Convolutional Neural Networks Applied to Antimony Quantification via Soil Laboratory Reflectance Spectroscopy in Northern Portugal: Opportunities and Challenges
Abstract
:1. Introduction
2. Background
2.1. Study Area
2.2. Spectral Reflectance
2.3. Convolutional Neural Network
3. Materials and Methods
3.1. Soil Collection and Preparation
3.2. Soil Reflectance Measuring
3.3. Spectral Pre-Processing
3.3.1. Continuum Removal for Normalisation
3.3.2. Spectral Pre-Treatment
3.3.3. Convert Waveform to Spectrogram
3.4. Application of Convolutional Neural Network
4. Results
4.1. ICP-MS Analysis
4.2. Spectral Pre-Processing and CNN Results
5. Discussion
6. Conclusions
Supplementary Materials
Author Contributions
Funding
Data Availability Statement
Acknowledgments
Conflicts of Interest
Appendix A
Sample | Sb (ppm) | As (ppm) | Pb (ppm) | Mn (ppm) | Zn (ppm) | Sample | Sb (ppm) | As (ppm) | Pb (ppm) | Mn (ppm) | Zn (ppm) |
---|---|---|---|---|---|---|---|---|---|---|---|
MDL | 0.02 | 0.2 | 0.02 | 1 | 0.2 | MDL | 0.02 | 0.2 | 0.02 | 1 | 0.2 |
RS001 | 13.38 | 25 | 33.49 | 13 | 12.7 | TP004 | 298.55 | 146.9 | 38.91 | 36 | 31.9 |
RS004 | 12.79 | 24.5 | 17.05 | 41 | 25.1 | TP007 | 80.07 | 67.9 | 21.95 | 29 | 42.9 |
RS007 | 10.71 | 18.7 | 22.23 | 68 | 24.5 | TP008 | 184.61 | 189.9 | 35.35 | 24 | 17.8 |
RS013 | 23.4 | 24.6 | 22.19 | 56 | 22.7 | TP009 | 31.39 | 46 | 18.38 | 29 | 34.6 |
RS015 | 14.55 | 16 | 28.89 | 60 | 49.1 | TP011 | 27.01 | 32.5 | 30.96 | 299 | 64.4 |
RS017 | 9.95 | 17.4 | 23.97 | 44 | 23.7 | TP013 | 251 | 167.4 | 24.41 | 17 | 30.8 |
RS019 | 9.5 | 21.3 | 21.08 | 22 | 14.7 | TP016 | 357.04 | 50.7 | 26.71 | 37 | 29.1 |
RS021 | 33.94 | 22.6 | 28.76 | 53 | 36.5 | TP017 | 1446 | 467.8 | 35.95 | 376 | 47.2 |
RS023 | 30.32 | 21 | 26.2 | 68 | 41.1 | TP027 | 52.6 | 70.3 | 25.98 | 23 | 19.7 |
RS027 | 16.87 | 17.9 | 24.53 | 51 | 31.1 | TP033 | 68.45 | 166.5 | 27.97 | 59 | 40.7 |
RS029 | 9.54 | 14.8 | 20.64 | 34 | 29.1 | TP035 | 715.49 | 78 | 49.92 | 142 | 68 |
RS031 | 25.73 | 20.3 | 20.93 | 57 | 29.8 | TP038 | 116.33 | 50 | 25.42 | 27 | 22.1 |
RS034 | 47.03 | 112.9 | 19.54 | 40 | 24.5 | TP040 | 258.4 | 74.5 | 62.37 | 63 | 26.9 |
RS037 | 575.47 | 1431.2 | 47.29 | 76 | 49.8 | TP044 | 259.18 | 65.7 | 42.6 | 844 | 146 |
RS040 | 59.42 | 28.6 | 33.97 | 261 | 50.9 | TP048 | 14.87 | 23.4 | 23.87 | 229 | 54.1 |
RS045 | 19.46 | 47.8 | 17.06 | 13 | 15 | TP049 | 207.65 | 30.9 | 25.6 | 27 | 19.7 |
RS049 | 162.61 | 111 | 24.82 | 35 | 28.3 | TP051 * | >4000 | 571.8 | 325.79 | 35 | 26.4 |
RS051 * | 2653 | 225 | 31.82 | 31 | 26.1 | TP055 | 50.4 | 27 | 25.04 | 51 | 31.3 |
RS052 | 2215 | 120.9 | 40.35 | 95 | 38.6 | TP064 | 103.82 | 34.8 | 33.38 | 106 | 65.3 |
RS055 | 50.68 | 21.2 | 12.64 | 15 | 23.6 | TP065 | 352.47 | 71 | 22.64 | 26 | 26 |
