Analytical science advances最新文献

{"title":"Precision evaluation of nitrogen isotope ratios by Raman spectrometry","authors":"Junji Yamamoto,&nbsp;Yuuki Hagiwara","doi":"10.1002/ansa.202200020","DOIUrl":"10.1002/ansa.202200020","url":null,"abstract":"<p>We measured Raman spectra of N₂ fluids obtained at 0.1–25 MPa at room temperature. The ¹⁴N¹⁵N peak in the Raman spectrum of a low-pressure N₂ fluid is difficult to detect because of the prevalence of a group of peaks attributed to rotational vibration of ¹⁴N₂. The Raman peaks of ¹⁴N¹⁵N and ¹⁴N₂ of N₂ fluid at 25 MPa were acquired at various exposure times. The mean values and standard deviations of the peak height ratios and peak area ones of ¹⁴N¹⁵N and ¹⁴N₂ were examined for each time. The standard deviations of the peak height ratios and peak area ones were 2.2% and 1.9%, respectively, for 20 spectra acquired with peak height of 1 million counts of ¹⁴N₂. The uncertainties are about two times higher than those predicted from the noise of a CCD. Improvement of the pixel resolution can enhance the precision of the nitrogen isotope ratios by Raman spectroscopy.</p>","PeriodicalId":93411,"journal":{"name":"Analytical science advances","volume":"3 9-10","pages":"269-277"},"PeriodicalIF":0.0,"publicationDate":"2022-10-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://chemistry-europe.onlinelibrary.wiley.com/doi/epdf/10.1002/ansa.202200020","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47615411","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Toward a greener approach to detect inorganic arsenic using the Gutzeit method and X-ray fluorescence spectroscopy","authors":"Helen Lin,&nbsp;Haochen Dai,&nbsp;Lili He","doi":"10.1002/ansa.202200014","DOIUrl":"10.1002/ansa.202200014","url":null,"abstract":"<p>Inorganic arsenic is a carcinogen repeatedly found in water and foods threatening global human health. Prior work applied the Gutzeit method and X-ray fluorescence spectroscopy to quantify inorganic arsenic based on a harmful chemical, i.e., mercury bromide, to capture the arsine gas. In this project, we explored silver nitrate as an alternative to mercury bromide for the capture and detection of inorganic arsenic. To compare the performance of mercury bromide and silver nitrate, two standard curves were established in the range from 0 to 33.3 µg/L after optimization of reaction conditions such as the quantity of reagents and reaction time. Our result shows silver nitrate-based standard curve had a lower limit of detection and limit of quantification at 1.02 µg/L and 3.40 µg/L, respectively, as compared to the one built upon mercury bromide that has limit of detection of 4.86 µg/L and limit of quantification of 16.2 µg/L. The relative higher sensitivity when using silver nitrate was contributed by the less interfering elements for X-ray fluorescence analysis and thus lower background signals. A commercial apple juice was studied for matrix inference, and the results show 85%–99% recoveries and 7.4%–24.5% relative standard deviation. In conclusion, we demonstrated silver nitrate is a better choice in terms of safety restrictions and detection capability at lower inorganic arsenic concentrations.</p>","PeriodicalId":93411,"journal":{"name":"Analytical science advances","volume":"3 9-10","pages":"262-268"},"PeriodicalIF":0.0,"publicationDate":"2022-09-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://chemistry-europe.onlinelibrary.wiley.com/doi/epdf/10.1002/ansa.202200014","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41888396","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Cross-validated permutation feature importance considering correlation between features","authors":"Hiromasa Kaneko","doi":"10.1002/ansa.202200018","DOIUrl":"10.1002/ansa.202200018","url":null,"abstract":"<p>In molecular design, material design, process design, and process control, it is important not only to construct a model with high predictive ability between explanatory features x and objective features y using a dataset but also to interpret the constructed model. An index of feature importance in x is permutation feature importance (PFI), which can be combined with any regressors and classifiers. However, the PFI becomes unstable when the number of samples is low because it is necessary to divide a dataset into training and validation data when calculating it. Additionally, when there are strongly correlated features in x, the PFI of these features is estimated to be low. Hence, a cross-validated PFI (CVPFI) method is proposed. CVPFI can be calculated stably, even with a small number of samples, because model construction and feature evaluation are repeated based on cross-validation. Furthermore, by considering the absolute correlation coefficients between the features, the feature importance can be evaluated appropriately even when there are strongly correlated features in x. Case studies using numerical simulation data and actual compound data showed that the feature importance can be evaluated appropriately using CVPFI compared to PFI. This is possible when the number of samples is low, when linear and nonlinear relationships are mixed between x and y when there are strong correlations between features in x, and when quantised and biased features exist in x. Python codes for CVPFI are available at https://github.com/hkaneko1985/dcekit.