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

高光谱成像 降维 人工智能 计算机科学 模式识别(心理学) 支持向量机 随机森林 特征选择 特征(语言学) 变量消去 数学 遥感 哲学 语言学 推论 地质学
作者
Zhiliang Kang,Jinping Geng,Rongsheng Fan,Chunyi Zhan,Jie Sun,Youli Wu,Lijia Xu,Cheng Liu
出处
期刊:Agriculture [Multidisciplinary Digital Publishing Institute]
卷期号:12 (9): 1337-1337 被引量:15
标识
DOI:10.3390/agriculture12091337
摘要

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 RC2 = 0.9710, RP2 = 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.
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