RS057 | 30.04 | 20.8 | 20.98 | 73 | 39.7 | TP068 | 217.94 | 47.9 | 20.33 | 37 | 31 |
RS061 | 82.11 | 30.8 | 25.48 | 24 | 25.9 | TP070 | 891.09 | 99.7 | 25.65 | 132 | 67 |
RS066 * | >4000 | 501.9 | 197.01 | 28 | 17.5 | TP072 | 6.3 | 16.4 | 29.52 | 57 | 33 |
RS067 * | 1103 | 129.6 | 28.5 | 18 | 21 | TP075 | 50.02 | 51.3 | 20.59 | 13 | 22.8 |
RS069 | 170.9 | 29.6 | 23.63 | 36 | 26.3 | TP077 | 258.22 | 27.4 | 22.34 | 26 | 29.4 |
RS073 | 342.44 | 84.8 | 34.84 | 27 | 36.1 | TP079 | 85.29 | 33.8 | 22.7 | 37 | 30.1 |
RS076 | 59.27 | 85.2 | 20.77 | 17 | 14 | TP082 | 31.88 | 22.8 | 14.67 | 51 | 32.7 |
RS078 * | >4000 | 680.5 | 171.89 | 143 | 51.8 | TP084 | 22.03 | 23.9 | 32.51 | 82 | 40.8 |
RS084 | 59.11 | 21.1 | 17.59 | 38 | 22.3 | TP087 | 619.71 | 92.3 | 103.39 | 78 | 38 |
RS088 | 89.74 | 34.8 | 20.79 | 30 | 22.8 | TP091 | 464.24 | 130.1 | 17.55 | 34 | 22.6 |
RS090 | 101.96 | 76.7 | 22.39 | 48 | 25 | TP093 | 38.45 | 19 | 9.26 | 767 | 128.8 |
RS094 | 79 | 91.5 | 24.93 | 29 | 26.5 | TP094 | 13.69 | 21.6 | 29.53 | 72 | 47.9 |
RS096 | 119.56 | 53.9 | 25.81 | 27 | 19.4 | TP099 * | >4000 | 1208.1 | 1040.14 | 143 | 110.8 |
RS100 | 127.62 | 43.8 | 18.54 | 35 | 28.3 | TP106 | 45.26 | 38.8 | 22.23 | 119 | 45.5 |
RS106 | 252.69 | 63.4 | 103.51 | 139 | 95.3 | TP109 * | 3786 | 240.8 | 190.75 | 387 | 55.4 |
RS108 | 61.45 | 72 | 30.65 | 28 | 28.9 | TP111 | 895 | 499 | 29.82 | 197 | 66.7 |
RS112 * | 1712 | 313.1 | 125.76 | 27 | 43.9 | TP115 | 93.15 | 37.9 | 18.36 | 271 | 76.2 |
RS115 | 118.07 | 147 | 25.04 | 49 | 33.4 | TP122 | 33.43 | 21.6 | 24.65 | 124 | 58.4 |
RS118 * | >4000 | 966.6 | 449.03 | 442 | 74.9 | TP125 | 44.84 | 19.4 | 19.12 | 44 | 22 |
RS126 | 131.8 | 48.8 | 23.47 | 22 | 25.5 | TP132 | 10.99 | 23.4 | 28.35 | 139 | 55.2 |
RS128 | 99.13 | 33.6 | 15.16 | 28 | 14.5 | TP133 | 38.25 | 25.5 | 26.92 | 61 | 33.9 |
RS129 | 565.34 | 81.6 | 359.75 | 88 | 26.7 | TP136 | 33.92 | 42 | 27.22 | 72 | 43.5 |
RS131 * | >4000 | 895.5 | 228.86 | 90 | 34.7 | TP138 | 48.68 | 17.7 | 20.54 | 41 | 42.9 |
RS134 | 151.98 | 91.1 | 37.56 | 78 | 30.1 | TP140 | 16.53 | 25.1 | 19.3 | 17 | 18.1 |
RS135 | 103.69 | 60.4 | 30.77 | 35 | 23.3 | TP147 | 26.18 | 30.8 | 43.43 | 116 | 78.4 |
RS143 * | >4000 | 671.9 | 221.68 | 163 | 41.1 | TP154 | 17.61 | 23.3 | 16.89 | 28 | 34.5 |
RS144 | 217.65 | 47.3 | 25.37 | 28 | 20.1 | TP156 | 13.44 | 27 | 15.95 | 33 | 27.4 |
RS148 | 48.3 | 36.2 | 16 | 11 | 11.1 | TP159 | 13.91 | 17.2 | 36.37 | 143 | 74.9 |
RS151 | 45.82 | 37.6 | 12.26 | 9 | 16.6 | TP167 | 11.39 | 20.3 | 12.68 | 15 | 20.6 |
RS156 * | 3500 | 361.7 | 50.46 | 46 | 28.6 | TP173 | 6.53 | 20.8 | 21.16 | 115 | 51.8 |
RS159 | 69.45 | 31 | 15.37 | 18 | 11.3 | TP175 | 16.54 | 17.9 | 15.22 | 16 | 18.2 |
RS162 | 68.91 | 28.6 | 26.37 | 55 | 34.2 | TP177 | 176.98 | 44.5 | 18.68 | 23 | 50.2 |
