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Gao W, Jiang Q, Guan Y, Huang H, Liu S, Ling S, Zhou L. Transfer learning improves predictions in lignin content of Chinese fir based on Raman spectra. Int J Biol Macromol 2024; 269:132147. [PMID: 38719007 DOI: 10.1016/j.ijbiomac.2024.132147] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/09/2024] [Revised: 04/20/2024] [Accepted: 05/05/2024] [Indexed: 05/13/2024]
Abstract
Lignin in biomass plays significant role in substitution of synthetic polymer and reduction of energy expenditure, and the lignin content was usually determined by wet chemical methods. However, the methods' heavy workload, low efficiency, huge consumption of chemicals and use of toxic reagents render them unsuitable for sustainable development and environmental protection. Chinese fir, a prevalent angiosperm tree, holds immense importance for various industries. Since our previous work found that Raman spectroscopy could accurately predict the lignin content in poplar, we propose that the lignin content of Chinese fir can be estimated by similar strategy. The results suggested that the peak at 2895 cm-1 is the optimal choice of internal standard peak and algorithm of XGBoost demonstrates the highest accuracy among all algorithms. Furthermore, transfer learning was successfully introduced to enhance the accuracy and robustness of the model. Ultimately, we report that a machine learning algorithm, combining transfer learning with XGBoost or LightGBM, offers an accurate, high-efficiency and environmental friendly method for predicting the lignin content of Chinese fir using Raman spectra.
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Affiliation(s)
- Wenli Gao
- School of Physical Science and Technology, ShanghaiTech University, 393 Middle Huaxia Road, Shanghai 201210, PR China
| | - Qianqian Jiang
- Bozhou University, 2266 Tangwang Avenue, Bozhou 236800, PR China
| | - Ying Guan
- Key Lab of State Forest and Grassland Administration of Wood Quality Improvement & Utilization, Hefei, Anhui 230036, PR China; School of Material Science and Chemistry, Anhui Agricultural University, Hefei, Anhui 230036, PR China
| | - Huahong Huang
- State Key Laboratory of Subtropical Silviculture, Zhejiang A&F University, Hangzhou 311300, Zhejiang, PR China
| | - Shengquan Liu
- Key Lab of State Forest and Grassland Administration of Wood Quality Improvement & Utilization, Hefei, Anhui 230036, PR China; School of Material Science and Chemistry, Anhui Agricultural University, Hefei, Anhui 230036, PR China
| | - Shengjie Ling
- School of Physical Science and Technology, ShanghaiTech University, 393 Middle Huaxia Road, Shanghai 201210, PR China; Shanghai Clinical Research and Trial Center, 201210 Shanghai, PR China; State Key Laboratory of Advanced Medical Materials and Devices, ShanghaiTech University, Shanghai 201210, PR China.
| | - Liang Zhou
- Key Lab of State Forest and Grassland Administration of Wood Quality Improvement & Utilization, Hefei, Anhui 230036, PR China; School of Material Science and Chemistry, Anhui Agricultural University, Hefei, Anhui 230036, PR China.
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Mohan M, Demerdash ON, Simmons BA, Singh S, Kidder MK, Smith JC. Physics-Based Machine Learning Models Predict Carbon Dioxide Solubility in Chemically Reactive Deep Eutectic Solvents. ACS OMEGA 2024; 9:19548-19559. [PMID: 38708262 PMCID: PMC11064036 DOI: 10.1021/acsomega.4c01175] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/05/2024] [Revised: 03/25/2024] [Accepted: 04/03/2024] [Indexed: 05/07/2024]
Abstract
Carbon dioxide (CO2) is a detrimental greenhouse gas and is the main contributor to global warming. In addressing this environmental challenge, a promising approach emerges through the utilization of deep eutectic solvents (DESs) as an ecofriendly and sustainable medium for effective CO2 capture. Chemically reactive DESs, which form chemical bonds with the CO2, are superior to nonreactive, physically based DESs for CO2 absorption. However, there are no accurate computational models that provide accurate predictions of the CO2 solubility in chemically reactive DESs. Here, we develop machine learning (ML) models to predict the solubility of CO2 in chemically reactive DESs. As training data, we collected 214 data points for the CO2 solubility in 149 different chemically reactive DESs at different temperatures, pressures, and DES molar ratios from published work. The physics-driven input features for the ML models include σ-profile descriptors that quantify the relative probability of a molecular surface segment having a certain screening charge density and were calculated with the first-principle quantum chemical method COSMO-RS. We show here that, although COSMO-RS does not explicitly calculate chemical reaction profiles, the COSMO-RS-derived σ-profile features can be used to predict bond formation. Of the models trained, an artificial neural network (ANN) provides the most accurate CO2 solubility prediction with an average absolute relative deviation of 2.94% on the testing sets. Overall, this work provides ML models that can predict CO2 solubility precisely and thus accelerate the design and application of chemically reactive DESs.
