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About

About

I graduated in Applied Physics (Optics and Lasers) at the University of Minho (1996), obtained the MSc in Optoelectronics and Lasers at the Physics Department of the University of Porto (2000); in 2006 I concluded a PhD program at Porto University in collaboration with the Department of Physics and Optical Sciences at the University of North Carolina at Charlotte, NC, USA, with work in luminescence based optical fibre systems for biochemical sensing applications using quantum dots. Since 1997 I have been involved in several research and technology transfer projects related to optical fibre sensing technology, developing new sensing configurations and interrogation techniques for optical sensors. I am, since 2007 a Senior researcher at INESC TEC reponsible for the Biochemical Sensors team, where we explore the potential of optical fibre and integrated optics technologies in environmental and medical applications framed by several R&D projects. I have more than 200 publications in the fields of sensors in national and international conferences and peer reviewed journals, I am author of 3 book chapters and also hold one patent. I am a member of SPIE and SPOF.

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Topics
Details

Details

  • Name

    Pedro Jorge
  • Role

    Area Manager
  • Since

    01st July 1997
031
Publications

2024

Autonomous and intelligent optical tweezers for improving the reliability and throughput of single particle analysis

Authors
Teixeira, J; Moreira, FC; Oliveira, J; Rocha, V; Jorge, PAS; Ferreira, T; Silva, NA;

Publication
MEASUREMENT SCIENCE AND TECHNOLOGY

Abstract
Optical tweezers are an interesting tool to enable single cell analysis, especially when coupled with optical sensing and advanced computational methods. Nevertheless, such approaches are still hindered by system operation variability, and reduced amount of data, resulting in performance degradation when addressing new data sets. In this manuscript, we describe the deployment of an automatic and intelligent optical tweezers setup, capable of trapping, manipulating, and analyzing the physical properties of individual microscopic particles in an automatic and autonomous manner, at a rate of 4 particle per min, without user intervention. Reproducibility of particle identification with the help of machine learning algorithms is tested both for manual and automatic operation. The forward scattered signal of the trapped PMMA and PS particles was acquired over two days and used to train and test models based on the random forest classifier. With manual operation the system could initially distinguish between PMMA and PS with 90% accuracy. However, when using test datasets acquired on a different day it suffered a loss of accuracy around 24%. On the other hand, the automatic system could classify four types of particles with 79% accuracy maintaining performance (around 1% variation) even when tested with different datasets. Overall, the automated system shows an increased reproducibility and stability of the acquired signals allowing for the confirmation of the proportionality relationship expected between the particle size and its friction coefficient. These results demonstrate that this approach may support the development of future systems with increased throughput and reliability, for biosciences applications.

2024

Identification of Relevant Spectral Ranges in Laser-Induced Breakdown Spectroscopy Imaging Using the Fourier Space

Authors
Lopes, T; Capela, D; Ferreira, MFS; Guimaraes, D; Jorge, PAS; Silva, NA;

Publication
APPLIED SPECTROSCOPY

Abstract
Laser-induced breakdown spectroscopy (LIBS) imaging has now a well-established position in the subject of spectral imaging, leveraging multi-element detection capabilities and fast acquisition rates to support applications both at academic and technological levels. In current applications, the standard processing pipeline to explore LIBS imaging data sets revolves around identifying an element that is suspected to exist within the sample and generating maps based on its characteristic emission lines. Such an approach requires some previous expert knowledge both on the technique and on the sample side, which hinders a wider and more transparent accessibility of the LIBS imaging technique by non-specialists. To address this issue, techniques based on visual analysis or peak finding algorithms are applied on the average or maximum spectrum, and may be employed for automatically identifying relevant spectral regions. Yet, maps containing relevant information may often be discarded due to low signal-to-noise ratios or interference with other elements. In this context, this work presents an agnostic processing pipeline based on a spatial information ratio metric that is computed in the Fourier space for each wavelength and that allows for the identification of relevant spectral ranges in LIBS. The results suggest a more robust and streamlined approach to feature extraction in LIBS imaging compared with traditional inspection of the spectra, which can introduce novel opportunities not only for spectral data analysis but also in the field of data compression.

