摘要
基于近地高光谱成像技术结合化学计量学方法，实现了黑豆品种的鉴别。实验以三种不同颜色豆芯的黑豆为研究对象，采用高光谱成像系统采集380～1030 nm波段范围的高光谱图像，提取高光谱图像中的样本感兴趣区域平均光谱信息作为样本的光谱进行分析，建立黑豆品种的判别分析模型。共采集180个黑豆样本的180条平均光谱曲线。剔除明显噪声部分之后以440～943 nm范围光谱为黑豆样本的光谱，采用多元散射校正（multiplicative scatter correction ，MSC）对光谱曲线进行预处理。分别以全部光谱数据、主成分分析（principal component analysis ，PCA）提取的光谱特征信息、小波分析（wavelet transform ，WT）提取的光谱特征信息建立了偏最小二乘判别分析法（partial least squares discriminant analysis ，PLS-DA ），簇类独立模式识别法（soft independent modeling of class analogy ，SIMCA），最邻近节点算法（K-nearest neighbor algo-rithm ，KNN），支持向量机（support vector machine ，SVM），极限学习机（extreme learning machine ，ELM）等判别分析模型。以全谱的判别分析模型中，ELM 模型效果最优；以PCA提取的光谱特征信息建立的模型中，ELM模型也取得了最优的效果；以WT 提取的光谱特征信息建立的模型中，ELM 模型结识别效果最好，建模集和预测集识别正确率达到100％。在所有的判别分析模型中，W T-EL M模型取得了最优的识别效果。实验结果表明以高光谱成像技术对黑豆品种进行无损鉴别是可行的，且WT 用于提取光谱特征信息以及ELM模型用于判别黑豆品种能取得较好的效果。
In the present study ,hyperspectral imaging combined with chemometrics was successfully proposed to identify differ-ent varieties of black bean .The varieties of black bean were defined based on the three different colors of the bean core .The hy-perspectral images in the spectral range of 380~1 030 nm of black bean were acquired using the developed hyperspectral imaging system ,and the reflectance spectra were extracted from the region of interest （ROI） in the images .The average spectrum of a ROI of the sample in the images was used to represent the spectrum of the sample and build classification models .In total ,180 spectra of 180 samples were extracted .The wavelengths from 440 to 943 nm were used for analysis after the removal of the spec-tral region with absolute noises ,and 440~943 nm spectra were preprocessed by multiplicative scatter correction （MSC） .Five classification methods ,including partial least squares discriminant analysis （PLS-DA） ,soft independent modeling of class analo-gy （SIMCA） ,K-nearest neighbor algorithm （KNN） ,support vector machine （SVM） and extreme learning machine （ELM） , were used to build discriminant models using the preprocessed full spectra ,the feature information extracted by principal compo-nent analysis （PCA） and the feature information extracted by wavelet transform （WT ） from the preprocessed spectra ,respec-tively .Among all the classification models using the preprocessed full spectra ,ELM models obtained the best performance ;among all the classification models using the feature information extracted from the preprocessed spectra by PCA ,ELM model also obtained the best classification accuracy ;and among all the classification models using the feature information extracted from the preprocessed spectra by WT ,ELM models obtained the best classification performance with 100% accuracy in both the cali-bration set and the prediction set .Among all classification models ,WT-ELM model obtained the best classification accuracy . The over
出处
《光谱学与光谱分析》
SCIE
EI
CAS
CSCD
北大核心
2014年第3期746-750,共5页
Spectroscopy and Spectral Analysis
基金
国家高技术研究发展计划(863计划)项目(2011AA100705)
中央高校基本科研业务费专项资金项目资助
关键词
黑豆
高光谱成像
判别分析模型
Black bean
Hyperspectral imaging
Discriminant model
