Genetic algorithm‐based wavelength selection method for spectral calibration

In this paper, we propose a genetic algorithm‐based wavelength selection (GAWLS) method for visible and near‐infrared (Vis/NIR) spectral calibration. The objective of GAWLS is to construct robust and predictive regression models by selecting informative wavelength regions. To demonstrate the ability of the proposed method, regression models for soil properties and sugar content of apples are constructed by using GAWLS and other variable selection methods. Copyright © 2010 John Wiley & Sons, Ltd.     

Partitioned partial least squares regression with application to a batch fermentation process

The performance of Partial Least Squares regression (PLS) in predicting the output with multivariate cross‐ and autocorrelated data is studied. With many correlated predictors of varying importance PLS does not always predict well and we propose a modified algorithm, Partitioned Partial Least Squares (PPLS). In PPLS the predictors are partitioned into smaller subgroups and the important subgroups with high prediction power are identified. Finally, regular PLS analysis using only those subgroups is performed. The proposed Partitioned PLS (PPLS) algorithm is used in the analysis of data from a real pharmaceutical batch fermentation process for which the process variables follow certain profiles during a specific fermentation period. We observed that PPLS leads to a more accurate prediction of the yield of the fermentation process and an easier interpretation, since fewer predictors are used in the final PLS prediction. In the application important issues such as alignment of the profiles from one batch to another and standardization of the predictors are also addressed. For instance, in PPLS noise magnification due to standardization does not seem to create problems as it might in regular PLS. Finally, PPLS is compared to several recently proposed functional PLS and PCR methods and a genetic algorithm for variable selection. More specifically for a couple of publicly available data sets with near infrared spectra it is shown that overall PPLS has lower cross‐validated error than PLS, PCR and the functional modifications hereof, and is similar in performance to a more complex genetic algorithm. Copyright © 2011 John Wiley & Sons, Ltd.     

复方氯丙那林质量检控的近红外光谱快速分析

基于近红外光纤漫反射技术,利用偏最小二乘法分别建立了复方氯丙那林胶囊的三种药效成分盐酸氯丙那林、盐酸溴己新和盐酸去氯羟嗪的快速同时测定方法。所建立的盐酸氯丙那林、盐酸溴己新和盐酸去氯羟嗪的定量分析多元校正模型的相关系数分别为99.7%、99.4%和99.0%,校正集的均方根残差分别为0.028、0.145和0.250,预测均方根误差分别为0.055、0.120和0.210。由于该方法是在不经任何预处理的情况下的光纤快速同时分析,因此可用于复方氯丙那林的过程质量控制。     

近红外光谱法测定黄芩提取物中黄芩苷含量

近红外光谱技术(NIR)是近年来快速发展的一种新型光谱分析技术,具有快速、高效、无污染、非破坏性以及实时分析等优点~([1]),已在农业、烟草、石油化工、医药等多领域得到广泛应用.尤其在药物分析方面,体现出近红外光谱分析的巨大潜力~([2]).     

检测维生素C的囊泡荧光传感器的制备

利用合成的含有识别基团苯硼酸和荧光读出基团萘的新型双亲化合物(DNMPBA)在THF/水选择性溶剂中自组织成囊泡,囊泡的相变温度为56.8℃;当向囊泡体系加维生素C时,DNMPBA囊泡中的萘生色基在345nm的荧光峰强度急剧减弱.荧光强度减弱归于所形成的硼酸酯增强了DNMPBA双亲化合物中一个氧原子孤对电子对萘生色基的淬灭作用.DNMPBA囊泡与维生素C的相互作用而导致体系荧光强度变化,使该体系有可能应用于检测生物物质如维生素C的化学传感器.     

