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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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Gao W, Zhou L, Liu S, Guan Y, Gao H, Hu J. Machine learning algorithms for rapid estimation of holocellulose content of poplar clones based on Raman spectroscopy. Carbohydr Polym 2022; 292:119635. [DOI: 10.1016/j.carbpol.2022.119635] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/06/2022] [Revised: 05/08/2022] [Accepted: 05/16/2022] [Indexed: 11/02/2022]
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Luo SH, Wang WL, Zhou ZF, Xie Y, Ren B, Liu GK, Tian ZQ. Visualization of a Machine Learning Framework toward Highly Sensitive Qualitative Analysis by SERS. Anal Chem 2022; 94:10151-10158. [PMID: 35794045 DOI: 10.1021/acs.analchem.2c01450] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Abstract
Surface-enhanced Raman spectroscopy (SERS), providing near-single-molecule-level fingerprint information, is a powerful tool for the trace analysis of a target in a complicated matrix and is especially facilitated by the development of modern machine learning algorithms. However, both the high demand of mass data and the low interpretability of the mysterious black-box operation significantly limit the well-trained model to real systems in practical applications. Aiming at these two issues, we constructed a novel machine learning algorithm-based framework (Vis-CAD), integrating visual random forest, characteristic amplifier, and data augmentation. The introduction of data augmentation significantly reduced the requirement of mass data, and the visualization of the random forest clearly presented the captured features, by which one was able to determine the reliability of the algorithm. Taking the trace analysis of individual polycyclic aromatic hydrocarbons in a mixture as an example, a trustworthy accuracy no less than 99% was realized under the optimized condition. The visualization of the algorithm framework distinctly demonstrated that the captured feature was well correlated to the characteristic Raman peaks of each individual. Furthermore, the sensitivity toward the trace individual could be improved by least 1 order of magnitude as compared to that with the naked eye. The proposed algorithm distinguished by the lesser demand of mass data and the visualization of the operation process offers a new way for the indestructible application of machine learning algorithms, which would bring push-to-the-limit sensitivity toward the qualitative and quantitative analysis of trace targets, not only in the field of SERS, but also in the much wider spectroscopy world. It is implemented in the Python programming language and is open-source at https://github.com/3331822w/Vis-CAD.
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Affiliation(s)
- Si-Heng Luo
- State Key Laboratory for Physical Chemistry of Solid Surfaces, College of Chemistry and Chemical Engineering, Xiamen University, Xiamen, Fujian 361005, China.,State Key Laboratory of Marine Environmental Science, Fujian Provincial Key Laboratory for Coastal Ecology and Environmental Studies, Center for Marine Environmental Chemistry & Toxicology, College of the Environment and Ecology, Xiamen University, Xiamen 361102, China
| | - Wei-Li Wang
- State Key Laboratory of Marine Environmental Science, Fujian Provincial Key Laboratory for Coastal Ecology and Environmental Studies, Center for Marine Environmental Chemistry & Toxicology, College of the Environment and Ecology, Xiamen University, Xiamen 361102, China
| | - Zhi-Fan Zhou
- State Key Laboratory of Marine Environmental Science, Fujian Provincial Key Laboratory for Coastal Ecology and Environmental Studies, Center for Marine Environmental Chemistry & Toxicology, College of the Environment and Ecology, Xiamen University, Xiamen 361102, China
| | - Yi Xie
