A Portable Smartphone-based Platform with an Offline Image-processing Tool for the Rapid Paper-based Colorimetric Detection of Glucose in Artificial Saliva

An Optoelectronic Sensing Real-Time Glucose Detection Film Using Photonic Crystal Enhanced Rare Earth Fluorescence and Additive Manufacturing

In this research, a novel detection method employing rare-earth upconversion nanoparticle (UCNP) as the core, coated with MnO2 nanosheets is designed, which formed a color and fluorescence dual-responsive UCNP composite material, MnO2-modified NaYF4:Yb,Tm@NaYF4. By enabling both colorimetric and fluorescence methods simultaneously, this composite material allows for the detection of glucose concentration under different conditions, while exhibiting strong resistance to environmental interference, chemical stability, and accuracy. To further enhance the sensitivity of the detection method, a photonic crystals (PCs)-PDMS array where polymethyl methacrylate PCs are deposited onto a substrate composed of PDMS-glass slice with hydrophobic surfaces is developed. This array can serve as a substrate that specifically reflected blue light while allowing other colors of light to pass through, which effectively reduced background signal interference and improved detection sensitivity (1.2 µm) with a wider linear range (20-800 µm). Finally, a portable fluorescence intensity detection device is designed to enhance the portability of the platform. Numerous experimental results demonstrated that this research significantly improved the sensitivity of glucose detection, providing new research directions for the field of fluid biomarker detectio.

Machine learning-aided evaluation of oxidative strength of cold atmospheric plasma-treated water

Plasma medicine is gaining attraction in the medical field, particularly the use of cold atmospheric plasma (CAP) in biomedicine. The chemistry of the plasma is complex, and the reactive oxygen species (ROS) within it are the basis for the biological effect of CAP on the target. Understanding how the oxidative power of ROS responds to diverse plasma parameters is vital for standardizing the effective application of CAP. The proven applicability of machine learning (ML) in the field of medicine is encouraging, as it can also be applied in the field of plasma medicine to correlate the oxidative strength of plasma-treated water (PTW) according to different parameters. In this study, plasma-treated water was mixed with potassium iodide-starch reagent for color formation that could be linked to the oxidative capacity of PTW. Corresponding images were captured resulting from the exposure of the color-forming agent to water treated with plasma for different time points. Several ML models were trained to distinguish the color changes sourced by the oxidative strength of ROS. The AdaBoost Classifier (ABC) algorithm demonstrated better performance among the classification models used by extracting color-based features from the images. Our results, with a test accuracy of 63.5%, might carry a potential for future standardization in the field of plasma medicine with an automated system that can be created to interpret the oxidative properties of ROS in different plasma treatment parameters via ML.

On-site colorimetric food spoilage monitoring with smartphone embedded machine learning

Real-time and on-site food spoilage monitoring is still a challenging issue to prevent food poisoning. At the onset of food spoilage, microbial and enzymatic activities lead to the formation of volatile amines. Monitoring of these amines with conventional methods requires sophisticated, costly, labor-intensive, and time consuming analysis. Here, anthocyanins rich red cabbage extract (ARCE) based colorimetric sensing system was developed with the incorporation of embedded machine learning in a smartphone application for real-time food spoilage monitoring. FG-UV-CD100 films were first fabricated by crosslinking ARCE-doped fish gelatin (FG) with carbon dots (CDs) under UV light. The color change of FG-UV-CD100 films with varying ammonia vapor concentrations was captured in different light sources with smartphones of various brands, and a comprehensive dataset was created to train machine learning (ML) classifiers to be robust and adaptable to ambient conditions, resulting in 98.8% classification accuracy. Meanwhile, the ML classifier was embedded into our Android application, SmartFood++, enabling analysis in about 0.1 s without internet access, unlike its counterpart using cloud operation via internet. The proposed system was also tested on a real fish sample with 99.6% accuracy, demonstrating that it has a great advantage as a potent tool for on-site real-time monitoring of food spoilage by non-specialized personnel.

Saliva Analysis Based on Microfluidics: Focusing the Wide Spectrum of Target Analyte

Saliva is one of the most critical human body fluids that can reflect the state of the human body. The detection of saliva is of great significance for disease diagnosis and health monitoring. Microfluidics, characterized by microscale size and high integration, is an ideal platform for the development of rapid and low-cost disease diagnostic techniques and devices. Microfluidic-based saliva testing methods have aroused considerable interest due to the increasing need for noninvasive testing and frequent or long-term testing. This review briefly described the significance of saliva analysis and generally classified the targets in saliva detection into pathogenic microorganisms, inorganic substances, and organic substances. By using this classification as a benchmark, the state-of-the-art research results on microfluidic detection of various substances in saliva were summarized. This work also put forward the challenges and future development directions of microfluidic detection methods for saliva.

A field-deployable water quality monitoring with machine learning-based smartphone colorimetry

Water quality monitoring is an increasing global concern as the pollution of water sources causes adverse effects on economic growth and human health. Traditional approaches to the detection of pollutants are time-consuming and labor-intensive due to the requirement of sophisticated equipment or laboratory settings. Therefore, portable devices featuring rapid response and easy operation are indispensable in water quality monitoring. Herein, smartphone-based colorimetric pollutant quantification is demonstrated in a machine learning (ML) framework. As a proof of concept, the presence of seven ions in water was analyzed using colorimetric strips. The color variation on the strip indicators was captured under eight lighting conditions with five smartphones, providing robustness against the illumination variation and camera optics for ML classifiers. Color and texture features were extracted from the images to train the classifiers. Among the twenty-three classifiers, K-Nearest Neighbors exhibits the best classification performance, leading to the integration with our custom-designed Android application called Hydro Sens. The proposed approach was also tested with real samples taken from local water sources. The results prove that incorporating color strips with ML with a smartphone application can be used for water quality monitoring, which offers promising alternatives for sophisticated equipment that is especially applicable in resource-limited settings.

