Near-Infrared Hyperspectral Imaging Spectroscopy to Detect Microplastics and Pieces of Plastic in Almond Flour

Authors: H. Apaza, L. Chévez, H. Loro

Abstract:

Plastic and microplastic pollution in human food chain is a big problem for human health that requires more elaborated techniques that can identify their presences in different kinds of food. Hyperspectral imaging technique is an optical technique than can detect the presence of different elements in an image and can be used to detect plastics and microplastics in a scene. To do this statistical techniques are required that need to be evaluated and compared in order to find the more efficient ones. In this work, two problems related to the presence of plastics are addressed, the first is to detect and identify pieces of plastic immersed in almond seeds, and the second problem is to detect and quantify microplastic in almond flour. To do this we make use of the analysis hyperspectral images taken in the range of 900 to 1700 nm using 4 unmixing techniques of hyperspectral imaging which are: least squares unmixing (LSU), non-negatively constrained least squares unmixing (NCLSU), fully constrained least squares unmixing (FCLSU), and scaled constrained least squares unmixing (SCLSU). NCLSU, FCLSU, SCLSU techniques manage to find the region where the plastic is found and also manage to quantify the amount of microplastic contained in the almond flour. The SCLSU technique estimated a 13.03% abundance of microplastics and 86.97% of almond flour compared to 16.66% of microplastics and 83.33% abundance of almond flour prepared for the experiment. Results show the feasibility of applying near-infrared hyperspectral image analysis for the detection of plastic contaminants in food.

Keywords: Food, plastic, microplastic, NIR hyperspectral imaging, unmixing.

References:

[1] Bouwmeester, H., Hollman, P. C., & Peters, R. J. “Potential health impact of environmentally released micro-and nanoplastics in the human food production chain: experiences from nanotoxicology”. Environmental science & technology, 49(15), 2015, pp. 8932-8947.
[2] Farrell, P., & Nelson, K., “Trophic level transfer of microplastic: Mytilus edulis (L.) to Carcinus maenas (L.)”. Environmental pollution, 177, 2013, pp.1-3.
[3] Shan, J., Zhao, J., Zhang, Y., Liu, L., Wu, F., & Wang, X. “Simple and rapid detection of microplastics in seawater using hyperspectral imaging technology”. Analytica Chimica Acta, 1050, 2019, 161-168.
[4] Kwon, J. H., Chang, S., Hong, S. H., & Shim, W. J., Microplastics as a vector of hydrophobic contaminants: Importance of hydrophobic additives. Integrated Environmental Assessment and Management, 13(3), 2017, pp. 494-499.
[5] Wright, S. L., Thompson, R. C., & Galloway, T. S., “The physical impacts of microplastics on marine organisms: a review”. Environmental pollution, 178, 2013, pp. 483-492.
[6] Law, K. L., & Thompson, R. C. (2014). Microplastics in the seas. Science, 345(6193), 2014, pp. 144-145.
[7] Sun, D. W. (Ed.), “Hyperspectral imaging for food quality analysis and control”. Elsevier, 2010.
[8] Sun, D. W. (Ed.), “Infrared spectroscopy for food quality analysis and control”. Academic press, 2009.
[9] Bajorski, P. “Second moment linear dimensionality as an alternative to virtual dimensionality”. IEEE transactions on geoscience and remote sensing, 49(2), 2010, pp. 672-678.
[10] Green, A. A., Berman, M., Switzer, P., & Craig, M. D. “A transformation for ordering multispectral data in terms of image quality with implications for noise removal”. IEEE Transactions on geoscience and remote sensing, 26(1), 1988, pp. 65-74.
[11] Chang, C. I., & Du, Q. “Estimation of number of spectrally distinct signal sources in hyperspectral imagery”. IEEE Transactions on geoscience and remote sensing, 42(3), 2004, pp. 608-619.
[12] Chang, C. I., Wu, C. C., Liu, W., & Ouyang, Y. C., “A new growing method for simplex-based endmember extraction algorithm”. IEEE transactions on geoscience and remote sensing, 44(10), 2006, 2804-2819.
[13] Polder, G., Van der Heijden, G. W. A. M., & Young, I. T., “Hyperspectral image analysis for measuring ripeness of tomatoes”, 2000.
[14] Van der Meer, F., & De Jong, S. M., “Improving the results of spectral unmixing of Landsat Thematic Mapper imagery by enhancing the orthogonality of end-members”. International Journal of Remote Sensing, 21(15), 2000, pp. 2781-2797.
[15] Chang, C. I., & Heinz, D. C., “Constrained subpixel target detection for remotely sensed imagery”. IEEE transactions on geoscience and remote sensing, 38(3), 2000, pp. 1144-1159.
[16] Heinz, D. C., “Fully constrained least squares linear spectral mixture analysis method for material quantification in hyperspectral imagery”. IEEE transactions on geoscience and remote sensing, 39(3), 2001, pp. 529-545.
[17] Veganzones, M. A., Drumetz, L., Tochon, G., Dalla Mura, M., Plaza, A., Bioucas-Dias, J., & Chanussot, J. “A new extended linear mixing model to address spectral variability”. In 2014 6th Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (WHISPERS), IEEE, 2014, pp. 1-4.
[18] Hong, D., Yokoya, N., Chanussot, J., & Zhu, X. X., “An augmented linear mixing model to address spectral variability for hyperspectral unmixing”. IEEE Transactions on Image Processing, 28(4), 2018, pp. 1923-1938.