紫外-可见-短波近红外漫反射光谱技术测定白芷中二氧化硫残留量的研究

建立紫外-可见-短波近红外漫反射光谱结合化学计量学测定白芷中二氧化硫残留量的方法。利用紫外-可见-短波近红外漫反射光谱技术并结合化学计量学建模预测二氧化硫残留量。偏最小二乘回归法(PLSR)建模优于支持向量回归法(SVR); Random Frog波段选择结合Auto-scaling预处理后PLS建模后预测效果最佳,校正集R~2为0. 99,交叉验证集R~2为0. 94,预测集R~2为0. 96。紫外-可见-短波近红外漫反射光谱结合化学计量学可以实现二氧化硫残留量的快速检测,为中药饮片的质量评价及监管提供一种技术手段。     

短波近红外光谱技术对葡萄酒中总糖含量快速测定的研究

采用短波近红外光谱技术结合偏最小二乘法(PLS),建立了葡萄酒中总糖含量的定量分析数学模型,讨论了光谱预处理方法和主成分数对PLS模型预报精度的影响.应用所建模型对预测集样本中总糖含量进行预报,结果令人满意.该方法方便快捷,并且具有较高的预报精度,可以用于葡萄酒中总糖含量的快速测定.     

偏最小二乘-近红外漫反射光谱法测定西米替丁药片

研究了应用偏最小二乘法(PLS)同近红外漫反射光谱法结合,对西米替丁片剂药品进行无损非破坏定量分析,建立了最佳的数学校正模型。讨论了波长间隔和主成分数对PLS定量预测能力的影响,预测了未知样品。     

近红外光谱法对头孢氨苄粉末药品的非破坏定量分析

采用近红外漫反射光谱法对头孢氨苄粉末药品中主要成分头孢氨苄进行快速、无损定量分析.采用偏最小二乘法建立近红外光谱信息与待测组分含量间的最佳数学校正模型.对3种光谱(SNV光谱、一阶导数、二阶导光谱)的预测结果进行了比较,讨论了光谱的预处理方法和主成分数对偏最小二乘法定量预测能力的影响,并对预测集样品进行预测.     

应用近红外漫反射光谱法的猕猴桃品质无损检测北大核心CSCD

应用近红外漫反射光谱技术和化学计量学,研究成熟期猕猴桃内部品质与其近红外漫反射光谱之间的关系。在室温(24±2)℃下,采集猕猴桃赤道区域不同测试部位在4 000~10 000 cm^(-1)范围内的光谱数据,用基于平滑处理、归一化及基线校正的组合式处理方法对原始光谱进行预处理;另应用偏最小二乘(PLS)法、主成分回归法和多元线性回归法等方法分别建立猕猴桃硬度、可溶性固形物含量(SSC)的校正模型。结果表明:采用组合预处理方法和PLS法建立的校正模型精度最高;硬度校正集相关系数R_c、均方根误差RMSEC和预测集相关系数R_p、均方根误差RMSEP达到了0.976 5,0.548 3,0.943 2,0.612 7;SSC校正集相关系数R_c、均方根误差RMSEC和预测集相关系数R_p、均方根误差RMSEP达到了0.916 6,0.539 6,0.901 2,0.619 0;试验结果验证了本法的可行性。     

偏最小二乘法测定复方乙酰水杨酸片中的有效成分

将偏最小二乘法(PLS)与近红外漫反射光谱法相结合,对复方乙酰水杨酸片进行无损非破坏定量分析．建立了最佳的数学校正模型,比较了样品中3种有效成分(乙酰水杨酸、非那西丁和咖啡因)同时测定和单独测定时的主成分数对PLS定量预测能力的影响,预测了未知样品。3种有效成分同时测定和单独测定建立的PLS模型具有相同的主成分数,PLS预报浓度与参考浓度具有相近的标准偏差,说明用PLS法同时测定3种组分的含量是可行的。     

应用近红外漫反射光谱法的猕猴桃品质无损检测

应用近红外漫反射光谱技术和化学计量学,研究成熟期猕猴桃内部品质与其近红外漫反射光谱之间的关系。在室温(24±2)℃下,采集猕猴桃赤道区域不同测试部位在4 000~10 000 cm~(-1)范围内的光谱数据,用基于平滑处理、归一化及基线校正的组合式处理方法对原始光谱进行预处理;另应用偏最小二乘(PLS)法、主成分回归法和多元线性回归法等方法分别建立猕猴桃硬度、可溶性固形物含量(SSC)的校正模型。结果表明:采用组合预处理方法和PLS法建立的校正模型精度最高;硬度校正集相关系数R_c、均方根误差RMSEC和预测集相关系数R_p、均方根误差RMSEP达到了0.976 5,0.548 3,0.943 2,0.612 7;SSC校正集相关系数R_c、均方根误差RMSEC和预测集相关系数R_p、均方根误差RMSEP达到了0.916 6,0.539 6,0.901 2,0.619 0;试验结果验证了本法的可行性。     

