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Method to characterize color of biochar and its prediction with biochar yield as model property

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Abstract

Biochar has trigged increasing attention in the last decade due to its multiple functionalities, in which, color is not only the visual appearance, but also a useful indicator of relevant properties as well as an important tool to predict biochar’s properties. However, biochar color characterization remains challenged currently. In this work, three different feedstock-derived biochars were produced and basic color information (from color spaces RGB, HSB and CIE L*a*b*) of their scanned images was extracted. Then principal component analysis (PCA) and nonmetric multidimensional scaling analysis (NMDS) were employed on the combinations of different color indexes to cluster biochars. With the assumption that clusters from both PCA and NMDS were as consistent as possible with that of visual intuition of biochar color, we identified feedstock-independent color indexes [(R + G-B)/(R + G + B), (R + B-G)/(R + G + B), (G + B-R)/(R + G + B), L, a, b] to characterize color of biochar, which can in microscopic perspective elucidate color differences with respect to pyrolysis temperature and feedstock type. Finally, prediction ability of color indexes with biochar yield as model property was explored and good performance was observed for both partial least squares regression (R2 = 0.8653) and neural network regression (R2 = 0.9720) with the latter being more powerful. The proposed color indexes will find more applications in prediction of biochar properties and functions in future.

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References

  • Amaral ESD, Silva DV, Anjos LD, Schilling AC, Dalmolin AC, Mielke MS (2019) Relationships between reflectance and absorbance chlorophyll indices with RGB (red, green, blue) image components in seedlings of tropical tree species at nursery stage. New for 50:377–388

    Article  Google Scholar 

  • Chen WH, Lin BJ, Lin YY, Chu YS, Ubando AT, Show PL, Ong HC, Chang JS, Ho SH, Culaba AB, Pétrissans A, Pétrissans M (2021) Progress in biomass torrefaction: principles, applications and challenges. Prog Energy Combust 82:100887

    Article  Google Scholar 

  • Cheng HD, Jiang XH, Sun Y, Wang JL (2001) Color image segmentation: advances and prospects. Pattern Recognit 34:2259–2281

    Article  Google Scholar 

  • Ghodake GS, Shinde SK, Kadam AA, Saratale RG, Saratale GD, Kumar M, Palem RR, Al-Shwaiman HA, Elgorban AM, Syed A, Kim DY (2021) Review on biomass feedstocks, pyrolysis mechanism and physicochemical properties of biochar: state-of-the-art framework to speed up vision of circular bioeconomy. J Clean Prod 297:126645

    Article  CAS  Google Scholar 

  • Gholizadeh A, Saberioon M, Viscarra Rossel RA, Boruvka L, Klement A (2020) Spectroscopic measurements and imaging of soil colour for field scale estimation of soil organic carbon. Geoderma 357:113972

    Article  CAS  Google Scholar 

  • Gonçalves Dias Diniz PH (2020) Chemometrics-assisted color histogram-based analytical systems. J Chemom 34:e3242

    Article  Google Scholar 

  • Gürgen A, Topaloglu E, Ustaömer D, Yıldız S, Ay N (2019) Prediction of the colorimetric parameters and mass loss of heattreated bamboo: comparison of multiple linear regression and artificial neural network method. Color Res Appl 44:824–833

    Article  Google Scholar 

  • Huang ZL, Jiang SL, Guo JY, Wang XR, Tan MJ, Xiong RX, Wang ZW, Wu ZJ, Li H (2020) Oxidative Torrefaction of Phragmites australis: gas-pressurized effects and correlation analysis based on color value. Energy Fuels 34:11073–11082

    Article  CAS  Google Scholar 

  • Ippolito JA, Cui LQ, Kammann C, Wrage-Mönnig N, Estavillo JM, Fuertes-Mendizabal T, Cayuela ML, Sigua G, Novak J, Spokas K, Borchard N (2020) Feedstock choice, pyrolysis temperature and type influence biochar characteristics: a comprehensive meta-data analysis review. Biochar 2:421–438

    Article  Google Scholar 

  • Li H, Wang SY, Yuan XZ, Xi YN, Huang ZL, Tan MJ, Li CZ (2018) The effects of temperature and color value on hydrochars’ properties in hydrothermal carbonization. Bioresour Technol 249:574–581

    Article  CAS  Google Scholar 

  • Li ZH, Han D, Yang CJ, Zhang TY, Yu HQ (2019) Probing operational conditions of mixing and oxygen deficiency using HSV color space. J Environ Manag 232:985–992

    Article  CAS  Google Scholar 

  • Li M, Rong LL, Zhou SF, Xiao XY, Wu LG, Fan YX, Lu CH, Zou XM (2020) Dissipation of sulfonamides in soil emphasizing taxonomy and function of microbiomes by metagenomic analysis. J Agric Food Chem 68:13594–13607

    Article  CAS  Google Scholar 

  • Lu L, Yu WT, Wang YF, Zhang K, Zhu XM, Zhang YC, Wu YJ, Ullah H, Xiao X, Chen BL (2020) Application of biochar-based materials in environmental remediation: from multi-level structures to specific devices. Biochar 2:1–13

    Article  Google Scholar 

  • Makled WA, Tahoun SS (2015) Digital quantification of the miospore coloration to assess the thermal maturity: novel RGB-based measuring technique. Mar Pet Geol 67:1–15

    Article  Google Scholar 

  • Moya R, Marín JD (2011) Grouping of Tectona grandis (L.f.) clones using wood color and stiffness. New for 42:329–345

