近红外光谱结合特征变量筛选方法用于农药乳油中毒死蜱含量的测定

为提高毒死蜱农药乳油中有效成分近红外光谱定量分析模型的精度和稳定性。采用联合区间偏最小二乘法(siPLS)结合遗传算法(GA)筛选特征变量,由交互验证法确定最佳主成分因子数及筛选的变量数。结果表明,从全光谱区优选出81个变量,主成分因子数为11时,能建立性能最优的模型,模型预测集的决定系数R_p~2为0.972,预测均方根误差(RMSEP)为0.353%。研究表明,利用siPLS结合GA方法优选特征变量,能大幅度地消除农药乳油光谱变量间的冗余信息和无关信息,降低模型的复杂度,提高农药有效成分预测模型的精度及稳定性。     

Applications of Vibrational Spectroscopy for Analysis of Connective Tissues

Advances in vibrational spectroscopy have propelled new insights into the molecular composition and structure of biological tissues. In this review, we discuss common modalities and techniques of vibrational spectroscopy, and present key examples to illustrate how they have been applied to enrich the assessment of connective tissues. In particular, we focus on applications of Fourier transform infrared (FTIR), near infrared (NIR) and Raman spectroscopy to assess cartilage and bone properties. We present strengths and limitations of each approach and discuss how the combination of spectrometers with microscopes (hyperspectral imaging) and fiber optic probes have greatly advanced their biomedical applications. We show how these modalities may be used to evaluate virtually any type of sample (ex vivo, in situ or in vivo) and how “spectral fingerprints” can be interpreted to quantify outcomes related to tissue composition and quality. We highlight the unparalleled advantage of vibrational spectroscopy as a label-free and often nondestructive approach to assess properties of the extracellular matrix (ECM) associated with normal, developing, aging, pathological and treated tissues. We believe this review will assist readers not only in better understanding applications of FTIR, NIR and Raman spectroscopy, but also in implementing these approaches for their own research projects.     

Compositional Analysis of Cement Raw Meal by Near-Infrared (NIR) Spectroscopy

Abstract

A rapid and accurate method is presented to determine CaCO₃, SiO₂, Fe₂O₃, and Al₂O₃ in cement raw meal using near-infrared (NIR) spectroscopy. Multiplicative scatter correction (MSC) was employed to eliminate the scattering signal and partial least squares (PLS) regression was used to build the analysis model. The results demonstrated good performance by this approach for the determination of CaCO₃, SiO₂, Fe₂O₃, and Al₂O₃. NIR spectroscopy exhibits the feasibility to characterize the quality of cement raw meal. Compared with prompt gamma neutron activation analysis (PGNAA) and X-ray fluorescence (XRF), this method is more efficient and safer.     

Quality Evaluation of Ephedrae herba by near Infrared Spectroscopy

ABSTRACT

A novel method was developed for the quality control of Ephedrae herba by near-infrared (NIR) spectroscopy. First, qualitative models established by discriminant analysis and support vector machine were used for the preliminary screening of unqualified samples of E. herba. Then quantitative models of ephedrine and the total alkali (ephedrine and pseudoephedrine) were established by partial least squares regression and particle swarm optimization based least square support vector machine. The contents of test samples were predicted by the established NIR quantitative models. As a result, the accuracies of unqualified identification were 98.9% by discriminant analysis and 100% by support vector machine. The performance of the particle swarm optimization based least square support vector machine models were better than the partial least squares regression models. The correlation coefficients were both more than 0.98 and relative standard errors of calibrations were less than 9% in the calibration sets of particle swarm optimization based least square support vector machine models. As for the test sets, the correlation coefficients were both more than 0.93 and the relative standard errors of prediction were less than 13%, indicating satisfactory predicted results. All of these results demonstrated that NIR spectroscopy may be a powerful tool for the quality control of E. herba.     

Quantitative Analysis of Chromium(VI) in Dilute Solution by Using Adsorption and Diffuse Reflectance Near‐infrared Spectroscopy

A method for quantitative determination of metal element in aqueous solution was developed by using adsorption and diffuse reflectance near‐infrared spectroscopy (DRNIRS). In this method, the analyte is firstly adsorbed onto the resin from the dilute solution, and then the adsorbed analyte is directly determined in the sorbent by using DRNIRS. Enrichment of the analyte is achieved by the adsorption from the dilute solution, and quantitative determination is accomplished by using multivariate calibration technique. Taking chromium(VI) in river water as the analytical target, adsorption conditions and the partial least squares (PLS) model was optimized. The results show that chromium(VI) can be immobilized onto the adsorbent and quantitatively measured by DRNIRS and multivariate calibration. With cross validation and external validation, the correlation coefficient between the reference and predicted concentration was found to be above 0.98 in the range of 0.75–29.90 mg·L⁻¹ for the PLS model, and the interference of the coexisting matrix was eliminated with the aid of multivariate calibration.     