近红外光谱-径向基神经网络在异烟肼片无损定量分析中的应用

应用异烟肼片粉末的近红外漫反射光谱数据分别结合偏最小二乘法(PLS)和径向基神经网络(RBFNN)建立定量分析模型,并用所建模型对预测集样品进行了预测,结果表明:应用RBFNN所建立的定量分析模型优于PLS模型,相关系数(r)值由0.99593提高到0.99734,交互验证均方根误差(RMSECV)值由0.00523下降到0.00423,预测均方根误差(RMSEP)值由0.00614下降到0.00501。     

PLS-BP法近红外光谱技术同时测定鲜乳中四种主成分

基于近红外光谱技术,将偏最小二乘法(Partial Least Squares,PLS)和单隐层的反向传播网络(Back-Propagation Network,BP)联用并测定了鲜乳中4种主成分含量.用PLS法将原始数据压缩为主成分,取前3个主成分的14个数据输入网络,以Kolmogorov定理为依据,经过实验确定中间层的神经元个数为29,初始训练迭代次数为1000,建立了脂肪、蛋白质、乳糖、牛乳总固体4种主成分含量的预测校正模型.PLS-BP模型对样品4个组分含量的预测决定系数(R2)分别为:0.961、0.974、0.951、0.997;本研究为近红外光谱技术在鲜乳多组分快速检测提供了新思路.     

近红外光谱法快速测定柴胡提取过程中的药效成分

利用近红外光谱(NIRS)技术对柴胡提取过程中的药效成分进行快速定量分析。共收集126个柴胡提取液样品,采用紫外-可见分光光度法测定总黄酮和多糖的含量,高效液相色谱法(HPLC)测定柴胡皂苷A及柴胡皂苷D的含量,以透射模式采集提取液的近红外光谱,运用偏最小二乘法(PLS)分别建立了近红外光谱与4种药效指标参考值之间的定量校正模型,并采用不同的预处理方法、光谱波段和主因子数对模型进行优化。结果表明,总黄酮、多糖、柴胡皂苷A和柴胡皂苷D 4种定量模型的近红外预测值与参考值之间的拟合性良好,模型预测精度较高,其中预测集相关系数(RP)均大于0.9;预测集误差均方根(RMSEP)分别为3.46 μg/mL、0.743 mg/mL、1.53 μg/mL、0.406 μg/mL;预测集相对偏差(RSEP)分别为1.65%、8.28%、5.74%、7.52%。该研究证实了NIRS结合PLS可成功应用于监测柴胡提取液中药效成分的含量变化,且方法具有快速、准确、无损和环保的特点。     

蛇床子香豆素的薄层分离-直接进样-质谱鉴定

用丙酮提取,石油醚冷冻结晶,从蛇床子中得到混合香豆素。经薄层分离 ,直接进样和质谱分析 ,鉴定出8种香豆素化合物:Ⅰ.蛇床子素(osthol) ,Ⅱ.佛手内酯(bergapten) ,Ⅲ.欧芹属素乙(imperatorin) ,Ⅳ.花椒毒素(xanthotoxin) ,Ⅴ.异回芹内酯(isopimpinellin) ,Ⅵ.别异英波托林(alloisoimperatorin) ,Ⅶ.6 -甲氧基 -8 -甲基香豆精(6_methoxy_8_methylcoumarin) ,Ⅷ.花椒毒酚(xanthotoxol)。其中Ⅵ、Ⅶ首次于蛇床子中发现。     

Preparative isolation and purification of bergapten and imperatorin from the medicinal plant Cnidium monnieri using high-speed counter-current chromatography by stepwise increasing the flow-rate of the mobile phase

A high-speed counter-current chromatography (HSCCC) method was developed for the preparative separation and purification of bergapten and imperatorin from the Chinese medicinal plant Cnidium monnieri (L.) Cusson. The crude extract was obtained by extraction with ethanol from the dried fruits of Cnidium monnieri (L.) Cusson under sonication. Preparative HSCCC with a two-phase solvent system composed of n-hexane-ethyl acetate-ethanol-water (5:5:5:5, v/v/v/v) was successfully performed by increasing the flow-rate of the mobile phase stepwise from 1.0 to 2.0 ml min(-1) after 180 min. The components purified and collected were analyzed by high-performance liquid chromatography. The method yielded 45.8 mg of bergapten at 96.5% purity and 118.3 mg of imperatorin at 98.2% purity from 500 mg of the crude extract in a single run. The recoveries of bergapten and imperatorin were 92.1 and 93.7%, respectively.     

