Rapid Lignin Thermal Property Prediction through Attenuated Total Reflectance‐Infrared Spectroscopy and Chemometrics
To expedite the valorisation of lignin as a sustainable component in materials applications, rapid and generally available analytical methods are essential to overcome the bottleneck of lignin characterisation. Where features of a lignin's chemical structure have previously been found to be pre...
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| Veröffentlicht in: | ChemSusChem Jg. 17; H. 9; S. e202301464 - n/a |
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| Abstract | To expedite the valorisation of lignin as a sustainable component in materials applications, rapid and generally available analytical methods are essential to overcome the bottleneck of lignin characterisation. Where features of a lignin's chemical structure have previously been found to be predicted by Partial Least Squares (PLS) regression models built on Infrared (IR) data, we now show for the first time that this approach can be extended to prediction of the glass transition temperature (Tg), a key physicochemical property. This methodology is shown to be convenient and more robust for prediction of Tg than prediction through empirically derived relationships (e. g., Flory‐Fox). The chemometric analysis provided root mean squared errors of prediction (RMSEP) as low as 10.0 °C for a botanically, and a process‐diverse set of lignins, and 6.2 °C for kraft‐only samples. The PLS models could separately predict both the Tg as well as the degree of allylation (%allyl) for allylated lignin fractions, which were all derived from a single lignin source. The models performed exceptionally well, delivering RMSEP of 6.1 °C, and 5.4 %, respectively, despite the conflicting influences of increasing molecular weight and %allyl on Tg. Finally, the method provided accurate determinations of %allyl with RMSEP of 5.2 %.
The variable nature of technical lignins and their suitability for materials applications, necessitates detailed characterisation of their structural and material performance properties, such as glass transition temperature. This typically requires a suite of high‐end analytical equipment for analysis. We show that readily‐accessible attenuated total reflectance‐FTIR combined with partial least squares regression analysis, provides rapid access to this important parameter. |
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| AbstractList | To expedite the valorisation of lignin as a sustainable component in materials applications, rapid and generally available analytical methods are essential to overcome the bottleneck of lignin characterisation. Where features of a lignin's chemical structure have previously been found to be predicted by Partial Least Squares (PLS) regression models built on Infrared (IR) data, we now show for the first time that this approach can be extended to prediction of the glass transition temperature ( T g ), a key physicochemical property. This methodology is shown to be convenient and more robust for prediction of T g than prediction through empirically derived relationships (e. g., Flory‐Fox). The chemometric analysis provided root mean squared errors of prediction (RMSEP) as low as 10.0 °C for a botanically, and a process‐diverse set of lignins, and 6.2 °C for kraft‐only samples. The PLS models could separately predict both the T g as well as the degree of allylation (% allyl ) for allylated lignin fractions, which were all derived from a single lignin source. The models performed exceptionally well, delivering RMSEP of 6.1 °C, and 5.4 %, respectively, despite the conflicting influences of increasing molecular weight and % allyl on T g . Finally, the method provided accurate determinations of % allyl with RMSEP of 5.2 %. To expedite the valorisation of lignin as a sustainable component in materials applications, rapid and generally available analytical methods are essential to overcome the bottleneck of lignin characterisation. Where features of a lignin's chemical structure have previously been found to be predicted by Partial Least Squares (PLS) regression models built on Infrared (IR) data, we now show for the first time that this approach can be extended to prediction of the glass transition temperature (Tg), a key physicochemical property. This methodology is shown to be convenient and more robust for prediction of Tg than prediction through empirically derived relationships (e. g., Flory‐Fox). The chemometric analysis provided root mean squared errors of prediction (RMSEP) as low as 10.0 °C for a botanically, and a process‐diverse set of lignins, and 6.2 °C for kraft‐only samples. The PLS models could separately predict both the Tg as well as the degree of allylation (%allyl) for allylated lignin fractions, which were all derived from a single lignin source. The models performed exceptionally well, delivering RMSEP of 6.1 °C, and 5.4 %, respectively, despite the conflicting influences of increasing molecular weight and %allyl on Tg. Finally, the method provided accurate determinations of %allyl with RMSEP of 5.2 %. To expedite the valorisation of lignin as a sustainable component in materials applications, rapid and generally available analytical methods are essential to overcome the bottleneck of lignin characterisation. Where features of a lignin's chemical structure have previously been found to be predicted by Partial Least Squares (PLS) regression models built on Infrared (IR) data, we now show for the first time that this approach can be extended to prediction of the glass transition temperature (Tg), a key physicochemical property. This methodology is shown to be convenient and more robust for prediction of Tg than prediction through empirically derived relationships (e. g., Flory-Fox). The chemometric analysis provided root mean squared errors of prediction (RMSEP) as low as 10.0 °C for a botanically, and a process-diverse set of lignins, and 6.2 °C for kraft-only samples. The PLS models could separately predict both the Tg as well as the degree of allylation (%allyl) for allylated lignin fractions, which were all derived from a single lignin source. The models