DETECTING CONTAMINANTS IN POST-CONSUMER PLASTIC PACKAGING WASTE BY A NIR HYPERSPECTRAL IMAGING-BASED CASCADE DETECTION APPROACH
- Giuseppe Bonifazi - Department of Chemical Engineering, Materials and Environment, Sapienza University of Rome, Italy
- Riccardo Gasbarrone - Department of Chemical Engineering, Materials and Environment, Sapienza University of Rome, Italy
- Silvia Serranti - Department of Chemical Engineering, Materials and Environment, Sapienza University of Rome, Italy
- Available online in Detritus - Volume 15 - June 2021
- Pages 94-106
Released under CC BY-NC-ND
Copyright: © 2020 CISA Publisher
Abstract
Recycling of post-consumer packaging wastes involves a complex chain of activities, usually based on three main stages, that is: i) collection from households or recovery from Municipal solid waste (MSW), ii) sorting and, finally, iii) mechanical recycling. The systematic identification of impurities inside plastic packaging waste streams, and the assessment of the different occurring materials, can be considered as one of the key issues to certify and to classify waste materials fed to recycling plants and to perform a full control of the resulting processed fractions and byproducts, that have to comply with market demands. The utilization of a Near InfraRed (NIR) – HyperSpectral Imaging (HSI) based methods, along with chemometrics and machine learning techniques, can fulfill these goals. In this paper, the HSI-based sorting logics, to apply, to implement and to set up to perform an automatic separation of paper, cardboard, plastics and multilayer packaging are investigated.Keywords
Editorial History
- Received: 15 Dec 2020
- Revised: 09 Mar 2021
- Accepted: 12 Mar 2021
- Available online: 06 Jun 2021
References
Agresti G, Bonifazi G., Calienno L., Capobianco G., Lo Monaco A., Pelosi C., Picchio R. and Serranti, S., 2013. Surface Investigation of Photo-Degraded Wood by Colour Monitoring, Infrared Spectroscopy, and Hyperspectral Imaging. Journal of Spectroscopy, 380536, 2314-4920.
DOI 10.1155/2013/380536
Ballabio D., and Todeschini R., 2009. Multivariate Classification for Qualitative Analysis. In S. Da-Wen, Infrared Spectroscopy for Food Quality Analysis and Control, 83-104
Bonifazi G., Palmieri R., Serranti S., 2015a, Short wave infrared hyperspectral imaging for recovered postconsumer single and mixed polymers characterization. Image Sensors and Imaging Systems 2015, Proc. of SPIE-IS&T Electronic Imaging, SPIE Vol. 9403, 94030R.
DOI 10.1117/12.2081362
Bonifazi G., Palmieri R. and Serranti S., 2015b. Hyperspectral imaging applied to end-of-life (EOL) concrete recycling, tm-Technisches Messen, 82 (12), 616–624
Bonifazi G., Serranti S., Bonoli A. and Dall’Ara A., 2009. Innovative recognition-sorting procedures applied to solid waste: the hyperspectral approach, Sustainable Development and Planning IV, 2, 885-889
Capobianco G., Prestileo F., Serranti S. and Bonifazi G., 2015, Hyperspectral imaging-based approach for the in-situ characterization of ancient Roman wall paintings, Periodico di Mineralogia, 3A (Special issue), 407-418
DV Optics S.r.l. 2008. Spectral Scanner Operative Manual (Version 2.0). Italy. Available at: http://www.dvoptic.com/index.html (Accessed: 5 March 2021)
Eigenvector, 2016. Using Cross-Validation. Available at: http://wiki.eigenvector.com/index.php?title=Using_Cross-Validation (Accessed: 5 March 2021)
Eigenvector, 2017. Advanced Preprocessing: Sample Normalization. Available at: http://wiki.eigenvector.com/index.php?title=Advanced_Preprocessing:_Sample_Normalization (Accessed: 5 March 2021)
Eigenvector, 2018. Preprocess. Available at: http://www.wiki.eigenvector.com/index.php?title=Preprocess (Accessed: 5 March 2021)
Fawcett T., 2006). An introduction to ROC analysis. Pattern Recognition Letters, 27 (8), 861-874
Hollstein F., Wohllebe M., Arnaiz S., 2015. Identification and Sorting of Plastics Film Waste by NIR-Hyperspectral-Imaging. near infrared spectroscopy: proceedings of the international conference, 2015, Foz do Iguaçu, Brazil
Hu B., Serranti S., Fraunolcz N., Di Maio F. and Bonifazi G., 2013. Recycling-oriented characterization of polyolefin packaging waste. Waste Management, 33 (3), 574-584.
DOI 10.1016/j.wasman.2012.11.018
Jansen M., Thoden van Velzen E.U. and Pretz Th., 2015. Handbook for sorting of plastic packaging waste concentrates: separation efficiencies of common plastic packaging objects in widely used separation machines at existing sorting facilities with mixed postconsumer plastic packaging waste as input. (Reports of Wageningen UR Food & Biobased Research; No. 1604) Wageningen UR Food & Biobased Research, Wageningen, Netherlands
Kumaravelu C., and Gopal A., 2015. A Review on the applications of Near-Infrared Spectrometer and Chemometrics for the Agro-Food Processing Industries. 2015 IEEE Technological Innovation in ICT for Agriculture and Rural Development (TIAR), Chennai, India, 8-12.
