Data Augmentation Techniques for Machine Learning Applied to Optical Spectroscopy Datasets in Agrifood Applications: A Comprehensive Review.

Machine learning (ML) and deep learning (DL) have achieved great success in different tasks. These include computer vision, image segmentation, natural language processing, predicting classification, evaluating time series, and predicting values based on a series of variables. As artificial intelligence progresses, new techniques are being applied to areas like optical spectroscopy and its uses in specific fields, such as the agrifood industry. The performance of ML and DL techniques generally improves with the amount of data available. However, it is not always possible to obtain all the necessary data for creating a robust dataset. In the particular case of agrifood applications, dataset collection is generally constrained to specific periods. Weather conditions can also reduce the possibility to cover the entire range of classifications with the consequent generation of imbalanced datasets. To address this issue, data augmentation (DA) techniques are employed to expand the dataset by adding slightly modified copies of existing data. This leads to a dataset that includes values from laboratory tests, as well as a collection of synthetic data based on the real data. This review work will present the application of DA techniques to optical spectroscopy datasets obtained from real agrifood industry applications. The reviewed methods will describe the use of simple DA techniques, such as duplicating samples with slight changes, as well as the utilization of more complex algorithms based on deep learning generative adversarial networks (GANs), and semi-supervised generative adversarial networks (SGANs).

Gas Sensor Based on Lossy Mode Resonances by Means of Thin Graphene Oxide Films Fabricated onto Planar Coverslips.

The use of planar waveguides has recently shown great success in the field of optical sensors based on the Lossy Mode Resonance (LMR) phenomenon. The properties of Graphene Oxide (GO) have been widely exploited in various sectors of science and technology, with promising results for gas sensing applications. This work combines both, the LMR-based sensing technology on planar waveguides and the use of a GO thin film as a sensitive coating, to monitor ethanol, water, and acetone. Experimental results on the fabrication and performance of the sensor are presented. The obtained results showed a sensitivity of 3.1, 2.0, and 0.6 pm/ppm for ethanol, water, and acetone respectively, with a linearity factor R2 > 0.95 in all cases.

Fiber Optic Gas Sensors Based on Lossy Mode Resonances and Sensing Materials Used Therefor: A Comprehensive Review.

Pollution in cities induces harmful effects on human health, which continuously increases the global demand of gas sensors for air quality control and monitoring. In the same manner, the industrial sector requests new gas sensors for their productive processes. Moreover, the association between exhaled gases and a wide range of diseases or health conditions opens the door for new diagnostic applications. The large number of applications for gas sensors has permitted the development of multiple sensing technologies. Among them, optical fiber gas sensors enable their utilization in remote locations, confined spaces or hostile environments as well as corrosive or explosive atmospheres. Particularly, Lossy Mode Resonance (LMR)-based optical fiber sensors employ the traditional metal oxides used for gas sensing purposes for the generation of the resonances. Some research has been conducted on the development of LMR-based optical fiber gas sensors; however, they have not been fully exploited yet and offer optimal possibilities for improvement. This review gives the reader a complete overview of the works focused on the utilization of LMR-based optical fiber sensors for gas sensing applications, summarizing the materials used for the development of these sensors as well as the fabrication procedures and the performance of these devices.

Optical Biosensors for the Detection of Rheumatoid Arthritis (RA) Biomarkers: A Comprehensive Review.

A comprehensive review of optical biosensors for the detection of biomarkers associated with rheumatoid arthritis (RA) is presented here, including microRNAs (miRNAs), C-reactive protein (CRP), rheumatoid factor (RF), anti-citrullinated protein antibodies (ACPA), interleukin-6 (IL-6) and histidine, which are biomarkers that enable RA detection and/or monitoring. An overview of the different optical biosensors (based on fluorescence, plasmon resonances, interferometry, surface-enhanced Raman spectroscopy (SERS) among other optical techniques) used to detect these biomarkers is given, describing their performance and main characteristics (limit of detection (LOD) and dynamic range), as well as the connection between the respective biomarker and rheumatoid arthritis. It has been observed that the relationship between the corresponding biomarker and rheumatoid arthritis tends to be obviated most of the time when explaining the mechanism of the optical biosensor, which forces the researcher to look for further information about the biomarker. This review work attempts to establish a clear association between optical sensors and rheumatoid arthritis biomarkers as well as to be an easy-to-use tool for the researchers working in this field.

A Comprehensive Review: Materials for the Fabrication of Optical Fiber Refractometers Based on Lossy Mode Resonance.

Lossy mode resonance based sensors have been extensively studied in recent years. The versatility of the lossy mode resonance phenomenon has led to the development of sensors based on different configurations that make use of a wide range of materials. The coating material is one of the key elements in the performance of a refractometer. This review paper intends to provide a global view of the wide range of coating materials available for the development of lossy mode resonance based refractometers.

Micro and Nanostructured Materials for the Development of Optical Fibre Sensors.

The measurement of chemical and biomedical parameters can take advantage of the features exclusively offered by optical fibre: passive nature, electromagnetic immunity and chemical stability are some of the most relevant ones. The small dimensions of the fibre generally require that the sensing material be loaded into a supporting matrix whose morphology is adjusted at a nanometric scale. Thanks to the advances in nanotechnology new deposition methods have been developed: they allow reagents from different chemical nature to be embedded into films with a thickness always below a few microns that also show a relevant aspect ratio to ensure a high transduction interface. This review reveals some of the main techniques that are currently been employed to develop this kind of sensors, describing in detail both the resulting supporting matrices as well as the sensing materials used. The main objective is to offer a general view of the state of the art to expose the main challenges and chances that this technology is facing currently.