Sensors

Photonics Research

Table of Contents

We have worked extensivly in various fields of Photonics, including (but not exclusively) Oxygen Sensing and Food Technology. But we are also working on application of Machine Learning on all kind of photonics use cases.

Research

Publications

Oxygen Sensing

  1. Venturini, F., Michelucci, U., & Baumgartner, M. (2021). Implementation of multi-task learning neural network architectures for robust industrial optical sensing. Optical Measurement Systems for Industrial Inspection XII, 11782, 117822H.
    @inproceedings{venturini2021implementation,
      title = {Implementation of multi-task learning neural network architectures for robust industrial optical sensing},
      author = {Venturini, Francesca and Michelucci, Umberto and Baumgartner, Michael},
      booktitle = {Optical Measurement Systems for Industrial Inspection XII},
      volume = {11782},
      pages = {117822H},
      year = {2021},
      bibtex_show = {true},
      abbr = {Sensing},
      topic = {oxygen},
      organization = {International Society for Optics and Photonics}
    }
    
  2. Michelucci, U., & Venturini, F. (2020). New Autonomous Intelligent Sensor Design Approach for Multiple Parameter Inference. Engineering Proceedings, 2(1). https://doi.org/10.3390/engproc2020002096

    The determination of multiple parameters via luminescence sensing is of great interest for many applications in different fields, like biosensing and biological imaging, medicine, and diagnostics. The typical approach consists in measuring multiple quantities and in applying complex and frequently just approximated mathematical models to characterize the sensor response. The use of machine learning to extract information from measurements in sensors have been tried in several forms before. But one of the problems with the approaches so far, is the difficulty in getting a training dataset that is representative of the measurements done by the sensor. Additionally, extracting multiple parameters from a single measurement has been so far an impossible problem to solve efficiently in luminescence. In this work a new approach is described for building an autonomous intelligent sensor, which is able to produce the training dataset self-sufficiently, use it for training a neural network, and then use the trained model to do inference on measurements done on the same hardware. For the first time the use of machine learning additionally allows to extract two parameters from one single measurement using multitask learning neural network architectures. This is demonstrated here by a dual oxygen concentration and temperature sensor.

    @article{engproc2020002096,
      author = {Michelucci, Umberto and Venturini, Francesca},
      title = {New Autonomous Intelligent Sensor Design Approach for Multiple Parameter Inference},
      journal = {Engineering Proceedings},
      volume = {2},
      year = {2020},
      number = {1},
      bibtex_show = {true},
      abbr = {Sensing},
      article-number = {96},
      link = {https://www.mdpi.com/2673-4591/2/1/96},
      issn = {2673-4591},
      topic = {oxygen},
      doi = {10.3390/engproc2020002096}
    }
    
  3. Venturini, F., Michelucci, U., & Baumgartner, M. (2020). Deep-learning for multi-parameter luminescence sensing: demonstration of dual sensor. Frontiers in Optics, FTu2B–5.
    @inproceedings{venturini2020deep,
      title = {Deep-learning for multi-parameter luminescence sensing: demonstration of dual sensor},
      author = {Venturini, Francesca and Michelucci, Umberto and Baumgartner, Michael},
      booktitle = {Frontiers in Optics},
      pages = {FTu2B--5},
      year = {2020},
      bibtex_show = {true},
      abbr = {Sensing},
      topic = {oxygen},
      organization = {Optical Society of America},
      link = {https://www.osapublishing.org/viewmedia.cfm?uri=FiO-2020-FTu2B.5&seq=0}
    }
    
  4. Venturini, F., Michelucci, U., & Baumgartner, M. (2020). Multi-task learning approach for optical luminescence sensing. Applied Machine Learning Days (AMLD), Lausanne, 25-29 January 2020.
    @inproceedings{venturini2020multi,
      title = {Multi-task learning approach for optical luminescence sensing},
      author = {Venturini, Francesca and Michelucci, Umberto and Baumgartner, Michael},
      booktitle = {Applied Machine Learning Days (AMLD), Lausanne, 25-29 January 2020},
      bibtex_show = {true},
      abbr = {Sensing},
      topic = {oxygen},
      year = {2020}
    }
    
  5. Venturini, F., Michelucci, U., & Baumgartner, M. (2020). Dual oxygen and temperature sensing with single indicator using multi-task-learning neural networks. Optical Sensing and Detection VI, 11354, 113541C.
    @inproceedings{venturini2020dual,
      title = {Dual oxygen and temperature sensing with single indicator using multi-task-learning neural networks},
      author = {Venturini, Francesca and Michelucci, Umberto and Baumgartner, Michael},
      booktitle = {Optical Sensing and Detection VI},
      volume = {11354},
      pages = {113541C},
      year = {2020},
      bibtex_show = {true},
      abbr = {Sensing},
      topic = {oxygen},
      organization = {International Society for Optics and Photonics},
      link = {https://www.spiedigitallibrary.org/conference-proceedings-of-spie/11354/113541C/Dual-oxygen-and-temperature-sensing-with-single-indicator-using-multi/10.1117/12.2554941.short}
    }
    