Polydimethylsiloxane: A General Matrix for High‐Performance Chromatographic NMR Spectroscopy

The detection and structural characterization of the components of a mixture is a challenging task. Therefore, the development of a facile and general method that enables both the separation and the structural characterization of the components is desired. Diffusion‐ordered NMR spectroscopy (DOSY) with the aid of a matrix is a promising tool for this purpose. However, because the currently existing matrices only separate limited components, the application of the DOSY technique is restricted. Herein we introduce a new versatile matrix, poly(dimethylsiloxane), which can fully separate many mixtures of different structural types by liquid‐state NMR spectroscopy. With poly(dimethylsiloxane), liquid‐state chromatographic NMR spectroscopy could become a general approach for the structural elucidation of mixtures of compounds.     

Detection and Quantification of Alprazolam Added to Long Drinks by Near Infrared Spectroscopy and Chemometrics

In the present work, a fast, relatively cheap, and green analytical strategy to identify and quantify the fraudulent (or voluntary) addition of a drug (alprazolam, the API of Xanax^®) to an alcoholic drink of large consumption, namely gin and tonic, was developed using coupling near-infrared spectroscopy (NIR) and chemometrics. The approach used was both qualitative and quantitative as models were built that would allow for highlighting the presence of alprazolam with high accuracy, and to quantify its concentration with, in many cases, an acceptable error. Classification models built using partial least squares discriminant analysis (PLS-DA) allowed for identifying whether a drink was spiked or not with the drug, with a prediction accuracy in the validation phase often higher than 90%. On the other hand, calibration models established through the use of partial least squares (PLS) regression allowed for quantifying the drug added with errors of the order of 2–5 mg/L.     

Insect Protein Content Analysis in Handcrafted Fitness Bars by NIR Spectroscopy. Gaussian Process Regression and Data Fusion for Performance Enhancement of Miniaturized Cost-Effective Consumer-Grade Sensors

Future food supply will become increasingly dependent on edible material extracted from insects. The growing popularity of artisanal food products enhanced by insect proteins creates particular needs for establishing effective methods for quality control. This study focuses on developing rapid and efficient on-site quantitative analysis of protein content in handcrafted insect bars by miniaturized near-infrared (NIR) spectrometers. Benchtop (Büchi NIRFlex N-500) and three miniaturized (MicroNIR 1700 ES, Tellspec Enterprise Sensor and SCiO Sensor) in hyphenation to partial least squares regression (PLSR) and Gaussian process regression (GPR) calibration methods and data fusion concept were evaluated via test-set validation in performance of protein content analysis. These NIR spectrometers markedly differ by technical principles, operational characteristics and cost-effectiveness. In the non-destructive analysis of intact bars, the root mean square error of cross prediction (RMSEP) values were 0.611% (benchtop) and 0.545–0.659% (miniaturized) with PLSR, and 0.506% (benchtop) and 0.482–0.580% (miniaturized) with GPR calibration, while the analyzed total protein content was 19.3–23.0%. For milled samples, with PLSR the RMSEP values improved to 0.210% for benchtop spectrometer but remained in the inferior range of 0.525–0.571% for the miniaturized ones. GPR calibration improved the predictive performance of the miniaturized spectrometers, with RMSEP values of 0.230% (MicroNIR 1700 ES), 0.326% (Tellspec) and 0.338% (SCiO). Furthermore, Tellspec and SCiO sensors are consumer-oriented devices, and their combined use for enhanced performance remains a viable economical choice. With GPR calibration and test-set validation performed for fused (Tellspec + SCiO) data, the RMSEP values were improved to 0.517% (in the analysis of intact samples) and 0.295% (for milled samples).     

Untargeted Metabolomics in Forensic Toxicology: A New Approach for the Detection of Fentanyl Intake in Urine Samples

