Continuous Hue-Based Self-Calibration of a Smartphone Spectrometer Applied to Optical Fiber Fabry-Perot Sensor Interrogation

Portable optical fiber biosensors integrated with smartphone: technologies, applications, and challenges [Invited]

The increasing demand for individualized health monitoring and diagnostics has prompted considerable research into the integration of portable optical fiber biosensors integrated with smartphones. By capitalizing on the benefits offered by optical fibers, these biosensors enable qualitative and quantitative biosensing across a wide range of applications. The integration of these sensors with smartphones, which possess advanced computational power and versatile sensing capabilities, addresses the increasing need for portable and rapid sensing solutions. This extensive evaluation thoroughly examines the domain of optical fiber biosensors in conjunction with smartphones, including hardware complexities, sensing approaches, and integration methods. Additionally, it explores a wide range of applications, including physiological and chemical biosensing. Furthermore, the review provides an analysis of the challenges that have been identified in this rapidly evolving area of research and concludes with relevant suggestions for the progression of the field.

Portuino-A Novel Portable Low-Cost Arduino-Based Photo- and Fluorimeter

A novel portable low-cost Arduino-controlled photo- and fluorimeter for on-site measurements has been developed. The device uses LEDs as a light source and a phototransistor as a light sensor. The circuit is based on the discharge of a capacitor with the photocurrent from the phototransistor. Validation experiments for absorbance measurements were performed by measuring protein concentration using the Bradford method and measuring phosphate ions in water using a commercial test kit. The emission light of the excited fluorescent dyes rhodamine 6G and calcofluor white was measured to validate the usability of the device as a fluorescence photometer. In all validation experiments, similar correlation coefficients and limit of detection could be achieved with the portable photo- and fluorimeter and a laboratory spectrometer and fluorimeter. Real sample analysis was performed, measuring phosphate concentration in freshwater and concentration of green fluorescent protein, extracted from Escherichia coli.

Cost-Effective Fiber Optic Solutions for Biosensing

In the last years, optical fiber sensors have proven to be a reliable and versatile biosensing tool. Optical fiber biosensors (OFBs) are analytical devices that use optical fibers as transducers, with the advantages of being easily coated and biofunctionalized, allowing the monitorization of all functionalization and detection in real-time, as well as being small in size and geometrically flexible, thus allowing device miniaturization and portability for point-of-care (POC) testing. Knowing the potential of such biosensing tools, this paper reviews the reported OFBs which are, at the moment, the most cost-effective. Different fiber configurations are highlighted, namely, end-face reflected, unclad, D- and U-shaped, tips, ball resonators, tapered, light-diffusing, and specialty fibers. Packaging techniques to enhance OFBs' application in the medical field, namely for implementing in subcutaneous, percutaneous, and endoscopic operations as well as in wearable structures, are presented and discussed. Interrogation approaches of OFBs using smartphones' hardware are a great way to obtain cost-effective sensing approaches. In this review paper, different architectures of such interrogation methods and their respective applications are presented. Finally, the application of OFBs in monitoring three crucial fields of human life and wellbeing are reported: detection of cancer biomarkers, detection of cardiovascular biomarkers, and environmental monitoring.

Visible Light Spectrum Extraction from Diffraction Images by Deconvolution and the Cepstrum

Accurate color determination in variable lighting conditions is difficult and requires special devices. We considered the task of extracting the visible light spectrum using ordinary camera sensors, to facilitate low-cost color measurements using consumer equipment. The approach uses a diffractive element attached to a standard camera and a computational algorithm for forming the light spectrum from the resulting diffraction images. We present two machine learning algorithms for this task, based on alternative processing pipelines using deconvolution and cepstrum operations, respectively. The proposed methods were trained and evaluated on diffraction images collected using three cameras and three illuminants to demonstrate the generality of the approach, measuring the quality by comparing the recovered spectra against ground truth measurements collected using a hyperspectral camera. We show that the proposed methods are able to reconstruct the spectrum, and, consequently, the color, with fairly good accuracy in all conditions, but the exact accuracy depends on the specific camera and lighting conditions. The testing procedure followed in our experiments suggests a high degree of confidence in the generalizability of our results; the method works well even for a new illuminant not seen in the development phase.

Spectral Classification Based on Deep Learning Algorithms

Convolutional neural networks (CNN) can achieve accurate image classification, indicating the current best performance of deep learning algorithms. However, the complexity of spectral data limits the performance of many CNN models. Due to the potential redundancy and noise of the spectral data, the standard CNN model is usually unable to perform correct spectral classification. Furthermore, deeper CNN architectures also face some difficulties when other network layers are added, which hinders the network convergence and produces low classification accuracy. To alleviate these problems, we proposed a new CNN architecture specially designed for 2D spectral data. Firstly, we collected the reflectance spectra of five samples using a portable optical fiber spectrometer and converted them into 2D matrix data to adapt to the deep learning algorithms’ feature extraction. Secondly, the number of convolutional layers and pooling layers were adjusted according to the characteristics of the spectral data to enhance the feature extraction ability. Finally, the discard rate selection principle of the dropout layer was determined by visual analysis to improve the classification accuracy. Experimental results demonstrate our CNN system, which has advantages over the traditional AlexNet, Unet, and support vector machine (SVM)-based approaches in many aspects, such as easy implementation, short time, higher accuracy, and strong robustness.

Smartphone-based optical analysis systems

During the past few decades, there has been a growing trend towards the use of smartphone-based analysis systems. This is mainly due to its ubiquity, its increasing computing capacity, its relatively low cost and the ability to acquire and process data at the same time. Furthermore, there are many sensors integrated into a smartphone, for example a complementary metal-oxide semiconductor (CMOS) sensor. A CMOS sensor enables optical analysis for example by using it as a colorimeter, photometer or spectrometer. This review explores the current state-of-the-art smartphone-based optical analysis systems in various areas of application. It is organized into three sections, each of which investigates one class of smartphone-based devices: (i) smartphone-based colorimeters (ii) smartphone-based photo- and spectrometers and (iii) smartphone-based fluorimeters.

Comparison of Pulse Wave Signal Monitoring Techniques with Different Fiber-Optic Interferometric Sensing Elements

Pulse wave (PW) measurement is a highly prominent technique, used in biomedical diagnostics. Development of novel PW sensors with increased accuracy and reduced susceptibility to motion artifacts will pave the way to more advanced healthcare technologies. This paper reports on a comparison of performance of fiber optic pulse wave sensors, based on Fabry–Perot interferometer, fiber Bragg grating, optical coherence tomography (OCT) and singlemode-multimode-singlemode intermodal interferometer. Their performance was tested in terms of signal to noise ratio, repeatability of demodulated signals and suitability of demodulated signals for extraction of information about direct and reflected waves. It was revealed that the OCT approach of PW monitoring provided the best demodulated signal quality and was most robust against motion artifacts. Advantages and drawbacks of all compared PW measurement approaches in terms of practical questions, such as multiplexing capabilities and abilities to be interrogated by portable hardware are discussed.