An International Study Evaluating Elemental Analysis
A statistical study on elemental analysis for 5 small organic compounds at 18 independent service providers across multiple countries demonstrates variation in the returned results that is outside journal guidelines (0.4%) in greater than 10% of measurements. The results indicate that a deviation of 0.4% is not a realistic journal requirement with the variability attributed to random error.
- Rupert E. H. Kuveke*Rupert E. H. Kuveke*Email: [email protected]Department of Mathematical and Physical Sciences, La Trobe University, Melbourne, Victoria 3086, AustraliaMore by Rupert E. H. Kuveke,
- Lachlan BarwiseLachlan BarwiseDepartment of Biochemistry and Chemistry, La Trobe Institute for Molecular Science, La Trobe University, Melbourne, Victoria 3086, AustraliaMore by Lachlan Barwise,
- Yara van IngenYara van IngenCardiff Catalysis Institute, School of Chemistry, Cardiff University, Cardiff, CF10 3AT Wales, United KingdomMore by Yara van Ingen,
- Kanika VashisthKanika VashisthDepartment of Chemistry and Biochemistry, Baylor University, One Bear Place #97348, Waco, Texas 76798, United StatesMore by Kanika Vashisth,
- Nicholas RobertsNicholas RobertsDepartment of Chemistry, Dalhousie University, Halifax, Nova Scotia B3H 4R2, CanadaMore by Nicholas Roberts,
- Saurabh S. Chitnis*Saurabh S. Chitnis*Email: [email protected]Department of Chemistry, Dalhousie University, Halifax, Nova Scotia B3H 4R2, CanadaMore by Saurabh S. Chitnis,
- Jason L. Dutton*Jason L. Dutton*Email: [email protected]Department of Biochemistry and Chemistry, La Trobe Institute for Molecular Science, La Trobe University, Melbourne, Victoria 3086, AustraliaMore by Jason L. Dutton,
- Caleb D. Martin*Caleb D. Martin*Email: [email protected]Department of Chemistry and Biochemistry, Baylor University, One Bear Place #97348, Waco, Texas 76798, United StatesMore by Caleb D. Martin, and
- Rebecca L. Melen*Rebecca L. Melen*Email: [email protected]Cardiff Catalysis Institute, School of Chemistry, Cardiff University, Cardiff, CF10 3AT Wales, United KingdomMore by Rebecca L. Melen
Synopsis
Based on statistical analysis of CHN combustion results of 18 international service providers, it is determined that the ±0.4% deviation most commonly required by chemistry journals is not justified.
Introduction
journal | guidelines |
---|---|
Nature Chemistry | Evidence of sample purity is requested for each new compound. Methods for purity analysis depend on the compound class. For most organic and organometallic compounds, purity may be demonstrated by high field 1H NMR or 13C NMR data, although elemental analysis (±0.4%) is encouraged for small molecules. |
Journal of Organic Chemistry | Found values for carbon, hydrogen, and nitrogen should be within 0.4% of the Calcd values for the proposed formula. The need to include fractional molecules of solvent or water in the molecular formula to improve the fit of the data usually reflects incomplete purification of the sample. |
Inorganic Chemistry | For all new compounds, evidence adequate to establish both identity and degree of purity (homogeneity) must be provided. For known compounds prepared by a new or modified synthetic procedure, the types of physical and spectroscopic data that were found to match cited literature data should be identified, and purity documentation should be provided. |
Organometallics | Organometallics strongly encourages the characterization of all new compounds by elemental analysis. For such data, agreement of calculated and found values within 0.4% (e.g., Calcd, 20.14%; Found, 20.54%) is considered acceptable. For deviations slightly outside the accepted range, authors are encouraged to provide an explanation in the relevant paragraph of the Experimental section, and to include a statement such as “although these results are outside the range viewed as establishing analytical purity, they are provided to illustrate the best values obtained to date.” |
