基于高光谱图谱融合的蓝莓可溶性固形物含量检测

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关键词：可溶性固形物含量；无损检测；信息融合；特征提取；机器学习 中图分类号：TS255.7；O439；TP183 文献标识码：A DOI： 10.7525/j.issn.1006-8023.2025.03.017

Abstract： Soluble solids content （SSC）is akey indicatorfor assessing the internal qualityoffruits.This study proposes anon-destructive detection method based on hyperspectral image fusion to predict the SSCof blueberries.Three widely used wavelength dimensionalityreduction algorithms areemployed：Monte Carlo uninformative variable elimination（MCUVE），Competitive Adaptive Reweighted Sampling（CARS），and Successive Projections Algorithm（SPA），，to identify optimal wavelengths.Additionally，astrategy integrating Local Binary Paterns（LBP）and GrayLevel Co-occurrence Matrix（GLCM） is proposed for feature extraction.Using spectral features，image features，and fused features，Partial Least Squares （PLS），Backpropagation Neural Network （BPNN），and Support Vector Machine（SVM） models are developed for SSC prediction.Theresults demonstrate that the BPNN model，utilizing spectral features extractedvia the CARS algorithm and image featuresderived from the LBP+GLCM algorithm，yields the highest prediction accuracy.The model'scoefficient of determination（ ）isO.9261，while the Root Mean Square Error ofPrediction（RMSEP）is 0.3641.Thisstudyindicates that hyperspectral image fusion technology holds significant potential forthenon-destructiveprediction of blueberry SSC.

Keywords：Soluble solidcontent；non-destructive assessment； information fusion；feature extraction;machine learning

0 引言

蓝莓作为重要的林下经济作物，因其独特的风味和丰富的营养，深受消费者喜欢[1-2]。（剩余14781字）