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%.

An Intra-Oral Optical Sensor for the Real-Time Identification and Assessment of Wine Intake.

Saliva has gained considerable attention as a diagnostics alternative to blood analyses. A wide spectrum of salivary compounds is correlated to blood concentrations of biomarkers, providing informative and discriminative data regarding the state of health. Intra-oral detection and assessment of food and beverage intake can be correlated and provides valuable information to forecast the formation and modification of salivary biomarkers. In this context, the present work proposes a novel intra-oral optical fiber sensor, developed around an optical coupler topology, and exemplified on the detection and assessment of wine intake, which is accounted for example for the formation of Nε-carboxymethyllysine Advanced Glycation End-products. A laboratory proof of concept validates the proposed solution on four white and four red wine samples. The novel optical sensor geometry shows good spectral properties, accounting for selectivity with respect to grape-based soft drinks. This enables intra-oral detection and objective quality assessment of wine. Moreover, its implementation exploits the advantages of fiber-optics sensing and facilitates integration into a mouthguard, holding considerable potential for real-time biomedical applications to investigate Advanced Glycation End-products in the saliva and their connection with consumption of wine, for the evaluation of risk factors in diet-related diseases.

Distributed fluorescent optical fiber proximity sensor: Towards a proof of concept.

Fluorescent fibers are optical fibers which emit light as a response to an incident phenomenon, usually an incident light. Operation depends on the doping dyes, which determine specific fluorescence and optical characteristics useful in the development of optical sensors. In this work we propose a low-cost distributed proximity sensor implemented using a red fluorescent fiber, to provide a security option for a surface plasmon resonance system. Operation of the proposed sensor relies on having the incident illumination intensity varied by the presence or absence of an obstacle in the vicinity of the sensing element. This will influence the radiated fluorescence accordingly. The proposed setup for the implementation of the optical proximity sensor assumes having a high brightness LED deployed for axial fiber illumination and a blue LED for side illumination. Electronic processing then accounts for gain and digitization. Measurement results of the prototype validate the proposed concept.