Guide to Semi-Quantitative Non-Targeted Screening Using LC/ESI/HRMS

Non-targeted screening (NTS) with reversed phase liquid chromatography electrospray ionization high resolution mass spectrometry (LC/ESI/HRMS) is increasingly employed as an alternative to targeted analysis; however, it is not possible to quantify all compounds found in a sample with analytical standards. As an alternative, semi-quantification strategies are, or at least should be, used to estimate the concentrations of the unknown compounds before final decision making. All steps in the analytical chain, from sample preparation to ionization conditions and data processing can influence the signals obtained, and thus the estimated concentrations. Therefore, each step needs to be considered carefully. Generally, less is more when it comes to choosing sample preparation as well as chromatographic and ionization conditions in NTS. By combining the positive and negative ionization mode, the performance of NTS can be improved, since different compounds ionize better in one or the other mode. Furthermore, NTS gives opportunities for retrospective analysis. In this tutorial, strategies for semi-quantification are described, sources potentially decreasing the signals are identified and possibilities to improve NTS are discussed. Additionally, examples of retrospective analysis are presented. Finally, we present a checklist for carrying out semi-quantitative NTS.     

Non-Targeted Screening Approaches for Profiling of Volatile Organic Compounds Based on Gas Chromatography-Ion Mobility Spectroscopy (GC-IMS) and Machine Learning

Due to its high sensitivity and resolving power, gas chromatography-ion mobility spectrometry (GC-IMS) is a powerful technique for the separation and sensitive detection of volatile organic compounds. It is a robust and easy-to-handle technique, which has recently gained attention for non-targeted screening (NTS) approaches. In this article, the general working principles of GC-IMS are presented. Next, the workflow for NTS using GC-IMS is described, including data acquisition, data processing and model building, model interpretation and complementary data analysis. A detailed overview of recent studies for NTS using GC-IMS is included, including several examples which have demonstrated GC-IMS to be an effective technique for various classification and quantification tasks. Lastly, a comparison of targeted and non-targeted strategies using GC-IMS are provided, highlighting the potential of GC-IMS in combination with NTS.     

The Analysis of Phenolic Compounds in Walnut Husk and Pellicle by UPLC-Q-Orbitrap HRMS and HPLC

Husk and pellicle as the agri-food waste in the walnut-product industry are in soaring demand because of their rich polyphenol content. This study investigated the differential compounds related to walnut polyphenol between husk and pellicle during fruit development stage. By using ultra-high performance liquid chromatography-quadrupole-orbitrap (UHPLC-Q-Orbitrap), a total of 110 bioactive components, including hydrolysable tannins, flavonoids, phenolic acids and quinones, were tentatively identified, 33 of which were different between husk and pellicle. The trend of dynamic content of 16 polyphenols was clarified during walnut development stage by high-performance liquid chromatography (HPLC). This is the first time to comprehensive identification of phenolic compounds in walnut husk and pellicle, and our results indicated that the pellicle is a rich resource of polyphenols. The dynamic trend of some polyphenols was consistent with total phenols. The comprehensive characterization of walnut polyphenol and quantification of main phenolic compounds will be beneficial for understanding the potential application value of walnut and for exploiting its metabolism pathway.     

