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Correction

Correction: Nag et al. Machine Learning-Based Classification of Lignocellulosic Biomass from Pyrolysis-Molecular Beam Mass Spectrometry Data. Int. J. Mol. Sci. 2021, 22, 4107

1
Computational Science Center, National Renewable Energy Laboratory, 15013 Denver West Pkwy, Golden, CO 80401, USA
2
Renewable Resources and Enabling Sciences Center, National Renewable Energy Laboratory, 15013 Denver West Pkwy, Golden, CO 80401, USA
*
Author to whom correspondence should be addressed.
These authors contributed equally.
Int. J. Mol. Sci. 2025, 26(19), 9482; https://doi.org/10.3390/ijms26199482
Submission received: 25 July 2025 / Accepted: 11 August 2025 / Published: 28 September 2025
(This article belongs to the Section Molecular Plant Sciences)
A bug was found in our R code which warrants some minor changes in some tables, figures, and the related text in the paper.
Error in Tables: In the original publication [1], there were multiple related mistakes in Table 3 as published. The first row in Table 3 should have a value of 22 in the “Counts” column, instead of 21, and there should be an additional element, 60, in the “m/z Values” column. The fifth row of Table 3 should have 137 in the “m/z Values” column instead of 60. The asterisk next to the 91 m/z value in the first row of Table 3, along with the asterisk in the Table 3 header and the text following it, should be removed. The sentence that immediately precedes Table 3 should be removed as well. The corrected version of Table 3 is provided below.
In the original publication, there were a couple of related mistakes in Table 5. The first row in Table 5 should have a value of 15 in the “Counts” column, instead of 14, and there should be an additional element, 154, in the “m/z Values” column. The corrected version of Table 5 is provided below.
Errors in Figures: In the original publication, there were minor mistakes in Figures 2 and 3. In the original publication, the intersection of all three sets—primary type, secondary type, and Sample ID, has 21 and 14 elements in Figure 2 and Figure 3, respectively. The corrected Figure 2 and Figure 3, provided below, have been updated to show that the intersection of all three sets—primary type, secondary type, and Sample ID, has 22 elements in Figure 2 and 15 elements in Figure 3, respectively.
Text Correction: There were the following errors in the original publication. The sentence “On the other hand, the spectral intensities at 21 m/z values, which are provided in the first row of Table 3, are important for all the three different classification problems and are typically abundant ions generated in biomass pyrolysis mass spectra.” in Section 2.3.1 on Page 9 of 22, in the last paragraph, refers to an incorrect number (21) of m/z values. A correction has been made to this sentence so that the updated sentence now reads as “On the other hand, the spectral intensities at 22 m/z values, which are provided in the first row of Table 3, are important for all the three different classification problems and are typically abundant ions generated in biomass pyrolysis mass spectra.”
The sentence “The spectral intensities at 14 m/z values, which are provided in the first row of Table 5, are important for all the three different classification problems and make up the only grouping of ions consisting of a number of annotated or otherwise abundant ions typically observed in the spectra.” in Section 2.3.2 on Page 13 of 22, in the last paragraph, also mentions in incorrect number (14) of m/z values. A correction has been made to this sentence so that the updated sentence now reads as “The spectral intensities at 15 m/z values, which are provided in the first row of Table 5, are important for all the three different classification problems and make up the only grouping of ions consisting of a number of annotated or otherwise abundant ions typically observed in the spectra.”
The sentence “Comparison of the p-value based feature sets before and after the RUV correction indicates that there are fewer spectral intensity features relevant only to the primary type classification (10 vs. 16) and only to the secondary type classification (6 vs. 11).” in Section 2.3.2 on Page 13 of 22, in the last paragraph, contains an incorrect word—“fewer”. This sentence is updated to “Comparison of the p-value based feature sets before and after the RUV correction indicates that there is an increase in the number of spectral intensity features relevant only to the primary type classification, (10 vs. 16), as well as in the number of spectral intensity features relevant only to the secondary type classification (6 vs. 11).”
The sentence “On the other hand, after the RUV correction, there is a reduction in the number of spectral intensity features in the p-value based feature set that are relevant only to the Sample ID classification problem (4 vs. 10) compared to the pre-RUV correction p-value based feature set.” in Section 2.3.2 on Page 13 of 22, in the last paragraph, contains an incorrect phrase “(4 vs. 10)”. This sentence is corrected to “On the other hand, comparison of the pre-RUV corrected versus post-RUV corrected p-value based feature sets indicates that there is a reduction in the number of spectral intensity features that are relevant only to the Sample ID classification problem, (10 vs. 4)”.
The sentence “Finally, the number of spectral intensities in the p-value based feature set that are relevant to all the three classification problems (primary type, secondary type and Sample ID) shrinks from 21 before the RUV correction to 14 after the RUV correction.” in Section 2.3.2 on Page 14 of 22, in the first paragraph, contains two incorrect values. This sentence is rectified to “Finally, the number of spectral intensities in the p-value based feature set that are relevant to all the three classification problems (primary type, secondary type and Sample ID) shrinks from 22 before the RUV correction to 15 after the RUV correction.”
The authors state that the scientific conclusions are unaffected. This correction was approved by the Academic Editor. The original publication has also been updated.