Preparative isolation of osthol and xanthotoxol from Common Cnidium Fruit (Chinese traditional herb) using stepwise elution by high-speed counter-current chromatography

Preparative high-speed counter-current chromatography (HSCCC) was successfully used for isolation and purification of osthol and xanthotoxol from Cnidium monnieri (L.) Cusson (Common Cnidium Fruit) using stepwise elution with a pair of two-phase solvent systems composed of n-hexane-ethyl acetate-methanol-water at (1:1:1:1, v/v), and (5:5:6:4, v/v), which had been selected by analytical high-speed counter-current chromatography. Using a preparative unit of the HSCCC centrifuge, about a 308 mg amount of the crude extract was separated, yielding 88.3 mg of osthol and 19.4 mg of xanthotoxol at a high purity of over 98%.     

Identification and quantification of the volatile constituents in Cnidium monnieri using supercritical fluid extraction followed by GC‐MS

The volatile components of Cnidium monnieri were obtained by supercritical fluid extraction (SFE) and analyzed by GC‐MS (identification and determination of metabolites). The compounds were identified according to their retention times and mass spectra. The effects of different parameters, such as extraction pressure, temperature, dynamic extraction time, flow rate of CO₂, on the SFE of C. monnieri extracts were investigated. A total of 14 compounds of SFE extracts were identified. Osthole (69.52%), bornyl acetate (10.03%), α‐pinene (4.71%), and imperatorin (2.42%) were the major compounds identified in C. monnieri SFE extracts. The quantitation of osthole and imperatorin were then accomplished. The linear calibration ranges were all 5–1000 μg/mL for osthole and imperatorin by GC‐MS analysis. The recovery of osthole and imperatorin were in the range 96.5–101.8%. The LODs for osthole and imperatorin were 1.0 and 0.6 μg/mL, respectively.     

The prediction of total anthocyanin concentration in red-grape homogenates using visible-near-infrared spectroscopy and artificial neural networks

This study compares the performance of partial least squares (PLS) regression analysis and artificial neural networks (ANN) for the prediction of total anthocyanin concentration in red-grape homogenates from their visible-near-infrared (Vis-NIR) spectra. The PLS prediction of anthocyanin concentrations for new-season samples from Vis-NIR spectra was characterised by regression non-linearity and prediction bias. In practice, this usually requires the inclusion of some samples from the new vintage to improve the prediction. The use of WinISI LOCAL partly alleviated these problems but still resulted in increased error at high and low extremes of the anthocyanin concentration range. Artificial neural networks regression was investigated as an alternative method to PLS, due to the inherent advantages of ANN for modelling non-linear systems. The method proposed here combines the advantages of the data reduction capabilities of PLS regression with the non-linear modelling capabilities of ANN. With the use of PLS scores as inputs for ANN regression, the model was shown to be quicker and easier to train than using raw full-spectrum data. The ANN calibration for prediction of new vintage grape data, using PLS scores as inputs, was more linear and accurate than global and LOCAL PLS models and appears to reduce the need for refreshing the calibration with new-season samples. ANN with PLS scores required fewer inputs and was less prone to overfitting than using PCA scores. A variation of the ANN method, using carefully selected spectral frequencies as inputs, resulted in prediction accuracy comparable to those using PLS scores but, as for PCA inputs, was also prone to overfitting with redundant wavelengths.     

A practical approach for near infrared spectral quantitative analysis of complex samples using partial least squares modeling

The number of latent variables (LVs) or the factor number is a key parameter in PLS modeling to obtain a correct prediction. Although lots of work have been done on this issue, it is still a difficult task to determine a suitable LV number in practical uses. A method named independent factor diagnostics (IFD) is proposed for investigation of the contribution of each LV to the predicted results on the basis of discussion about the determination of LV number in PLS modeling for near infrared (NIR) spectra of complex samples. The NIR spectra of three data sets of complex samples, including a public data set and two tobacco lamina ones, are investigated. It is shown that several high order LVs constitute main contributions to the predicted results, albeit the contribution of the low order LVs should not be neglected in the PLS models. Therefore, in practical uses of PLS for analysis of complex samples, it may be better to use a slightly large LV number for NIR spectral analysis of complex samples. Supported by the National Natural Science Foundation of China (Grant Nos. 20775036 & 20835002)     

Micelle-mediated extraction and cloud-point preconcentration of osthole and imperatorin from Cnidium monnieri with analysis by high performance liquid chromatography

