Detection of lethal fake liquors using digitally labelled gas-phase Fourier transform infrared spectroscopy

Fake liquors have caused severe body injuries or even deaths worldwide, rapid detection of such lethal drinks is thus quite necessary. Methanol has been identified as a primary cause of the problem, so methanol monitoring is critical to the detection of fake liquors. The present work provides an effective strategy for rapid detection of different lethal fake liquors. Using gas-phase Fourier transform infrared spectroscopy in a digitally labeled approach, the spectral bands of methanol were extracted by the iterative discrete wavelet transform for classification, which is named as digitally labeled gas-phase Fourier transform infrared spectroscopy. In digitally labeled gas-phase Fourier transform infrared spectroscopy, principal component analysis and least square support vector machine were combined to discriminate problematic samples using the iterative discrete wavelet transform filtered signals. As a result, the method could cleverly extract spectral features of methanol from the alcoholic drinks in the presence of uncontrolled matrix effects. The recognition accuracy was higher than 97.0%, and each measurement was done within 3?min. The results illustrate that the digitally labeled gas-phase Fourier transform infrared spectroscopy method serves well to rapidly discriminate fake liquors as an efficient and promising tool, and could be well extended to detection of any other targeted volatile substance in complicated systems.     

Differential Kinetic Spectrophotometric Determination of Methamidophos and Fenitrothion in Water and Food Samples by Use of Chemometrics

A spectrophotometric method for simultaneous analysis of methamidophos and fenitrothion was proposed by application of chemometrics to the spectral kinetic data, which was based upon the difference in the inhibitory effect of the two pesticides on acetylcholinesterase (AChE) and the use of 5,5′‐dithiobis(2‐nitrobenzoic acid) (DTNB) as a chromogenic reagent for the thiocholine iodide (TChI) released from the acetylthiocholine iodide (ATChI) substrate. The absorbance of the chromogenic product was measured at 412 nm. The different experimental conditions affecting the development and stability of the chromogenic product were carefully studied and optimized. Linear calibration graphs were obtained in the concentration range of 0.5–7.5 ng·mL^?1 and 5–75 ng·mL^?1 for methamidophos and fenitrothion, respectively. Synthetic mixtures of the two pesticides were analysed, and the data obtained processed by chemometrics, such as partial least square (PLS), principal component regression (PCR), back propagation‐artificial neural network (BP‐ANN), radial basis function‐artificial neural network (RBF‐ANN) and principal component‐radial basis function‐artificial neural network (PC‐RBF‐ANN). The results show that the RBF‐ANN gives the lowest prediction errors of the five chemometric methods. Following the validation of the proposed method, it was applied to the determination of the pesticides in several commercial fruit and vegetable samples; and the standard addition method yielded satisfactory recoveries.     

Innovative Combination of Dispersive Solid Phase Extraction Followed by NIR-Detection and Multivariate Data Analysis for Prediction of Total Polyphenolic Content

Recently polyphenols attracted great interest in the field of food and nutrition as well as in the pharmaceutical and cosmetics industries due to their health benefits through antioxidative behavior in the human body. However, because of the high number of compounds characterized as phenols and their structural diversity, quantification of polyphenols turns out to be a highly complex task. Although, a wide variety of analytical methods are used for the determination of total polyphenolic content, they are all found to be lacking in a variety of different tasks, such as their limits of detection and quantification, repeatability, accuracy and specificity. For this reason, a novel approach combining the advantages of solid phase purification, near infrared analysis and multivariate data analysis was investigated for the prediction of total polyphenolic content, suitable for a wide range of sample matrices. Dispersive solid phase extraction was performed and optimized using polyvinylpyrrolidone as sorbent, known to selectively bind polyphenols. Near-infrared detection of adsorbed polyphenols was carried out subsequently. Furthermore, the method was in-house validated, examining selectivity, repeatability and accuracy, working range, as well as multivariate limit of detection and limit of quantification, comparing it with two routinely used methods—namely, Folin–Ciocalteu photometric assay and Löwenthal titration. The novel established method was applied for the prediction of total polyphenolic content in tea and wine samples.     