Organic Letters | To support the molecular formula assignment, either the HRMS data accurate within 5 ppm, or combustion elemental analysis data accurate within 0.4%, must be reported for new compounds. |
Journal of the American Chemical Society | Evidence for elemental constitution must be provided by either elemental analysis (e.g., combustion analysis, microprobe analysis) or mass spectrometry. While an X-ray diffraction structure is not considered definitive proof of elemental composition, it is acceptable evidence for composition providing that the results of other physical methods concerning the characterization are conclusive. |
Angewandte Chemie | Data should be provided to an accuracy within ±0.4%. |
European Journal of Inorganic Chemistry | Data should be provided to an accuracy within ±0.4%. |
Chemistry─A European Journal | Data should be provided to an accuracy within ±0.4%. |
Chemical Science | For identification purposes for new compounds, an accuracy to within ±0.3% is expected, and in exceptional cases, to within ±0.5% is required. If a molecular weight is to be included, the appropriate form is [Found: C, 63.1; H, 5.4%; M (mass spectrum), 352 (or simply M+, 352). C13H13NO4 requires C, 63.2; H, 5.3%; M, 352]. |
Chemical Communications | Elemental analysis (within ±0.4% of the calculated value) is required to confirm 95% sample purity and corroborate isomeric purity. |
Dalton Transactions | This should include elemental analyses that agree to within ±0.4% of the calculated values. |
European Journal of Organic Chemistry | Data should be provided to an accuracy within ±0.4%. |
Organic and Biomolecular Chemistry | High-resolution mass spectroscopy (HRMS), with a found value within 0.003 m/z unit of the calculated value of a parent-derived ion. Elemental analysis data may be provided if HRMS is not available. |
Chem Catalysis | Evidence of purity is a requirement for all new compounds. The appropriate methods are dependent on the type of compounds reported. For organic and organometallic compounds, high-field 1H and 13C NMR can be used to show purity. Ideally, elemental analysis (±0.4%) should be included for small molecules. |
Chem | Evidence of purity is a requirement for all new compounds. The appropriate methods are dependent on the type of compounds reported. For organic and organometallic compounds, high-field 1H and 13C NMR can be used to show purity. Ideally, elemental analysis (±0.4%) should be included for small molecules. |
Results and Discussion

sample size | fail results | fail results (%) | 95% CI for fail results (%) | |
---|---|---|---|---|
all | 436 | 47 | 10.78 | (8.18–14.06) |
dl-tryptophan | 81 | 6 | 7.41 | (3.15–15.53) |
succinimide | 87 | 10 | 11.49 | (6.18–20.07) |
2-hydroxybenzimidazole | 90 | 11 | 12.22 | (6.8–20.74) |
bisoctrizole | 95 | 13 | 13.68 | (8.04–22.15) |
diacetylpyridine | 83 | 7 | 8.43 | (3.89–16.66) |
carbon | 146 | 24 | 16.44 | (11.24–23.35) |
hydrogen | 146 | 3 | 2.05 | (0.43–6.14) |
nitrogen | 144 | 20 | 13.89 | (9.1–20.56) |
Figure 1

Figure 1. Box plot visual guide, included for reference purposes.
Figure 2

Figure 2. Box plots of differences between analysis and theoretical values for each element, across the five different compounds, with the C outlier from Midwest Micro Analytical Laboratories not shown.
Figure 3

Figure 3. Percentages of “Acceptable” and “Fail” results for each of the 18 service providers used, across all 5 compounds for C, H, and N.
Figure 4

Figure 4. Box plots of observed differences between analysis and theoretical values for C, for all service providers in this study, with the C outlier from Midwest Micro Analytical Laboratories not shown.
Figure 5

Figure 5. Box plots of observed differences between analysis and theoretical values for H, for all service providers in this study.
Figure 6

Figure 6. Box plots of observed differences between analysis and theoretical values for N, for all service providers in this study.
Experimental Section
Conclusions
Supporting Information
The Supporting Information is available free of charge at https://pubs.acs.org/doi/10.1021/acscentsci.2c00325.
