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

Zhiliang Kang, Jinping Geng, Rongsheng Fan, Yan Hu, Jie Sun, Youli Wu, Lijia Xu () and Cheng Liu
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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

Agriculture, 2022, vol. 12, issue 9, 1-21

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.

Keywords: mango; dry matter; fluorescence hyperspectral; OSC algorithm; BPNN; nondestructive detection (search for similar items in EconPapers)
JEL-codes: Q1 Q10 Q11 Q12 Q13 Q14 Q15 Q16 Q17 Q18 (search for similar items in EconPapers)
Date: 2022
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