Determination of optimal source-detector separation in measuring chromophores in layered tissue with diffuse reflectance

Based on analysis of the relation between mean penetration depth and source-detector separation in a three-layer model with the method of Monte-Carlo simulation, an optimal source-detector separation is derived from the mean penetration depth referring to monitoring the change of chromophores concentration of the sandwiched layer. In order to verify the separation, we perform Monte-Carlo simulations with varied absorption coefficient of the sandwiched layer. All these diffuse reflectances are used to construct a calibration model with the method of partial least square (PLS). High correlation coefficients and low root mean square error of prediction (RMSEP) at the optimal separation have confirmed correctness of the selection. This technique is expected to show light on noninvasive diagnosis of near-infrared spectroscopy.     

基于紫外-可见光谱的水质TOC定量分析方法

地表水资源安全关系到国民健康、生态环境稳定和经济可持续发展,具有重要战略意义。总有机碳（TOC）是一种衡量水体中有机物含量的综合指标,其在水环境监管和治理中具有重要价值。传统检测方法通过高温催化氧化测定水样中TOC含量具有耗时较长、操作复杂的局限性,紫外-可见光谱技术具有检测速度快、操作简单的优势,因而在水质在线检测中具有较好的应用前景。国内外对地表水中TOC浓度的在线检测目前大多采用与COD浓度间的相关关系进行间接推算得到,这类方法对水体成分的稳定性要求较高。相比于常规的间接推算方法,采用光谱定量分析方法建立TOC与紫外-可见光谱间的分析模型具有更好的鲁棒性和分析精度,便于实现水质无人值守在线监测。实验配置了TOC样本溶液,设计了为期两天的实验,在4个时间段采集得到样品光谱数据集（分别记为D1,D2,…,D6）。首先,通过分组实验将D1作为训练集建立TOC偏最小二乘（PLS）回归模型,预测同一时间段测试集D2的TOC浓度,得到平均绝对相对误差（MAPE）不超过0.78%,表明建立的TOC定量分析模型具有较高的精度。然后,为验证PLS建立的TOC模型对仪器状态变化的鲁棒性,选择不同时间段采集的光谱数据分别作为训练集和测试集,进行不同仪器状态交叉实验,4组实验中测试集样品TOC浓度预测值的MAPE分别为3.82%,3.75%,3.43%和0.98%。实验表明,采用PLS算法建立的TOC紫外-可见光谱定量分析模型具有较好的分析精度和鲁棒性,分组实验和不同仪器状态交叉实验中预测浓度的MAPE均不超过3.82%,优于常规的间接推算法。此外,建立的光谱定量分析模型不依赖COD与TOC间的推算关系,因此在水环境变化时较常规推算方法具有更好的适应能力。最后,PLS算法建模过程简单,运算速度快,为浸入式在线检测设备的开发和维护提供了便利。     

人体血糖浓度近红外光谱无创检测过程中温度对结果的影响

利用近红外光谱进行人体血糖浓度无创检测过程中,检测结果会受到很多的因素影响,如测量温度,压力等,另外还有人体的状态.在这诸多的影响因素中,温度是一个不可忽视的因素.为了研究温度对测量结果的定量影响,文章采用葡萄糖水溶液,在15,20,25,30,35,40℃等六个温度点处做了研究,样品浓度范围为10～200 mg·dL-1,浓度间隔为10 mg·dL-1,波长范围为1 100～1 700 nm.在各温度点处建立模型,并利用不同的模型进行相互预测.建模结果为RMSEP值最大为11.227 9 mg·dL-1,最小为3.298 8 mg·dL-1,相关系数在0.98左右.当温度改变1℃时,预测结果会有平均△c=2.662(mg·dL-1·℃-1)的预测误差,这结果表明当建立模型温度与测试时的温度一致时误差最小.另外,文章提出了可以采用两种方法来减小或者补偿由温度给测量结果来的影响.     

