A NOVEL METHODOLOGY TOWARDS ACCURATE AND … · 2020. 11. 3. · A NOVEL METHODOLOGY TOWARDS...
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A NOVEL METHODOLOGY TOWARDS ACCURATE AND AUTOMATIC MICROPLASTICS IDENTIFICATION FROM THE AQUATIC ENVIRONMENT V. Morgado (1)(2) , C. Palma (1) , R.J.N. Bettencourt da Silva (2) (1) Instituto Hidrográfico, Rua das Trinas, 49, 1249-093 Lisboa, Portugal ([email protected]) (2) Centro de Química Estrututal, Faculdade de Ciências, Universidade de Lisboa, Campo Grande, 1749-016 Lisboa, Portugal BACKGROUND Statistics point that more than 60 % of the global composition of marine litter is plastic and about 1.15 to 2.41 million tons of plastic are dumped into oceans every year from rivers [1,2]. Microplastics are any synthetic solid particle or polymeric matrix, with (ir)regular shape and with size ranging from 1 μm to 5 mm, of either primary or secondary manufacturing origin, which are insoluble in water [3]. Microplastics are characterised according to their physical and chemical properties and can be identified from collected infrared spectrum. This spectrum is unique for a specific plastic type, working as a molecular fingerprint. The interpretation of the infrared spectra can be very complex and time-consuming. PROBLEM ADDITIVES & CONTAMINANTS INGESTION BIOACCUMULATION SAMPLING AREA Mondego river estuary Portugal AUTOMATIC METHODOLOGY FOR POLYMER IDENTIFICATION 0 50 100 150 600 1100 1600 2100 2600 3100 3600 R (%) Wavenumber, ̃ (cm -1 ) PP Reference or + 1. Collection of μ-FTIR spectra PA + PC + PE + PS + PTFE 2. Match determination by different algorithms: › (Un)weighted Pearson, Spearman or Alternative CC › Original, First or Second Derivative of Signal NEGATIVE CASES | non-PP Particles: 0 50 100 R (%) 0 10 20 30 600 1100 1600 2100 2600 3100 3600 Wavenumber, ̃ (cm -1 ) POSITIVE CASES | PP Particles NEGATIVE CASES POSTIVE CASES 3. Simulation of Match distribution: › Bootstrap method 4. Estimation of Distribution parameters: P5»P from Positive Cases P95»N from Negative Cases 5. Estimation of Validation parameters: FP & LR(+) POSITIVE CASES NEGATIVE CASES P95»N P5»P + FP > 5 % FP < 5 % P5»P < P95»N P5»P > P95»N or PA: Polyacrilamide PC: Polycarbonate PE: Polyethylene PP: Polypropylene PS: Polystyrene PTFE: Polytetrafluoroethylene Note: P5»P is associated to a TP of 95 % LR(+) > 19 FP < 5 % P 5 »P > P 95 »N 6. Decision on method validity PP identification methods Match type P5»P P95»N FP (%) LR(+) Signal type Algorithm 1 st derivative Complementar Unweighted Spearman 0.317 0.153 0.0023 41337 Original 0.316 0.163 0.0032 30157 Inverse 0.321 0.188 0.0091 10487 Original Unweighted Pearson 0.209 0.112 0.14 660 Complementar 0.209 0.112 0.14 660 Original Unweighted Alternative 0.209 0.112 0.14 656 Complementar 0.209 0.112 0.14 656 Inverse Weighted Spearman 0.533 0.418 0.18 537 2 nd derivative Inverse 0.500 0.426 0.31 308 1 st derivative Original 0.398 0.325 0.93 102 P95»N: 95th percentile of negative cases P5»P: 5th percentile of positive cases TP: True postive result rate FP: False positive result rate LR: Likelihood ratio «Since the methodology satisfies quality requirements appropriate to its purpose, it is considered valid .» Minimum Match = P5»P = 0.317 References: V. Morgado, C. Palma, R.J.N. Bettencourt da Silva, Microplastics identification by Infrared spectroscopy – Evaluation of identification criteria and uncertainty by the Bootstrap method, Talanta (In press, 2020). DOI: 10.1016/j.talanta.2020.121814. [1] Alfred-Wegener-Institut, Litterbase, Online Portal for Marine Litter. Available at: https ://litterbase.awi.de/. [2] Lebreton, L., van der µZwet, J., Damsteeg, J., Slat, B., Andrady, A., Reisser, J. (2017). River plastic emissions to the world’s oceans. Nature Communications, 8, 15611. [3] Frias, J., and Nash, R. (2019). Microplastics: Finding a consensus on the definition. Marine Pollution Bulletin, 138, 145-147. [4] Gomes, G.B., Morgado, V., Palma, C. (2020). Preliminary Data on Polymer Type Identification from Estuarine Environmental Samples. En: Cocca, M., Pace, E.D., Errico, M.E., Gentile, G., Montarsolo, A., Mossotti, R., Avella, M. (eds.) Proceedings of the 2nd International Conference on Microplastic Pollution in the Mediterranean Sea. Springer, Switzerland, 170-174. Background Photo credits: grupo-interacao.com. Funding: This work was supported by Universidade de Lisboa through a PhD Scholarship 2018, the Operational Program Mar2020 through project “AQUIMAR – Caracterização geral de áreas aquícolas para estabelecimento de culturas marinhas” (MAR2020 nº MAR-02.01.01-FEAMP-0107) and Fundacão para a Ciência e Tecnologia (FCT) through projects UIDB/00100/2020 and UIDP/00100/2020.
