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基于高光谱成像技术的蓝莓糖度无损检测模型优化研究

Optimization Research on Non-destructive Detection Model of Blueberry Sugar Content based on Hyperspectral Imaging Technology

投稿时间：2025-02-05 修订日期：2025-05-09

DOI：

中文关键词: 蓝莓糖度无损检测高光谱成像技术机器学习

英文关键词:Blueberry sugar content Non destructive testing Hyperspectral imaging technology Machine learning

基金项目:中央高校基本科研业务经费创新团队项目(2572023CT15)。国家自然科学(32202147)。

作者	单位
郭欣悦	东北林业大学机电工程学院
陈国良	东北林业大学计算机与控制工程学院
朱良宽	东北林业大学机电工程学院
刘大洋	东北林业大学计算机与控制工程学院
孙枭雄	东北林业大学机电工程学院

Author	Institution
guo xinyue	Northeast Forestry University，College of Mechanical and Electrical Engineering
chen guoliang	Northeast Forestry University, College of Computer and Control Engineering
zhu liangkuan	Northeast Forestry University，College of Mechanical and Electrical Engineering
liu dayang	Northeast Forestry University, College of Computer and Control Engineering
sun xiaoxiong	Northeast Forestry University, College of Computer and Control Engineering

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中文摘要:

目的本研究旨在基于高光谱成像技术，优化蓝莓糖度的无损检测模型，以提高糖度预测的准确性和效率，满足市场对蓝莓品质控制的需求。方法以产自丹东的L25品种蓝莓为试验对象，采用高光谱成像技术获取900~1700 nm波长范围内的蓝莓高光谱图像，并计算感兴趣区域的平均光谱作为原始数据。通过多元散射矫正（Multiple Scatter Correction, MSC）、标准正态变量变换（Standard Normal Variable , SNV）和Savitzky - Golay（SG）三种预处理方法改善光谱数据质量。基于预处理后的全波长数据，分别建立偏最小二乘回归法（Partial Least Squares, PLS）、BP神经网络、支持向量机回归法（Support Vector Regression, SVR）等糖度预测模型。结果实验结果表明，在MSC和SNV预处理下，PLSR模型在糖度预测过程中表现最为优异，均方根误差（RMSEP）分别为0.3586、0.3599。结论本研究优化了基于高光谱成像技术的蓝莓糖度无损检测模型，为蓝莓糖度的快速、准确预测提供了有效的技术支持，具有较强的实际应用潜力。

英文摘要:

ABSTRACT: Objective This study aims to optimize a non-destructive model for predicting blueberry sugar content using hyperspectral imaging technology, to enhance the accuracy and efficiency of sugar content prediction and meet the market demand for blueberry quality control. Methods The L25 variety of blueberries from Dandong was selected as the subject, and hyperspectral images were acquired in the wavelength range of 900–1700 nm. The average spectrum of the region of interest was calculated as the raw data. Three preprocessing methods, including Multiple Scatter Correction (MSC), Standard Normal Variate (SNV), and Savitzky-Golay (SG), were applied to improve the spectral data quality. Non-destructive sugar content prediction models were established using Partial Least Squares Regression (PLSR), BP Neural Networks, and Support Vector Regression (SVR) based on the full-wavelength data after preprocessing. Results The experimental results demonstrated that the PLSR model, with MSC and SNV preprocessing, exhibited the best performance, achieving Root Mean Square Error of Prediction (RMSEP) values of 0.3586 and 0.3599, respectively. Conclusion This study provides an optimized non-destructive detection model for blueberry sugar content, offering effective technical support for rapid and accurate sugar content prediction with significant practical potential.

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