  6. Venturini, F., Michelucci, U., & Baumgartner, M. (2020). Dual oxygen and temperature luminescence learning sensor with parallel inference. Sensors, 20(17), 4886.
    @article{venturini2020duam,
      title = {Dual oxygen and temperature luminescence learning sensor with parallel inference},
      author = {Venturini, Francesca and Michelucci, Umberto and Baumgartner, Michael},
      journal = {Sensors},
      volume = {20},
      number = {17},
      pages = {4886},
      year = {2020},
      bibtex_show = {true},
      abbr = {Sensing},
      topic = {oxygen},
      publisher = {Multidisciplinary Digital Publishing Institute},
      link = {https://www.mdpi.com/1424-8220/20/17/4886/pdf}
    }
    
  7. Venturini, F., Baumgartner, M., & Michelucci, U. (2019). New approach for luminescence sensing based on machine learning. Optical Data Science II, 10937, 109370H.
    @inproceedings{venturini2019new,
      title = {New approach for luminescence sensing based on machine learning},
      author = {Venturini, Francesca and Baumgartner, Michael and Michelucci, Umberto},
      booktitle = {Optical Data Science II},
      volume = {10937},
      pages = {109370H},
      year = {2019},
      bibtex_show = {true},
      abbr = {Sensing},
      topic = {oxygen},
      organization = {International Society for Optics and Photonics},
      link = {https://www.spiedigitallibrary.org/conference-proceedings-of-spie/10937/109370H/New-approach-for-luminescence-sensing-based-on-machine-learning/10.1117/12.2508969.short}
    }
    
  8. Michelucci, U., & Venturini, F. (2019). Multi-task learning for multi-dimensional regression: application to luminescence sensing. Applied Sciences, 9(22), 4748.
    @article{michelucci2019multi,
      title = {Multi-task learning for multi-dimensional regression: application to luminescence sensing},
      author = {Michelucci, Umberto and Venturini, Francesca},
      journal = {Applied Sciences},
      volume = {9},
      number = {22},
      pages = {4748},
      year = {2019},
      bibtex_show = {true},
      abbr = {Sensing},
      topic = {oxygen},
      publisher = {Multidisciplinary Digital Publishing Institute},
      link = {https://www.mdpi.com/2076-3417/9/22/4748/pdf}
    }
    
  9. Michelucci, U., Baumgartner, M., & Venturini, F. (2019). Optical oxygen sensing with artificial intelligence. Sensors, 19(4), 777.
    @article{michelucci2019optical,
      title = {Optical oxygen sensing with artificial intelligence},
      author = {Michelucci, Umberto and Baumgartner, Michael and Venturini, Francesca},
      journal = {Sensors},
      volume = {19},
      number = {4},
      pages = {777},
      year = {2019},
      publisher = {Multidisciplinary Digital Publishing Institute},
      link = {https://www.mdpi.com/1424-8220/19/4/777/pdf},
      bibtex_show = {true},
      topic = {oxygen},
      abbr = {Sensing}
    }
    

Food Technology

  1. Venturini, F., Sperti, M., Michelucci, U., Gucciardi, A., Martos, V. M., & Deriu, M. A. (2022). Extraction of physicochemical properties from the fluorescence spectrum with 1D convolutional neural networks: Application to olive oil. Journal of Food Engineering, 336, 111198.
    @article{venturini2023extraction,
      title = {Extraction of physicochemical properties from the fluorescence spectrum with 1D convolutional neural networks: Application to olive oil},
      author = {Venturini, Francesca and Sperti, Michela and Michelucci, Umberto and Gucciardi, Arnaud and Martos, Vanessa M. and Deriu, Marco A.},
      journal = {Journal of Food Engineering},
      volume = {336},
      pages = {111198},
      year = {2022},
      month = sep,
      publisher = {Elsevier},
      selected = {true},
      bibtex_show = {true},
      abbr = {Food},
      topic = {food}
    }
    
  2. Sperti, M., Gucciardi, A., Michelucci, U., Venturini, F., & Deriu, M. A. (2022). Chemical analysis of olive oils from fluorescence spectra thanks to one-dimensional convolutional neural networks. Optical Sensing and Detection VII, 12139.
    @inproceedings{sperti2022chemical,
      title = {Chemical analysis of olive oils from fluorescence spectra thanks to one-dimensional convolutional neural networks},
      author = {Sperti, Michela and Gucciardi, Arnaud and Michelucci, Umberto and Venturini, Francesca and Deriu, Marco A},
      booktitle = {Optical Sensing and Detection VII},
      volume = {12139},
      year = {2022},
      bibtex_show = {true},
      abbr = {Food},
      topic = {food},
      organization = {SPIE}
    }
    