- Fujian Key Laboratory of Sensing and Computing for Smart City, School of Information Science and Engineering, Xiamen University, Xiamen, Fujian 361005, China.,Shenzhen Research Institute of Xiamen University, Xiamen University, Shenzhen 518000, China
| | - Bin Ren
- State Key Laboratory for Physical Chemistry of Solid Surfaces, College of Chemistry and Chemical Engineering, Xiamen University, Xiamen, Fujian 361005, China
| | - Guo-Kun Liu
- State Key Laboratory of Marine Environmental Science, Fujian Provincial Key Laboratory for Coastal Ecology and Environmental Studies, Center for Marine Environmental Chemistry & Toxicology, College of the Environment and Ecology, Xiamen University, Xiamen 361102, China
| | - Zhong-Qun Tian
- State Key Laboratory for Physical Chemistry of Solid Surfaces, College of Chemistry and Chemical Engineering, Xiamen University, Xiamen, Fujian 361005, China
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Mazurek S, Włodarczyk M, Pielorz S, Okińczyc P, Kuś PM, Długosz G, Vidal-Yañez D, Szostak R. Quantification of Salicylates and Flavonoids in Poplar Bark and Leaves Based on IR, NIR, and Raman Spectra. Molecules 2022; 27:3954. [PMID: 35745076 PMCID: PMC9229158 DOI: 10.3390/molecules27123954] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/20/2022] [Revised: 06/15/2022] [Accepted: 06/16/2022] [Indexed: 12/10/2022] Open
Abstract
Poplar bark and leaves can be an attractive source of salicylates and other biologically active compounds used in medicine. However, the biochemical variability of poplar material requires a standardization prior to processing. The official analytical protocols used in the pharmaceutical industry rely on the extraction of active compounds, which makes their determination long and costly. An analysis of plant materials in their native state can be performed using vibrational spectroscopy. This paper presents for the first time a comparison of diffuse reflectance in the near- and mid-infrared regions, attenuated total reflection, and Raman spectroscopy used for the simultaneous determination of salicylates and flavonoids in poplar bark and leaves. Based on 185 spectra of various poplar species and hybrid powdered samples, partial least squares regression models, characterized by the relative standard errors of prediction in the 4.5-9.9% range for both calibration and validation sets, were developed. These models allow for fast and precise quantification of the studied active compounds in poplar bark and leaves without any chemical sample treatment.
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Affiliation(s)
- Sylwester Mazurek
- Department of Chemistry, University of Wrocław, 14 F. Joliot-Curie, 50-383 Wrocław, Poland; (S.P.); (R.S.)
| | - Maciej Włodarczyk
- Department of Pharmacognosy and Herbal Medicines, Faculty of Pharmacy, Wroclaw Medical University, 211a Borowska, 50-556 Wrocław, Poland; (P.O.); (P.M.K.); (G.D.); (D.V.-Y.)
| | - Sonia Pielorz
- Department of Chemistry, University of Wrocław, 14 F. Joliot-Curie, 50-383 Wrocław, Poland; (S.P.); (R.S.)
| | - Piotr Okińczyc
- Department of Pharmacognosy and Herbal Medicines, Faculty of Pharmacy, Wroclaw Medical University, 211a Borowska, 50-556 Wrocław, Poland; (P.O.); (P.M.K.); (G.D.); (D.V.-Y.)
| | - Piotr M. Kuś
- Department of Pharmacognosy and Herbal Medicines, Faculty of Pharmacy, Wroclaw Medical University, 211a Borowska, 50-556 Wrocław, Poland; (P.O.); (P.M.K.); (G.D.); (D.V.-Y.)
| | - Gabriela Długosz
- Department of Pharmacognosy and Herbal Medicines, Faculty of Pharmacy, Wroclaw Medical University, 211a Borowska, 50-556 Wrocław, Poland; (P.O.); (P.M.K.); (G.D.); (D.V.-Y.)
| | - Diana Vidal-Yañez
- Department of Pharmacognosy and Herbal Medicines, Faculty of Pharmacy, Wroclaw Medical University, 211a Borowska, 50-556 Wrocław, Poland; (P.O.); (P.M.K.); (G.D.); (D.V.-Y.)
- Faculty of Pharmacy, University of Barcelona, Joan XXIII, 27-31, 08014 Barcelona, Spain
| | - Roman Szostak
- Department of Chemistry, University of Wrocław, 14 F. Joliot-Curie, 50-383 Wrocław, Poland; (S.P.); (R.S.)
