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Automatic Selection of Reference Lines for Spectrometer Calibration with Recurrent Neural Networks

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Part of the Communications in Computer and Information Science book series (CCIS,volume 1705)


Instrument calibration is a critical but time-consuming process in many scientific fields. In this paper, we present an approach using recurrent neural networks (RNNs) for automatically detecting reference lines required to calibrate a spectrometer with well-known wavelengths of mercury and neon spectra. RNNs are a type of neural network that is best suited for processing sequential data. We collect a dataset of spectral images by taking images with cameras of different resolutions to train the neural network. Moreover, we prove that RNNs can learn to predict spectra lines in the calibration process with high precision. We match spectrometer measurements to their corresponding wavelengths by fitting a polynomial with these predicted reference lines. We validate our method using a 3D-printed spectrometer and compare the results with the NIST Atomic Spectra Database. The automatic selection of neon or mercury reference lines helps the calibration procedure to become faster, thus avoiding any manual selection. Our proposed technique is suitable for spectrometry applications where the speed is critical and the calibration process needs to be performed frequently.


  • Wavelength calibration
  • Spectrometer
  • Recurrent Neural Networks

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  1. 1.

  2. 2.

  3. 3.

  4. 4.


  1. Lesani, A., et al.: Quantification of human sperm concentration using machine learning-based spectrophotometry. Comput. Biol. Med. 127, 104061 (2020)

    CrossRef  Google Scholar 

  2. Kokilambigai, K.S., Lakshmi, K.S.: Utilization of green analytical chemistry principles for the simultaneous estimation of paracetamol, aceclofenac and thiocolchicoside by UV spectrophotometry. Green Chem. Lett. Rev. 14(1), 99–107 (2021)

    CrossRef  Google Scholar 

  3. Noor, A.M., Norali, A.N., Zakaria, Z., Fook, C.Y., Cahyadi, B.N.: An open-source, miniature UV to NIR spectrophotometer for measuring the transmittance of liquid materials. In: Md. Zain, Z., Sulaiman, M.H., Mohamed, A.I., Bakar, M.S., Ramli, M.S. (eds.) Proceedings of the 6th International Conference on Electrical, Control and Computer Engineering. Lecture Notes in Electrical Engineering, vol. 842, pp. 407–416. Springer, Singapore (2022).

  4. Tsotsou, G.E., Potiriadi, I.: A UV/Vis spectrophotometric methodology for quality control of stevia-based extracts in the food industry. Food Control 137, 108932 (2022)

    Google Scholar 

  5. Zhang, Y., Zhang, T., Li, H.: Application of laser-induced breakdown spectroscopy (LIBS) in environmental monitoring. Spectrochim. Acta, Part B 181, 106218 (2021)

    CrossRef  Google Scholar 

  6. Post, C., et al.: Application of laser-induced, deep UV Raman spectroscopy and artificial intelligence in real-time environmental monitoring-solutions and first results. Sensors 21(11), 3911 (2021)

    CrossRef  Google Scholar 

  7. Young-Gu, J.: Fabrication of a low-cost and high-resolution papercraft smartphone spectrometer. Phys. Educ. 55(3), 035005 (2020)

    CrossRef  Google Scholar 

  8. Ormachea, O., Villazón, A., Escalera, R.: A spectrometer based on smartphones and a low-cost kit for transmittance and absorbance measurements in real-time. Optica Pura y Aplicada 50(3), 239–249 (2017)

    CrossRef  Google Scholar 

  9. Muñoz-Hernández, T.C., Valencia, E.G., Torres, P., Ramírez. D.L.A.: Low-Cost Spectrometer for Educational Applications using Mobile Devices. Optica Pura y Aplicada, 50(3), 221–228 (2017)

    Google Scholar 

  10. McGonigle, A., et al.: Smartphone spectrometers. Sensors 18(2), 223 (2018)

    CrossRef  Google Scholar 

  11. Wang, L.-J., Chang, Y.-C., Sun, R., Li, L.: A multichannel smartphone optical biosensor for high-throughput point-of-care diagnostics. Biosens. Bioelectron. 87, 686–692 (2017)

    CrossRef  Google Scholar 

  12. Dutta, S., Saikia, K., Nath, P.: Smartphone based LSPR sensing platform for bio-conjugation detection and quantification. RSC Adv. 6(26), 21871–21880 (2016)

    CrossRef  Google Scholar 

  13. Liu, D., Hennelly, B.M.: Improved wavelength calibration by modeling the spectrometer. Appl. Spectrosc. 76, 1283–1299 (2022)

    CrossRef  Google Scholar 

  14. Rumelhart, D.E., Hinton, G.E., Williams, R.J.: Learning representations by back-propagating errors. Nature 323(6088), 533–536 (1986)

    CrossRef  MATH  Google Scholar 

  15. Podpora, M., Korba’s, G., Kawala-Janik, A.: YUV vs RGB - choosing a color space for human-machine interaction. Ann. Comput. Sci. Inf. Syst. 3(29–34), 09 (2014)

    Google Scholar 

  16. Liu, K., Feihong, Yu.: Accurate wavelength calibration method using system parameters for grating spectrometers. Opt. Eng. 52(1), 013603 (2013)

    CrossRef  Google Scholar 

  17. Tan, M., Pang, R., Le, Q.V.: Efficientdet: scalable and efficient object detection. In: 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 10778–10787 (2020)

    Google Scholar 

  18. Kramida, A., and the NIST ASD Team. NIST Atomic Spectra Database (ver. 5.9). [2022, August 12]. National Institute of Standards and Technology, Gaithersburg, MD (2021)

  19. Jian, D., et al.: Sunlight based handheld smartphone spectrometer. Biosens. Bioelectron. 143, 111632 (2019)

    CrossRef  Google Scholar 

  20. Markvart, A., Liokumovich, L., Medvedev, I., Ushakov, N.: Continuous hue-based self-calibration of a smartphone spectrometer applied to optical fiber fabry-perot sensor interrogation. Sensors 20(21), 6304 (2020)

    CrossRef  Google Scholar 

  21. Villazon, A., Ormachea, O., Zenteno, A., Orellana, A.: A low-cost spectrometry remote laboratory. In: Auer, M.E., El-Seoud, S.A., Karam, O.H. (eds.) Artificial Intelligence and Online Engineering. REV 2022. Lecture Notes in Networks and Systems, vol. 524, pp. 198–208 Springer, Cham (2023)

    Google Scholar 

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This work was partially funded by the Erasmus+ Project “EUBBC-Digital” (No. 618925-EPP-1-2020-1-BR-EPPKA2-CBHE-JP).

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Correspondence to Alex Villazón .

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Zenteno, A., Orellana, A., Villazón, A., Ormachea, O. (2023). Automatic Selection of Reference Lines for Spectrometer Calibration with Recurrent Neural Networks. In: Narváez, F.R., Urgilés, F., Bastos-Filho, T.F., Salgado-Guerrero, J.P. (eds) Smart Technologies, Systems and Applications. SmartTech-IC 2022. Communications in Computer and Information Science, vol 1705. Springer, Cham.

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  • Print ISBN: 978-3-031-32212-9

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