Brian L. Lee, Fatemeh Shahin, Alyaa Selim, Mark Berjanskii, Claudia Torres-Calzada, Prashanthi Kovur, Rupasri Mandal and David S. Wishart*,
{"title":"Automated Beer Analysis by NMR Spectroscopy","authors":"Brian L. Lee, Fatemeh Shahin, Alyaa Selim, Mark Berjanskii, Claudia Torres-Calzada, Prashanthi Kovur, Rupasri Mandal and David S. Wishart*, ","doi":"10.1021/acsfoodscitech.4c0089610.1021/acsfoodscitech.4c00896","DOIUrl":null,"url":null,"abstract":"<p >Previously, we reported on the development of MagMet, a tool capable of automatically processing and quantifying 1D <sup>1</sup>H NMR spectra of complex chemical mixtures, including biofluids such as human serum or plasma and, more recently, beverages such as wine. In this article, we present an extension of MagMet, called MagMet-B, for the automated profiling of 1D <sup>1</sup>H NMR spectra of beer. We curated a comprehensive 1D <sup>1</sup>H NMR spectral library comprising 81 more abundant metabolites commonly found in beer samples and optimized the MagMet algorithm to accurately fit these compounds. A comparison with manual profiling using the Chenomx NMR Suite (Version 8.3) showed a strong correlation between the manually measured and automated MagMet metabolite concentrations, with a mean absolute percent error of 13% and a median absolute percent error of 9%. Time-to-process comparisons show that MagMet-B is up to 45× faster than manual analysis. The MagMet-B Web server, which is specifically tailored for profiling beer NMR spectra at 700 MHz, is now accessible at https://magmet.ca.</p>","PeriodicalId":72048,"journal":{"name":"ACS food science & technology","volume":"5 1","pages":"378–388 378–388"},"PeriodicalIF":2.6000,"publicationDate":"2024-12-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"ACS food science & technology","FirstCategoryId":"1085","ListUrlMain":"https://pubs.acs.org/doi/10.1021/acsfoodscitech.4c00896","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"FOOD SCIENCE & TECHNOLOGY","Score":null,"Total":0}
引用次数: 0
Abstract
Previously, we reported on the development of MagMet, a tool capable of automatically processing and quantifying 1D 1H NMR spectra of complex chemical mixtures, including biofluids such as human serum or plasma and, more recently, beverages such as wine. In this article, we present an extension of MagMet, called MagMet-B, for the automated profiling of 1D 1H NMR spectra of beer. We curated a comprehensive 1D 1H NMR spectral library comprising 81 more abundant metabolites commonly found in beer samples and optimized the MagMet algorithm to accurately fit these compounds. A comparison with manual profiling using the Chenomx NMR Suite (Version 8.3) showed a strong correlation between the manually measured and automated MagMet metabolite concentrations, with a mean absolute percent error of 13% and a median absolute percent error of 9%. Time-to-process comparisons show that MagMet-B is up to 45× faster than manual analysis. The MagMet-B Web server, which is specifically tailored for profiling beer NMR spectra at 700 MHz, is now accessible at https://magmet.ca.