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Rapid estimation of the chemical composition of rice straw using FTIR spectroscopy: a chemometric investigation

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Abstract

The efficiency of lignocellulosic biomass conversion to fuels and chemicals is highly dependent on the structure and chemical composition of the biomass. So, rapid estimation of the chemical composition of biomass will be highly beneficial for biorefineries to fine-tune the feedstock and other processing parameters like enzymatic loading for a better yield of 2G ethanol. The conventional wet chemistry method for composition analysis is time-consuming, costly and laborious. This study aims to develop rapid, non-laborious, low-cost, and industrially applicable chemometric models like principal component regression (PCR) and partial least squares regression (PLSR) based on FTIR spectroscopy to determine the chemical composition of one such typical lignocellulosic biomass, viz., rice straw (RS). However, the results suggest that PCR and PLSR models constructed using the unprocessed FTIR spectra show poor performance in prediction. So, the spectra were processed, and an exploratory spectral analysis helped identify a specific region from 750 to 1800 cm−1 (SR-1) that accounts for significant variation between the RS samples. Moreover, 58 critical peaks in SR-1 were identified using a novel peak identification method proposed in this study. Further, results suggest that PLSR models developed using SR-1 of the processed spectra and the peaks as excellent prediction models (\({\mathrm{R}}_{\mathrm{cv}}^{2}\) > 95% and RPD > 4) and successful prediction models (90% < \({\mathrm{R}}_{\mathrm{cv}}^{2}\)< 95% and 3 < RPD < 4). Hence this study demonstrates that fine-tuned PLSR models based on processed FTIR spectra can be used as a tool for high-throughput screening of RS samples in biorefineries to improve the yield.

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The datasets generated during and/or analyzed for the current study were provided as supplementary materials.

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Acknowledgements

SRP acknowledges and thank the Council of Scientific and Industrial Research (CSIR), New Delhi, India, and the University Grants Commission (UGC), New Delhi, India, for CSIR-UGC Junior Research Fellowship (2019 - 2021) and CSIR-UGC Senior Research Fellowship (2021 - 2023). Also, CSIR-NIIST, Thiruvananthapuram, for providing the research facility.

Funding

SM received financial support from CSIR, New Delhi, India. The authors received financial support from RKS of the Department of Science and Technology, Government of India, for the project DST/INT/AUS/GCP-5/13(G) and the Council of Scientific and Industrial Research, Government of India, for Project 33/2018/MD-FFT&FTC-ANB.

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  1. Sreejith R P and Meena Sankar contributed equally to this work.

Authors and Affiliations

  1. Material Science and Technology Division, CSIR-National Institute for Interdisciplinary Science and Technology, Industrial Estate P.O, Thiruvananthapuram, Kerala-695019, India

    Sreejith R P & Sivaraman Savithri

  2. Academy of Scientific and Innovative Research (AcSIR), Ghaziabad-201002, India

    Sreejith R P, Meena Sankar, Rajeev K. Sukumaran & Sivaraman Savithri

  3. Biofuels and Biorefineries Section, Microbial Processes and Technology Division, CSIR-National Institute for Interdisciplinary Science and Technology, Industrial Estate P.O, Thiruvananthapuram, Kerala-695019, India

    Meena Sankar & Rajeev K. Sukumaran

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Contributions

Sreejith R P: conceptualization, methodology, software, formal analysis, data curation, investigation, visualization, writing - original draft, project administration.

Meena Sankar: methodology, formal analysis, investigation, writing - original draft.

Rajeev K Sukumaran: conceptualization, methodology, resources, funding, validation, writing - original draft.

Sivaraman Savithri: conceptualization, methodology, resources, funding, supervision, validation, writing - original draft.

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Correspondence to Sivaraman Savithri.

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R P, S., Sankar, M., Sukumaran, R.K. et al. Rapid estimation of the chemical composition of rice straw using FTIR spectroscopy: a chemometric investigation. Biomass Conv. Bioref. 14, 11829–11847 (2024). https://doi.org/10.1007/s13399-022-03508-8

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