    Article  Google Scholar 

  • Nguyen TT, Nguyen THV, Ji XD, Yuan BN, Trinh HM, Do KTL, Guo MH (2019) Prediction of the color change of heat-treated wood during artificial weathering by artificial neural network. Eur J Wood Wood Prod 77:1107–1116

    Article  Google Scholar 

  • Parks DH, Beiko RG (2010) Identifying biologically relevant differences between metagenomic communities. Bioinformatics 26:715–721

    Article  CAS  Google Scholar 

  • Parks DH, Tyson GW, Hugenholtz P, Beiko RG (2014) STAMP: statistical analysis of taxonomic and functional profiles. Bioinformatics 30:3123–3124

    Article  CAS  Google Scholar 

  • Peng YS, Wang Y (2019) Prediction of the chlorophyll content in pomegranate leaves based on digital image processing technology and stacked sparse autoencoder. Int J Food Prop 22:1720–1732

    Article  CAS  Google Scholar 

  • Phillips CL, Meyer KM, Hanson CV, Biraud SC, Trippe KM (2021) Manipulating rangeland soil microclimate with juniper biochar for improved native seedling establishment. Soil Sci Soc Am J 85:1–15

    Article  Google Scholar 

  • Qi RY, Chen ZW, Wang MF, Wu RT, Jiang EC (2019) Prediction method for torrefied rice husk based on gray-scale analysis. ACS Omega 4:17837–17842

    Article  CAS  Google Scholar 

  • Sgarbossa A, Costa C, Menesatti P, Antonucci F, Pallottino F, Zanetti M, Grigolato S, Cavalli S (2014) Colorimetric patterns of wood pellets and their relations with quality and energy parameters. Fuel 137:70–76

    Article  CAS  Google Scholar 

  • Sigmund G, Gharasoo M, Hüffer T, Hofmann T (2020) Deep learning neural network approach for predicting the sorption of ionizable and polar organic pollutants to a wide range of carbonaceous materials. Environ Sci Technol 54:4583–4591

    Article  CAS  Google Scholar 

  • Taheri-Garavand A, Fatahi S, Shahbazi F, de la Guardia M (2019) A nondestructive intelligent approach to real-time evaluation of chicken meat freshness based on computer vision technique. J Food Process Eng 42:e13039

    Article  Google Scholar 

  • Tan MJ, Li H, Huang ZL, Wang ZW, Xiong RX, Jiang SL, Zhang JC, Wu ZJ, Li CZ, Luo L (2021) Comparison of atmospheric and gas-pressurized oxidative torrefaction of heavy-metal-polluted rice straw. J Clean Prod 283:124636

    Article  CAS  Google Scholar 

  • Thompson LC, Ciesielski PN, Jarvis MW, Mukarakate C, Nimlos MR, Donohoe BS (2017) Estimating the temperature experienced by biomass particles during fast pyrolysis using microscopic analysis of biochars. Energy Fuels 31:8193–8201

    Article  CAS  Google Scholar 

  • Xi YN, Yuan XZ, Tan MJ, Jiang SL, Wang ZL, Huang ZL, Wang H, Jiang LB, Li H (2021) Properties of oxidatively torrefied Chinese fir residue: color dimension, pyrolysis kinetics, and storage behavior. Fuel Process Technol 213:106663

    Article  CAS  Google Scholar 

  • Zeng JZ, Cao LQ, Xu MY, Zhu T, Hang JZH (2020) Complex reaction processes in combustion unraveled by neural network-based molecular dynamics simulation. Nat Commun 11:5713

    Article  CAS  Google Scholar 

Download references

Acknowledgements

This work was financially supported by National Natural Science Foundation of China (31760165, 41661095, 51969008), Outstanding Scholarship of Jiangxi Scientific Committee (20192BCBL23014), Natural Science Foundation of Jiangxi Province (20202BABL203024), Education Department of Jiangxi Province (GJJ190540), Foundation of Key Laboratory of Yangtze River Water Environment Ministry of Education (Tongji University) (YRWEF201907), Foundation for postdoctoral research in Jiangxi Province (2018KY48), Foundation of President of the Zhongke-Ji’an Institute for Eco-Environmental Sciences (ZJIEES-2020-05), College student innovation and entrepreneurship project of China (202010419023) and College student innovation and entrepreneurship project of Jiangxi Province (S202010419039). We also greatly appreciate the thoughtful comments and constructive suggestions from the two reviewers for improving the quality of the manuscript.

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Authors and Affiliations

  1. School of Life Science, Jinggangshan University, Ji’an, 343009, China

    Yuxing Fan, Yingying Xiong, Yingying Zhang, Zhangsong Jiang, Haihui Tang, Ligui Wu, Mi Li, Xiaoyu Xiao, Cui Hu & Xiaoming Zou

  2. College of Environmental Science and Engineering, Guilin University of Technology, Guilin, 541004, China

    Yuxing Fan, Zhangsong Jiang, Haihui Tang & Xiaoming Zou

  3. Zhongke-Ji’an Institute for Eco-Environmental Sciences, Ji’an, 343016, China

    Xiaoyu Xiao

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  1. Yuxing Fan
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  2. Yingying Xiong
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  4. Zhangsong Jiang
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  5. Haihui Tang
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  7. Mi Li
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  9. Cui Hu
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  10. Xiaoming Zou
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Correspondence to Mi Li or Xiaoming Zou.

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Fan, Y., Xiong, Y., Zhang, Y. et al. Method to characterize color of biochar and its prediction with biochar yield as model property. Biochar 3, 687–699 (2021). https://doi.org/10.1007/s42773-021-00119-w

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  • DOI: https://doi.org/10.1007/s42773-021-00119-w

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