The misuse of fentanyl, and novel synthetic opioids (NSO) in general, has become a public health emergency, especially in the United States. The detection of NSO is often challenged by the limited diagnostic time frame allowed by urine sampling and the wide range of chemically modified analogues, continuously introduced to the recreational drug market. In this study, an untargeted metabolomics approach was developed to obtain a comprehensive “fingerprint” of any anomalous and specific metabolic pattern potentially related to fentanyl exposure. In recent years, in vitro models of drug metabolism have emerged as important tools to overcome the limited access to positive urine samples and uncertainties related to the substances actually taken, the possible combined drug intake, and the ingested dose. In this study, an in vivo experiment was designed by incubating HepG2 cell lines with either fentanyl or common drugs of abuse, creating a cohort of 96 samples. These samples, together with 81 urine samples including negative controls and positive samples obtained from recent users of either fentanyl or “traditional” drugs, were subjected to untargeted analysis using both UHPLC reverse phase and HILIC chromatography combined with QTOF mass spectrometry. Data independent acquisition was performed by SWATH in order to obtain a comprehensive profile of the urinary metabolome. After extensive processing, the resulting datasets were initially subjected to unsupervised exploration by principal component analysis (PCA), yielding clear separation of the fentanyl positive samples with respect to both controls and samples positive to other drugs. The urine datasets were then systematically investigated by supervised classification models based on soft independent modeling by class analogy (SIMCA) algorithms, with the end goal of identifying fentanyl users. A final single-class SIMCA model based on an RP dataset and five PCs yielded 96% sensitivity and 74% specificity. The distinguishable metabolic patterns produced by fentanyl in comparison to other opioids opens up new perspectives in the interpretation of the biological activity of fentanyl.     

Green Multi-Platform Solution for the Quantification of Levodopa Enantiomeric Excess in Solid-State Mixtures for Pharmacological Formulations

The aim of the present work was to develop a green multi-platform methodology for the quantification of l-DOPA in solid-state mixtures by means of MIR and NIR spectroscopy. In order to achieve this goal, 33 mixtures of racemic and pure l-DOPA were prepared and analyzed. Once spectra were collected, partial least squares (PLS) was exploited to individually model the two different data blocks. Additionally, three different multi-block approaches (mid-level data fusion, sequential and orthogonalized partial least squares, and sequential and orthogonalized covariance selection) were used in order to simultaneously handle data from the different platforms. The outcome of the chemometric analysis highlighted the quantification of the enantiomeric excess of l-DOPA in enantiomeric mixtures in the solid state, which was possible by coupling NIR and PLS, and, to a lesser extent, by using MIR. The multi-platform approach provided a higher accuracy than the individual block analysis, indicating that the association of MIR and NIR spectral data, especially by means of SO-PLS, represents a valid solution for the quantification of the l-DOPA excess in enantiomeric mixtures.     

基于遗传算法优化的约束背景双线性分解算法用于改进高效液相色谱灰色分析体系的校正结果

该文采用约束背景双线性分解算法(CBBL)对以高效液相色谱(HPLC)方法分离分析的灰色分析体系进行了多元校正研究。针对采用包括CBBL在内的矩阵校正方法处理HPLC灰色分析体系的固有缺陷,即在相关组分的色谱保留时间重现性较低的情形下多元校正的结果不理想,对CBBL方法进行了改进,即将待测组分的浓度与组分的色谱保留时间同时作为优化的参量引入CBBL,并采用遗传算法(GA)优化CBBL,对于模拟的组分保留时间飘移严重的HPLC灰色分析体系及保留时间重现性不佳的多种酚类化合物组成的实际HPLC灰色分析体系进行了多元校正分析,成功克服了经典CBBL的固有缺陷,取得了较理想的多元校正结果。另外,该研究所建议的方法的校正结果也显著优于传统的残差双线性分解法(RBL)以及秩消失因子分析法(RAFA)。     

A Gas Liquid Chromatographic Fluorescent Procedure for the Analysis of Benzo(A)Pyrene in 24 Hour Atmospheric Particulate Samples

Abstract

This paper describes a selective method for the assay of benzo(a)pyrene (BaP) in 24 hour particulate samples. Benzo(a)pyrene is partially separated from the other polynuclear aromatic hydrocarbons via gas chromatography and then is completely resolved optically from traditionally difficult to resolve compounds (benzo(e)pyrene, perylene, benzo(k)fluoranthene) by means of a gas phase fluorescent detector. Ambient air concentrations as low as 20 picograms per cubic meter of air can be assayed with this gas chromatographic - gas phase fluorescent detection system.     

Non-destructive Phenotyping of Chili Pepper Ripening Using Spectroscopic Probes: A Potential Approach for Shelf-Life Measurement

This study explores the application of spectroscopic techniques (laser induced fluorescence, Raman and attenuated total reflectance Fourier transform infrared) coupled with principal component analysis for the nondestructive, extraction free and rapid evaluation of biochemical changes associated with ripening of chili peppers at four stages (mature, pre-ripe, ripe and post-ripe). The analysis of the fluorescence spectra of the exocarp of chili pepper shows a decrease in the intensity of chlorophyll bands at 685 and 735?nm and an increase in the intensity of carotenoid fluorescence bands at 490–500?nm and 565–580?nm with progress in the ripening stages. These changes are regarded to be significant phenotypic markers for the ripening of chili peppers. The observed changes in the position of carotenoid bands in Raman spectra at 1004, 1156, 1188, and 1524?cm^?1 with increase in their intensity indicating the accumulation of carotenoids and change in the carotenoid composition from β-carotene in the mature chilis to capsanthin in the ripe chilis. In addition, the infrared spectra show changes in the carbohydrates, amide II, amide I and cutin at various stages of ripening. Also, the variation in the position of pectin bands indicates change in its molecular mass with decreasing content. The determined spectral signatures can be used as biomolecular index for effective monitoring of the ripening of chili peppers. The commercial application of noninvasive spectroscopic probes will be advantageous for the phenotyping of economically important plant parts, screening, grading, shelf life estimation and quality standardization.     