Transcript of A NOVEL METHODOLOGY TOWARDS ACCURATE AND … · 2020. 11. 3. · A NOVEL METHODOLOGY TOWARDS...
A NOVEL METHODOLOGY TOWARDS ACCURATE AND AUTOMATIC
MICROPLASTICS IDENTIFICATION FROM THE AQUATIC ENVIRONMENT
V. Morgado(1)(2), C. Palma(1), R.J.N. Bettencourt da Silva(2)
(1) Instituto Hidrográfico, Rua das Trinas, 49, 1249-093 Lisboa, Portugal ([email protected])
(2) Centro de Química Estrututal, Faculdade de Ciências, Universidade de Lisboa, Campo Grande, 1749-016 Lisboa, Portugal
BACKGROUND
Statistics point that more than 60 % of the global
composition of marine litter is plastic and about 1.15
to 2.41 million tons of plastic are dumped into oceans
every year from rivers [1,2].
Microplastics are any synthetic solid particle or
polymeric matrix, with (ir)regular shape and with size
ranging from 1 µm to 5 mm, of either primary or
secondary manufacturing origin, which are insoluble in
water [3].
Microplastics are characterised according to their
physical and chemical properties and can be identified
from collected infrared spectrum. This spectrum is
unique for a specific plastic type, working as a
molecular fingerprint. The interpretation of the infrared
spectra can be very complex and time-consuming.
PROBLEM
ADDITIVES &
CONTAMINANTS
INGESTION
BIO
AC
CU
MU
LA
TIO
N
SAMPLING AREA
Mondego river estuaryPortugal
AUTOMATIC METHODOLOGY FOR POLYMER IDENTIFICATION
0
50
100
150
600110016002100260031003600
R (%)
Wavenumber, �̃� (cm-1)
PP Reference
or+
1. Collection of µ-FTIR spectra
PA+ PC+ PE + PS+ PTFE
2. Match determination by different algorithms:
› (Un)weighted Pearson, Spearman or Alternative CC
› Original, First or Second Derivative of Signal
NEGATIVE CASES | non-PP Particles:
0
50
100
R (%)
0
10
20
30
600110016002100260031003600
Wavenumber, �̃� (cm-1)
POSITIVE CASES | PP Particles
NEGATIVE CASESPOSTIVE CASES
3. Simulation of Match
distribution:
› Bootstrap method
4. Estimation of Distribution parameters:
P5»P from Positive Cases
P95»N from Negative Cases
5. Estimation of Validation parameters: FP & LR(+)
POSITIVE CASES
NEGATIVE CASES
P95»N P5»P
+
FP > 5 %
FP < 5 %
P5»P < P95»N
P5»P > P95»N
or
PA: Polyacrilamide
PC: Polycarbonate
PE: Polyethylene
PP: Polypropylene
PS: Polystyrene
PTFE: Polytetrafluoroethylene
Note: P5»P is associated to a TP of 95 %
LR(+) > 19FP < 5 %P5»P > P95»N6. Decision on method validity
PP identification methodsMatch type
P5»P P95»N FP (%) LR(+)Signal type Algorithm
1st derivative
ComplementarUnweighted
Spearman
0.317 0.153 0.0023 41337Original 0.316 0.163 0.0032 30157Inverse 0.321 0.188 0.0091 10487Original Unweighted
Pearson
0.209 0.112 0.14 660Complementar 0.209 0.112 0.14 660
Original Unweighted
Alternative
0.209 0.112 0.14 656Complementar 0.209 0.112 0.14 656
InverseWeighted
Spearman
0.533 0.418 0.18 5372nd derivative Inverse 0.500 0.426 0.31 3081st derivative Original 0.398 0.325 0.93 102
P95»N: 95th percentile of negative cases
P5»P: 5th percentile of positive cases
TP: True postive result rate
FP: False positive result rate
LR: Likelihood ratio«Since the methodology satisfies quality
requirements appropriate to its purpose,
it is considered valid.»
Minimum Match = P5»P = 0.317
References: V. Morgado, C. Palma, R.J.N. Bettencourt da Silva, Microplastics identification by Infrared
spectroscopy – Evaluation of identification criteria and uncertainty by the Bootstrap method, Talanta (In press,
2020). DOI: 10.1016/j.talanta.2020.121814.
[1] Alfred-Wegener-Institut, Litterbase, Online Portal for Marine Litter. Available at: https://litterbase.awi.de/.
[2] Lebreton, L., van der µZwet, J., Damsteeg, J., Slat, B., Andrady, A., Reisser, J. (2017). River plastic
emissions to the world’s oceans. Nature Communications, 8, 15611. [3] Frias, J., and Nash, R. (2019).
Microplastics: Finding a consensus on the definition. Marine Pollution Bulletin, 138, 145-147. [4] Gomes, G.B.,
Morgado, V., Palma, C. (2020). Preliminary Data on Polymer Type Identification from Estuarine Environmental
Samples. En: Cocca, M., Pace, E.D., Errico, M.E., Gentile, G., Montarsolo, A., Mossotti, R., Avella, M. (eds.)
Proceedings of the 2nd International Conference on Microplastic Pollution in the Mediterranean Sea. Springer,
Switzerland, 170-174. Background Photo credits: grupo-interacao.com.
Funding: This work was supported by Universidade de Lisboa through a PhD Scholarship 2018, the Operational
Program Mar2020 through project “AQUIMAR – Caracterização geral de áreas aquícolas para estabelecimento
de culturas marinhas” (MAR2020 nº MAR-02.01.01-FEAMP-0107) and Fundacão para a Ciência e Tecnologia
(FCT) through projects UIDB/00100/2020 and UIDP/00100/2020.