performed exceptionally well, delivering RMSEP of 6.1 °C, and 5.4 %, respectively, despite the conflicting influences of increasing molecular weight and %allyl on Tg. Finally, the method provided accurate determinations of %allyl with RMSEP of 5.2 %.To expedite the valorisation of lignin as a sustainable component in materials applications, rapid and generally available analytical methods are essential to overcome the bottleneck of lignin characterisation. Where features of a lignin's chemical structure have previously been found to be predicted by Partial Least Squares (PLS) regression models built on Infrared (IR) data, we now show for the first time that this approach can be extended to prediction of the glass transition temperature (Tg), a key physicochemical property. This methodology is shown to be convenient and more robust for prediction of Tg than prediction through empirically derived relationships (e. g., Flory-Fox). The chemometric analysis provided root mean squared errors of prediction (RMSEP) as low as 10.0 °C for a botanically, and a process-diverse set of lignins, and 6.2 °C for kraft-only samples. The PLS models could separately predict both the Tg as well as the degree of allylation (%allyl) for allylated lignin fractions, which were all derived from a single lignin source. The models performed exceptionally well, delivering RMSEP of 6.1 °C, and 5.4 %, respectively, despite the conflicting influences of increasing molecular weight and %allyl on Tg. Finally, the method provided accurate determinations of %allyl with RMSEP of 5.2 %. To expedite the valorisation of lignin as a sustainable component in materials applications, rapid and generally available analytical methods are essential to overcome the bottleneck of lignin characterisation. Where features of a lignin's chemical structure have previously been found to be predicted by Partial Least Squares (PLS) regression models built on Infrared (IR) data, we now show for the first time that this approach can be extended to prediction of the glass transition temperature (Tg), a key physicochemical property. This methodology is shown to be convenient and more robust for prediction of Tg than prediction through empirically derived relationships (e. g., Flory‐Fox). The chemometric analysis provided root mean squared errors of prediction (RMSEP) as low as 10.0 °C for a botanically, and a process‐diverse set of lignins, and 6.2 °C for kraft‐only samples. The PLS models could separately predict both the Tg as well as the degree of allylation (%allyl) for allylated lignin fractions, which were all derived from a single lignin source. The models performed exceptionally well, delivering RMSEP of 6.1 °C, and 5.4 %, respectively, despite the conflicting influences of increasing molecular weight and %allyl on Tg. Finally, the method provided accurate determinations of %allyl with RMSEP of 5.2 %. The variable nature of technical lignins and their suitability for materials applications, necessitates detailed characterisation of their structural and material performance properties, such as glass transition temperature. This typically requires a suite of high‐end analytical equipment for analysis. We show that readily‐accessible attenuated total reflectance‐FTIR combined with partial least squares regression analysis, provides rapid access to this important parameter. To expedite the valorisation of lignin as a sustainable component in materials applications, rapid and generally available analytical methods are essential to overcome the bottleneck of lignin characterisation. Where features of a lignin's chemical structure have previously been found to be predicted by Partial Least Squares (PLS) regression models built on Infrared (IR) data, we now show for the first time that this approach can be extended to prediction of the glass transition temperature (T ), a key physicochemical property. This methodology is shown to be convenient and more robust for prediction of T than prediction through empirically derived relationships (e. g., Flory-Fox). The chemometric analysis provided root mean squared errors of prediction (RMSEP) as low as 10.0 °C for a botanically, and a process-diverse set of lignins, and 6.2 °C for kraft-only samples. The PLS models could separately predict both the T as well as the degree of allylation (% ) for allylated lignin fractions, which were all derived from a single lignin source. The models performed exceptionally well, delivering RMSEP of 6.1 °C, and 5.4 %, respectively, despite the conflicting influences of increasing molecular weight and % on T . Finally, the method provided accurate determinations of % with RMSEP of 5.2 %. |
| Author | Riddell, Luke A. Meirer, Florian Lindner, Jean‐Pierre B. Peinder, Peter Bruijnincx, Pieter C. A. |
| Author_xml | – sequence: 1 givenname: Luke A. surname: Riddell fullname: Riddell, Luke A. organization: Utrecht University – sequence: 2 givenname: Jean‐Pierre B. surname: Lindner fullname: Lindner, Jean‐Pierre B. organization: Group Research – sequence: 3 givenname: Peter surname: Peinder fullname: Peinder, Peter organization: Utrecht University – sequence: 4 givenname: Florian surname: Meirer fullname: Meirer, Florian email: f.meirer@uu.nl organization: Utrecht University – sequence: 5 givenname: Pieter C. A. orcidid: 0000-0001-8134-0530 surname: Bruijnincx fullname: Bruijnincx, Pieter C. A. email: P.C.A.Bruijnincx@uu.nl organization: Utrecht University |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/38194292$$D View this record in MEDLINE/PubMed |
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| CitedBy_id | crossref_primary_10_3390_polym16233325 crossref_primary_10_1002_cssc_202400938 crossref_primary_10_1016_j_chemolab_2025_105467 crossref_primary_10_1038_s41596_025_01139_7 |
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| SubjectTerms | Allyl compounds Chemical reactions Chemometrics Glass transition temperature Lignin Physicochemical properties Regression models Thermodynamic properties |
| Title | Rapid Lignin Thermal Property Prediction through Attenuated Total Reflectance‐Infrared Spectroscopy and Chemometrics |
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