摘要
光谱分析方法以其灵敏、快速、准确的检测特点,在各个领域得到广泛应用。血液和尿液的成分含量是判断人体健康状况的客观依据,低成本血液和尿液成分快速检测方法具有重要的学术意义和社会价值。基于多光程光谱比单一光程光谱能够提供更多样本成分含量信息从而提高测量精度的理论,对多光程光谱法血液及尿液成分检测进行了初步研究和探索。
设计并搭建了自动多光程光谱检测系统以获得多光程光谱数据。该系统由自动微位移装置、光谱仪和微机(上位机)构成,通过编写的上位机VB.net程序及微控制器控制程序协同工作,精确控制自动微位移装置微小位移,实现了多光程光谱数据的自动采集。
在血液成分含量检测实验中,采集200例血清样本近红外多光程光谱数据,并对样本葡萄糖、总胆固醇、总蛋白、白蛋白含量进行建模分析,r2分别为0.8686、0.9432、0.8953和0.8993, RMSEP分别为0.6792mmol/L、0.2064mmol/L、2.8469g/L和1.7942g/L。多光程光谱提取特征波长的初步研究表明光程分段挑选波长方法的建模效果最优,可将光谱数据维数缩减为原始数据的1/40,对多光程光谱降维方法的研究有一定的积极意义。
在光谱尿液成分检测实验中,选择肾脏损伤敏感指标尿微量白蛋白作为检测对象。采集标准品配制的27个尿样和207例实际尿样多光程可见-近红外光谱信息。配制尿样使用可见-近红外双波段多光程光谱建立的回归模型优于使用单一波段建模,r2达0.9905,最大预测绝对误差为7.56mg/L。实际尿样建模中充分考虑光谱数据的非线性因素,使用PLS-ANN联立建模,r2为0.9511,RMSEP为5.02 mg/L,提高了定量分析精度。
课题设计自动多光程检测系统以减小误操作发生的几率,提高了实验效率和准确度,具有良好的稳定性和可重复性,并通过初步对多光程光谱数据建模分析、特征提取等数据挖掘手段的研究,可以较准确的预测血液中葡萄糖、总胆固醇、总蛋白、白蛋白四种成分和尿液中微量白蛋白的含量,为进一步探索血液和尿液多光程光谱采集分析的检测方法奠定了基础。
Spectral analysis method, with sensitive, rapid and accurate features, is widely used in various fields. The concentration level of blood and urine components is the main judgment basis of human health condition. Low-cost and rapid detection of blood and urine components has important academic significance and social value. In theory, the multiple optical path length spectra, with the nonlinear feature, can provide more information of the sample than single optical path length spectra and can improve the accuracy of measurement, using the abundant information. Based on this theory, the preliminary research and exploration on the measurement of blood and urine components was carried out in this work.
To obtain the multiple optical path length spectra data, the automatic detection system was established. The system consists of an automatic micro-displacement device, two spectrometers and a PC. By the cooperative work of the software designed based on VB.net language and the micro-controller, the minute optical length can be changed accurately and stably and the data obtaining processing can be performed automatically.
In the measuring experiment of blood components, near-infrared multiple optical path length spectra of 200 serum samples were collected. The models of glucose, total cholesterol, total protein and albumin were established. The r2 of prediction are 0.8686, 0.9432, 0.8953 and 0.8993, and the RMSEP are 0.6792 mmol/L, 0.2064 mmol/L, 2.8469 g/L and 1.7942 g/L, respectively. The result of the preliminary study on the feature extraction shows that picking up the special wavelengths from group of optical path length has the best performance. The dimension of spectral data decreases to 1/40 of the original data. This method has the positive significance to the modeling of multiple optical path length spectra.
In the measuring experiment of urine components, UMALB, the sensitive indicator of renal injury, was selected as the measuring object. 27 standard samples of urine and 207 human urine samples were acquired. The modeling results of standard samples show that the model established by both visible and near-infrared spectral information is better than that using a single band. Its r2 is up to 0.9905, and the maximum absolute prediction error is 7.56mg/L. Considering the nonlinear factors of spectral data, the PLS-ANN modeling method was involved to increase the accuracy of quantitative analysis. The r2 of the human urine samples is 0.9511, and RMSEP is 5.02 mg/L.
This detection system designed in this work reduced the probability of misoperation, and improved the efficiency and accuracy of experiments greatly. The prediction accuracy of glucose, total cholesterol, total protein, albumin and UMALB concentration is improved according to the preliminary study of data mining method, such as model establishment and feature extraction, which is basis of the follow-up blood and urine multiple optical path length spectra collection and analysis.
