近红外光谱法同时测定盐酸氯丙嗪和盐酸异丙嗪

采用近红外光谱法同时测定盐酸氯丙嗪和盐酸异丙嗪。对光谱经过10种不同预处理后,分别建立了测定盐酸氯丙嗪和盐酸异丙嗪的PLS校正模型。根据模型评价参数选择最佳模型,并用该模型对未知样品进行了预测。结果显示,对浓度范围在1～13mg.mL-1的盐酸氯丙嗪和盐酸异丙嗪,其近红外的最佳数学模型的相关系数分别为0.9432和0.9670,预测模型的均方根误差(RMSEP)分别为1.05和1.11。     

偏最小二乘法结合傅里叶变换近红外光谱同时测定卷烟焦油、烟碱和一氧化碳的释放量

采用多元校正技术-偏最小二乘法结合具有代表性的烟气总粒相物萃取液的傅里叶变换近红外透射光谱220个,分别建立了焦油、烟碱和一氧化碳的校正模型,并对光谱数据预处理方法和异常值诊断进行了讨论。所建的焦油、烟碱和一氧化碳模型相关系数分别为0．9922、0．9828和0．9734;均方预测残差(RMSECV)分别为0．3392、0．0329和0．3478;预测范围分别为：预测范围分别为：11．0～15．0mg、1．15～1．45mg和13．0～17．5mg。通过对模型进行t-检验,在显著性水平大于0．05的条件下,其测定结果与标准方法的测定结果对比,两者无显著性差异。该分析方法应用于同时测定卷烟焦油、烟碱和一氧化碳的释放量,结果令人满意。     

改进的重铬酸盐法测定水和废水中化学需氧量

对重铬酸盐法测定水和废水中化学需氧量的国家标准方法作了改进。改进方法较原方法的取样量由20.00mL减少为10.00mL,氯离子掩蔽剂硫酸汞由固体改为溶液;改进方法受氯离子干扰的程度与国标方法相似,当氯离子质量浓度小于1 000mg·L-1时对测定结果无影响。改进方法的检出限为3 mg·L-1,对标准物质和标准溶液进行测定,测定结果的相对误差在0.96%~2.70%之间,标准偏差在0.80~5.6 mg·L-1之间,相对标准偏差(n=6)在0.80%~5.9%之间。方法应用于水样的测定,测定值与国标法测定值相符,测定值的标准偏差在0.94~20.5mg·L-1之间,相对标准偏差(n=6)在0.53%~11%之间。     

无汞分光光度法快速测定含氯废水化学需氧量

建立无汞分光光度法快速测定含氯废水的化学需氧量。氯离子在测定化学需氧量反应中会增大重铬酸钾的消耗量,进而影响测定的准确性。传统的化学需氧量测定需要使用硫酸汞掩蔽氯化物。通过添加过量硫酸银以减少汞的使用,建立化学需氧量的无汞测定法。研究表明添加硫酸银含量1.03%的Ag2SO4-H2SO4试剂可以掩蔽500 mg/L及以下浓度氯离子的干扰。运用传统的测定方法和无汞测定法对理论化学需氧量为100 mg/L的邻苯二甲酸氢钾溶液进行测定,无汞测定法测定值略高于传统方法测定值。在氯离子浓度低于500 mg/L时,无汞方法测定值的准确度优于传统方法。另外,化学需氧量无汞测定法将化学药品的单位样本使用成本从0.61元降至0.47元。无汞分光光度法适用于含氯废水化学需氧量的快速测定。     

Al~(3+)-核固红-蛋白质体系的光谱特性及其分析应用

在pH为4.0时,Al3+与核固红形成的螯合物能够通过静电作用力和疏水作用力在蛋白质表面聚集形成聚集体。据此,提出了共振瑞利散射光谱法测定蛋白质的新方法。研究了体系的吸收光谱、荧光光谱及共振散射光谱。采用共振光散射技术测定了金属螯合物的组成。探讨了反应机理和共振光散射增强的主要原因。在440 nm处,人血清白蛋白(HSA)和牛血清白蛋白(BSA)的线性范围分别是0.2~12.0μg·mL-1和0.6~26.0μg·mL-1,对应的检测限分别是19.9 ng·mL-1和40.9 ng·mL-1。在584 nm处HSA和BSA的线性范围分别是0.05~6.0μg·mL-1和0.2~14.0μg·mL-1,检测限分别是11.5 ng·mL-1和23.3 ng·mL-1。方法可用于人血清、唾液和尿样中蛋白质含量的测定。将该方法的测定结果与考马斯亮蓝法的测定结果进行对照,经t-检验证明两种方法无显著性差异。     