RS164 | 79.33 | 24.6 | 19.59 | 57 | 31.3 | TP179 | 10.66 | 50.7 | 15.55 | 17 | 23.3 |
RS169 | 72.12 | 20.7 | 15.76 | 21 | 24.8 | BLK | <0.02 | 0.3 | 0.04 | <1 | 0.3 |
Reference Material STD OREAS45H | 1.12 | 16.3 | 11.39 | 416 | 38.9 | ||||||
Reference Material STD OREAS501D | 2.56 | 13.4 | 24.43 | 379 | 81.7 | ||||||
Reference Material STD OREAS45H | 0.84 | 16.1 | 11.49 | 426 | 39.3 | ||||||
Reference Material STD OREAS501D | 2.43 | 11.4 | 24.3 | 371 | 83.2 | ||||||
Soil Pulp RS090 | 101.96 | 76.7 | 22.39 | 48 | 25 | ||||||
Soil Replicate RS090 | 96.97 | 76.5 | 22.25 | 46 | 24.9 | ||||||
Soil Pulp RS037 | 575.47 | 1431.2 | 47.29 | 76 | 49.8 | ||||||
Soil Replicate RS037 | 564.07 | 1426.4 | 48.43 | 77 | 50.8 |
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Element | Sb | As | Pb | Mn | Zn |
---|---|---|---|---|---|
Sb | 1 | - | - | - | - |
As | 0.9 | 1 | - | - | - |
Pb | 0.63 | 0.73 | 1 | - | - |
Mn | 0.15 | 0.28 | 0.42 | 1 | - |
Zn | 0.25 | 0.41 | 0.72 | 0.66 | 1 |
Sb (ppm) | As (ppm) | Pb (ppm) | Mn (ppm) | Zn (ppm) | |
---|---|---|---|---|---|
Mean | 132 | 69 | 30 | 75 | 36 |
Std. Deviation | 184 | 156 | 38 | 123 | 23 |
Minimum | 6.3 | 15 | 9 | 9 | 11 |
Maximum | 895 | 1431 | 360 | 844 | 146 |
Q1 | 26 | 23 | 20 | 27 | 23 |
Q2 | 59 | 34 | 24 | 39 | 30 |
Q3 | 155 | 69 | 29 | 72 | 42 |
Element | R2 | RMSE Train | RMSE Validation | Training Epochs |
---|---|---|---|---|
Sb | 0.7 | 0.0014 | 173 | 1000 |
As | 0.96 | 0.01 | 46 | 1000 |
Pb | 0.83 | 0.04 | 20 | 750 |
Mn | 0.93 | 0.0006 | 41 | 600 |
Zn | 0.78 | 0.0002 | 18 | 1000 |
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Carvalho, M.; Cardoso-Fernandes, J.; Lima, A.; Teodoro, A.C. Convolutional Neural Networks Applied to Antimony Quantification via Soil Laboratory Reflectance Spectroscopy in Northern Portugal: Opportunities and Challenges. Remote Sens. 2024, 16, 1964. https://doi.org/10.3390/rs16111964
Carvalho M, Cardoso-Fernandes J, Lima A, Teodoro AC. Convolutional Neural Networks Applied to Antimony Quantification via Soil Laboratory Reflectance Spectroscopy in Northern Portugal: Opportunities and Challenges. Remote Sensing. 2024; 16(11):1964. https://doi.org/10.3390/rs16111964
Chicago/Turabian StyleCarvalho, Morgana, Joana Cardoso-Fernandes, Alexandre Lima, and Ana C. Teodoro. 2024. "Convolutional Neural Networks Applied to Antimony Quantification via Soil Laboratory Reflectance Spectroscopy in Northern Portugal: Opportunities and Challenges" Remote Sensing 16, no. 11: 1964. https://doi.org/10.3390/rs16111964
APA StyleCarvalho, M., Cardoso-Fernandes, J., Lima, A., & Teodoro, A. C. (2024). Convolutional Neural Networks Applied to Antimony Quantification via Soil Laboratory Reflectance Spectroscopy in Northern Portugal: Opportunities and Challenges. Remote Sensing, 16(11), 1964. https://doi.org/10.3390/rs16111964