A model transfer strategy based on screening stable wavelength for quantitative analysis of holocellulose and lignin content distribution in pulpwood
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OpenAlex topics: Spectroscopy and Chemometric Analyses · Wood Treatment and Properties · Wood and Agarwood Research
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References (36)
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1. Tsuchikawa S, Schwanninger M (2013) A review of recent near-infrared research for wood and paper (part 2). Appl Spectrosc Rev 48(7):560–58710.1080/05704928.2011.621079 ↗
2. Liang L, Fang G, Deng Y, Xiong Z, Wu T (2019) Determination of moisture content and basic density of poplar wood chips under various moisture conditions by near-infrared spectroscopy. For Sci 65(5):548–555
3. Sheng X, Zan J, Jiang Y, Shen S, Li L, Yuan H (2023) Data fusion strategy for rapid prediction of moisture content during drying of black tea based on micro-NIR spectroscopy and machine vision. Optik 276:17064510.1016/j.ijleo.2023.170645 ↗
4. Schwanninger M, Rodrigues JC, Fackler K (2011) A review of band assignments in near infrared spectra of wood and wood components. J Near Infrared Spectrosc 19(5):287–30810.1255/jnirs.955 ↗
5. Yang SY, Han Y, Chang YS, Park JH, Park Y, Chung H, Yeo H (2018) Classification of the hot air heat treatment degree of larch wood using a multivariate analysis of near-infrared spectroscopy. J Wood Sci 64:220–22510.1007/s10086-018-1706-z ↗
6. Yang H, Liu Y, Xiong Z, Liang L (2020) Rapid determination of holocellulose and lignin in wood by near infrared spectroscopy and kernel extreme learning machine. Anal Lett 53(7):1140–115410.1080/00032719.2019.1700267 ↗
7. Santos AJ, Alves AM, Simões RM, Pereira H, Rodrigues J, Schwanninger M (2012) Estimation of wood basic density of Acacia melanoxylon (R. Br.) by near infrared spectroscopy. J Near Infrared Spectrosc 20(2):267–27410.1255/jnirs.986 ↗
8. He W, Hu H (2013) Prediction of hot-water-soluble extractive, pentosan and cellulose content of various wood species using FT-NIR spectroscopy. Biores Technol 140:299–30510.1016/j.biortech.2013.04.115 ↗
9. Ishizuka S, Sakai Y, Tanaka-Oda A (2014) Quantifying lignin and holocellulose content in coniferous decayed wood using near-infrared reflectance spectroscopy. J For Res 19(1):233–23710.1007/s10310-012-0386-6 ↗
10. Yang J, Li J, Hu J, Yang W, Zhang X, Xu J, Ying Y (2022) An interpretable deep learning approach for calibration transfer among multiple near-infrared instruments. Comput Electron Agric 192:10658410.1016/j.compag.2021.106584 ↗
11. Feudale RN, Woody NA, Tan H, Myles AJ, Brown SD, Ferré J (2002) Transfer of multivariate calibration models: a review. Chemom Intell Lab Syst 64(2):181–19210.1016/S0169-7439(02)00085-0 ↗
12. Fan S, Li J, Xia Y, Tian X, Guo Z, Huang W (2019) Long-term evaluation of soluble solids content of apples with biological variability by using near-infrared spectroscopy and calibration transfer method. Postharvest Biol Technol 151:79–8710.1016/j.postharvbio.2019.02.001 ↗
13. Rehman TU, Ma D, Wang L, Zhang L, Jin J (2020) Predictive spectral analysis using an end-to-end deep model from hyperspectral images for high-throughput plant phenotyping. Comput Electron Agric 177:10571310.1016/j.compag.2020.105713 ↗
14. Liu Y, Cai W, Shao X (2016) Linear model correction: a method for transferring a near-infrared multivariate calibration model without standard samples. Spectrochim Acta Part A Mol Biomol Spectrosc 169:197–20110.1016/j.saa.2016.06.041 ↗
15. Ajala EM (2012) Calibration transfer between different analytical methods. In: The African symposium: an online journal of the African educational research network, vol 12, no 1, pp 141–149
16. Zhang L, Small GW, Arnold MA (2003) Multivariate calibration standardization across instruments for the determination of glucose by Fourier transform near-infrared spectrometry. Anal Chem 75(21):5905–591510.1021/ac034495x ↗
17. Parrott AJ, McIntyre AC, Holden M, Colquhoun G, Chen ZP, Littlejohn D, Nordon A (2022) Calibration model transfer in mid-infrared process analysis with in situ attenuated total reflectance immersion probes. Anal Methods 14(19):1889–189610.1039/D2AY00116K ↗
18. Zhang Z, Li Y, Li C, Wang Z, Chen Y (2022) Algorithm of stability-analysis-based feature selection for NIR calibration transfer. Sensors 22(4):165910.3390/s22041659 ↗