近红外光谱快速分析法在小儿清热止咳口服液中的应用

应用近红外光谱透射分析技术建立了小儿清热止咳口服液的快速定性和其活性成分快速定量的分析新方法。采用聚类分析模型鉴别口服液品牌,且9种品牌的小儿清热止咳口服液能准确地被鉴别;利用偏最小二乘法(PLS)可对小儿清热止咳口服液中的活性成分黄芩苷和甘草酸进行同时检测。以黄芩苷和甘草酸为指标成分建立的定量校正模型预测性能较好,校正模型的相关系数(R2)分别为94.96、91.86,内部交叉验证均方差(RMSECV)分别为0.104、0.0930mg·mL-1,外部预测均方根误差(RM-SEP)分别为0.124、0.0699mg·mL-1。结果证明,近红外光谱快速分析法用于小儿清热止咳口服液的定性、定量分析,准确度较高,能够满足对小儿清热止咳口服液进行质量检测的需要。     

分光光度法与火焰原子吸收光谱法测定钢中硼的比较

提出了萃取-分光光度法和火焰原子吸收光谱法测定钢中硼的两种方法,比较了两者的线性范围、检出限、样品测定结果等.试验发现:分光光度法和火焰原子吸收光谱法在硼含量分别为0.11～3.2 mg·L-1和0.05～3.24 mg·L-1范围内时,吸光度与其质量浓度呈线性关系,检出限(3S/N)分别为0.033,0.036 mg·L-1.经检验,两种方法的测定结果之间无显著性差异.     

酸化吹气-重铬酸钾法测定高氯地表水中化学需氧量

为研究酸化吹气-重铬酸钾法测定高氯地表水中化学需氧量的适用性,6家实验室使用酸化吹气-重铬酸钾法对高氯地表水中化学需氧量进行测定。结果表明,方法的检出限为4mg/L,6家实验室对3种浓度的标准溶液(氯化物浓度均为20 000mg/L)进行精密度测定,实验室内相对标准偏差范围分别为3.4%～9.6%、2.9%～4.4%、1.7%～7.5%,实验室间相对标准偏差分别为3.2%、2.3%、3.4%;对2个不同浓度的高氯地表水实际样品进行精密度测定,实验室内相对标准偏差范围为2.7%～5.7%、2.1%～7.5%,实验室间相对标准偏差分别为4.1%、13.4%;对3种浓度的标准溶液(氯化物浓度均为20 000mg/L)进行准确度测定,实验室内相对标准偏差范围为-1.0%～11.0%、-3.0%～3.5%、-5.3%～4.0%,室间相对误差的标准偏差分别为4.0%、2.3%、3.4%。方法去除氯离子干扰效果好,精密度、准确度及检出限较好,适用于高氯地表水中化学需氧量的测定。     

反相高效液相色谱法测定一种口腔炎喷雾剂中绿原酸和咖啡酸的含量

取1mL口腔炎喷雾剂样品,用甲醇-水(1+1)溶液定量稀释至10mL,分取此溶液25μL供反相高效液相色谱法测定其绿原酸和咖啡酸含量。采用Shim-packVP-ODS色谱柱分离,以甲醇-0.012mol·L-1磷酸(27+73)混合溶液为流动相,柱温为30℃,流量为0.8mL·min-1,紫外检测器波长为323nm。结果表明:绿原酸在0.51～91.80mg·mL-1,咖啡酸在2.0～360.0mg·mL-1范围内呈线性关系,用标准加入法测定方法的回收率,结果依次在95.9%～97.4%和100.1%～100.9%范围内,检出限(3S/N)依次为0.10,0.40mg·mL-1,测定下限(10S/N)依次为0.36,1.40mg·mL-1。     

不用汞盐溶液的废水化学需氧量的快速测定

对测定废水的化学需氧量(COD)的标准方法(GB/T 11914-1989)作了两点改进:用硫酸锰代替硫酸银作催化剂和用少量银盐代表汞盐作为氯离子的掩蔽剂.这一改进使氧化时间由原来的2 h缩短为6 min,而且彻底消除了由汞离子所带来的污染问题.试验所得优化的测定条件为:加热温度为160℃;回流时间为6 min;酸度为15 mol·L-1;硫酸锰的加入量为7.5 g·L-1及测定下限为100 mg·L-1COD.     

