Holographic Sensor Based on Bayfol HX200 Commercial Photopolymer for Ethanol and Acetic Acid Detection.

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.

Classification of Food Additives Using UV Spectroscopy and One-Dimensional Convolutional Neural Network.

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%.