本文關(guān)鍵詞：基于iPLS和SiPLS算法的人體血清膽紅素含量的可見-近紅外光譜建模　出處：《光電子·激光》2016年10期 　論文類型：期刊論文

  更多相關(guān)文章： 可見-近紅外(NIR)光譜 間隔偏最小二乘法(iPLS) 協(xié)合區(qū)間偏最小二乘法(SIPLS) 波段優(yōu)選 血清膽紅素(BR)

【摘要】：為了建立血清膽紅素(BR,bilirubin)樣品總膽紅素(TBIL)、直接膽紅素(DBIL)和間接膽紅素(IBIL)近紅外(NIR)光譜分析最優(yōu)模型,利用可見-NIR透射光譜技術(shù)與間隔偏最小二乘法(iPLS)及協(xié)合區(qū)間偏最小二乘法(SiPLS)算法相結(jié)合對(duì)建模區(qū)域進(jìn)行優(yōu)選,實(shí)現(xiàn)血清光譜特征波段選擇,建立光譜與血清BR成分之間的定量預(yù)測(cè)模型,以均方根誤差(RMSE)作為模型評(píng)價(jià)標(biāo)準(zhǔn)。結(jié)果表明:SiPLS模型效果更佳,TBIL、DBIL和IBIL的最優(yōu)建模波長(zhǎng)范圍分別為400~536nm、1 366~1 502nm和2 324~2 460nm,400~502nm、608~710nm和1 644~1 746nm,400~502nm和1 746~1 848nm;3種BR最優(yōu)預(yù)測(cè)模型的RMSE分別為0.598 9、0.207 2和0.386 2μmol/L;波段優(yōu)選對(duì)提高預(yù)測(cè)結(jié)果的準(zhǔn)確性有重要的意義;采用SiPLS建立TBIL、DBIL和IBIL定量分析模型,不僅可以提高模型的預(yù)測(cè)精度,而且克服了iPLS單一區(qū)間建模的缺點(diǎn),優(yōu)選出的特征譜區(qū)還可為設(shè)計(jì)小型專用光譜分析儀器提供依據(jù)。
[Abstract]:In order to establish the serum bilirubin (BR, bilirubin) samples of total bilirubin (TBIL), direct bilirubin (DBIL) and indirect bilirubin (IBIL) near infrared (NIR) spectral analysis of the optimal model, using -NIR visible transmission spectroscopy and interval partial least squares (iPLS) and synergy interval partial least squares (SiPLS) algorithm combining to optimize the modeling area, realize the serum spectral characteristics of band selection, establishment of quantitative prediction model between spectrum and serum BR components, with root mean square error (RMSE) as a model of evaluation criteria. The results show that the better effect of SiPLS model, TBIL modeling, optimal wavelength range of DBIL and IBIL were 400~536nm, 1 366~1 and 2 502nm 324~2 460nm, 400~502nm, 608~710nm and 1 644~1 746nm, 400~502nm 746~1 and 1 848nm; 3 BR optimal prediction model of RMSE were 0.598 9,0.207 2 and 0.3862 mol/L; waveband selection for improving the prediction results. Sex is of great importance. Using SiPLS to establish TBIL, DBIL and IBIL quantitative analysis models can not only improve the prediction accuracy of the model, but also overcome the shortcomings of iPLS single interval modeling, and the optimized characteristic spectral area can also provide a basis for designing small and special spectral analysis instruments.

【作者單位】： 暨南大學(xué)光電工程系;暨南大學(xué)第一附屬醫(yī)院臨床檢驗(yàn)中心;
【基金】：國(guó)家自然科學(xué)基金(31371785) 廣東省自然科學(xué)基金(S2011040001850) 廣東省戰(zhàn)略新興產(chǎn)業(yè)核心技術(shù)攻關(guān)(2012A032300016) 高等學(xué)校博士學(xué)科點(diǎn)專項(xiàng)科研基金(20124401120005) 廣東光陣光電科技有限公司光電信息工程院士工作站(2014B090905001)資助項(xiàng)目
【分類號(hào)】：R446.1;O657.33
【正文快照】： tant significance to improve prediction accuracy.The SiPLS-based quantitative analysis model of TBIL,DBIL and IBIL in serum has high prediction accuracy and overcomes shortcomings of the iPLS-basedmodel.Furthermore,the optimized characteristic spectrum r，

本文編號(hào)：1437924