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Gao W, Zhou L, Liu S, Guan Y, Gao H, Hui B. Machine learning prediction of lignin content in poplar with Raman spectroscopy. BIORESOURCE TECHNOLOGY 2022; 348:126812. [PMID: 35131461 DOI: 10.1016/j.biortech.2022.126812] [Citation(s) in RCA: 15] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/03/2022] [Revised: 01/29/2022] [Accepted: 01/31/2022] [Indexed: 06/14/2023]
Abstract
Based on features extracted from Raman spectra, regularization algorithms, SVR, DT, RF, LightGBM, CatBoost, and XGBoost were used to develop prediction models for lignin content in poplar. Firstly, Raman features extracted from FT-Raman spectra after data processing were used as input of models and determined lignin contents were output. Secondly, grid-search combined with cross-validation was used to adjust the hyper-parameters of models. Finally, the predictive models were built by aforementioned algorithms. The results indicated regularization algorithms, SVR, DT held test R2 were >0.80 which means the predictive values from model still deviate from measured ones. Meanwhile, RF, LightGBM, CatBoost, and XGBoost were better than above algorithms, and their test R2 were >0.91 which suggesting the predictive values was nearly close to measured ones. Therefore, fast and accurate methods for predicting lignin content were obtained and will be useful for screening suitable lignocellulosic resource with expected lignin content.
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Affiliation(s)
- Wenli Gao
- School of Forestry and Landscape Architecture, Anhui Agricultural University, Hefei, Anhui 230036, PR China; Key Lab of State Forest and Grassland Administration on Wood Quality Improvement & High Efficient Utilization, Hefei, Anhui 230036, PR China
| | - Liang Zhou
- School of Forestry and Landscape Architecture, Anhui Agricultural University, Hefei, Anhui 230036, PR China; Key Lab of State Forest and Grassland Administration on Wood Quality Improvement & High Efficient Utilization, Hefei, Anhui 230036, PR China.
| | - Shengquan Liu
- School of Forestry and Landscape Architecture, Anhui Agricultural University, Hefei, Anhui 230036, PR China; Key Lab of State Forest and Grassland Administration on Wood Quality Improvement & High Efficient Utilization, Hefei, Anhui 230036, PR China
| | - Ying Guan
- School of Forestry and Landscape Architecture, Anhui Agricultural University, Hefei, Anhui 230036, PR China; Key Lab of State Forest and Grassland Administration on Wood Quality Improvement & High Efficient Utilization, Hefei, Anhui 230036, PR China
| | - Hui Gao
- School of Forestry and Landscape Architecture, Anhui Agricultural University, Hefei, Anhui 230036, PR China; Key Lab of State Forest and Grassland Administration on Wood Quality Improvement & High Efficient Utilization, Hefei, Anhui 230036, PR China
| | - Bin Hui
- State Key Laboratory of Bio-Fibers and Eco-Textiles, Institute of Marine Biobased Materials, School of Materials Science and Engineering, Qingdao University, Qingdao 266071, PR China
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Gao W, Shu T, Guan Y, Ling S, Liu S, Zhou L. Novel strategy for establishment of an FT-Raman spectroscopy based quantitative model for poplar holocellulose content determination. Carbohydr Polym 2022; 277:118793. [PMID: 34893223 DOI: 10.1016/j.carbpol.2021.118793] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/15/2021] [Revised: 10/13/2021] [Accepted: 10/17/2021] [Indexed: 01/07/2023]
Abstract
Raman spectroscopy is effective for studying the ultrastructure, lignin content, and cellulose crystallinity of lignocellulosic materials. However, the quantitative analysis of holocellulose in lignocellulosic materials by this technique is challenging. In this study, based on Fourier-transform Raman (FT-Raman) spectroscopy, a novel strategy for building poplar holocellulose content quantitative model was proposed. Different algorithms were applied, including Principal component regression (PCR), partial least square regression (PLSR), ridge regression (RR), lasso regression (LR), and elastic net regression (ENR). Combined with different algorithms, twelve candidates of internal standard were selected. Sixty models combined by five regression algorithms and twelve internal standards were performed by five-fold cross validation. Consequently, the models constructed through RR, LR, and ENR combined with the internal standard of peak intensity of 2945 cm-1 were credible (Rp > 0.9, RMSEp < 1.0, and MAEp < 0.9). Credible models were obtained, indicating the high potential of FT-Raman spectroscopy for predicting the holocellulose content of lignocellulosic materials.