一种可绝对定量核酸的数字PCR微流控芯片

构建了一种新型的可进行核酸单分子扩增和核酸绝对定量的数字聚合酶链式反应(数字PCR)微流控芯片. 应用多层软光刻技术, 以聚二甲基硅氧烷(PDMS)作为芯片材料, 盖玻片作为基底制作了具有3层结构以及微阀控制功能的微流控芯片. 芯片的大小与载玻片相当, 可同时检测4个样品, 每个样品通入芯片后平均分配到640个反应小室, 每个小室的体积为6 nL. 以从肺癌细胞A549中提取的18sRNA为样品检测了该芯片的可行性. 将样品稀释数倍后通入芯片, 核酸分子随机分布在640个小室中并扩增. 核酸分子在芯片中的分布符合泊松分布原理, 当样品中待测核酸分子平均拷贝数低于0.5个/小室时, 则每个反应小室包含0个或1个分子. 经过PCR扩增后, 有模板分子的小室检测结果为阳性反应, 而无模板分子的小室为阴性反应, 最后通过计数阳性反应室的个数, 可绝对定量原始待测样品中的目标DNA分子拷贝数. 实验结果表明, 该数字 PCR芯片可实现DNA单分子反应和核酸绝对定量, 具有成本低、 灵敏度高、 节省时间和试剂以及操作简单等优点, 为数字PCR方法在普通实验室的应用提供了一种新途径, 可用于癌症及感染性疾病的早期诊断、 单细胞分析、 产前诊断以及各种细菌病毒的核酸检验等研究.     

Use of Passive and Grab Sampling and High-Resolution Mass Spectrometry for Non-Targeted Analysis of Emerging Contaminants and Their Semi-Quantification in Water

Different groups of organic micropollutants including pharmaceuticals and pesticides have emerged in the environment in the last years, resulting in a rise in environmental and human health risks. In order to face up and evaluate these risks, there is an increasing need to assess their occurrence in the environment. Therefore, many studies in the past couple of decades were focused on the improvements in organic micropollutants’ extraction efficiency from the different environmental matrices, as well as their mass spectrometry detection parameters and acquisition modes. This paper presents different sampling methodologies and high-resolution mass spectrometry-based non-target screening workflows for the identification of pharmaceuticals, pesticides, and their transformation products in different kinds of water (domestic wastewater and river water). Identification confidence was increased including retention time prediction in the workflow. The applied methodology, using a passive sampling technique, allowed for the identification of 85 and 47 contaminants in the wastewater effluent and river water, respectively. Finally, contaminants’ prioritization was performed through semi-quantification in grab samples as a fundamental step for monitoring schemes.     

Assessment of Micro- and Nanoplastic Composition (Polymers and Additives) in the Gastrointestinal Tracts of Ebro River Fishes

One of the main routes of fish exposure to micro- and nanoplastics (MNPLs) is their ingestion. MNPLs can act as reservoirs of organic contaminants that are adsorbed onto their surfaces, or that can leach from their complex formulations, with potential impacts on biota and along the aquatic food chain. While MNPLs have been reported in fishes worldwide, complete information on MNPL compositions, polymers and additives continues to be scarce. In this work, the presence of MNPLs in the gastrointestinal tracts (GIT) of fish from the Ebro River (Spain) was investigated using a double suspected screening approach to assess and quantify polymers and additives. The sample-preparation procedure consisted of sequential alkaline and acidic digestions with KOH and HNO₃, followed by ultrasonic-assisted extraction (USAE) with toluene. The analysis of polymers was carried out with size-exclusion chromatography followed by high-resolution mass spectrometry using an atmospheric pressure photoionization source, operating in negative and positive ionisation modes (SEC-(±)-APPI-HRMS) using full-scan acquisition (FS). Plastic additives were assessed using high-performance liquid chromatography with a C18 analytical column coupled to HRMS equipped with an electrospray ionisation source operating under positive and negative conditions (LC-(±ESI)-HRMS). The acquisition was performed in parallel with full-scan (FS) and data-dependent scan (ddMS²) modes, working under positive and negative ionisation modes. The polymers most frequently detected and quantified in fish GITs were polysiloxanes, polyethylene (PE), polypropylene (PP) and polystyrene (PS). PE was detected in 84% of the samples, with a concentration range from 0.55 to 3545 µg/g. On the other hand, plasticisers such as phthalates and stabilisers such as benzotriazoles were the most frequently identified plastic additives.     

