Rui Costa Martins

Abstract

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.

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.

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.

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.