DOI 10.1109/TIAR.2015.7358523
Larrechi M.S., and Callao M.P., 2003. Strategy for introducing NIR spectroscopy and multivariate calibration techniques in industry. Trends in Analytical Chemistry, 22 (9), 634-640.
DOI 10.1016/S0165-9936(03)01005-7
Palmieri R., Bonifazi G., Serranti S., 2014. Recycling-oriented characterization of plastic frames and printed circuit boards from mobile phones by electronic and chemical imaging, Waste Management, 34 (11), 2120–2130.
DOI 10.1016/j.wasman.2014.06.003
Pasquini C., 2003. Near Infrared Spectroscopy: Fundamentals, Practical Aspects and Analytical Applications, Journal of the Brazilian Chemical Society, 14 (2), 198-219.
DOI 10.1590/S0103-50532003000200006
Rinnan Å., van den Berg F. and Engelsen S.B., 2009. Review of the most common pre-processing techniques for near-infrared spectra, TrAC Trends in Analytical Chemestry, 28 (10), 1201-1222.
DOI 10.1016/j.trac.2009.07.007
Risvik, H., 2007. Principal Component Analysis (PCA) & NIPALS algorithm. Available at: http://neuroinformatics.be/wp-content/uploads/2016/01/pca_nipals.pdf (Accessed: 5 March 2021)
Roggo Y., Chalus P., Maurer L., Lema-Martinez C., Edmond A. and Jent N., 2007. A review of near infrared spectroscopy and chemometrics in pharmaceutical technologies, Journal of Pharmaceutical and Biomedical Analysis, 44 (3), 683–700.
DOI 10.1016/j.jpba.2007.03.023
Savitzky, A. and Golay, M.J.E., 1964. Smoothing and Differentiation of Data by Simplified Least-Squares Procedures. Analytical Chemistry, 36 (8), 1627-1639.
DOI 10.1021/ac60214a047
Serranti S. and Bonifazi G., 2010. Post-Consumer Polyolefins (PP-PE) Recognition by Combined Spectroscopic Sensing Techniques, The Open Waste Management Journal, 3, 35-45
Serranti S., Bonifazi G., Gasbarrone R., 2018a. Kiwifruits ripening assessment by portable hyperspectral devices. Proc. SPIE 10665, Sensing for Agriculture and Food Quality and Safety X, Orlando, Florida, United States, 106650S
Serranti S., Bonifazi G., Gasbarrone R., 2018b, Olive fruit ripening evaluation and quality assessment by hyperspectral sensing devices. Proc. SPIE 10665, Sensing for Agriculture and Food Quality and Safety X, Orlando, Florida, United States, 106650R
Serranti S., Gargiulo A., and Bonifazi G., 2011. Characterization of post-consumer polyolefin wastes by hyperspectral imaging for quality control in recycling processes. Waste Management, 31 (11), 2217-2227.
DOI 10.1016/j.wasman.2011.06.007
Serranti S., Palmieri R., Bonifazi G., 2013. Opto-electronic combined characterization of end-of-life mobile phone wastes for a sustainable processing. Proceedings Sardinia 2013, Fourteenth International Waste Management and Landfill Symposium, S. Margherita di Pula, Cagliari, Italy
Serranti S., Palmieri R., Bonifazi G., 2015. Hyperspectral imaging applied to demolition waste recycling: an innovative approach for quality control, Journal of Electronic Imaging, 24 (4), 043003-1_043003-9.
DOI 10.1117/1.JEI.24.4.043003
Silva E. J., Britto A. S., Oliveira L. S., Enembreck F., Sabourin R. and Koerich A. L., 2017. A two-step cascade classification method, 2017 International Joint Conference on Neural Networks (IJCNN), Anchorage, Alaska, United States, 573-580
Tsuchikawa S. and Kobori H., 2015. A review of recent application of near infrared spectroscopy to wood science and technology, Journal of Wood Science, 61, 213–220.
DOI 10.1007/s10086-015-1467-x
Ulrici A., Serranti S., Ferrari C., Cesare D., Foca G. and Bonifazi G., 2013. Efficient chemometric strategies for PET-PLA discrimination in recycling plants using hyperspectral imaging, Chemometrics and Intelligent laboratory Systems, 122, 31-39.
DOI 10.1016/j.chemolab.2013.01.001
Wise B. M., Gallagher N. B., Bro R., Shaver J. M., Windig W., Koch R.S., 2008. PLS_Toolbox 4.2 Reference Manual for use with MATLAB™, Eigenvector Research Incorporated Inc, Wenatchee, Washington, United States
Wold, S., Esbensen, K. and Geladi, P., 1987. Principal Component Analysis, Chemometrics and Intelligent Laboratory Systems, 2, 37-52.
DOI 10.1016/0169-7439(87)80084-9
Yamane, T., 1967. Statistics: An Introductory Analysis, 2nd edition, Harper and Row, New York, United States