  3. Arnaud, G., Michelucci, U., Venturini, F., Sperti, M., & Deriu, M. A. (2022). Compact optical fluorescence sensor for food quality control using artificial neural networks: application to olive oil. Optical Sensing and Detection VII, 12139.
    @inproceedings{arnaud2022compact,
      title = {Compact optical fluorescence sensor for food quality control using artificial neural networks: application to olive oil},
      author = {Arnaud, Gucciardi and Michelucci, Umberto and Venturini, Francesca and Sperti, Michela and Deriu, Marco Agostino},
      booktitle = {Optical Sensing and Detection VII},
      volume = {12139},
      year = {2022},
      bibtex_show = {true},
      abbr = {Food},
      topic = {food},
      organization = {SPIE}
    }
    
  4. Venturini, F., Michelucci, U., Sperti, M., Gucciardi, A., & Deriu, M. A. (2022). One-dimensional convolutional neural networks design for fluorescence spectroscopy with prior knowledge: explainability techniques applied to olive oil fluorescence spectra. Optical Sensing and Detection VII, 12139, 326–333.
    @inproceedings{venturini2022one,
      title = {One-dimensional convolutional neural networks design for fluorescence spectroscopy with prior knowledge: explainability techniques applied to olive oil fluorescence spectra},
      author = {Venturini, Francesca and Michelucci, Umberto and Sperti, Michela and Gucciardi, Arnaud and Deriu, Marco A},
      booktitle = {Optical Sensing and Detection VII},
      volume = {12139},
      pages = {326--333},
      year = {2022},
      bibtex_show = {true},
      abbr = {Food},
      topic = {food},
      organization = {SPIE}
    }
    
  5. Venturini, F., Sperti, M., Michelucci, U., Herzig, I., Baumgartner, M., Caballero, J. P., Jimenez, A., & Deriu, M. A. (2021). Exploration of Spanish Olive Oil Quality with a Miniaturized Low-Cost Fluorescence Sensor and Machine Learning Techniques. Foods, 10(5). https://doi.org/10.3390/foods10051010

    Extra virgin olive oil (EVOO) is the highest quality of olive oil and is characterized by highly beneficial nutritional properties. The large increase in both consumption and fraud, for example through adulteration, creates new challenges and an increasing demand for developing new quality assessment methodologies that are easier and cheaper to perform. As of today, the determination of olive oil quality is performed by producers through chemical analysis and organoleptic evaluation. The chemical analysis requires advanced equipment and chemical knowledge of certified laboratories, and has therefore limited accessibility. In this work a minimalist, portable, and low-cost sensor is presented, which can perform olive oil quality assessment using fluorescence spectroscopy. The potential of the proposed technology is explored by analyzing several olive oils of different quality levels, EVOO, virgin olive oil (VOO), and lampante olive oil (LOO). The spectral data were analyzed using a large number of machine learning methods, including artificial neural networks. The analysis performed in this work demonstrates the possibility of performing the classification of olive oil in the three mentioned classes with an accuracy of 100%. These results confirm that this minimalist low-cost sensor has the potential to substitute expensive and complex chemical analysis.

    @article{foods10051010,
      author = {Venturini, Francesca and Sperti, Michela and Michelucci, Umberto and Herzig, Ivo and Baumgartner, Michael and Caballero, Josep Palau and Jimenez, Arturo and Deriu, Marco Agostino},
      title = {Exploration of Spanish Olive Oil Quality with a Miniaturized Low-Cost Fluorescence Sensor and Machine Learning Techniques},
      journal = {Foods},
      volume = {10},
      year = {2021},
      number = {5},
      article-number = {1010},
      link = {https://www.mdpi.com/2304-8158/10/5/1010},
      pubmedid = {34066453},
      bibtex_show = {true},
      abbr = {Food},
      topic = {food},
      issn = {2304-8158},
      doi = {10.3390/foods10051010}
    }
    

Spectroscopy Algorithms

  1. Michelucci, U., & Venturini, F. (2017). Novel semi-parametric algorithm for interference-immune tunable absorption spectroscopy gas Sensing. Sensors, 17(10), 2281.
    @article{michelucci2017novel,
      title = {Novel semi-parametric algorithm for interference-immune tunable absorption spectroscopy gas Sensing},
      author = {Michelucci, Umberto and Venturini, Francesca},
      journal = {Sensors},
      volume = {17},
      number = {10},
      pages = {2281},
      year = {2017},
      publisher = {Multidisciplinary Digital Publishing Institute},
      bibtex_show = {true},
      topic = {mltheory},
      abbr = {Algorithms}
    }