摘要
应用近红外光谱技术结合波长选择方法(modeling power,MP)实现了黑木耳产地的快速准确判别。共收集4个产地240个黑木耳样本,通过光谱扫描,建立了最优的偏最小二乘(PLS)判别模型。同时应用MP选择对黑木耳产地判别的有效波长,作为输入变量,建立最小二乘-支持向量机(MP-LS-SVM)模型。比较了3种MP选择波长的阈值方法,分别为MP值大于0.95,0.90和(0.90+Peak),并建立了相应的MP-LS-SVM模型。以预测集样本的准确判别率作为模型评价标准,分别设定预测的残差绝对值标准0.1,0.2和0.5。预测结果表明,MP-LS-SVM(0.90+Peak)模型在残差标准为0.1,0.2和0.5时的判别效果均为最优,正确判别率分别为98.3%,100%和100%。说明ModelingPower是一种非常有效的波长选择方法,应用近红外光谱技术结合MP-LS-SVM进行黑木耳产地判别是可行的,并获得了满意的判别精度。
Near infrared （NIR） spectroscopy combined with variable selection method of modeling power was investigated for the fast and accurate geographical origin discrimination of auricularia auricula. A total of 240 samples of auriculari auricula were collected in the market, and the spectra of all samples were scanned within the spectral region of 1100-2 500 nm. The calibration set was composed of 180 （45 samples for each origin） samples, and the remaining 60 samples were employed as the validation set. The optimal partial least squares （PLS） discriminant model was achieved after performance comparison of different prepro cessing （Savitzky-Golay smoothing, standard normal variate, 1-derivative, and 2-derivative）. The effective wavelengths, which were selected by modeling power （MP） and used as input data matrix of least squares-support vector machine （LS-SVM）, were employed for the development of modeling power-least squares-support vector machine （MP-LS-SVM） model. Radial basis function （RBF） kernel was applied as kernel function. Three threshold methods for variable selection by modeling power were applied in MP-LS-SVM models, and there were the values of modeling power higher than 0. 95, higher than 0. 90, and higher than 0. 90 combined with peak location （0. 90＋Peak）. The correct recognition ratio in the validation set was used as evaluation stand-ards. The absolute error of prediction was set as 0. 1, 0. 2 and 0. 5, which showed the wrong recognition threshold value. The results indicated that the MP-LS-SVM （0. 90＋Peak） model could achieve the optimal performance in all three absolute error standards （0. 1, 0.2 and 0. 5）, and the correct recognition ratio was 98. 3%, 100% and 100%, respectively. The variable selection threshold （0. 90± Peak） was the most suitable one in the application of modeling power. It was concluded that modeling power was an effective variable selection method, and near infrared spectroscopy combined with MP-LS-SVM model was suc cessfully applied for the origin discrimination of auricularia auricula, and an excellent prediction precision was also achieved.
出处
《光谱学与光谱分析》
SCIE
EI
CAS
CSCD
北大核心
2010年第1期62-65,共4页
Spectroscopy and Spectral Analysis
基金
国家科技支撑项目(2006BAD10A04)
国家高技术研究发展计划"863"项目(2006AA10Z234
2007AA10Z210)
浙江省自然科学基金项目(Y506152)资助
关键词
近红外光谱
黑木耳
产地判别
MODELING
POWER
最小二乘-支持向量机
Near infrared spectroscopy
Auricularia auricula
Geographical origin discriminatiom Modeling power
Leastsquares-support vector machine