引文
[1]凌明胜,钱志余,梁超英,血糖浓度荧光光谱检测研究,量子电子学报,2007,24(5):635~639.
[2] Enejder A M K,Scecina T G,Oh J,et al. Raman spectroscopy for noninvasive glucose measurements,Journal of Biomedical Optics,2005,10(3):31114~31119.
[3] Katsuhiko Maruo,Mitsuhiro Tsurugi,Jakusei Chin,et al. Noninvasive Blood Glucose Assay Using a Newly Developed Near-Infrared System,IEEE Jouranal of Selected Topics in Quantum Electronics,2003,9(2):322~329.
[4]李刚,王焱,李秋霞,动态光谱法对提高近红外无创血液成份检测精度的理论分析,红外与毫米波学报,2006,25(5):345~348.
[5]张志勇,门剑龙,李刚,等.动态光谱法用于人体血红蛋白浓度的无创测量,光谱学与光谱分析,2010,30(1):150~153.
[6]雷猛,冯新泸,何滔,广义二维相关光谱技术及其应用,光谱实验室,2008,25(5):996~1002.
[7]刘浩,黄艳萍,高鸿彬,等.含氮芳香类药物的近红外及二维相关光谱分析,计算机与应用化学,2008,25(6):724~728.
[8] Goetz A F H,Rowan L C,Geologic remote sensing,Science,1981,221:81~791.
[9] Goetz A F H,Remote,Three decades of hyperspectral remote sensing of the Earth: A personal view,Sensing of Environment,2009,113:S5~S16.
[10]Wold S,Albano C,Dunn M,et al. Pattern recognition: finding and using regularities in multivariate data,Food Research and Data Analysis,Applied Science,1983:147~188.
[11]丁丽,相玉红,张卓勇,近红外光谱定量预测奶粉中三聚氰胺的含量,首都师范大学学报(自然科学版),2010,31(1):26~29.
[12]王俊,陈国庆,魏柏林,等.基于BP神经网络的日落黄溶液荧光光谱的研究,光学技术,2009,35(6):868~871.
[13]吴新生,谢益民,支持向量机回归法在近红外光谱测定植物纤维原料中甲氧基含量中的应用,化学分析计量,2009,18(2):17~20.
[14]吴桂芳,黄凌霞,何勇,葡萄浆果糖度可见/近红外光谱检测的研究,光谱学与光谱分析,2008,28(9):2090~2093.
[15]方利民,林敏,基于独立分量和神经网络的近红外多组分分析方法,分析化学,2008,36(6):815~818.
[16]黄康,汪辉君,徐惠荣,基于最小二乘支持向量机的番茄汁糖酸度分析研究,光谱学与光谱分析,2009,29(4):931~934.
[17]吴迪,何勇,冯水娟,基于LS-SVM的红外光谱技术在奶粉脂肪含量无损检测中的应用,红外与毫米波学报,2008,27(3):180~184.
[18]侯瑞,吉海彦,张录达,基于OSC-PLS算法对大麦蛋白质含量进行定量分析的研究,光谱学与光谱分析,2009,29(7):1840~1843.
[19]陈全胜,郭志明,赵杰文,等.基于净分析物预处理算法的绿茶中儿茶素的近红外光谱定量分析,红外与毫米波学报,2009,28(5):357~361.
[20]朱殿明,杨鸿鹏,骆晓森,等.基于小波变换的人血清血卟啉荧光光谱分析法,光谱学与光谱分析,2007,27(12):2553~2557.
[21]丁小平,孟超,王建林,等.人血清吸收光谱的研究,光谱学与光谱分析,1999,19(2):225~226.
[22]Wolfgang Petrich,Arnulf Staib,Matthias Otto,et al. Correlation between the state of health of blood donors and the corresponding mid-intrared spectra of the serum,Vibrational Spectroscopy,2002,28:117~129.
[23]Yoen-Joo Kim,Gilwon Yoon,Multicomponent assay for human serum using mid-infrared transmission spectroscopy based on component-optimized spectral region selected by a first loading vector analysis in partial least-squares regression,APPLIED SPECTROSCOPY,2002,56(5):625~632.
[24]Daniel Rohleder,Wolfgang Kiefer,Wolfgang Perich,Quantitative analysis of serum and serum ultrafiltrate by means of Raman spectroscopy,Analyst,2004,129:906~911.
[25]S. Kasemsumran,Y. P. Du,K. Murayama,et al. Near-infrared spectroscopic determination of human serum albumin,γ-globulin, and glucose in a control serum solution with searching combination moving window partial least squares,Analytica Chimica Acta,2004,512:223~230.