Review on Compounds Isolated from Eriocaulaceae Family and Evaluation of Biological Activities by Machine Learning

Eriocaulaceae is a pantropical family whose main center of biodiversity is in Brazil. In general, the family has about 1200 species, in which phytochemical and biological studies have shown a variety of structures and activities. The aim of this research is to compile the compounds isolated in the Eriocaulaceae family and carry out a computational study on their biological targets. The bibliographic research was carried out on six databases. Tables were built and organized according to the chemical class. In addition, a summary of the methods of isolating the compounds was also made. In the computational study were used ChEMBL platform, DRAGON 7.0, and the KNIME 4.4.0 software. Two hundred and twenty-two different compounds have been isolated in sixty-eight species, divided mainly into flavonoids and naphthopyranones, and minor compounds. The ligand-based virtual screening found promising molecules and molecules with multitarget potential, such as xanthones 194, 196, 200 and saponin 202, with xanthone 194 as the most promising. Several compounds with biological activities were isolated in the family, but the chemical profiles of many species are still unknown. The selected structures are a starting point for further studies to develop new antiparasitic and antiviral compounds based on natural products.     

Investigating Bacterial Volatilome for the Classification and Identification of Mycobacterial Species by HS-SPME-GC-MS and Machine Learning

Species of Mycobacteriaceae cause disease in animals and humans, including tuberculosis and leprosy. Individuals infected with organisms in the Mycobacterium tuberculosis complex (MTBC) or non-tuberculous mycobacteria (NTM) may present identical symptoms, however the treatment for each can be different. Although the NTM infection is considered less vital due to the chronicity of the disease and the infrequency of occurrence in healthy populations, diagnosis and differentiation among Mycobacterium species currently require culture isolation, which can take several weeks. The use of volatile organic compounds (VOCs) is a promising approach for species identification and in recent years has shown promise for use in the rapid analysis of both in vitro cultures as well as ex vivo diagnosis using breath or sputum. The aim of this contribution is to analyze VOCs in the culture headspace of seven different species of mycobacteria and to define the volatilome profiles that are discriminant for each species. For the pre-concentration of VOCs, solid-phase micro-extraction (SPME) was employed and samples were subsequently analyzed using gas chromatography–quadrupole mass spectrometry (GC-qMS). A machine learning approach was applied for the selection of the 13 discriminatory features, which might represent clinically translatable bacterial biomarkers.     

高效液相色谱-非特异性同位素稀释质谱联用技术定量分析全血中总血红蛋白含量

建立了基于同位素稀释(ID)的高效液相色谱(HPLC)-电感耦合等离子体质谱(ICP-MS)联用技术,通过测量血红蛋白(HGB)中铁(Fe)元素含量实现了全血中总HGB准确定量的策略.该策略包括2种方法,第一种方法为在线HPLC-ID-ICP-MS定量技术:全血中HGB经液相色谱分离后,通过柱后在线添加浓缩54Fe同位素稀释剂的方式,与液相色谱洗脱液经三通混合后进入ICP-MS在线测量54Fe/56Fe的同位素比值,并根据同位素稀释质谱法公式及蛋白中Fe的含量计算HGB的浓度.针对全血样品中存在的其它主要含铁蛋白[如转铁蛋白(Tf)],在实现色谱完全分离的基础上,通过在线优化改变54Fe稀释剂流速实现了HGB和Tf的同时定量.在另一种方法中,先对全血样品进行消解并通过ID-ICP-MS测定其中Fe的总量,然后根据全血样品的HPLC-ICP-MS分析结果,通过计算HGB质谱峰面积占总峰面积的比值从而得到HGB中Fe含量占总Fe含量的比例,实现了其中HGB的准确定量. 2种定量方法得到的结果分别为(115.3±2.4) mg/g和(115.5±2.1) mg/g,结果吻合良好,方法的检出限...     

