Abstract

Particle pollution is a global health challenge that is linked to around three million premature deaths per year. There is therefore great interest in the development of sensors capable of precisely quantifying both the number and type of particles. Here, we demonstrate an approach that leverages machine learning in order to identify particulates directly from their scattering patterns. We show the capability for producing a 2D sample map of spherical particles present on a coverslip, and also demonstrate real-time identification of a range of particles including those from diesel combustion.

Published by The Optical Society under the terms of the Creative Commons Attribution 4.0 License. Further distribution of this work must maintain attribution to the author(s) and the published article's title, journal citation, and DOI.

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References

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2018 (6)

2017 (4)

A. Sinha, J. Lee, S. Li, and G. Barbastathis, “Lensless computational imaging through deep learning,” Optica 4(9), 1117–1125 (2017).
[Crossref]

Y. Jo, S. Park, J. Jung, J. Yoon, H. Joo, M. H. Kim, S.-J. Kang, M. C. Choi, S. Y. Lee, and Y. Park, “Holographic deep learning for rapid optical screening of anthrax spores,” Sci. Adv. 3(8), e1700606 (2017).
[Crossref] [PubMed]

G. Satat, M. Tancik, O. Gupta, B. Heshmat, and R. Raskar, “Object classification through scattering media with deep learning on time resolved measurement,” Opt. Express 25(15), 17466–17479 (2017).
[Crossref] [PubMed]

L. A. Philips, D. B. Ruffner, F. C. Cheong, J. M. Blusewicz, P. Kasimbeg, B. Waisi, J. R. McCutcheon, and D. G. Grier, “Holographic characterization of contaminants in water: Differentiation of suspended particles in heterogeneous dispersions,” Water Res. 122, 431–439 (2017).
[Crossref] [PubMed]

2016 (3)

R. Sussarellu, M. Suquet, Y. Thomas, C. Lambert, C. Fabioux, M. E. Pernet, N. Le Goïc, V. Quillien, C. Mingant, Y. Epelboin, C. Corporeau, J. Guyomarch, J. Robbens, I. Paul-Pont, P. Soudant, and A. Huvet, “Oyster reproduction is affected by exposure to polystyrene microplastics,” Proc. Natl. Acad. Sci. U.S.A. 113(9), 2430–2435 (2016).
[Crossref] [PubMed]

L. Fierce, T. C. Bond, S. E. Bauer, F. Mena, and N. Riemer, “Black carbon absorption at the global scale is affected by particle-scale diversity in composition,” Nat. Commun. 7, 12361 (2016).
[Crossref] [PubMed]

C. Wang, F. C. Cheong, D. B. Ruffner, X. Zhong, M. D. Ward, and D. G. Grier, “Holographic characterization of colloidal fractal aggregates,” Soft Matter 12(42), 8774–8780 (2016).
[Crossref] [PubMed]

2015 (3)

J. Lelieveld, J. S. Evans, M. Fnais, D. Giannadaki, and A. Pozzer, “The contribution of outdoor air pollution sources to premature mortality on a global scale,” Nature 525(7569), 367–371 (2015).
[Crossref] [PubMed]

M. Loxham, “Harmful effects of particulate air pollution: identifying the culprits,” Respirology 20(1), 7–8 (2015).
[Crossref] [PubMed]

M. Cole, P. Lindeque, E. Fileman, C. Halsband, and T. S. Galloway, “The impact of polystyrene microplastics on feeding, function and fecundity in the marine copepod Calanus helgolandicus,” Environ. Sci. Technol. 49(2), 1130–1137 (2015).
[Crossref] [PubMed]

2014 (2)

A. Yevick, M. Hannel, and D. G. Grier, “Machine-learning approach to holographic particle characterization,” Opt. Express 22(22), 26884–26890 (2014).
[Crossref] [PubMed]

N. Srivastava, G. Hinton, A. Krizhevsky, I. Sutskever, and R. Salakhutdinov, “Dropout: A simple way to prevent neural networks from overfitting,” J. Mach. Learn. Res. 15(1), 1929–1958 (2014).