[26]陈华才,杨仲国,陈星旦,等.傅里叶变换近红外光谱法快速检测人血清生化成分,分析实验室,2005,24(7):17~20.
[27]Dahu Qi,Andrew J.Berger,Chemical concentration measurement in blood serum and urine samples using liquid-core optical fiber Raman Spectroscopy,APPLIED OPTICS,2007,46(10):1726~1734.
[28]Kan-Zhi Liu,Angela Man,Thomas C. Dembinski,Quantification of serum apolipoprotein B by infrared spectroscopy , Analytical and Bioanalytical Chemistry,2007,387:1809~1814.
[29]兰秀凤,刘莹,朱拓,等.人血清中总胆固醇的光谱学研究,光子学报,2008,37(3):547~551.
[30]朱卫华,赵志新,郭昕,等.基于血清紫外-可见吸收光谱的胆固醇含量研究,光谱学与光谱分析,2009,29(4):1004~1007.
[31]Meyer TW,Bennett PH,Nelson RG,Podocyte number predicts long-term urinary albumin excretion in Pima Indians with Type II diabetes and microalbuminuria, DABETOLOGIA,1999,42 (11) : 1341~1344.
[32]Brantsma AH,Bakker SJL,de Zeeuw D,et al. Urinary albumin excretion as a predictor of the development of hypertension in the general population,Journal of The American Society of Nephrology,2006,17 (2) : 331~335.
[33]Comper WD,Osicka TM,Clark M,et al. Earlier detection of micro albuminuria in diabetic patients using a new urinary albumin assay,Kidney International,2004,65(5): 1850~1855.
[34]Borch-Johnsen K,Feldt-Rasmussen B,Strandgaard S,et al. Urinary albumin excretion - An independent predictor of ischemic heart disease,Arteriosclerosis Thrombosis and Vascular Biology,1999,19(8) : 1992~1997.
[35]Comper WD,Jerums G,Osicka TM,Differences in urinary albumin detected by four immunoassays and high-performance liquid chromatography, Clinical Biochemistry,2004,37(2): 105~111.
[36]徐未,魏言春,邢达,等.基于高灵敏探针海萤荧光素类似物的化学发光法检测尿微量白蛋白,分析化学,2008,36(1): 57~60.
[37]R.Anthony Shaw,Steven Kotowich,Henry H.Mantsch,et al. Quantitation of protein,creatinine,and urea in urine by near-infrared spectroscopy,Clinical Biochemistry,1996,29(1):11~19.
[38]R.Anthony Shaw,Sarah Low-Ying,Michzel Leroux,et al. Toward reagent-free clinical analysis: Quantitation of urine urea,creatinine,and total protein from mid-infrared spectra of dried urine films,Clinical Chemistry,2000,46:1493~1495.
[39]X.Dou,Y. Yamaguchi,H. Yamamoto,et al. Quantitative analysis of metabolites id urine using a highly precise,compact near-infrared Raman spectrometer,Vibrational Spectroscopy,1996,13:83~89.
[40]刘伟玲,张思祥,虞启琏,等.近红外光谱法用于尿液多成分分析,分析仪器,2004,1:47~50.
[41]汪嚥,卢延辉,王蕊,等.光程长对光谱测量误差的影响,天津大学学报,2004,37(10):906~909..
[42]于海燕,应义斌,谢丽娟,等.光程对黄酒金属元素近红外透射光谱分析精度的影响,光谱学与光谱分析,2007,27(6):1118~1120.
[43]姜礼义,刘福莉,陈华才,等.绿茶汤中茶多酚近红外定量分析的光程选择,中国计量学院学报,2009,20(2):135~138.
[44]林涛,于海燕,徐惠荣,等.光程变化对给予近红外光谱的黄酒品质检测的影响,光谱学与光谱分析,2009,29(4):950~955.
[45]李刚,刘玉良,林凌,王焱,采用多光程长建模方法检测血液成分含量,分析化学,2007,35(10):1495~1498.
[46]李刚,李静瑶,林凌,变光程长方法测量物质的光学参数,光电子·激光,2010,21(3):403~405.
[47]朱大洲,籍保平,史波林,等.基于小波变换的苹果汁多光程近红外光谱信息提取研究,红外与毫米波学报,2009,28(5):271~275.
[48]吴桂芳,黄凌霞,何勇,葡萄浆果糖度可见/近红外光谱检测的研究,光谱学与光谱分析,2008,28(9):2090~2093.