NMR spectra of samples as shipped from The Dutton Lab, a spreadsheet of all the data obtained from the service providers, details of all statistical analyses, and additional tables and figures (PDF)
Transparent Peer Review report available (PDF)
Raw data file (XLSX)
Terms & Conditions
Most electronic Supporting Information files are available without a subscription to ACS Web Editions. Such files may be downloaded by article for research use (if there is a public use license linked to the relevant article, that license may permit other uses). Permission may be obtained from ACS for other uses through requests via the RightsLink permission system: http://pubs.acs.org/page/copyright/permissions.html.
References
This article references 15 other publications.
- 1Gabbaï, F. P.; Chirik, P. J.; Fogg, D. E.; Meyer, K.; Mindiola, D. J.; Schafer, L. L.; You, S. An editorial about elemental analysis. Organometallics 2016, 35, 3255– 3256, DOI: 10.1021/acs.organomet.6b00720
- 2Tyner, T.; Francis, J. ACS Reagent Chemicals Specifications and Procedures for Reagents and Standard-Grade Reference Materials; American Chemical Society, 2017.
- 3Drahl, C.; Ritter, S. K. Insert data here ... but make it up first. C&EN 2013. https://cen.acs.org/articles/91/web/2013/08/Insert-Data-Make-First.html.Google ScholarThere is no corresponding record for this reference.
- 4Schulz, W. G. Reports Detail A Massive Case of Fraud. C&EN , Nov 30, 2011. http://pubsapp.acs.org/cen/news/88/i49/8849news2.html?.Google ScholarThere is no corresponding record for this reference.
- 5Ellison, S. L. R.; Hardcastle, W. A. Causes of error in analytical chemistry: results of a web-based survey of proficiency testing participants. Accred. Qual. Assur. 2012, 17, 453– 464, DOI: 10.1007/s00769-012-0894-2
- 6Kandioller, W.; Theiner, J.; Keppler, B. K.; Kowol, C. R. Elemental analysis: an important purity control but prone to manipulations. Inorg. Chem. Front. 2022, 9, 412– 416, DOI: 10.1039/d1qi01379c[Crossref], [CAS], Google Scholar6https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BB3MXislyns7fP&md5=308329f7a86f89d9e037f4af300af8caElemental analysis: an important purity control but prone to manipulationsKandioller, Wolfgang; Theiner, Johannes; Keppler, Bernhard K.; Kowol, Christian R.Inorganic Chemistry Frontiers (2022), 9 (3), 412-416CODEN: ICFNAW; ISSN:2052-1553. (Royal Society of Chemistry)Elemental anal. provides a powerful anal. tool for purity detn. of compds. and is a prerequisite for publication in many journals dealing with (bio)inorg. synthetic chem. However, in contrast to other anal. methods, there is no requirement to prove the presented values e.g. with chromatograms, making this essential anal. prone to manipulations. Our personal observation in the review process of numerous manuscripts over the last years revealed that the amt. of questionable data is constantly increasing. Within this article we discuss what realistic measured deviations from the theor. compn. of a compd. are and present an approach to provide original elemental anal. data to support the listed values in the exptl. sections. This would enable reviewers, editors and readers to better judge the presented results in the future.
- 7Pearson, K. On the criterion that a given system of deviations from the probable in the case of a correlated system of variables is such that it can be reasonably supposed to have arisen from random sampling. Philos. Mag. 1900, 50, 157– 175, DOI: 10.1080/14786440009463897
- 8Agresti, A.; Coull, B. A. Approximate is Better than “Exact” for Interval Estimation of Binomial Proportions. American Statistician 1998, 52 (2), 119– 126, DOI: 10.1080/00031305.1998.10480550
- 9Brown, L. D.; Cai, T. T.; DasGupta, A. Interval Estimation for a Binomial Proportion. Statistical Science 2001, 16 (2), 101– 133, DOI: 10.1214/ss/1009213286
- 10Choi, K. P.; Xia, A. Approximating the number of successes in independent trials: Binomial versus Poisson. Annals of Applied Probability 2002, 12 (4), 1139– 1148, DOI: 10.1214/aoap/1037125856
- 11Cohen, J. Statistical power analysis for the behavioural sciences; ISBN 97801-134-74270-7; Routledge, 1988.Google ScholarThere is no corresponding record for this reference.