2023

Intelligent grids for faster elemental mapping with Laser-induced breakdown spectroscopy

Authors
Capela, D; Ferreira, M; Lima, A; Jorge, P; Guimarães, D; Silva, NA;

Publication
Results in Optics

Abstract
Laser-induced breakdown spectroscopy is a spectroscopic technique that allows for fast elemental mapping of heterogeneous samples. Yet, detailed maps need high-resolution sampling grids, which can turn the task into a time-consuming process and can increase sample damage. In this work, we present the implementation of an imaged-based intelligent mesh algorithm that makes use of superpixel segmentation to optimize elemental mapping processes. Our results show that the approach can increase the elemental mapping resolution and decrease acquisition times, fostering opportunities for applications that benefit from minimal sample damage such as heritage analysis, or timely analysis such as industrial applications. © 2022 The Author(s)

2023

Compact biosensor system for the quantification of hydrogen peroxide in milk

Authors
Vasconcelos, H; Matias, A; Mendes, J; Araujo, J; Dias, B; Jorge, PAS; Saraiva, C; de Almeida, JMMM; Coelho, LCC;

Publication
TALANTA

Abstract
Hydrogen peroxide is usually added to products to delay the development of microorganisms mainly in milk, hence increasing its stability over time, however the side effects can become devastating to human health.A technique is presented consisting of detecting hydrogen peroxide as an adulterant in milk through a sensor where pretreatment of the sample is not necessary, using a single use membrane. The detection of hydrogen peroxide in fresh-raw, whole, semi-skimmed and skimmed milk was performed using a luminol chem-iluminescence reaction.For hydrogen peroxide water solutions, a linear response was attained from 1.0 x 10-4 to 9.0 x 10-3 %w/w and an LOD (limit of detection) of 3.0 x 10-5 %w/w was determined. An R-squared value of 0.97 and a relative standard deviation lower than 10%, were achieved.Hydrogen peroxide concentration as low as 1.0 x 10-3 %w/w was measured for fresh-raw, skim and whole milk and for semi-skimmed milk, as low as 2.0 x 10-3 %w/w.The methodology presented, as long as our knowledge, is original, rapid, ecological and inexpensive. In regard of the sensitivity obtained, the methodology has great possibility to be applied in the detection of hydrogen peroxide in several areas. It is envisaged monitoring of food quality, agriculture systems and environment pollution.

2023

Interactive three-dimensional chemical element maps with laser-induced breakdown spectroscopy and photogrammetry

Authors
Lopes, T; Rodrigues, P; Cavaco, R; Capela, D; Ferreira, MFS; Guimaraes, D; Jorge, PAS; Silva, NA;

Publication
SPECTROCHIMICA ACTA PART B-ATOMIC SPECTROSCOPY

Abstract
Imaging the spatial distribution of chemical elements at a sample surface is a common application of laserinduced breakdown spectroscopy with vast scientific and technological applications. Yet, typical imaging solutions only explore the creation of two-dimensional maps, which can limit the interpretability of the results and further diagnostics in three-dimensional settings. Within this context, this work explores the combination of spectral imaging techniques and photogrammetry to deploy a versatile solution for the creation of threedimensional spectral imaging models. First, by making use of a numerical algorithm that is able to match features in the spectral image with those of the three-dimensional model, we show how to match the mesh from distinct sensor modalities. Then, we describe a possible visualization workflow, making use of dedicated photogrammetry and visualization software to easily deploy interactive models. Overall, the results demonstrate the versatility of our approach and pave for the development of novel spectral imaging diagnostic strategies that are able to deliver better qualitative analysis and insight in the three-dimensional space.

Supervised
thesis

2023

Magnetophotonics for Electromagnetic Surface Waves Sensors

Author
João Pedro Miranda Carvalho

Institution
UP-FCUP

2023

New tools for high performance LIBS Imaging

Author
Tomás José Moreira Lopes

Institution
UP-FCUP

2023

Optical Tweezers: From automatic manipulation to multimodal sensing

Author
Joana Magalhães Baptista Teixeira

Institution
UP-FCUP

2023

Fiber Laser Plasma Spectroscopy for Real-Time

Author
Miguel Fernandes Soares Ferreira

Institution
UP-FCUP

2023

Towards direct measurement of the refractive index of suspended nanoparticles by Back-Scattering Interferometry

Author
João Pedro de Azevedo Calçada

Institution
UP-FCUP