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Details

  • Nationality

    Portugal
  • Centre

    Applied Photonics
  • Contacts

    +351220402301
    rui.c.martins@inesctec.pt
010
Publications

2019

Path Planning Algorithms Benchmarking for Grapevines Pruning and Monitoring

Authors
Magalhães, SA; dos Santos, FN; Martins, RC; Rocha, LF; Brito, J;

Publication
Progress in Artificial Intelligence - Lecture Notes in Computer Science

Abstract

2019

Optical Sensing of Nitrogen, Phosphorus and Potassium: A Spectrophotometrical Approach Toward Smart Nutrient Deployment

Authors
Monteiro Silva, F; Jorge, PAS; Martins, RC;

Publication
Chemosensors

Abstract
The feasibility of a compact, modular sensing system able to quantify the presence of nitrogen, phosphorus and potassium (NPK) in nutrient-containing fertilizer water was investigated. Direct UV-Vis spectroscopy combined with optical fibers were employed to design modular compact sensing systems able to record absorption spectra of nutrient solutions resulting from local producer samples. N, P, and K spectral interference was studied by mixtures of commercial fertilizer solutions to simulate real conditions in hydroponic productions. This study demonstrates that the use of bands for the quantification of nitrogen with linear or logarithmic regression models does not produce analytical grade calibrations. Furthermore, multivariate regression models, i.e., Partial Least Squares (PLS), which consider specimens interference, perform poorly for low absorbance nutrients. The high interference present in the spectra has proven to be solved by an innovative self-learning artificial intelligence algorithm that is able to find interference modes among a spectral database to produce consistent predictions. By correctly modeling the existing interferences, analytical grade quantification of N, P, and K has proven feasible. The results of this work open the possibility of real-time NPK monitoring in Micro-Irrigation Systems.

2019

Application of a novel LIBS prototype as an analytical grade tool for Li quantification in pegmatite samples

Authors
Guimaraes, D; Ferreira, MFS; Ribeiro, R; Dias, C; Lima, A; Martins, RC; Jorge, PAS;

Publication
Proceedings of SPIE - The International Society for Optical Engineering

Abstract
A high-resolution advanced laser induced breakdown spectroscopy prototype was used to quantify lithium (Li) in lithiniferous rocks. Samples were collected from Barroso's mine (Portugal), claimed as Western Europe's largest spodumene Li discovery. 51 samples from a reverse circulation drill were collected, one for each meter interval, dried, milled, pressed into pellets and further analyzed by laser induced breakdown spectroscopy. Quantification was attempted using either linear models based on the intensity of selected Li spectral lines or advanced chemometrics methods. The latter was very successful, with correlation coefficients of 0.97 against certified laboratory results. © 2019 SPIE.

2019

Plasma control by pattern recognition in laser induced breakdown spectroscopy

Authors
Ferreira, MFS; Guimaraes, D; Jorge, PAS; Martins, RC;

Publication
Proceedings of SPIE - The International Society for Optical Engineering

Abstract
A low-computational intensive laser control approach is proposed for implementing an embedded control system, using pattern recognition by relevant principal component analysis for laser induced breakdown spectroscopy applications. The laser energy is directly related to the resulting spectral pattern and is determined by iterations in the feature space. Results show that single shot iterations until optimum energy can be significantly reduced by pattern recognition. A performance benchmark with minerals, alloys and pellets from material collected from a drill demonstrated an average of 50% improvement, significantly reducing sample deterioration and improving measurement safety. © 2019 SPIE.

2019

Unscrambling Complex Sample Composition, Variability and Multi-scale Interference in Optical Spectroscopy

Authors
Martins, RC;

Publication
FOURTH INTERNATIONAL CONFERENCE ON APPLICATIONS OF OPTICS AND PHOTONICS

Abstract
Spectral information is characterized by multi-scaled interference, convolution and variability. Spectral lines are fragmented and diffused along the spectra. In many cases, matrix and physical effects do not allow to determine specific bands. Despite this limitation, the observed spectra contains significant amounts of information about the sample composition and characteristics, which once understood, can make spectroscopy an ideal technology for analyzing complex samples, such as bodyfluids and tissues. Breaking down and deciphering the structure of spectral information is paramount for the development of reagent-free point-of-care devices. A self-learning artificial intelligence was developed to take advantage of spectral complex information structure. It determines the relationships between composition and/or spectral features in high-dimensional space, where different sub-spaces correlate to specific constituents or characteristics. It also establishes a knowledgebase, by feature space transformations and optimizing co-variance search direction under the correct 'matrix effect' context. An example of hemogram analysis with erythrocyte and leucocyte counts is presented to demonstrate the advantages of the developed methodology.

Supervised
thesis

2020

Metodologias e Tecnologias para a previsão dinâmica da qualidade dafruta -do campo ao prato

Author
Ana Patrícia Ferreira Vicente da Silva

Institution
UP-FCUP

2020

Fiber Laser Plasma Spectroscopy for Real-Time

Author
Miguel Fernandes Soares Ferreira

Institution
UP-FCUP

2019

Fiber Laser Plasma Spectroscopy for Real-Time

Author
Miguel Fernandes Soares Ferreira

Institution
UP-FCUP