Reference

  1. Nag, A.; Gerritsen, A.; Doeppke, C.; Harman-Ware, A.E. Machine Learning-Based Classification of Lignocellulosic Biomass from Pyrolysis-Molecular Beam Mass Spectrometry Data. Int. J. Mol. Sci. 2021, 22, 4107. [Google Scholar] [CrossRef] [PubMed]
Figure 2. Venn diagram of py-MBMS spectral ion feature sets for different classification levels of biomass types.
Figure 2. Venn diagram of py-MBMS spectral ion feature sets for different classification levels of biomass types.
Ijms 26 09482 g002
Figure 3. Venn diagram of py-MBMS spectral ion feature sets as RUV corrected spectra for different classification levels of biomass types.
Figure 3. Venn diagram of py-MBMS spectral ion feature sets as RUV corrected spectra for different classification levels of biomass types.
Ijms 26 09482 g003
Table 3. Spectral features (ions) specific to classification problems using spectra before RUV correction.
Table 3. Spectral features (ions) specific to classification problems using spectra before RUV correction.
Primary TypeSecondary TypeSample IDCountsm/z Values
TrueTrueTrue2260, 84, 85, 86, 91, 93, 94, 105, 114, 123, 124, 126, 135, 136, 139, 140, 144, 165, 184, 205, 209, 302
FalseTrueTrue755, 58, 64, 115, 119, 125, 131
TrueFalseTrue466, 197, 200, 296
FalseFalseTrue1079, 80, 100, 106, 113, 116, 161, 190, 211, 212
TrueTrueFalse1274, 92, 99, 103, 107, 110, 121, 129, 137, 148, 149, 166
FalseTrueFalse677, 97, 111, 117, 153, 162
TrueFalseFalse1065, 70, 83, 109, 112, 174, 177, 192, 203, 219
Table 5. Spectral features (ions) specific to classification problems using RUV-corrected data.
Table 5. Spectral features (ions) specific to classification problems using RUV-corrected data.
Primary TypeSecondary TypeSample IDCountsm/z Value
TrueTrueTrue1573, 93, 94, 105, 107, 114, 123, 124, 126, 135, 140, 154, 162, 219, 302
FalseTrueTrue1258, 91, 95, 97, 100, 119, 131, 136, 139, 190, 200, 332
TrueFalseTrue460, 79, 165, 203
FalseFalseTrue455, 80, 113, 418
TrueTrueFalse1157, 66, 84, 85, 86, 92, 120, 144, 149, 174, 192
FalseTrueFalse1168, 103, 106, 117, 122, 129, 130, 148, 153, 209, 211
TrueFalseFalse1665, 72, 74, 78, 81, 83, 98, 109, 110, 115, 161, 163, 166, 197, 205, 296
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MDPI and ACS Style

Nag, A.; Gerritsen, A.; Doeppke, C.; Harman-Ware, A.E. Correction: Nag et al. Machine Learning-Based Classification of Lignocellulosic Biomass from Pyrolysis-Molecular Beam Mass Spectrometry Data. Int. J. Mol. Sci. 2021, 22, 4107. Int. J. Mol. Sci. 2025, 26, 9482. https://doi.org/10.3390/ijms26199482

AMA Style

Nag A, Gerritsen A, Doeppke C, Harman-Ware AE. Correction: Nag et al. Machine Learning-Based Classification of Lignocellulosic Biomass from Pyrolysis-Molecular Beam Mass Spectrometry Data. Int. J. Mol. Sci. 2021, 22, 4107. International Journal of Molecular Sciences. 2025; 26(19):9482. https://doi.org/10.3390/ijms26199482

Chicago/Turabian Style

Nag, Ambarish, Alida Gerritsen, Crissa Doeppke, and Anne E. Harman-Ware. 2025. "Correction: Nag et al. Machine Learning-Based Classification of Lignocellulosic Biomass from Pyrolysis-Molecular Beam Mass Spectrometry Data. Int. J. Mol. Sci. 2021, 22, 4107" International Journal of Molecular Sciences 26, no. 19: 9482. https://doi.org/10.3390/ijms26199482

APA Style

Nag, A., Gerritsen, A., Doeppke, C., & Harman-Ware, A. E. (2025). Correction: Nag et al. Machine Learning-Based Classification of Lignocellulosic Biomass from Pyrolysis-Molecular Beam Mass Spectrometry Data. Int. J. Mol. Sci. 2021, 22, 4107. International Journal of Molecular Sciences, 26(19), 9482. https://doi.org/10.3390/ijms26199482

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