</p>","PeriodicalId":93411,"journal":{"name":"Analytical science advances","volume":"3 9-10","pages":"278-287"},"PeriodicalIF":0.0,"publicationDate":"2022-09-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://chemistry-europe.onlinelibrary.wiley.com/doi/epdf/10.1002/ansa.202200018","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47450935","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Front Cover: Resonance Raman analysis of intracellular vitamin B12 analogs in methanogenic archaea","authors":"","doi":"10.1002/ansa.202200900","DOIUrl":"https://doi.org/10.1002/ansa.202200900","url":null,"abstract":"<p>The cover image is based on the Research Article <i>Resonance Raman analysis of intracellular vitamin B₁₂ analogs in methanogenic archaea</i> by Nanako Kanno et al., https://doi.org/10.1002/ansa.202100042.\u0000\u0000 <figure>\u0000 <div><picture>\u0000 <source></source></picture><p></p>\u0000 </div>\u0000 </figure></p>","PeriodicalId":93411,"journal":{"name":"Analytical science advances","volume":"3 5-6","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-06-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://chemistry-europe.onlinelibrary.wiley.com/doi/epdf/10.1002/ansa.202200900","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"137712597","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Helping reviewers assess statistical analysis: A case study from analytic methods","authors":"Ron S. Kenett,&nbsp;Bernard G. Francq","doi":"10.1002/ansa.202000159","DOIUrl":"10.1002/ansa.202000159","url":null,"abstract":"<p>Analytic methods development, like many other disciplines, relies on experimentation and data analysis. Determining the contribution of a paper or report on a study incorporating data analysis is typically left to the reviewer's experience and good sense, without reliance on structured guidelines. This is amplified by the growing role of machine learning driven analysis, where results are based on computer intensive algorithm applications. The evaluation of a predictive model where cross validation was used to fit its parameters adds challenges to the evaluation of regression models, where the estimates can be easily reproduced. This lack of structure to support reviews increases uncertainty and variability in reviews. In this paper, aspects of statistical assessment are considered. We provide checklists for reviewers of applied statistics work with a focus on analytic method development. The checklist covers six aspects relevant to a review of statistical analysis, namely: (1) study design, (2) algorithmic and inferential methods in frequentism analysis, (3) Bayesian methods in Bayesian analysis (if relevant), (4) selective inference aspects, (5) severe testing properties and (6) presentation of findings. We provide a brief overview of these elements providing references for a more elaborate treatment. The robustness analysis of an analytical method is used to illustrate how an improvement can be achieved in response to questions in the checklist. The paper is aimed at both engineers and seasoned researchers.</p>","PeriodicalId":93411,"journal":{"name":"Analytical science advances","volume":"3 5-6","pages":"212-222"},"PeriodicalIF":0.0,"publicationDate":"2022-06-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://chemistry-europe.onlinelibrary.wiley.com/doi/epdf/10.1002/ansa.202000159","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43247494","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Adaptive soft sensor based on transfer learning and ensemble learning for multiple process states","authors":"Nobuhito Yamada,&nbsp;Hiromasa Kaneko","doi":"10.1002/ansa.202200013","DOIUrl":"10.1002/ansa.202200013","url":null,"abstract":"<p>The objective of this study is to develop an adaptive software sensor technique that can predict objective process variables for a target grade in a plant while also considering information related to various other grades. We use a dataset of the target grade as the target domain and those of the other grades as source domains to perform transfer learning. Multiple models or sub-models are constructed by setting a source domain for each grade and changing the number of samples used as the source domain. Furthermore, to prevent the negative transfer, the use of a source domain is automatically judged. In this study, we constructed sub-models using the locally weighted partial least squares approach as an adaptive soft sensor technique. The values of an objective variable were predicted with ensemble learning using sub-models. The effectiveness of the proposed method was verified using a dataset measured in an actual incineration plant, and the proposed method was able to accurately predict the product quality even when the plant was operated in five grades and when a new grade was produced.