Essential oils are a valuable raw material for several industries. Low-cost methods cannot detect its adulteration; specialised equipment is required. In this paper, we proposed the use of gas sensors to detect the adulteration process in the essential oil of Cistus ladanifer. Gas sensors are used in a measuring chamber to measure pure and adulterated oils. We compare the suitability of the tested sensors for detecting adulterated oil and the required measuring time. A total of five samples are determined, with a measuring time of 12 h. Each gas sensor is configured to be sensitive to different compounds. Even though sensors are not specific to detect the volatile organic compounds (VOCs) present in the essential oil, our objective is to evaluate if these VOCs might interact with the sensors as an interferent. Results indicate that various gas sensors sensitive to the same chemical compound offered different values. It might indicate that the interaction of VOCs is different among the tested sensors or that the location of the sensors and the heterogeneous distribution of VOCs along the measurement chamber impact the data. Regarding the performed analyses, we can affirm that identifying the adulterated essential oil is possible using the generated data. Moreover, the results suggest that most of the data, even for different compounds and sensors, are highly correlated, allowing a reduction in the studied variables. According to the high correlation, data are reduced, and 100% of correct classification can be obtained even when only the MQ3 and MQ8 are used.

The COVID-19 pandemic has been a worldwide catastrophe. Its impact, not only economically, but also socially and in terms of human lives, was unexpected. Each of the many mechanisms to fight the contagiousness of the illness has been proven to be extremely important. One of the most important mechanisms is the use of facemasks. However, the wearing the facemasks incorrectly makes this prevention method useless. Artificial Intelligence (AI) and especially facial recognition techniques can be used to detect misuses and reduce virus transmission, especially indoors. In this paper, we present an intelligent method to automatically detect when facemasks are being worn incorrectly in real-time scenarios. Our proposal uses Convolutional Neural Networks (CNN) with transfer learning to detect not only if a mask is used or not, but also other errors that are usually not taken into account but that may contribute to the virus spreading. The main problem that we have detected is that there is currently no training set for this task. It is for this reason that we have requested the participation of citizens by taking different selfies through an app and placing the mask in different positions. Thus, we have been able to solve this problem. The results show that the accuracy achieved with transfer learning slightly improves the accuracy achieved with convolutional neural networks. Finally, we have also developed an Android-app demo that validates the proposal in real scenarios.

The monitoring of the coastal environment is a crucial factor in ensuring its proper management. Nevertheless, existing monitoring technologies are limited due to their cost, temporal resolution, and maintenance needs. Therefore, limited data are available for coastal environments. In this paper, we present a low-cost multiparametric probe that can be deployed in coastal areas and integrated into a wireless sensor network to send data to a database. The multiparametric probe is composed of physical sensors capable of measuring water temperature, salinity, and total suspended solids (TSS). The node can store the data in an SD card or send them. A real-time clock is used to tag the data and to ensure data gathering every hour, putting the node in deep sleep mode in the meantime. The physical sensors for salinity and TSS are created for this probe and calibrated. The calibration results indicate that no effect of temperature is found for both sensors and no interference of salinity in the measuring of TSS or vice versa. The obtained calibration model for salinity is characterised by a correlation coefficient of 0.9 and a Mean Absolute Error (MAE) of 0.74 g/L. Meanwhile, different calibration models for TSS were obtained based on using different light wavelengths. The best case was using a simple regression model with blue light. The model is characterised by a correlation coefficient of 0.99 and an MAE of 12 mg/L. When both infrared and blue light are used to prevent the effect of different particle sizes, the determination coefficient of 0.98 and an MAE of 57 mg/L characterised the multiple regression model.

The development of low-cost systems for measuring medical parameters is currently an important issue since this type of system ensures that any sector of the population can access these technologies. Likewise, pandemic situations, such as the one experienced some months ago due to SARS-CoV-2, require the rapid availability of diagnostic devices. One of the devices, which has turned out to be the most relevant in the early detection of respiratory problems, is the finger pulse oximeter. However, the default information that these devices offer is limited. This paper presents the process carried out to analyze the data measured by a commercial pulse oximeter and takes advantage of them to extract relevant information about the vital parameters of the patient that is being monitored, such as peripheral oxygen saturation (SpO2), pulse rate (bpm) (PR), respiratory rate (RR/min), perfusion index (%) (Pi), plethysmography wave, plethysmographic variation index (%) (PVi), the shape of the dicrotic fissure, and the area under the curve. To do this, the Bluetooth frames generated by the device are analyzed through reverse engineering and processed to obtain the aforementioned parameters. Finally, an application for Android devices is developed in order to facilitate the collection and reading of the parameters. The system is tested with different patients whose results are validated by a physician.

In coastal water monitoring, abrupt pH changes might indicate different pollution sources. Existing sensors for pH monitoring in coastal waters at low cost are mainly based on a glass membrane and a reference electrode. Virtual sensors are elements capable of measuring certain parameters based on data from other parameters or variables. The aim of this paper is to propose the use of a virtual pH sensor based on measuring different physical effects of H+ on the electromagnetic field generated by an inductor. Double inductors based on two solenoids of 40 and 80 spires are used as sensing elements. Samples with pH from 4 to 11 are used, and the effect of temperature is evaluated using samples from 10 to 40 °C. The induced voltage and the delay of the signal are measured for powering frequencies from 100 to 500 kHz. These data of delay, induced voltage, frequency, and temperature are included in a probabilistic neural network to classify these data according to the pH. The results indicate low accuracy for samples with a pH of 11. A second analysis, excluding these data, offered correctly classified cases of 88.9%. The system can achieve considerable high accuracy (87.5%) using data gathered at a single frequency, from 246 to 248 kHz. The predicted versus observed data is correlated with a linear model characterized by an R2 of 0.69, which is similar to the ones observed in other virtual sensors.