A new method based on micelle-mediated extraction and cloud-point preconcentration was developed for the separation and determination of hydrophobic compounds osthole and imperatorin from Cnidium monnieri by high performance liquid chromatography with photodiode array detection. The non-ionic surfactant C(13)E(8) (Genapol X-080) was chosen as the extract solvent. Various experimental conditions were investigated to evaluate and optimize the extraction and preconcentration process. The chromatographic separation was accomplished on a Zorbax SB-C(18) analytical column (150 mm x 4.6mm i.d., 5 microm particle diameter) maintained at 30 degrees C and detected by UV absorption at 320 nm. The gradient elution was achieved with a mobile phase composed of 0.1% phosphoric acid and acetonitrile at a flow rate of 1.0 mL min(-1). Under the optimum conditions, the calibration curve for both analytes was linear in the range of 0.52-33.5 microg mL(-1) with the correlation coefficients greater than 0.9996. The intra-day and inter-day precision (RSD) is below 5.3% and the limits of detection (LOD) for the analytes are 93 and 124 ng mL(-1)(S/N=3). The proposed technique is a low cost, simple and sensitive method with high clean-up effect. Finally, the method was successfully applied to separate and determine osthole and imperatorin from C. monnieri, respectively.     

Isolation and purification of osthole and imperatorin from Fructus Cnidii by semi-preparative supercritical fluid chromatography

Fructus Cnidii, the dried ripe fruit of Cnidium monnieri (L.) Cusson., has been widely used in traditional Chinese medicine. Osthole and imperatorin are the main active ingredients of Fructus Cnidii and had been found of antispasmodic, anti-HIV, anti-fungal, anti-viral, anti-tumor, anti-mutagenic, anti-arrhythmic, hypotensive, and broad-spectrum antimicrobial effects. A supercritical fluid chromatography (SFC) method for isolation and purification of osthole and imperatorin from Fructus Cnidii was established in this work. The separation conditions, including the stationary phase, the organic modifier, the composition and the flow rate of the mobile phase, column backpressure and column temperature, were optimized on analytical scale at first. And then a semi-preparative SFC (SP-SFC) method was developed based on the conditions of analytical scale SFC. SP-SFC was accomplished on YMC-Pack NH₂ column. Ethanol was used as the modifier and its percentage in the mobile phase was 3%. The flow rate of the mobile phase was 20?mL/min, column backpressure was 13?MPa, column temperature was 318?K, detection wavelength was 310?nm, and injection volume was 0.2?mL. Under the optimum conditions, osthole and imperatorin were obtained with high purities as determined by high performance liquid chromatography. The chemical structures of the obtained compounds were identified by nuclear magnetic resonance and mass spectrum analysis.     

Optimized sample-weighted partial least squares

In ordinary multivariate calibration methods, when the calibration set is determined to build the model describing the relationship between the dependent variables and the predictor variables, each sample in the calibration set makes the same contribution to the model, where the difference of representativeness between the samples is ignored. In this paper, by introducing the concept of weighted sampling into partial least squares (PLS), a new multivariate regression method, optimized sample-weighted PLS (OSWPLS) is proposed. OSWPLS differs from PLS in that it builds a new calibration set, where each sample in the original calibration set is weighted differently to account for its representativeness to improve the prediction ability of the algorithm. A recently suggested global optimization algorithm, particle swarm optimization (PSO) algorithm is used to search for the best sample weights to optimize the calibration of the original training set and the prediction of an independent validation set. The proposed method is applied to two real data sets and compared with the results of PLS, the most significant improvement is obtained for the meat data, where the root mean squared error of prediction (RMSEP) is reduced from 3.03 to 2.35. For the fuel data, OSWPLS can also perform slightly better or no worse than PLS for the prediction of the four analytes. The stability and efficiency of OSWPLS is also studied, the results demonstrate that the proposed method can obtain desirable results within moderate PSO cycles.     

Components Analysis of Biochar Based on Near Infrared Spectroscopy Technology

This study aims to establish a rapid quantitative analysis method for biochar based on near infrared spectroscopy (NIRS) technology. Near infrared spectra of 163 samples in the 10000–3800 cm^–1 (1000–2632 nm) range were collected, and the contents of fixed carbon (FC), volatile matter (VM) and ash of samples were also analyzed. A partial least square (PLS) model for FC, VM and Ash was established after the model spectral ranges were optimized, the optimal factors were determined, and the raw spectra were pretreated by multiple scatter correction and second derivative (MSC + SD) method. Finally, the prediction performance of predictive model was evaluated. The results showed that the PLS model had a good prediction ability, and the predicted coefficient R²_p of actual values vs prediction values for FC, VM and ash were 0.9423, 0.9517 and 0.9265, respectively. Root mean square error of prediction (RMSEP) was 0.1074, 0.1201 and 0.1243, and ratios of prediction to deviation (RPD) were 3.51, 4.28 and 2.03, respectively. The PLS model had good accuracy and precision for both of FC and VM, and could be used as a quantitative method for FC and VM contents analysis. Nevertheless, PLS model need to improve the precision for Ash analysis according to RPD value. This method provides a fast and effective technical means for the quantitative analysis of biochar components.