</p>","PeriodicalId":93411,"journal":{"name":"Analytical science advances","volume":"3 5-6","pages":"205-211"},"PeriodicalIF":0.0,"publicationDate":"2022-06-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://chemistry-europe.onlinelibrary.wiley.com/doi/epdf/10.1002/ansa.202200013","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48047879","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A validated liquid chromatography-tandem mass spectroscopy method for the quantification of tolinapant in human plasma","authors":"Vanessa Martins,&nbsp;Nicola Wilsher,&nbsp;Song Lin,&nbsp;Aram Oganesian","doi":"10.1002/ansa.202200009","DOIUrl":"10.1002/ansa.202200009","url":null,"abstract":"<p>Tolinapant (ASTX660), a pan-selective inhibitor of apoptosis protein antagonist with dual cIAP/XIAP activity, was identified as a clinical candidate in preclinical efficacy, pharmacokinetic and safety studies. In order to assess tolinapant in first-in-human Phase I/II clinical trials, a validated bioanalytical method was required to determine plasma pharmacokinetics. Tolinapant and d₄-tolinapant were extracted from human plasma using liquid-liquid extraction. Separation chromatography was performed on a Acquity BEH C18 1.7 µM, 50 mm × 2.1 mm i.d. column, using a mobile phase of 0.1% formic acid in water and 0.1% formic acid in acetonitrile. Mass spectrometry detection was performed by positive turbo ion spray ionisation, in multiple reaction monitoring mode. The method was validated according to the US Food and Drug Administration (FDA) guidelines. The method has a quantifiable linear range of 1–500 ng/mL (<i>r</i>² = 0.999). The intra- and inter-day coefficients of variation were &lt; 11.4%. Dilution QC samples agreed with prepared concentrations, with a precision of 1.5% and accuracy of 101%. Tolinapant mean recoveries ranged from 85.1–94.4 % with negligible matrix effects. A highly sensitive and selective LC-MS/MS bioanalytical method was developed and validated. The method was successfully applied in Phase 1/2 clinical trials to determine the human pharmacokinetic profile of tolinapant.</p>","PeriodicalId":93411,"journal":{"name":"Analytical science advances","volume":"3 5-6","pages":"198-204"},"PeriodicalIF":0.0,"publicationDate":"2022-06-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://chemistry-europe.onlinelibrary.wiley.com/doi/epdf/10.1002/ansa.202200009","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41385035","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Artificial amniotic fluid for nuclear magnetic resonance spectroscopy studies","authors":"Doshina Naila,&nbsp;Siddharth Sadanand,&nbsp;Dafna Sussman","doi":"10.1002/ansa.202100055","DOIUrl":"10.1002/ansa.202100055","url":null,"abstract":"<p>Amniocentesis is the process of retrieving the nutrient-rich amniotic fluid (AF) that encompasses the growing fetus in order to diagnose fetal diseases and developmental disorders. Currently, it is only performed on pregnant persons at risk and is invasive with the potential for infection and in some cases, miscarriage. A non-invasive alternative is needed and could be developed using magnetic resonance spectroscopy (MRS). To develop such MRS sequences, ample testing and training are needed and could be most efficiently conducted on a phantom. We propose a protocol for creating such a synthetic AF phantom for MRS testing and optimization. The proposed AF is validated using nuclear magnetic resonance (NMR) proving it produces spectra comparable to those in the literature. The results from this study can aid in developing a non-invasive fetal diagnostic tool to replace amniocentesis.</p>","PeriodicalId":93411,"journal":{"name":"Analytical science advances","volume":"3 5-6","pages":"174-187"},"PeriodicalIF":0.0,"publicationDate":"2022-04-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://chemistry-europe.onlinelibrary.wiley.com/doi/epdf/10.1002/ansa.202100055","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44912938","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Annual reviews: Recent advances in analytical sciences","authors":"Sebastiaan Eeltink","doi":"10.1002/ansa.202200011","DOIUrl":"10.1002/ansa.202200011","url":null,"abstract":"&lt;p&gt;Mass spectrometry (MS) is an extremely powerful analytical technique, which plays a central role in many scientific fields. The Heaney research group critically reviewed the 2021 applications of ambient ionization mass spectrometry (AIMS), as a follow-up to the previous year's review paper.