Statistics of the Popularity of Chemical Compounds in Relation to the Non-Target Analysis

The idea of popularity/abundance of chemical compounds is widely used in non-target chemical analysis involving environmental studies. To have a clear quantitative basis for this idea, frequency distributions of chemical compounds over indicators of their popularity/abundance are obtained and discussed. Popularity indicators are the number of information sources, the number of chemical vendors, counts of data records, and other variables assessed from two large databases, namely ChemSpider and PubChem. Distributions are approximated by power functions, special cases of Zipf distributions, which are characteristic of the results of human/social activity. Relatively small group of the most popular compounds has been denoted, conventionally accounting for a few percent (several million) of compounds. These compounds are most often explored in scientific research and are practically used. Accordingly, popular compounds have been taken into account as first analyte candidates for identification in non-target analysis.     

Predicting Regioselectivity in Radical C−H Functionalization of Heterocycles through Machine Learning

Radical C?H bond functionalization provides a versatile approach for elaborating heterocyclic compounds. The synthetic design of this transformation relies heavily on the knowledge of regioselectivity, while a quantified and efficient regioselectivity prediction approach is still elusive. Herein, we report the feasibility of using a machine learning model to predict the transition state barrier from the computed properties of isolated reactants. This enables rapid and reliable regioselectivity prediction for radical C?H bond functionalization of heterocycles. The Random Forest model with physical organic features achieved 94.2 % site accuracy and 89.9 % selectivity accuracy in the out‐of‐sample test set. The prediction performance was further validated by comparing the machine learning results with additional substituents, heteroarene scaffolds and experimental observations. This work revealed that the combination of mechanism‐based computational statistics and machine learning model can serve as a useful strategy for selectivity prediction of organic transformations.     

Application of Anodic Stripping Differential Alternative Pulses Voltammetry for Simultaneous Species Quantification

The second order voltammetric technique Differential Alternative Pulses Voltammetry (DAPV) was applied in anodic stripping mode for simultaneous quantification of traces of species having close E_1/2. The potential‐time waveform and the signal processing allowing the DAPV application in stripping mode are presented. The pulses widths and amplitudes were optimized to obtain maximal sensitivity and resolution at traces of In³⁺ and Cd²⁺ (having E_1/2 difference of 45 mV) simultaneous quantification in presence of excess of Pb²⁺. Precise results for both species concentrations were obtained up to In³⁺ to Cd²⁺ concentration ratio as high as 1 to 10 without any sample pre‐treatment in purified industrial waste waters using 0.1 mol L⁻¹ HCl as supporting electrolyte.     

Quantification and methodology issues in multivariate analysis of ToF‐SIMS data for mixed organic systems

Multivariate methods, such as principal component analysis (PCA) and multivariate curve resolution (MCR), are often employed to aid the analysis of large complex data sets such as time‐of‐flight secondary ion mass spectrometry (ToF‐SIMS) images. There is, however, much confusion over the most appropriate choice of method for any given application and the effects of data preprocessing, which is exacerbated by the confusing terminologies and the use of jargon in this field. In the present study, a simple model system consisting of a ToF‐SIMS image of an immiscible polymer blend is used to evaluate PCA and MCR in the accurate identification, localisation and quantification of the phase‐separated polymer domains, using four data preprocessing methods (no scaling, normalisation, variance scaling and Poisson scaling). This highlights significant issues and challenges in the quantitative multivariate analysis of mixed organic systems, including the discrimination of chemically significant features from experimental noise, the resolution of weak chemical contributions and potential bias introduced by data preprocessing. Multivariate analysis using Poisson scaling, identified as the most suitable data preprocessing method for both PCA and MCR, demonstrates a marked improvement upon traditional (manual) analysis and provides valuable additional information that is difficult to detect using traditional analysis. Using these results, we present recommendations for the optimum use of multivariate analysis by analysts and provide guidance on selecting the most appropriate methods. Confusing terminology is also clarified. © Crown copyright 2008. Reproduced with the permission of Her Majesty's Stationery Office. Published by John Wiley & Sons, Ltd.     