新型含苯硼酸和喹啉的囊泡荧光多糖传感器的制备

利用合成的含有识别基团苯硼酸和荧光读出基团喹啉的新型双亲化合物对硼酸苯甲基-8-十六烷氧基溴化喹啉(BHQB)在水中自组织成囊泡,囊泡的相变温度为52.4℃;当向囊泡体系加糖时,BHQB囊泡中的喹啉生色基在508nm的荧光峰强度急剧减弱,425nm处荧光逐渐增强.荧光强度变化可能归于所形成的硼酸酯改变了双亲化合物中硼原子的杂化轨道形式,进一步引起了整个分子内部的电子云排布所致.BHQB囊泡与糖的相互作用而导致体系荧光强度变化,并且这种变化的幅度与加入糖的种类和量均有关.因此体系有可能应用于检测生物物质如糖的化学传感器.     

肾上腺素电化学氧化生成肾上腺素红反应机理的薄层光谱电化学研究

应用长光程薄层电解池(石墨电极和铂片电极),使用循环伏安法(CV)和单电位阶跃计时吸收法(SPS/CA),研究了肾上腺在pH=2.5～7.0的McIlvaine缓冲溶液中电氧化生成肾上腺素红的反应机理,测定了表观速率常数kobs.结果表明:溶液pH值对肾上腺素的电氧化反应有很大影响,肾上腺素生成肾上腺素红的kobs随溶液pH值的增加而增大.     

硫化锌中硫酸根含量标准物质的研制

介绍了红外光学材料用ZnS粉体标准物质的研制工作。以ZnS粉体为基体材料,通过提纯方法进行ZnS粉体中硫酸根含量标准物质的制备。经过均匀性检验、稳定性考察后,采用离子色谱法进行定值。制备的标准物质硫酸根含量在0.1%~0.5%范围呈3个梯度分布,相对扩展不确定度U不大于2%(k=2)。     

Discrimination of Ephedra plants with diffuse reflectance FT-NIRS and multivariate analysis

The secondary metabolites of different Ephedra plants are various. Therefore, the discrimination of different Ephedra plants is significant. An objective, easy-to-use, rapid and pollution-free approach is proposed for discriminating Ephedra plants of different species, habitats and picking times on the basis of diffuse reflectance Fourier transform near infrared spectroscopy (FT-NIRS) measurements and multivariate analysis. The Fourier transform near infrared diffuse reflectance spectra (NIRDRS) were acquired from 37 pulverized samples of Ephedra plants put in glass vials in the near infrared (NIR) region between 10 000 and 4000 cm⁻¹, averaging 64 scans per spectrum at a resolution of 4 cm⁻¹. After spectra processing and data pre-processing, spectral data were analyzed respectively with three multivariate analysis techniques: discriminant analysis (DA), self-organizing map (SOM) and back-propagation artificial neural network (BP-ANN). The proposed method could distinguish not only the Ephedra plants of three species and two habitats but also the plants picked at different times of day without special sample treatment and the use of chemical reagents. The performance indexes of the DA model were 84.2-91.9% and the prediction accuracies of both the SOM and the BP-ANN models reached 93.3-100.0%.     

Chemometrical strategies for feature selection and data compression applied to NIR and MIR spectra of extra virgin olive oils for cultivar identification

The possibility provided by Chemometrics to extract and combine (fusion) information contained in NIR and MIR spectra in order to discriminate monovarietal extra virgin olive oils according to olive cultivar (Casaliva, Leccino, Frantoio) has been investigated.Linear discriminant analysis (LDA) was applied as a classification technique on these multivariate and non-specific spectral data both separately and jointly (NIR and MIR data together).In order to ensure a more appropriate ratio between the number of objects (samples) and number of variables (absorbance at different wavenumbers), LDA was preceded either by feature selection or variable compression. For feature selection, the SELECT algorithm was used while a wavelet transform was applied for data compression.Correct classification rates obtained by cross-validation varied between 60% and 90% depending on the followed procedure. Most accurate results were obtained using the fused NIR and MIR data, with either feature selection or data compression.Chemometrical strategies applied to fused NIR and MIR spectra represent an effective method for classification of extra virgin olive oils on the basis of the olive cultivar.