&lt;span&gt;&lt;sup&gt;1&lt;/sup&gt;&lt;/span&gt; AIMS enables the surface analysis of samples in their native environment in a high-throughput fashion and is widely used in disease diagnostics, forensics, homeland security applications, and the environmental sciences. Heaney et al. provide an overview of ambient ionization techniques and highlight the applicability in a wide range of scientific fields, either carried out in the laboratory or out in the field. High-performance liquid chromatography (HPLC) hyphenated to MS is the method of choice to profile oligonucleotide therapeutics. Hannauer et al. summarized the advancements in separation science considering ion-pairing reversed-phase chromatography, hydrophilic interaction chromatography, and two-dimensional liquid chromatography. In particular, the effect of chromatographic elution conditions and column chemistries on retention, resolving power, and MS compatibility are outlined. In addition, recent software developments for the tandem MS analysis of oligonucleotides are discussed. Minkus, Bieber, and Letzel provided a detailed review of the processing of mass-spectrometric non-target screening (NTS) data. NTS is an untargeted comprehensive analysis methodology based on high-resolution MS, where the entire mass range of small organic molecules of anthropogenic origin is recorded within a very short cycling time, leading to complex and large data sets. In particular, Minkus et al. review major contributions that concern the processing of NTS data, prioritization of features, as well as (semi-) quantitative methods that do not require analytical standards.&lt;/p&gt;&lt;p&gt;Terry et al. provided a detailed overview of applications of surface-enhanced Raman spectroscopy (SERS) in the environmental sciences. The inherent low intensity provided by Raman signals can be amplified by many orders of magnitude through electromagnetic-field and chemical/electronic enhancements when the analyte molecule is in close proximity with metal nanostructures. SERS is a promising technique that overcomes inherent limitations of MS, where information of molecular mass is obtained but isomeric differentiation is problematic, and UV-VIS detection, where contaminants can interfere with the acquired analyte spectra. Terry et al. highlight the characteristics of effective SERS nanosubstrates and methods for the SERS detection of inorganic, organic, and biological contaminants. Moreover, the pros and cons of SERS in environmental detection are discussed and possible avenues for future investigation are provided.&lt;/p&gt;&lt;p&gt;Two reviews were included that focus on the design, development, and applicability of novel stationary phases for LC. Valko described the recent deve","PeriodicalId":93411,"journal":{"name":"Analytical science advances","volume":"3 3-4","pages":"65-66"},"PeriodicalIF":0.0,"publicationDate":"2022-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://chemistry-europe.onlinelibrary.wiley.com/doi/epdf/10.1002/ansa.202200011","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48698068","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Biomimetic chromatography—A novel application of the chromatographic principles","authors":"Klara L Valko","doi":"10.1002/ansa.202200004","DOIUrl":"10.1002/ansa.202200004","url":null,"abstract":"<p>Biomimetic chromatography is the name of the High Performance Liquid Chromatography (HPLC) methods that apply stationary phases containing proteins and phospholipids that can mimic the biological environment where drug molecules distribute. The applied mobile phases are aqueous organic with a pH of 7.4 to imitate physiological conditions that would be encountered in the human body. The calibrated retention of molecules on biomimetic stationary phases reveals a compound's affinity to proteins and phospholipids, which can be used to model the biological and environmental fate of molecules. This technology, when standardised, enables the prediction of in vivo partition and distribution behaviour of compounds and aids the selection of the best compounds for further studies to become a drug molecule. Applying biomimetic chromatographic measurements helps reduce the number of animal experiments during the drug discovery process. New biomimetic stationary phases, such as sphingomyelin and phosphatidylethanolamine, widen the application to the modelling of blood–brain barrier distribution and lung tissue binding. Recently, the measured properties have also been used to predict toxicity, such as phospholipidosis and cardiotoxicity. The aquatic toxicity of drugs and pesticides can be predicted using biomimetic chromatographic data. Biomimetic chromatographic separation methods may also be extended in the future to predict protein and receptor binding kinetics. The development of new biomimetic stationary phases and new prediction models will further accelerate the widespread application of this analytical method.</p>","PeriodicalId":93411,"journal":{"name":"Analytical science advances","volume":"3 3-4","pages":"146-153"},"PeriodicalIF":0.0,"publicationDate":"2022-04-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://chemistry-europe.onlinelibrary.wiley.com/doi/epdf/10.1002/ansa.202200004","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43949138","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}