2013 (1)

F. R. Cassee, M.-E. Héroux, M. E. Gerlofs-Nijland, and F. J. Kelly, “Particulate matter beyond mass: recent health evidence on the role of fractions, chemical constituents and sources of emission,” Inhal. Toxicol. 25(14), 802–812 (2013).
[Crossref] [PubMed]

2012 (4)

F. J. Kelly and J. C. Fussell, “Size, source and chemical composition as determinants of toxicity attributable to ambient particulate matter,” Atmos. Environ. 60, 504–526 (2012).
[Crossref]

Z. D. Ristovski, B. Miljevic, N. C. Surawski, L. Morawska, K. M. Fong, F. Goh, and I. A. Yang, “Respiratory health effects of diesel particulate matter,” Respirology 17(2), 201–212 (2012).
[Crossref] [PubMed]

M. Kulmala, T. Petäjä, T. Nieminen, M. Sipilä, H. E. Manninen, K. Lehtipalo, M. Dal Maso, P. P. Aalto, H. Junninen, P. Paasonen, I. Riipinen, K. E. Lehtinen, A. Laaksonen, and V. M. Kerminen, “Measurement of the nucleation of atmospheric aerosol particles,” Nat. Protoc. 7(9), 1651–1667 (2012).
[Crossref] [PubMed]

R. W. Perry, G. Meng, T. G. Dimiduk, J. Fung, and V. N. Manoharan, “Real-space studies of the structure and dynamics of self-assembled colloidal clusters,” Faraday Discuss. 159(1), 211–234 (2012).
[Crossref]

2011 (2)

2010 (1)

K. Giewekemeyer, P. Thibault, S. Kalbfleisch, A. Beerlink, C. M. Kewish, M. Dierolf, F. Pfeiffer, and T. Salditt, “Quantitative biological imaging by ptychographic x-ray diffraction microscopy,” Proc. Natl. Acad. Sci. U.S.A. 107(2), 529–534 (2010).
[Crossref] [PubMed]

2008 (2)

V. Ramanathan and G. Carmichael, “Global and regional climate changes due to black carbon,” Nat. Geosci. 1(4), 221–227 (2008).
[Crossref]

B. Mills, C. F. Chau, E. T. F. Rogers, J. Grant-Jacob, S. L. Stebbings, M. Praeger, A. M. de Paula, C. A. Froud, R. T. Chapman, T. J. Butcher, J. J. Baumberg, W. S. Brocklesby, and J. G. Frey, “Direct measurement of the complex refractive index in the extreme ultraviolet spectral region using diffraction from a nanosphere array,” Appl. Phys. Lett. 93(23), 231103 (2008).
[Crossref]

2007 (1)

2006 (2)

G. E. Hinton and R. R. Salakhutdinov, “Reducing the dimensionality of data with neural networks,” Science 313(5786), 504–507 (2006).
[Crossref] [PubMed]

F. Pfeiffer, T. Weitkamp, O. Bunk, and C. David, “Phase retrieval and differential phase-contrast imaging with low-brilliance X-ray sources,” Nat. Phys. 2(4), 258–261 (2006).
[Crossref]

2004 (2)

H. M. L. Faulkner and J. M. Rodenburg, “Movable aperture lensless transmission microscopy: a novel phase retrieval algorithm,” Phys. Rev. Lett. 93(2), 023903 (2004).
[Crossref] [PubMed]

J. Gorodkin, “Comparing two K-category assignments by a K-category correlation coefficient,” Comput. Biol. Chem. 28(5-6), 367–374 (2004).
[Crossref] [PubMed]

2003 (1)

J. I. Levy, D. H. Bennett, S. J. Melly, and J. D. Spengler, “Influence of traffic patterns on particulate matter and polycyclic aromatic hydrocarbon concentrations in Roxbury, Massachusetts,” J. Expo. Anal. Environ. Epidemiol. 13(5), 364–371 (2003).
[Crossref] [PubMed]

1998 (3)

1997 (1)

S. Lawrence, C. L. Giles, A. C. Tsoi, and A. D. Back, “Face recognition: A convolutional neural-network approach,” IEEE Trans. Neural Netw. 8(1), 98–113 (1997).
[Crossref] [PubMed]

1995 (1)

S.-B. Cho and J. H. Kim, “Combining multiple neural networks by fuzzy integral for robust classification,” IEEE Trans. Syst. Man Cybern. 25(2), 380–384 (1995).
[Crossref]

1993 (1)

1991 (1)

D. F. Specht, “A general regression neural network,” IEEE Trans. Neural Netw. 2(6), 568–576 (1991).
[Crossref] [PubMed]

1983 (1)

1982 (1)

Aalto, P. P.