- 12Plotly, Box traces with R. 2022. https://plotly.com/r/reference/box/.Google ScholarThere is no corresponding record for this reference.
- 13Kruskal, W. H.; Wallis, W. A. Use of Ranks in One-Criterion Variance Analysis. Journal of the American Statistical Association 1952, 47 (260), 583– 621, DOI: 10.1080/01621459.1952.10483441
- 14Dunn, O. J. Multiple Comparisons Using Rank Sums. Technometrics 1964, 6 (3), 241– 252, DOI: 10.1080/00401706.1964.10490181
- 15Benjamini, Y.; Hochberg, Y. Controlling the False Discovery Rate: A Practical and Powerful Approach to Multiple Testing. Journal of the Royal Statistical Society: Series B (Methodological) 1995, 57 (1), 289– 300, DOI: 10.1111/j.2517-6161.1995.tb02031.x
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- Stephen Proctor, Sergio Lovera, Anton Tomich, Vincent Lavallo. Searching for the Truth: Elemental Analysis–A Powerful but Often Poorly Executed Technique. ACS Central Science 2022, 8 (7) , 874-876. https://doi.org/10.1021/acscentsci.2c00761
- Billy Oktora Abdilah Fauzi, Mitsuru Kondo, Mohamed I. Elzagheid, Lydia Rhyman, Ponnadurai Ramasami. Functionalization of Two-Dimensional Coordination Polymer in Small Organic Matter Removal from Organic Wastewater. Journal of Inorganic and Organometallic Polymers and Materials 2022, 1 https://doi.org/10.1007/s10904-022-02435-5
Abstract
Figure 1
Figure 1. Box plot visual guide, included for reference purposes.
Figure 2
Figure 2. Box plots of differences between analysis and theoretical values for each element, across the five different compounds, with the C outlier from Midwest Micro Analytical Laboratories not shown.
Figure 3
Figure 3. Percentages of “Acceptable” and “Fail” results for each of the 18 service providers used, across all 5 compounds for C, H, and N.
Figure 4
Figure 4. Box plots of observed differences between analysis and theoretical values for C, for all service providers in this study, with the C outlier from Midwest Micro Analytical Laboratories not shown.
Figure 5
Figure 5. Box plots of observed differences between analysis and theoretical values for H, for all service providers in this study.
Figure 6
Figure 6. Box plots of observed differences between analysis and theoretical values for N, for all service providers in this study.
References
ARTICLE SECTIONSThis article references 15 other publications.
- 1Gabbaï, F. P.; Chirik, P. J.; Fogg, D. E.; Meyer, K.; Mindiola, D. J.; Schafer, L. L.; You, S. An editorial about elemental analysis. Organometallics 2016, 35, 3255– 3256, DOI: 10.1021/acs.organomet.6b00720
- 2Tyner, T.; Francis, J. ACS Reagent Chemicals Specifications and Procedures for Reagents and Standard-Grade Reference Materials; American Chemical Society, 2017.
- 3Drahl, C.; Ritter, S. K. Insert data here ... but make it up first. C&EN 2013. https://cen.acs.org/articles/91/web/2013/08/Insert-Data-Make-First.html.Google ScholarThere is no corresponding record for this reference.
- 4Schulz, W. G. Reports Detail A Massive Case of Fraud. C&EN , Nov 30, 2011. http://pubsapp.acs.org/cen/news/88/i49/8849news2.html?.Google ScholarThere is no corresponding record for this reference.