Application of transcutaneous diffuse reflectance spectroscopy in the measurement of blood glucose concentration

In this paper, the propagation characteristics of near-infrared (NIR) light in the palm tissue are analyzed,and the principle and feasibility of using transcutaneous diffuse reflectance spectroscopy for non-invasive blood glucose detection are presented. An optical probe suitable for measuring the diffuse reflectance spectrum of human palm and a non-invasive blood glucose detection system using NIR spectroscopy are designed. Based on this system, oral glucose tolerance tests are performed to measure the blood glucose concentrations of two young healthy volunteers. The partial least square calibration model is then constructed by all individual experimental data. The final result shows that correlation coefficients of the two experiments between the predicted blood glucose concentrations and the reference blood glucose concentrations are 0.9870 and 0.9854, respectively. The root mean square errors of prediction of full cross validation are 0.54 and 0.52 mmol/1, respectively.     

奇异点快速检测在牛奶成分近红外光谱测量中的应用

近红外光谱作为一种依靠模型对物化性质进行分析的技术,对光谱数据的准确性进行快速准确的判断是得到可靠分析结果的前提。但是光谱数据中奇异点的存在会在很大程度上影响多变量校正模型的准确性,从而影响模型的预测效果。文章综合利用半数重采样法(Resampling by Half-Mean,RHM)和最小半球体积法(Smallest Half-Volume,SHV)成功剔除了被测量的牛奶成分近红外光谱中的奇异点,其效果远优于传统的奇异点剔除方法,并且该方法具有简单快速、计算量小、数值稳定等特点,非常适用于在线分析和其他类型的光谱数据中奇异点的检测。     

Application of transcutaneous diffuse reflectance spectroscopy in the measurement of blood glucose concentration

In this paper, the propagation characteristics of near-infrared (NIR) light in the palm tissue are analyzed, and the principle and feasibility of using transcutaneous diffuse reflectance spectroscopy for non-invasive blood glucose detection are presented. An optical probe suitable for measuring the diffuse reflectance spectrum of human palm and a non-invasive blood glucose detection system using NIR spectroscopy are designed. Based on this system, oral glucose tolerance tests are performed to measure the blood glucose concentrations of two young healthy volunteers. The partial least square calibration model is then constructed by all individual experimental data. The final result shows that correlation coefficients of the two experiments between the predicted blood glucose concentrations and the reference blood glucose concentrations are 0.9870 and 0.9854, respectively. The root mean square errors of prediction of full cross validation are 0.54 and 0.52 mmol/1, respectively.     

地表水总有机碳含量紫外-可见光谱检测方法

总有机碳是以碳含量评价水质有机污染的指标,可以反映水体受污染程度。目前地表水总有机碳检测多采用现场取样后实验室分析检测方法,该方法存在费时费力、操作复杂、二次化学污染等缺点。紫外-可见光谱法具有环保、操作简便、可实时在线原位检测等优点,在地表水总有机碳检测中具有很好的应用前景。针对总有机碳检测问题,采用了一种基于自适应增强学习的区间偏最小二乘回归方法,该方法将总有机碳吸收光谱波段分为若干子区间,初始化训练样本权重, 依次在各子区间建立偏最小二乘回归模型,根据子区间模型预测误差率计算该子区间预测结果的权重系数,并更新下一子区间训练样本权重,最后将各子区间模型预测结果线性加权得到总有机碳的检测结果。实验配制总有机碳标准溶液浓度25～150 mg·L-1共43个样品,第一时间段采集35个总有机碳标准样品光谱分为训练集和测试集,建立并验证总有机碳检测算法模型。为评价算法模型鲁棒性,在另一时间段采集剩余的8个标准样品光谱进行反测验证。实验结果表明,采用基于自适应增强学习的区间偏最小二乘回归法建立的总有机碳定量模型具有较高的精度和鲁棒性,分组验证和反测验证的预测均方根误差分别为1.304和1.533 mg·L-1,均优于偏最小二乘回归和极限学习机方法。为进一步验证该方法的有效性,使用该建模方法预测生活污水的总有机碳含量。实际地表水样本取样于河北石家庄藁城污水处理厂排污口污水及河北先河公司园区的生活污水,经稀释后共获得50组地表水样本,采用SPXY方法分为训练集33组水样,测试集17组水样。在实际水样检测中,采用净信号分析方法进行光谱预处理,降低总有机碳与其他水质参数间的交叉干扰;分组验证预测均方根误差为3.26 mg·L-1,平均绝对值百分比误差为3.46%。综上所述,基于自适应增强学习的区间偏最小二乘回归方法,可以快速准确地对地表水中总有机碳进行检测,为在线水质总有机碳检测提供了方法支撑。