, Ya’an 625000, China)

  • Lijia Xu

    (College of Mechanical and Electrical Engineering, Sichuan Agriculture University, Ya’an 625000, China)

  • Cheng Liu

    (College of Mechanical and Electrical Engineering, Sichuan Agriculture University, Ya’an 625000, China)

Abstract

The dry matter test of mango has important practical significance for the quality classification of mango. Most of the common fruit and vegetable quality nondestructive testing methods based on fluorescence hyperspectral imaging technology use a single algorithm in algorithms such as Uninformative Variable Elimination (UVE), Random Frog (RF), Competitive Adaptive Reweighted Sampling (CARS) and Continuous Projection Algorithm (SPA) to extract feature spectral variables, and the use of these algorithms alone can easily lead to the insufficient stability of prediction results. In this regard, a nondestructive detection method for the dry matter of mango based on hyperspectral fluorescence imaging technology was carried out. Taking the ‘Keitt’ mango as the research object, the mango samples were numbered in sequence, and their fluorescence hyperspectral images in the wavelength range of 350–1100 nm were collected, and the average spectrum of the region of interest was used as the effective spectral information of the sample. Select SPXY algorithm to divide samples into a calibration set and prediction set, and select Orthogonal Signal Correction (OSC) as preprocessing method. For the preprocessed spectra, the primary dimensionality reduction (UVE, SPA, RF, CARS), the primary combined dimensionality reduction (UVE + RF, CARS + RF, CARS + SPA), and the secondary combined dimensionality reduction algorithm ((CARS + SPA)-SPA, (UVE + RF)-SPA) and other 12 algorithms were used to extract feature variables. Separately constructed predictive models for predicting the dry matter of mangoes, namely, Support Vector Regression (SVR), Extreme Learning Machine (ELM), and Back Propagation Neural Network (BPNN) model, were used; The results show that (CARS + RF)-SPA-BPNN has the best prediction performance for mango dry matter, its correlation coefficients were R C 2 = 0.9710, R P 2 = 0.9658, RMSEC = 0.1418, RMSEP = 0.1526, this method provides a reliable theoretical basis and technical support for the non-destructive detection, and precise and intelligent development of mango dry matter detection.

Suggested Citation

  • Zhiliang Kang & Jinping Geng & Rongsheng Fan & Yan Hu & Jie Sun & Youli Wu & Lijia Xu & Cheng Liu, 2022. "Nondestructive Testing Model of Mango Dry Matter Based on Fluorescence Hyperspectral Imaging Technology," Agriculture, MDPI, vol. 12(9), pages 1-21, August.
  • Handle: RePEc:gam:jagris:v:12:y:2022:i:9:p:1337-:d:901321
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    References listed on IDEAS

    as
    1. Yan Hu & Lijia Xu & Peng Huang & Xiong Luo & Peng Wang & Zhiliang Kang, 2021. "Reliable Identification of Oolong Tea Species: Nondestructive Testing Classification Based on Fluorescence Hyperspectral Technology and Machine Learning," Agriculture, MDPI, vol. 11(11), pages 1-19, November.
    2. Linsheng Huang & Yong Liu & Wenjiang Huang & Yingying Dong & Huiqin Ma & Kang Wu & Anting Guo, 2022. "Combining Random Forest and XGBoost Methods in Detecting Early and Mid-Term Winter Wheat Stripe Rust Using Canopy Level Hyperspectral Measurements," Agriculture, MDPI, vol. 12(1), pages 1-16, January.
    3. Xiaohui Wang & Lijia Xu & Heng Chen & Zhiyong Zou & Peng Huang & Bo Xin, 2022. "Non-Destructive Detection of pH Value of Kiwifruit Based on Hyperspectral Fluorescence Imaging Technology," Agriculture, MDPI, vol. 12(2), pages 1-18, February.
    4. Xiong Luo & Lijia Xu & Peng Huang & Yuchao Wang & Jiang Liu & Yan Hu & Peng Wang & Zhiliang Kang, 2021. "Nondestructive Testing Model of Tea Polyphenols Based on Hyperspectral Technology Combined with Chemometric Methods," Agriculture, MDPI, vol. 11(7), pages 1-15, July.
    5. Sri Lakshmi Sesha Vani Jayanthi & Venkata Reddy Keesara & Venkataramana Sridhar, 2022. "Prediction of Future Lake Water Availability Using SWAT and Support Vector Regression (SVR)," Sustainability, MDPI, vol. 14(12), pages 1-17, June.
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