- 5Ellison, S. L. R.; Hardcastle, W. A. Causes of error in analytical chemistry: results of a web-based survey of proficiency testing participants. Accred. Qual. Assur. 2012, 17, 453– 464, DOI: 10.1007/s00769-012-0894-2
- 6Kandioller, W.; Theiner, J.; Keppler, B. K.; Kowol, C. R. Elemental analysis: an important purity control but prone to manipulations. Inorg. Chem. Front. 2022, 9, 412– 416, DOI: 10.1039/d1qi01379c[Crossref], [CAS], Google Scholar6https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BB3MXislyns7fP&md5=308329f7a86f89d9e037f4af300af8caElemental analysis: an important purity control but prone to manipulationsKandioller, Wolfgang; Theiner, Johannes; Keppler, Bernhard K.; Kowol, Christian R.Inorganic Chemistry Frontiers (2022), 9 (3), 412-416CODEN: ICFNAW; ISSN:2052-1553. (Royal Society of Chemistry)Elemental anal. provides a powerful anal. tool for purity detn. of compds. and is a prerequisite for publication in many journals dealing with (bio)inorg. synthetic chem. However, in contrast to other anal. methods, there is no requirement to prove the presented values e.g. with chromatograms, making this essential anal. prone to manipulations. Our personal observation in the review process of numerous manuscripts over the last years revealed that the amt. of questionable data is constantly increasing. Within this article we discuss what realistic measured deviations from the theor. compn. of a compd. are and present an approach to provide original elemental anal. data to support the listed values in the exptl. sections. This would enable reviewers, editors and readers to better judge the presented results in the future.
- 7Pearson, K. On the criterion that a given system of deviations from the probable in the case of a correlated system of variables is such that it can be reasonably supposed to have arisen from random sampling. Philos. Mag. 1900, 50, 157– 175, DOI: 10.1080/14786440009463897
- 8Agresti, A.; Coull, B. A. Approximate is Better than “Exact” for Interval Estimation of Binomial Proportions. American Statistician 1998, 52 (2), 119– 126, DOI: 10.1080/00031305.1998.10480550
- 9Brown, L. D.; Cai, T. T.; DasGupta, A. Interval Estimation for a Binomial Proportion. Statistical Science 2001, 16 (2), 101– 133, DOI: 10.1214/ss/1009213286
- 10Choi, K. P.; Xia, A. Approximating the number of successes in independent trials: Binomial versus Poisson. Annals of Applied Probability 2002, 12 (4), 1139– 1148, DOI: 10.1214/aoap/1037125856
- 11Cohen, J. Statistical power analysis for the behavioural sciences; ISBN 97801-134-74270-7; Routledge, 1988.Google ScholarThere is no corresponding record for this reference.
- 12Plotly, Box traces with R. 2022. https://plotly.com/r/reference/box/.Google ScholarThere is no corresponding record for this reference.
- 13Kruskal, W. H.; Wallis, W. A. Use of Ranks in One-Criterion Variance Analysis. Journal of the American Statistical Association 1952, 47 (260), 583– 621, DOI: 10.1080/01621459.1952.10483441
- 14Dunn, O. J. Multiple Comparisons Using Rank Sums. Technometrics 1964, 6 (3), 241– 252, DOI: 10.1080/00401706.1964.10490181
- 15Benjamini, Y.; Hochberg, Y. Controlling the False Discovery Rate: A Practical and Powerful Approach to Multiple Testing. Journal of the Royal Statistical Society: Series B (Methodological) 1995, 57 (1), 289– 300, DOI: 10.1111/j.2517-6161.1995.tb02031.x
Supporting Information
Supporting Information
ARTICLE SECTIONSThe Supporting Information is available free of charge at https://pubs.acs.org/doi/10.1021/acscentsci.2c00325.
NMR spectra of samples as shipped from The Dutton Lab, a spreadsheet of all the data obtained from the service providers, details of all statistical analyses, and additional tables and figures (PDF)
Transparent Peer Review report available (PDF)
Raw data file (XLSX)
Terms & Conditions
Most electronic Supporting Information files are available without a subscription to ACS Web Editions. Such files may be downloaded by article for research use (if there is a public use license linked to the relevant article, that license may permit other uses). Permission may be obtained from ACS for other uses through requests via the RightsLink permission system: http://pubs.acs.org/page/copyright/permissions.html.