M. Kulmala, T. Petäjä, T. Nieminen, M. Sipilä, H. E. Manninen, K. Lehtipalo, M. Dal Maso, P. P. Aalto, H. Junninen, P. Paasonen, I. Riipinen, K. E. Lehtinen, A. Laaksonen, and V. M. Kerminen, “Measurement of the nucleation of atmospheric aerosol particles,” Nat. Protoc. 7(9), 1651–1667 (2012).
[Crossref] [PubMed]

Back, A. D.

S. Lawrence, C. L. Giles, A. C. Tsoi, and A. D. Back, “Face recognition: A convolutional neural-network approach,” IEEE Trans. Neural Netw. 8(1), 98–113 (1997).
[Crossref] [PubMed]

Baker, J. A. G.

Baluja, S.

H. A. Rowley, S. Baluja, and T. Kanade, “Neural network-based face detection,” IEEE Trans. Pattern Anal. Mach. Intell. 20(1), 23–38 (1998).
[Crossref]

Barbastathis, G.

Bauer, S. E.

L. Fierce, T. C. Bond, S. E. Bauer, F. Mena, and N. Riemer, “Black carbon absorption at the global scale is affected by particle-scale diversity in composition,” Nat. Commun. 7, 12361 (2016).
[Crossref] [PubMed]

Baumberg, J. J.

B. Mills, C. F. Chau, E. T. F. Rogers, J. Grant-Jacob, S. L. Stebbings, M. Praeger, A. M. de Paula, C. A. Froud, R. T. Chapman, T. J. Butcher, J. J. Baumberg, W. S. Brocklesby, and J. G. Frey, “Direct measurement of the complex refractive index in the extreme ultraviolet spectral region using diffraction from a nanosphere array,” Appl. Phys. Lett. 93(23), 231103 (2008).
[Crossref]

Beerlink, A.

K. Giewekemeyer, P. Thibault, S. Kalbfleisch, A. Beerlink, C. M. Kewish, M. Dierolf, F. Pfeiffer, and T. Salditt, “Quantitative biological imaging by ptychographic x-ray diffraction microscopy,” Proc. Natl. Acad. Sci. U.S.A. 107(2), 529–534 (2010).
[Crossref] [PubMed]

Bennett, D. H.

J. I. Levy, D. H. Bennett, S. J. Melly, and J. D. Spengler, “Influence of traffic patterns on particulate matter and polycyclic aromatic hydrocarbon concentrations in Roxbury, Massachusetts,” J. Expo. Anal. Environ. Epidemiol. 13(5), 364–371 (2003).
[Crossref] [PubMed]

Bertrand, F. E.

Blusewicz, J. M.

L. A. Philips, D. B. Ruffner, F. C. Cheong, J. M. Blusewicz, P. Kasimbeg, B. Waisi, J. R. McCutcheon, and D. G. Grier, “Holographic characterization of contaminants in water: Differentiation of suspended particles in heterogeneous dispersions,” Water Res. 122, 431–439 (2017).
[Crossref] [PubMed]

Bond, T. C.

L. Fierce, T. C. Bond, S. E. Bauer, F. Mena, and N. Riemer, “Black carbon absorption at the global scale is affected by particle-scale diversity in composition,” Nat. Commun. 7, 12361 (2016).
[Crossref] [PubMed]

Brock, R. S.

Brocklesby, W. S.

