[关键词]
[摘要]
为了探究快速识别不同成熟度李果实的有效方法,本研究以李果实作为研究对象,基于高光谱成像技术对不同成熟度的李果实(未熟、半熟、成熟、过熟)样本的光谱信息(420~1000 nm)进行采集,对采集样本的光谱信息进行平滑处理(Smoothing)与标准正态变量校正(SNV)相结合的方法预处理光谱数据,分别以预处理后的全光谱(FS)数据和采用主成分分析(PCA)法提取主成分、采用连续投影算法(SPA)提取特征波长作为输入变量,建立偏最小二乘法(PLS)模型,比较不同判别模型的准确性。结果表明,FS-PLS建立的模型判别准确率最高,综合准确率达到了91.88%;但考虑实验计算量及复杂程度来说,SPA-PLS建立的模型判别准确率最优,综合准确率达到91.25%。该研究为李果实成熟度的判别检测提供了新的理论基础。
[Key word]
[Abstract]
In order to investigate an effective method for rapid discrimination of plum fruits at different maturity levels, plum fruits were used as the research object of this study, and spectral information (420~1000 nm) of plum fruits at different maturity levels (unripe, mid-ripe, ripe, over-ripe) was collected using hyperspectral imaging technology. A combination of smoothing and standard normalized variate (SNV) was applied to pretreat spectral data. Then, the partial least squares method(PLS) model was established for comparing the accuracy of different discriminant models, through using the pre-treated full spectrum (FS) data, the principal components extracted by the principal component analysis (PCA) method, and the feature wavelengths extracted by the successive projection algorithm (SPA) technique as input variables. The results revealed that the model established by FS-PLS had the highest discriminative accuracy and the accuracy rate reached 91.88%. However, considering the amount and complexity of the experimental calculations, the accuracy rate of the model established by SPA-PLS was the best with the comprehensive accuracy reaching 91.25%. This study provides a new theoretical basis for discriminative detection of plums maturity.
[中图分类号]
[基金项目]
国家自然科学基金资助项目(31271973);山西省自然科学基金资助项目(2012011030-3);山西农业大学青年科技创新项目(2016005)