Modeling the performance of “up-flow anaerobic sludge blanket” reactor based wastewater treatment plant using linear and nonlinear approaches—A case study

The paper describes linear and nonlinear modeling of the wastewater data for the performance evaluation of an up-flow anaerobic sludge blanket (UASB) reactor based wastewater treatment plant (WWTP). Partial least squares regression (PLSR), multivariate polynomial regression (MPR) and artificial neural networks (ANNs) modeling methods were applied to predict the levels of biochemical oxygen demand (BOD) and chemical oxygen demand (COD) in the UASB reactor effluents using four input variables measured weekly in the influent wastewater during the peak (morning and evening) and non-peak (noon) hours over a period of 48 weeks. The performance of the models was assessed through the root mean squared error (RMSE), relative error of prediction in percentage (REP), the bias, the standard error of prediction (SEP), the coefficient of determination (R²), the Nash-Sutcliffe coefficient of efficiency (E_f), and the accuracy factor (A_f), computed from the measured and model predicted values of the dependent variables (BOD, COD) in the WWTP effluents. Goodness of the model fit to the data was also evaluated through the relationship between the residuals and the model predicted values of BOD and COD. Although, the model predicted values of BOD and COD by all the three modeling approaches (PLSR, MPR, ANN) were in good agreement with their respective measured values in the WWTP effluents, the nonlinear models (MPR, ANNs) performed relatively better than the linear ones. These models can be used as a tool for the performance evaluation of the WWTPs.     

Optimization of solar photocatalytic biodegradability of seawater using statistical modelling

The performance of zinc oxide (ZnO) as a photocatalyst was evaluated for the treatment of pollutants present in seawater. Batch experimental studies were carried out by varying the dosage of photocatalyst (1–4 ​g/L). The effect of reaction time, pH and the dosage of photocatalyst was evaluated with the percentage removal efficiencies of chemical oxygen demand (COD), biological oxygen demand (BOD), total organic carbon (TOC) and the biodegradability (BOD/COD) of the seawater. Response surface methodology-central composite design (RSM-CCD) and artificial neural network-Levenberg Marquardt (ANN-LM) statistical models were employed to optimize the photocatalytic biodegradability (BOD/COD). A quadratic polynomial statistical model was obtained to predict the percentage removal efficiencies of COD, TOC, BOD and biodegradability. For the experimental runs, the maximum percentage removal efficiencies for COD, TOC, BOD was found to be 62.3, 40.1, and 18.8%, respectively. Whereas, the maximum biodegradability was 0.036. As per RSM-CCD and ANN-LM statistical model method the maximum percentage removal efficiencies were found to be COD ​= ​58.14, 60.39%, TOC ​= ​33.74, 40.09%, BOD ​= ​18.47, 18.7% and Biodegradability ​= ​0.0315, 0.0360, respectively. The predicted values from statistical models were well correlated with experimental values. ANN modelling predicted better values for the responses with an average of R² ​= ​0.99697 than RSM modelling with average R² ​= ​0.8948.     

中药材三七提取液近红外光谱的支持向量机回归校正方法

提出近红外光谱的支持向量机回归校正建模方法.以中药材三七渗漉提取液为实际分析对象,对其近红外光谱数据进行预处理和主成分分析后,用支持向量机回归算法建立人参皂苷R_g1,R_b1和R_d以及三七总皂苷的近红外光谱校正模型.以R_g1,R_b1和R_d的HPLC测定值及三七总皂苷的比色法测定值为参照,将本文方法与偏最小二乘回归和径向基神经网络建模方法相比较,结果表明,本文所建模型的预测准确性优于后两者,可推广应用于中药提取过程的近红外光谱分析.     