B. Mills, C. F. Chau, E. T. F. Rogers, J. Grant-Jacob, S. L. Stebbings, M. Praeger, A. M. de Paula, C. A. Froud, R. T. Chapman, T. J. Butcher, J. J. Baumberg, W. S. Brocklesby, and J. G. Frey, “Direct measurement of the complex refractive index in the extreme ultraviolet spectral region using diffraction from a nanosphere array,” Appl. Phys. Lett. 93(23), 231103 (2008).
[Crossref]

Bunk, O.

F. Pfeiffer, T. Weitkamp, O. Bunk, and C. David, “Phase retrieval and differential phase-contrast imaging with low-brilliance X-ray sources,” Nat. Phys. 2(4), 258–261 (2006).
[Crossref]

Butcher, T. J.

B. Mills, C. F. Chau, E. T. F. Rogers, J. Grant-Jacob, S. L. Stebbings, M. Praeger, A. M. de Paula, C. A. Froud, R. T. Chapman, T. J. Butcher, J. J. Baumberg, W. S. Brocklesby, and J. G. Frey, “Direct measurement of the complex refractive index in the extreme ultraviolet spectral region using diffraction from a nanosphere array,” Appl. Phys. Lett. 93(23), 231103 (2008).
[Crossref]

Carmichael, G.

V. Ramanathan and G. Carmichael, “Global and regional climate changes due to black carbon,” Nat. Geosci. 1(4), 221–227 (2008).
[Crossref]

Cassee, F. R.

F. R. Cassee, M.-E. Héroux, M. E. Gerlofs-Nijland, and F. J. Kelly, “Particulate matter beyond mass: recent health evidence on the role of fractions, chemical constituents and sources of emission,” Inhal. Toxicol. 25(14), 802–812 (2013).
[Crossref] [PubMed]

Chapman, H. N.

Chapman, R. T.

B. Mills, C. F. Chau, E. T. F. Rogers, J. Grant-Jacob, S. L. Stebbings, M. Praeger, A. M. de Paula, C. A. Froud, R. T. Chapman, T. J. Butcher, J. J. Baumberg, W. S. Brocklesby, and J. G. Frey, “Direct measurement of the complex refractive index in the extreme ultraviolet spectral region using diffraction from a nanosphere array,” Appl. Phys. Lett. 93(23), 231103 (2008).
[Crossref]

Chau, C. F.

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Figures (6)

Fig. 1
Fig. 1 Proposed application of a NN to quantify an object directly from the scattering pattern. A NN, which can approximate any function if provided with appropriate training data has the potential to directly determine specific sample parameters from the associated scattering pattern.
Fig. 2
Fig. 2 Collecting the training data set and training the neural network. The schematic shows the collection of scattering patterns and associated microscope images for 568 positions across a substrate, which was used to form the training data set. Each scattering pattern was used as an input for the NN, which then predicted the material and number of particles. The feedback from the comparison function was used to optimize the weighting between the neurons in the NN, via an automatic process known as backpropagation [43], in order to increase the prediction accuracy.
Fig. 3
Fig. 3 Diagram of the NN used for the microspheres and real-time experiments.
Fig. 4
Fig. 4 Highlighting the differences between scattering patterns for different materials and numbers of microspheres. (a) Contrasts the scattering patterns from a single (left) polystyrene and (right) silicon dioxide microsphere. (b) Shows that two particles of polystyrene for two orientations had similar but rotated scattering patterns. (c) Shows three polystyrene microspheres in a line (left) and in a triangle (right). In all cases, the associated microscope image is inset.
Fig. 5
Fig. 5 Particle maps created via application of the trained NN to scattering patterns from a range of positions across the substrate. The material and number at each position, as predicted by the NN, was used to form the sample maps. The maps correspond to regions of (a) polystyrene, and (b) silicon dioxide spheres. The colour map corresponds to the NN confidence percentage for each prediction.
Fig. 6
Fig. 6 Real-time sensing of pollen, diesel and wood ash particulates. (a) Prediction of the pollutant type as a function of measurement number. The y-axis corresponds to the NN confidence percentage for each prediction. (b) Confusion matrix showing the performance of classification. (c) Examples of SEM images of, and scattering patterns from, the four types of pollutant.

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