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Affiliation(s)
- Mood Mohan
- Biosciences
Division and Center for Molecular Biophysics, Oak Ridge National Laboratory, Oak Ridge, Tennessee 37831, United States
| | - Omar N. Demerdash
- Biosciences
Division and Center for Molecular Biophysics, Oak Ridge National Laboratory, Oak Ridge, Tennessee 37831, United States
| | - Blake A. Simmons
- Deconstruction
Division, Joint BioEnergy Institute, 5885 Hollis Street, Emeryville, California 94608, United States
- Biological
Systems and Engineering Division, Lawrence
Berkeley National Laboratory, 1 Cyclotron Road, Berkeley, California 94720, United States
| | - Seema Singh
- Deconstruction
Division, Joint BioEnergy Institute, 5885 Hollis Street, Emeryville, California 94608, United States
| | - Michelle K. Kidder
- Manufacturing
Science Division, Oak Ridge National Laboratory, Oak Ridge, Tennessee 37831-6201, United States
| | - Jeremy C. Smith
- Biosciences
Division and Center for Molecular Biophysics, Oak Ridge National Laboratory, Oak Ridge, Tennessee 37831, United States
- Department
of Biochemistry and Cellular and Molecular Biology, University of Tennessee, Knoxville, Tennessee 37996, United States
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Li S, Li T, Cai Y, Yao Z, He M. Rapid quantitative analysis of Rongalite adulteration in rice flour using autoencoder and residual-based model associated with portable Raman spectroscopy. SPECTROCHIMICA ACTA. PART A, MOLECULAR AND BIOMOLECULAR SPECTROSCOPY 2024; 304:123382. [PMID: 37725883 DOI: 10.1016/j.saa.2023.123382] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/21/2023] [Revised: 09/05/2023] [Accepted: 09/08/2023] [Indexed: 09/21/2023]
Abstract
Rice flour is a raw material for various foods and is used as a substitute for wheat flour. However, some merchants adulterate rice flour with the illegal additive Rongalite to extend the shelf life and earn illegal profits. Rongalite is highly carcinogenic, and ingestion of more than 10 g can even cause death. high-performance liquid chromatography (HPLC) and mass spectrometry (MS) are currently the main methods for detecting food adulteration, however, the existing methods have many limitations, complex operation, expensive instrumentation, etc. Raman spectroscopy has the advantages of convenience and non-destructive samples, but Raman spectroscopy can be affected by interference such as fluorescence background that affects detection, in addition to the problem of difficult quantitative analysis due to nonlinear bias. In this article, we used the preprocessing method of Savitzky-Golay smoothing filtering and VTPspline to improve the quality of the spectra and proposed the SARNet, which combines autoencoder and residual network to achieve the quantitative analysis of Rongalite content in rice flour. The new model combines a linear model with a nonlinear model, which can solve the nonlinear problem effectively. Experiments showed that the new SARNet model achieved state-of-the-art results, achieving the best R2 of 0.9703 and RMSEP of 0.0075. The lowest Rongalite concentration detected by the portable Raman spectrometer was 0.49%. In summary, the proposed method using portable Raman spectroscopy combined with machine learning has low detection bias and high accuracy, which can realize quantitative analyses of adulterated Rongalite in rice flour quickly. The method provides an accurate and nondestructive analytical tool in the field of food detection.
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Affiliation(s)
- Shiwen Li
- College of Engineering and Design, Hunan Normal University, Changsha, Hunan 410081, PR China
| | - Tian Li
- College of Engineering and Design, Hunan Normal University, Changsha, Hunan 410081, PR China
| | - Yaoyi Cai
- College of Engineering and Design, Hunan Normal University, Changsha, Hunan 410081, PR China
| | - Zekai Yao
- College of Engineering and Design, Hunan Normal University, Changsha, Hunan 410081, PR China
| | - Miaolei He
- College of Engineering and Design, Hunan Normal University, Changsha, Hunan 410081, PR China.
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Miranda-Valdez IY, Viitanen L, Intyre JM, Puisto A, Koivisto J, Alava M. Predicting effect of fibers on thermal gelation of methylcellulose using Bayesian optimization. Carbohydr Polym 2022; 298:119921. [DOI: 10.1016/j.carbpol.2022.119921] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/15/2022] [Revised: 07/22/2022] [Accepted: 07/22/2022] [Indexed: 11/29/2022]
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