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Affiliation(s)
- Wenli Gao
- School of Forestry and Landscape Architecture, Anhui Agricultural University, Hefei, Anhui 230036, China; Key Lab of State Forest and Grassland Administration on Wood Quality Improvement & High Efficient Utilization, Hefei, Anhui 230036, China
| | - Ting Shu
- School of Physical Science and Technology, Shanghai Tech University, 393 Middle Huaxia Road, Shanghai 201210, China
| | - Ying Guan
- School of Forestry and Landscape Architecture, Anhui Agricultural University, Hefei, Anhui 230036, China
| | - Shengjie Ling
- School of Physical Science and Technology, Shanghai Tech University, 393 Middle Huaxia Road, Shanghai 201210, China
| | - Shengquan Liu
- School of Forestry and Landscape Architecture, Anhui Agricultural University, Hefei, Anhui 230036, China; Key Lab of State Forest and Grassland Administration on Wood Quality Improvement & High Efficient Utilization, Hefei, Anhui 230036, China.
| | - Liang Zhou
- School of Forestry and Landscape Architecture, Anhui Agricultural University, Hefei, Anhui 230036, China; Key Lab of State Forest and Grassland Administration on Wood Quality Improvement & High Efficient Utilization, Hefei, Anhui 230036, China.
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van Wyngaard E, Blancquaert E, Nieuwoudt H, Aleixandre-Tudo JL. Infrared Spectroscopy and Chemometric Applications for the Qualitative and Quantitative Investigation of Grapevine Organs. FRONTIERS IN PLANT SCIENCE 2021; 12:723247. [PMID: 34539716 PMCID: PMC8448193 DOI: 10.3389/fpls.2021.723247] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/10/2021] [Accepted: 08/09/2021] [Indexed: 05/12/2023]
Abstract
The fourth agricultural revolution is leading us into a time of using data science as a tool to implement precision viticulture. Infrared spectroscopy provides the means for rapid and large-scale data collection to achieve this goal. The non-invasive applications of infrared spectroscopy in grapevines are still in its infancy, but recent studies have reported its feasibility. This review examines near infrared and mid infrared spectroscopy for the qualitative and quantitative investigation of intact grapevine organs. Qualitative applications, with the focus on using spectral data for categorization purposes, is discussed. The quantitative applications discussed in this review focuses on the methods associated with carbohydrates, nitrogen, and amino acids, using both invasive and non-invasive means of sample measurement. Few studies have investigated the use of infrared spectroscopy for the direct measurement of intact, fresh, and unfrozen grapevine organs such as berries or leaves, and these studies are examined in depth. The chemometric procedures associated with qualitative and quantitative infrared techniques are discussed, followed by the critical evaluation of the future prospects that could be expected in the field.
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Affiliation(s)
- Elizma van Wyngaard
- South African Grape and Wine Research Institute (SAGWRI), Department of Viticulture and Oenology, Stellenbosch University, Stellenbosch, South Africa
| | - Erna Blancquaert
- South African Grape and Wine Research Institute (SAGWRI), Department of Viticulture and Oenology, Stellenbosch University, Stellenbosch, South Africa
| | - Hélène Nieuwoudt
- South African Grape and Wine Research Institute (SAGWRI), Department of Viticulture and Oenology, Stellenbosch University, Stellenbosch, South Africa
| | - Jose Luis Aleixandre-Tudo
- South African Grape and Wine Research Institute (SAGWRI), Department of Viticulture and Oenology, Stellenbosch University, Stellenbosch, South Africa
- Instituto de Ingeniería de Alimentos para el Desarrollo (IIAD), Departamento de Tecnologia de Alimentos, Universidad Politécnica de Valencia, Valencia, Spain
- *Correspondence: Jose Luis Aleixandre-Tudo,
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