Qualitative and quantitative analysis of multiple components for quality control of Deng‐Zhan‐Sheng‐Mai capsules by ultra high performance liquid chromatography tandem mass spectrometry method coupled with chemometrics

Deng‐Zhan‐Sheng‐Mai capsules are a well‐known traditional Chinese patent medicine that was developed in China for the treatment of ischemic stroke. Its quality control focuses on Erigerontis Herba but ignores the contributions of Ginseng Radix et Rhizoma, Schisandrae Chinensis Fructus, and Ophiopogonis Radix. To improve the quality standards for this medicine, this work reports the application of a systematic ultra high performance liquid chromatography tandem mass spectrometric method coupled with chemometrics. Three qualitative and quantitative parameters are established for the evaluation of quality: chemical profiling, the relationship between the contents of 18 compounds and the antioxidant activity, and chemometric analysis. A total of 55 compounds, including 20 phenolic acids, 10 flavonoids, 15 saponins, and 10 lignans, were identified. The method for the quantitative determination of the aforementioned 18 compounds was validated. The limit of quantification ranged from 0.13 to 9.60 ng/mL. The overall recoveries ranged from 95.31 to 103.54%. Hierarchical cluster analysis and principal component analysis were applied to the data of 18 components in ten batches of samples. Nine compounds, including scutellarin, 3,5‐O‐dicaffeoylquinic acid, 4,5‐O‐dicaffeoylquinic acid, ginsenoside Rb1, ginsenoside Re, ginsenoside Rg1, ophiopogonin D, schisandrin, and schisandrol B, are suggested as chemical markers for evaluating the quality.     

Geographical classification of Epimedium based on HPLC fingerprint analysis combined with multi‐ingredients quantitative analysis

A reliable and comprehensive method for identifying the origin and assessing the quality of Epimedium has been developed. The method is based on analysis of HPLC fingerprints, combined with similarity analysis, hierarchical cluster analysis (HCA), principal component analysis (PCA) and multi‐ingredient quantitative analysis. Nineteen batches of Epimedium , collected from different areas in the western regions of China, were used to establish the fingerprints and 18 peaks were selected for the analysis. Similarity analysis, HCA and PCA all classified the 19 areas into three groups. Simultaneous quantification of the five major bioactive ingredients in the Epimedium samples was also carried out to confirm the consistency of the quality tests. These methods were successfully used to identify the geographical origin of the Epimedium samples and to evaluate their quality.     

Total detection of Tianma Toutong tablets for quality consistency by a five‐wavelength fusion fingerprint and chemometrics

A fingerprint method was developed and combined with chemometrics for quality evaluation of Tianma Toutong tablets, which are herbal medicine tablets used to treat migraine. Samples were analyzed by high‐performance liquid chromatography, where five single‐wavelength profiles (203, 232, 254, 280 and 310 nm) were fused to generate a five‐wavelength fusion fingerprint and were also used for the quantitative analysis of seven chemical markers (gastrodin, caffeic acid, hesperidin, isoimperatorin, chlorogenic acid, ferulic acid and imperatorin). A systematic quantitative fingerprint method and principal component analysis were used to analyze the generated data. Samples could be well distinguished from different manufacturers by analyzing the chromatographic data sets. In addition, the partial least squares model can serve as an antioxidant activity evaluation of Tianma Toutong tablets, as well as a reference for the selection of active constituents to analyze the spectrum–activity relationship. In summary, the integrated use of the fingerprint and chemometric analysis provides a reliable method for the identification of markers and the quality control of Tianma Toutong tablets.     

Raman spectroscopy for identification and quantification analysis of essential oil varieties: a multivariate approach applied to lavender and lavandin essential oils

Lavender (Lavandula angustifolia) is used for cosmetics, perfumes and medicine (antimicrobial activity and relaxant properties) while lavandin (sterile hybrid of L. angustifolia P. Mill. × Lavandula latifolia (L.f.) Medikus) is used for air fresheners, deodorants and soaps. These plants are widely cultivated for essential oil production. In this study, 104 samples were analyzed including 62 lavandin and 42 lavender oil samples from several varieties. The Raman spectra are similar but can be differentiated by chemometrics treatment. Data structure may be studied by PCA. A PLS regression model was used for quantitative analysis of the main compounds such as linalyl acetate, linalool and eucalyptol. The reference data were obtained by gas chromatography. The performance of the method was also tested to discriminate between the two species and the seven varieties (Abrial, Fine, Grosso, Maillette, Matherone, Sumian and Super) by PLS‐DA regression. The examination of PLS and PLS‐DA regression coefficients allowed for the identification of species and of the varieties' metabolomic markers. Copyright © 2015 John Wiley & Sons, Ltd.     

