Abstract
New paths to increase the sensing performance of plasmonic sensors have been reported in recent years. There are several methodologies to achieve such purpose, namely by optimizing the nanostructure, nanomaterial and even the sensing platform. Recently the use nanoparticles over plasmonic thin films have been reported and shown sensitivity enhancement, when compared to a bare thin film. Nevertheless, a nanomaterial combination between NP and thin film has not been studied. In this work it was studied such plasmonic materials in order to optimize not only refractometric sensitivity but also decrease the resultant plasmonic band width. It was found that for Au, Ag and Cu thin films, the deposition of plasmonic nanoparticles resulted in an overall refractometric sensitivity and figure of merit (FOM) increase. The larger FOM increase was obtained for the Ag thin film, from 42 to 162 when coupled to Si nanoparticles. The greater sensitivity increase was achieved for a Cu thin film coupled to a Si nanoparticle, with an increase from 1745 to 3230 nm/RIU. © Published under licence by IOP Publishing Ltd.

Abstract
The identification of olive-tree cultivars is a lengthy and expensive process, therefore, the proposed work presents a new strategy for identifying different cultivars of olive trees using their leaf and machine learning algorithms. In this initial case, four autochthonous cultivars of the Tras-os-Montes region in Portugal are identified (Cobrancosa, Madural, Negrinha e Verdeal). With the use of this type of algorithm, it is expected to replace the previous techniques, saving time and resources for farmers. Three different machine learning algorithms (Decision Tree, SVM, Random Forest) were also compared and the results show an overall accuracy rate of the best algorithm (Random Forest) of approximately 93%.

Abstract
A new (bio)sensing platform based on differential refractometric measurements was developed. The sensing scheme is based on the combination LPFGs/MIP/NIP, involving a dual channel system for real-time compensation of non-specific interactions. The correction system improves the sensor behavior by reducing the response to interferents by 30%. © 2022 The Author(s).

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.

Abstract
Biochemical-chemical sensing with plasmonic sensors is widely performed by tracking the responses of surface plasmonic resonance peaks to changes in the medium. Interestingly, consistent sensitivity and resolution improvements have been demonstrated for gold nanoparticles by analyzing other spectral features, such as spectral inflection points or peak curvatures. Nevertheless, such studies were only conducted on planar platforms and were restricted to gold nanoparticles. In this work, such methodologies are explored and expanded to plasmonic optical fibers. Thus, we study-experimentally and theoretically-the optical responses of optical fiber-doped gold or silver nanospheres and optical fibers coated with continuous gold or silver thin films. Both experimental and numerical results are analyzed with differentiation methods, using total variation regularization to effectively minimize noise amplification propagation. Consistent resolution improvements of up to 2.2x for both types of plasmonic fibers are found, demonstrating that deploying such analysis with any plasmonic optical fiber sensors can lead to sensing resolution improvements.

Abstract
Reinforced concrete structures are prevalent in infrastructure and are of significant economic and social importance to humanity. However, they are prone to decay from cement paste carbonation. pH sensors have been developed to monitor cement paste carbonation, but their adoption by the industry remains limited. This work introduces two new methods for monitoring cement paste carbonation in real time that have been validated through the accelerated carbonation of cement paste samples. Both configurations depart from traditional pH monitoring. In the first configuration, the carbonation depth of a cement paste sample is measured using two CO2 optical fiber sensors. One sensor is positioned on the surface of the sample, while the other is embedded in the middle. As the carbonation depth progresses and reaches the embedded CO2 sensor, the combined response of the sensors changes. In the second configuration, a multimode fiber is embedded within the paste, and its carbonation is monitored by observing the increase in reflected light intensity (1.6-18%) resulting from the formation of CaCO3. Its applicability in naturally occurring carbonation is tested at concentrations of 3.2% CO2, and the influence of water is positively evaluated; thus, this setup is suitable for real-world testing and applications.