Ioana-Adriana Potărniche

Outdoor solutions aiding the navigation of visually impaired individuals can seamlessly transition to indoor environments. Take, for instance, the adaptation of special lanes and configurations on the floor. However, these existing solutions fall short when it comes to addressing obstacles above ground level, such as open windows, as highlighted in a previous article on the use of ultrasonic glove for visually impaired users. In response, the present proposal is a user-friendly, cost-effective solution that is capable of detecting elevated obstacles. Importantly, this solution aligns with a user’s language preferences, eliminating the need for learning new languages or possessing IT skills. Users simply specify their desired language for the prototype to communicate in, ensuring a personalized experience. The system alerts users to the presence of obstacles through varying levels of warning, calculated based on the distance between the obstacle and the user’s current position. This approach not only enhances safety but also prioritizes accessibility and ease of use.

This paper presents a holographic sensor based on reflection holograms recorded in the commercial photopolymer Bayfol^® HX 200. The recording geometry and index modulation of the hologram were optimised to improve accuracy for this specific application. The sensor was subjected to tests using various analytes, and it exhibited sensitivity to acetic acid and ethanol. The measurements revealed a correlation between the concentration of the analyte in contact with the sensor’s surface and the resulting wavelength shift of the diffracted light. The minimum detectable concentrations were determined to be above 0.09 mol/dm³ for acetic acid and 5% (v/v) for ethanol. Notably, the sensors demonstrated a rapid response time. Given that ethanol serves as a base for alcoholic beverages, and acetic acid is commonly found in commercial vinegar, these sensors hold promise for applications in food quality control.

Food additives are utilized in countless food products available for sale. They enhance or obtain a specific flavor, extend the storage time, or obtain a desired texture. This paper presents an automatic classification system for five food additives based on their absorbance in the ultraviolet domain. Solutions with different concentrations were created by dissolving a measured additive mass into distilled water. The analyzed samples were either simple (one additive solution) or mixed (two additive solutions). The substances presented absorbance peaks between 190 nm and 360 nm. Each substance presents a certain number of absorbance peaks at specific wavelengths (e.g., acesulfame potassium presents an absorbance peak at 226 nm, whereas the peak associated with potassium sorbate is at 254 nm). Therefore, each additive has a distinctive spectrum that can be used for classification. The sample classification was performed using deep learning techniques. The samples were associated with numerical labels and divided into three datasets (training, validation, and testing). The best classification results were obtained using CNN (convolutional neural network) models. The classification of the 404 spectra with a CNN model with three convolutional layers obtained a mean testing accuracy of 92.38% ± 1.48%, whereas the mean validation accuracy was 93.43% ± 2.01%.