Colorimetric detection of H2O2 with Fe3O4@Chi nanozyme modified µPADs using artificial intelligence

Peroxidase mimicking Fe₃O₄@Chitosan (Fe₃O₄@Chi) nanozyme was synthesized and used for high-sensitive enzyme-free colorimetric detection of H₂O₂. The nanozyme was characterized in comparison with  Fe₃O₄ nanoparticles (NPs) using X-ray diffraction, Fourier-transform infrared spectroscopy, dynamic light scattering, and thermogravimetric analysis. The catalytic performance of Fe₃O₄@Chi nanozyme was first evaluated by UV-Vis spectroscopy using 3,3',5,5'-tetramethylbenzidine. Unlike Fe₃O₄NPs, Fe₃O₄@Chi nanozyme exhibited an intrinsic peroxidase activity with a detection limit of 69 nM. Next, the nanozyme was applied to a microfluidic paper-based analytical device (µPAD) and colorimetric analysis was performed at varying concentrations of H₂O₂ using a machine learning-based smartphone app called "Hi-perox Sens++ ." The app with machine learning classifiers made the system user-friendly as well as more robust and adaptive against variation in illumination and camera optics. In order to train various machine learning classifiers, the images of the µPADs were taken at 30 s and 10 min by four smartphone brands under seven different illuminations. According to the results, linear discriminant analysis exhibited the highest classification accuracy (98.7%) with phone-independent repeatability at t = 30 s and the accuracy was preserved for 10 min. The proposed system also showed excellent selectivity in the presence of various interfering molecules and good detection performance in tap water.

Electrospun nanofiber-based glucose sensors for glucose detection

Diabetes is a chronic, systemic metabolic disease that leads to multiple complications, even death. Meanwhile, the number of people with diabetes worldwide is increasing year by year. Sensors play an important role in the development of biomedical devices. The development of efficient, stable, and inexpensive glucose sensors for the continuous monitoring of blood glucose levels has received widespread attention because they can provide reliable data for diabetes prevention and diagnosis. Electrospun nanofibers are new kinds of functional nanocomposites that show incredible capabilities for high-level biosensing. This article reviews glucose sensors based on electrospun nanofibers. The principles of the glucose sensor, the types of glucose measurement, and the glucose detection methods are briefly discussed. The principle of electrospinning and its applications and advantages in glucose sensors are then introduced. This article provides a comprehensive summary of the applications and advantages of polymers and nanomaterials in electrospun nanofiber-based glucose sensors. The relevant applications and comparisons of enzymatic and non-enzymatic nanofiber-based glucose sensors are discussed in detail. The main advantages and disadvantages of glucose sensors based on electrospun nanofibers are evaluated, and some solutions are proposed. Finally, potential commercial development and improved methods for glucose sensors based on electrospinning nanofibers are discussed.

Colorimetric food spoilage monitoring with carbon dot and UV light reinforced fish gelatin films using a smartphone application

The objective of this study was to develop novel colorimetric films for food freshness monitoring. UV light irradiation (365 nm) and carbon dots (CDs) were tested as the potential crosslinkers in the fabrication of anthocyanins doped fish gelatin (FG) films. The effect of crosslinkers on the optical, surface, structural, barrier and mechanical properties of FG films was investigated. The incorporation of CD under UV irradiation improved the tested properties of FG films. The kinetic colorimetric responses of FG films against ammonia vaporwere studied to simulate the food spoilage and determine the ammonia sensitivity of the films. Among the tested films, UV-treated FG films containing 100 mg/l (FG-UV-CD100) indicated the best properties. Later, the color difference of FG-UV-CD100 films was observed to correlate well with microbial growth and TVB-N release in skinless chicken breast samples. At the same time, a custom-designed smartphone application (SmartFood) was also developed to be used with the FG-UV-CD100 film for quantitative estimation of food freshness in real-time. The proposed food freshness monitoring platform reveals a great potential to minimize global food waste and the outbreak of foodborne illness.

Non-enzymatic colorimetric detection of hydrogen peroxide using a μPAD coupled with a machine learning-based smartphone app

In the present study, iodide-mediated 3,3',5,5'-tetramethylbenzidine (TMB)-H₂O₂ reaction system was applied to a microfluidic paper-based analytical device (μPAD) for non-enzymatic colorimetric determination of H₂O₂. The proposed system is portable and incorporates a μPAD with a machine learning-based smartphone app. A smartphone app called "Hi-perox Sens" capable of image capture, cropping and processing was developed to make the system simple and user-friendly. Briefly, circular μPADs were designed and tested with varying concentrations of H₂O₂. Following the color change, the images of the μPADs were taken with four different smartphones under seven different illumination conditions. In order to make the system more robust and adaptive against illumination variation and camera optics, the images were first processed for feature extraction and then used to train machine learning classifiers. According to the results, TMB + KI showed the highest classification accuracy (97.8%) with inter-phone repeatability at t = 30 s under versatile illumination and maintained its accuracy for 10 minutes. In addition, the performance of the system was also comparable to two different commercially available H₂O₂ kits in real samples.