Comparison of multivariate linear regression methods in micro‐Raman spectrometric quantitative characterization

Chemical imaging was used in this study as a powerful analytical tool to characterize pharmaceuticals in solid form. The majority of analyses are evaluated with bilinear modelling using only the pure component spectra or just the chemical images themselves to estimate the concentrations in each pixel, which are far from true quantitative determination. Our aim was to create more accurate concentration images using regression methods. For the first time in chemical imaging, variable selections with interval partial least squares (PLS) and with genetic algorithms (PLS‐GA) were applied to increase the efficiency of the models. These were compared to numerous bilinear modelling and multivariate linear regression methods such as univariate regression, classical least squares (CLS), multivariate curve resolution–alternating least squares (MCR‐ALS), principal component regression (PCR) and partial least squares (PLS). Two component spray‐dried pharmaceuticals were used as a model. The paper is shown that, in contrast to the usual way of using either external validation or cross‐validation, both should be performed simultaneously in order to get a clear picture of the prediction errors and to be able to select the appropriate models. Using PLS with variable selection, the root mean square errors were reduced to 3% per pixel by keeping only those peaks that are truly necessary for the estimation of concentrations. It is also shown that interval PLS can point out the best peak for univariate regression, and can thereby be of great help even when regulations allow only univariate models for product quality testing. Variable selection, besides yielding more accurate overall concentrations across a Raman map, also reduces the deviation among pixel concentrations within the images, thereby increasing the sensitivity of homogeneity studies. Copyright © 2015 John Wiley & Sons, Ltd.     

Screening and quantifying the quality markers of DuHuo by fingerprint modeling

Abstract

With environmental health and safety becoming a concern for people, supercritical fluid chromatography has been regarded as a “green chemistry” method that gradually has peaked people’s interest. The purpose of this study was to establish a method to screen and quantify the quality markers of Duhuo (DH) through an environmentally friendly ultra-performance convergence chromatography (UPC²) method. Firstly, the fingerprint of different batches of DH samples was established by UPC². Then, the quality markers that can represent the whole information of DH was screened through chemometrics. Finally, the content of the selected quality markers was determined using the UPC² method. From the fingerprint analysis, 24 common peaks were identified. The quantitative analysis results showed that the four compounds could be used as quality markers to represent the overall information and to monitor the quality of DH. This is the first time a traditional Chinese medicine (TCM) fingerprint has been established by UPC² and also the first time to screen the quality markers in DH. The method established in this study can be used for a comprehensive and overall analysis of DH or other natural medicines, and it could serve as an efficient method to contribute to the expansion of the global market for TCMs.     

In situ synthesis Bismarck brown R reductive products-immobilised AgNPs assisted by catalytic activity of AgNPs as colorimetric probe for Hg2+ detection in water

A novel, selective and sensitive colorimetric detection method for Hg²⁺ is developed using Bismarck brown R (BBR) reductive products-immobilised silver nanoparticles (BBR products-AgNPs) as a sensing probe. In this research, the BBR reductive products were synthesised in situ and immediately immobilised on the surface of AgNPs. A surface plasmon resonance band of BBR products-AgNPs was observed at 396 nm. The absorbance at 396 nm gradually decreased, and a remarkable blue shift from 396 to 368 nm and a colour change from yellow to colourless by increasing the Hg²⁺ concentration were observed. This observation is due to the oxidation of Ag° to Ag⁺ and the reduction of Hg²⁺ to Hg° during the process, and thus Ag–Hg nanoalloy is formed. In addition, an exploratory study of the designed system is done by chemometrics approaches with the aim of making a clear perspective from the reaction system. The effective parameters for detection of Hg²⁺ based on the BBR products-AgNPs as optical probe were optimised. Under optimal conditions, the sensor exhibited a high selectivity and sensitivity with limit of detection of 6.1 nM (1.66 µg L^?1) and the linear range from 0.01–19.0 µM (2.72–5160 µg L^?1). The proposed method was simple, rapid and cost-effective without any complicated operation. Finally, it was successfully used for detection of Hg²⁺ in water samples with recoveries ranges of 86.7–103.6% and relative standard deviation slower than 10.1%.