Development of a fast LC–MS/MS method for quantification of rilmenidine in human serum: elucidation of fragmentation pathways by HRMS

Rilmenidine is an alpha 2 adrenoreceptor agonist used in the treatment of mild and moderate hypertension. In this study, a fast and accurate liquid chromatographic method with tandem mass spectrometric detection has been validated in order to assure quantification of rilmenidine in human serum. The fragmentation pathway of protonated rilmenidine was studied using high‐resolution mass spectrometry (HRMS). This study compared selectivity, linearity, accuracy, precision, extraction efficiency, matrix effect and sensitivity using common liquid–liquid extraction (LLE) and solid‐phase extraction (SPE) procedures. The limit of quantitation for both extraction techniques was 0.1 ng/ml. Several differences between the LLE and SPE have been observed in terms of linearity, accuracy, precision and matrix effect. Additionally, the advantages of SPE included less manual work load and increased recovery of rilmenidine in human serum to approximately 80% (LLE, 57%). The developed method involving SPE was found to be accurate (relative error (RE) < 5%), reproducible (relative standard deviation, RSD < 7%), robust and suitable for quantitative analysis of rilmenidine in serum samples obtained from patients under antihypertensive treatment. Copyright © 2010 John Wiley & Sons, Ltd.     

Screening and Quantification of Organic Pollutants in Soil Using Comprehensive Two-dimensional Gas Chromatography with Time-of-flight Mass Spectrometry

The identification and quantification of organic compounds in leaching basin soil is important for the evaluation of soil pollution. In this study, a non-target screening strategy and a quantitative analytical method were developed based on the accelerated solvent extraction method followed by comprehensive two-dimensional gas chromatography with time-of-flight mass spectrometry. First, a screening method for potential risk compounds in soil samples was established, and the major compounds were screened under the conditions such as matching similarity, signal-to-noise ratio, and relative area ratio. Second, a quantitative method was further developed by internal calibration curves for 50 main organic pollutants in the soil samples, including 27 polycyclic aromatic hydrocarbons and their derivatives (PAHs), 10 phthalic acid esters, eight phenolic compounds, and five benzene derivatives. The quantitative procedure exhibited good selectivity, accuracy, precision, low limits of detection (0.03–1.02?ng/g), and quantification (0.1–3.0?ng/g) for all target compounds. Finally, the proposed strategy was applied to the soil samples that were collected from a leaching basin and polluted by electroplating wastewater. Abundant PAHs and phenolic compounds were detected in the topsoil sample, which were mainly released from the electroplating wastewater. The application of this multi-dimensional strategy in leaching basin soil samples can also be used for the assessment of organic pollution in other complex soil samples.     

Absolute quantitative analysis for sorbic acid in processed foods using proton nuclear magnetic resonance spectroscopy

An analytical method using solvent extraction and quantitative proton nuclear magnetic resonance (qHNMR) spectroscopy was applied and validated for the absolute quantification of sorbic acid (SA) in processed foods. The proposed method showed good linearity. The recoveries for samples spiked at the maximum usage level specified for food in Japan and at 0.13 g kg⁻¹ (beverage: 0.013 g kg⁻¹) were larger than 80%, whereas those for samples spiked at 0.063 g kg⁻¹ (beverage: 0.0063 g kg⁻¹) were between 56.9 and 83.5%. The limit of quantification was 0.063 g kg⁻¹ for foods (and 0.0063 g kg⁻¹ for beverages containing Lactobacillus species). Analysis of the SA content of commercial processed foods revealed quantities equal to or greater than those measured using conventional steam-distillation extraction and high-performance liquid chromatography quantification. The proposed method was rapid, simple, accurate, and precise, and provided International System of Units traceability without the need for authentic analyte standards. It could therefore be used as an alternative to the quantification of SA in processed foods using conventional method.