本文關(guān)鍵詞： 血流感染 傅里葉變換近紅外光譜 偏最小二乘判別分析 最小二乘-支持向量機 競爭性自適應(yīng)重加權(quán)算法 病原菌鑒別　出處：《福州大學(xué)學(xué)報(自然科學(xué)版)》2017年05期 　論文類型：期刊論文

【摘要】：利用傅里葉變換近紅外光譜(FT-NIR)收集1 000~1 852 nm范圍內(nèi)3種常見病原菌大腸桿菌(ATCC25922)、金黃色葡萄球菌(ATCC 29213)、銅綠假單胞菌(ATCC 27853)的近紅外透射光譜,采用競爭性自適應(yīng)重加權(quán)算法(CARS)對波長變量進行篩選,并分別結(jié)合偏最小二乘判別分析(PLS-DA)、最小二乘-支持向量機(LS-SVM)建立鑒別模型.比較兩種鑒別模型在進行波長變量優(yōu)選前后的性能發(fā)現(xiàn),采用全波段建模的PLS-DA與LS-SVM兩種模型的預(yù)測性能較低;利用CARS對波長變量進行篩選后,對優(yōu)選的24個特征波長分別建立兩種鑒別模型,模型預(yù)測性能明顯提高,其中以LS-SVM模型最優(yōu),3種病原菌準確率分別為85.0%,100%和100%.研究結(jié)果表明,利用CARS能夠有效去除光譜無用信息,減少模型復(fù)雜度,增強模型預(yù)測性能,結(jié)合LS-SVM可為臨床利用近紅外快速檢測血流感染病原菌提供一種新的方法.
[Abstract]:The near infrared transmission spectra of Escherichia coli ATCC25922, Staphylococcus aureus ATCC29213and Pseudomonas aeruginosa ATCC27853 were collected by Fourier transform near infrared spectroscopy (FT-NIR). A competitive adaptive reweighting algorithm (CARSs) is used to screen the wavelength variables. Combined with partial least squares discriminant analysis (PLS-DAA) and least squares support vector machine (LS-SVM), the identification models were established, and the performance of the two discriminant models before and after optimal selection of wavelength variables were compared. The prediction performance of PLS-DA and LS-SVM models based on full-band modeling is low, and the prediction of the two models can be improved obviously by using CARS to screen the wavelength variables and to establish two discriminant models for the 24 characteristic wavelengths selected separately. The accuracy of LS-SVM model was 85.0% and 100%, respectively. The results showed that CARS could effectively remove spectral useless information, reduce the complexity of model, and enhance the performance of model prediction. The combination of LS-SVM can provide a new method for rapid detection of blood stream infection pathogens by near infrared spectroscopy.
【作者單位】： 福州大學(xué)電氣工程與自動化學(xué)院;福建省醫(yī)療器械和醫(yī)藥技術(shù)重點實驗室;福建醫(yī)科大學(xué)醫(yī)學(xué)技術(shù)與工程學(xué)院;
【基金】：國家自然科學(xué)基金資助項目(61403319) 福建省科技廳國際合作資助項目(2015I003) 福建省教育廳科技資助項目(JK2014001)
【分類號】：O657.33;R446.5
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本文編號：1525285