19. Yu Y, Huang J, Liu S, Zhu J, Liang S (2021) Cross target attributes and sample types quantitative analysis modeling of near-infrared spectroscopy based on instance transfer learning. Measurement 177:10934010.1016/j.measurement.2021.109340 ↗
20. Ni L, Xiao L, Zhang L (2018) Calibration transfer of near infrared spectral models without standards based on spectrum ratio analysis. J Instrum Anal 5:539–546
21. Zhang L, Li Y, Huang W, Ni L, Ge J (2020) The method of calibration model transfer by optimizing wavelength combinations based on consistent and stable spectral signals. Spectrochim Acta Part A Mol Biomol Spectrosc 227:11764710.1016/j.saa.2019.117647 ↗
22. Xing Z, Du C, Shen Y, Ma F, Zhou J (2021) A method combining FTIR-ATR and Raman spectroscopy to determine soil organic matter: improvement of prediction accuracy using competitive adaptive reweighted sampling (CARS). Comput Electron Agric 191:10654910.1016/j.compag.2021.106549 ↗
23. Hao Y, Sun XD, Pan YY, Gao RJ, Liu YD (2011) Detection of firmness and surface color of pear by near infrared spectroscopy based on Monte Carlo uninformative variables elimination method. Spectrosc Spectr Anal 31(5):1225–1229
24. Liang L, Wei L, Fang G, Xu F, Deng Y, Shen K, Zhu B (2020) Prediction of holocellulose and lignin content of pulp wood feedstock using near infrared spectroscopy and variable selection. Spectrochim Acta Part A Mol Biomol Spectrosc 225:11751510.1016/j.saa.2019.117515 ↗
25. Bi Y, Yuan K, Xiao W, Wu J, Shi C, Xia J, Zhou G (2016) A local pre-processing method for near-infrared spectra, combined with spectral segmentation and standard normal variate transformation. Anal Chim Acta 909:30–4010.1016/j.aca.2016.01.010 ↗
26. Li Y, Pan T, Li H, Chen S (2020) Non-invasive quality analysis of thawed tuna using near infrared spectroscopy with baseline correction. J Food Process Eng 43(8):e1344510.1111/jfpe.13445 ↗
27. Mehmood T, Liland KH, Snipen L, Sæbø S (2012) A review of variable selection methods in partial least squares regression. Chemom Intell Lab Syst 118:62–6910.1016/j.chemolab.2012.07.010 ↗
28. Breure TS, Prout JM, Haefele SM, Milne AE, Hannam JA, Moreno-Rojas S, Corstanje R (2022) Comparing the effect of different sample conditions and spectral libraries on the prediction accuracy of soil properties from near-and mid-infrared spectra at the field-scale. Soil Tillage Res 215:10519610.1016/j.still.2021.105196 ↗
29. Guan X, Liu J, Huang K, Kuang J, Liu D (2019) Evaluation of moisture content in processed apple chips using NIRS and wavelength selection techniques. Infrared Phys Technol 98:305–31010.1016/j.infrared.2019.01.010 ↗
30. Hao Y, Li Z, Ding N, Tang X, Zhang C (2022) A new near-infrared fluorescence probe synthesized from IR-783 for detection and bioimaging of hydrogen peroxide in vitro and in vivo. Spectrochim Acta Part A Mol Biomol Spectrosc 268:12064210.1016/j.saa.2021.120642 ↗
31. Rossel RV, Behrens T (2010) Using data mining to model and interpret soil diffuse reflectance spectra. Geoderma 158(1–2):46–5410.1016/j.geoderma.2009.12.025 ↗
32. Wang H, Xiong Z, Hu Y, Liu Z, Liang L (2022) Transfer strategy for near infrared analysis model of holocellulose and lignin based on improved slope/bias algorithm. BioResources 17(4):647610.15376/biores.17.4.6476-6489 ↗
33. He X, Ding G (2023) Development of a CH2-dependent analytical method using near-infrared spectroscopy via the integration of two algorithms: non-dominated sorting genetic-II and competitive adaptive reweighted sampling (NSGAII-CARS). Anal Methods 15(10):1286–129610.1039/D2AY02072F ↗
34. Wang H, Xiong Z, Hu Y, Liu Z, Liang L (2023) Application of wavelength selection strategy based on SWCSS-UVE algorithm in improving model transfer for near infrared analysis of holocellulose and lignin. Wood Mater Sci Eng 18(4):1405–141410.1080/17480272.2022.2144760 ↗
35. Hein PRG, Campos ACM, Mendes RF, Mendes LM, Chaix G (2011) Beurteilung physikalischer und mechanischer Eigenschaften von agro-basierten Spanplatten mittels Nahinfrarot-Spektroskopie. Eur J Wood Wood Prod 69:431–44210.1007/s00107-010-0471-5 ↗
36. Dwivedi P, Vivekanand V, Pareek N, Sharma A, Singh RP (2010) Bleach enhancement of mixed wood pulp by xylanase–laccase concoction derived through co-culture strategy. Appl Biochem Biotechnol 160:255–26810.1007/s12010-009-8654-4 ↗