Analysis of questioned documents: A review

During the last years (2000–2014), many publications concerning the forensic analysis of questioned documents have been published, and new techniques and methodologies are nowadays employed to overcome forensic caseworks. This article reviews a comprehensive collection of the works focused on this issue, including dating studies, the analysis of inks from pens and printers, the analysis of paper, the analysis of other samples related to questioned documents and studies on intersecting lines. These sections highlight the most relevant analytical studies by a wide range of analytical techniques. Separation and spectrometric techniques are critically discussed and compared, emphasizing the advantages and disadvantages of each one. Finally, concluding remarks on the research published are included.     

Application of carbon nanotubes in extraction and chromatographic analysis: A review

Carbon nanotube (CNT), a well-known carbon-based nanomaterial has drawn much attention in many application fields including chemistry in the last few decades. Many researchers and scientists have shown huge interest to improve the extraction methodologies and adopt their applications in combination with chromatography technique. With respect to this, the exceptional applications of CNTs have been introduced as extraction sorbent due to their excellent inborn physical and chemical properties. In particular, CNTs have consistently been used as adsorbents in various techniques including solid-phase micro-extraction, solid-phase extraction, micro dispersive slid phase extraction, magnetic dispersive solid phase extraction, analytes enrichment, sample fractionation and clean-up as well as support for many derivatization reactions. Many research papers have discussed the successful use of CNTs to overcome the limitations of the extraction techniques due to their excellent sorbent capacity. In addition, considering the clear need to make chromatographic technique more successful, the applications of CNTs have been reported in the literatures in details as stationary and pseudo-stationary phases for the separation and extraction of challenging compounds. Because of the higher thermal and chemical stability, CNTs have been anticipated as stationary phase modifier for chromatographic applications to avoid bleeding of the columns and enable the analysis even at very high temperature (1200 °C). In liquid chromatography CNTs have primarily been used in combination with other packing materials (silica) and sometimes incorporated in a porous polymeric monolith. Therefore, the recent utilizations of CNTs as extraction materials and stationary phases have been illustrated in the current review and a table listing the details applications of CNTs in aforementioned field is provided as well. We believe that the review will help researcher to gain vast knowledge about application of carbon nanotubes in the field of separation chemistry.     

Liquid chromatography coupled to on-line post column derivatization for the determination of organic compounds: A review on instrumentation and chemistries

Analytical derivatization either in pre or post column modes is one of the most widely used sample pretreatment techniques coupled to liquid chromatography. In the present review article we selected to discuss the post column derivatization mode for the analysis of organic compounds. The first part of the review focuses to the instrumentation of post-column setups including not only fundamental components such as pumps and reactors but also less common parts such as static mixers and back-pressure regulators; the second part of the article discusses the most popular “chemistries” that are involved in post column applications, including reagent-less approaches and new sensing platforms such as the popular gold nanoparticles. Some representative recent applications are also presented as tables.     

Characterization of carbon nanotubes and analytical methods for their determination in environmental and biological samples: A review

In the present paper, a critical overview of the most commonly used techniques for the characterization and the determination of carbon nanotubes (CNTs) is given on the basis of 170 references (2000–2014). The analytical techniques used for CNT characterization (including microscopic and diffraction, spectroscopic, thermal and separation techniques) are classified, described, and illustrated with applied examples. Furthermore, the performance of sampling procedures as well as the available methods for the determination of CNTs in real biological and environmental samples are reviewed and discussed according to their analytical characteristics. In addition, future trends and perspectives in this field of work are critically presented.     

Analytical techniques for wine analysis: An African perspective; a review

Analytical chemistry is playing an ever-increasingly important role in the global wine industry. Chemical analysis of wine is essential in ensuring product safety and conformity to regulatory laws governing the international market, as well as understanding the fundamental aspects of grape and wine production to improve manufacturing processes. Within this field, advanced instrumental analysis methods have been exploited more extensively in recent years. Important advances in instrumental analytical techniques have also found application in the wine industry. This review aims to highlight the most important developments in the field of instrumental wine and grape analysis in the African context. The focus of this overview is specifically on the application of advanced instrumental techniques, including spectroscopic and chromatographic methods. Recent developments in wine and grape analysis and their application in the African context are highlighted, and future trends are discussed in terms of their potential contribution to the industry.