水质监测中BOD5与COD值相关性研究

通过对大量水质监测数据进行分析整理，得出不同水质的BOD5与COD值之间的线性回归方程，由线性回归方程预测出的BOD5值进行验证，无论BOD5与COD线性关系显著与否，若用预测的BOD5值作为中间值，只需估算出一个稀释比就可以满足BOD5的测定要求。     

基于聚类分析的局部建模方法在茶叶近红外光谱分析中的应用

为提高茶叶中咖啡碱、氨基酸近红外光谱分析模型的预测精度,采用基于聚类分析的局部建模方法。先提取茶叶样品光谱数据的特征因子,使用聚类分析对样品进行硬划分,经样品间距离和类间距离判别,确定单个模型定标样品个数。完成特征谱带的分析并进行波段选择后,随机抽取15个样品,偏最小二乘法局部建模结果显示:咖啡碱、氨基酸的预测平均相对偏差分别由聚类前的5.80%和6.14%下降为聚类后的2.75%和2.44%,模型预测精度显著提高。     

Development of Near-Infrared Reflectance Spectroscopy Models for Quantitative Determination of Water-Soluble Carbohydrate Content in Wheat Stem and Glume

Water-soluble carbohydrate (WSC) reserved in stem is an important agronomic trait for crop improvement. The intact samples and pieces of chipped samples were employed to determine WSC content by near-infrared reflectance spectroscopy (NIRS). Three NIRS models were developed to predict WSC content in wheat stem lower internode, upmost internode, and wheat glume, respectively. Moreover, a mixed model was developed for WSC quantitative analysis in the mixed sample of the three wheat organs. Statistics analysis indicated that the four models showed a high determination coefficient (R² ≥ 0.97) and ratio of standard deviation to RMSECV (RPD ≥ 5.99). The NIRS models would allow rapid and high throughout assessments and selections of WSC contents in wheat genetics and breeding programs.     

应用近红外技术测定黄豆粕中水分、蛋白质和粗脂肪

应用近红外分析仪（NIR）快速测定黄豆粕中水分、蛋白质和粗脂肪的含量,通过光谱扫描统计分析,分别找出测量黄豆粕水分、蛋白质、粗脂肪的波长及校准常数值,经数理统计分析结果表明：近红外法与常规水分测定法经典凯氏定氮法、索氏浸提法所测定的结果呈密切的线性相关,检测结果令人满意。     

近红外光谱法快速测定附子中6个生物碱的含量

应用近红外光谱(NIRS)技术结合偏最小二乘(PLS)和最小二乘支持向量机(LS-SVM)建立了附子中多指标成分的快速无损检测方法。选取38批样品建立了同时测定附子样品中6种成分含量的高效液相色谱(HPLC)方法;通过采集附子样品的NIRS图,分别采用PLS和LS-SVM建立了各个成分HPLC测定值与NIRS图的定量校正模型。所建立的苯甲酰新乌头原碱、苯甲酰乌头原碱、苯甲酰次乌头原碱、新乌头碱、次乌头碱、乌头碱、单酯型生物碱总量和双酯型生物碱总量LS-SVM模型的相对预测偏差(RPD)分别为3.3、3.2、4.1、7.7、8.8、7.6、4.0和8.6;验证集相关系数(rpre)分别为0.9486、0.9475、0.9668、0.9909、0.9946、0.9969、0.9669和0.9927,且LS-SVM模型优于PLS模型,说明NIRS模型验证集与HPLC测定值具有良好的非线性关系,模型预测效果良好。采用NIRS技术结合LS-SVM模型可以快速对附子中的上述6个生物碱含量以及单酯型生物碱总量和双酯型生物碱总量进行检测,方法操作简便,对控制附子中的生物碱含量具有一定的指导作用。     

Multi-way partial least squares modeling of water quality data

A 10 years surface water quality data set pertaining to a polluted river was analyzed using partial least squares (PLS) regression models. Both the unfold-PLS and N-PLS (tri-PLS and quadri-PLS) models were calibrated through leave-one out cross-validation method. These were applied to the multivariate, multi-way data array with a view to assess and compare their predictive capabilities for biochemical oxygen demand (BOD) of river water in terms of their relative mean squares error of cross-validation, prediction and variance captured. The sum of squares of residuals and leverages were computed and analyzed to identify the sites, variables, years and months which may have influence on the constructed model. Both the tri- and quadri-PLS models yielded relatively low validation error as compared to unfold-PLS and captured high variance in model. Moreover, both of these methods produced acceptable model precision and accuracy. In case of tri-PLS the root mean squares errors were 1.65 and 2.17 for calibration and prediction, respectively; whereas these were 2.58 and 1.09 for quadri-PLS. At a preliminary level it seems that BOD can be predicted but a different data arrangement is needed. Moreover, analysis of the scores and loadings plots of the N-PLS models could provide information on time evolution of the river water quality.