Gold Nanoparticles-Based Colorimetric Assays for Environmental Monitoring and Food Safety Evaluation

Development and validation of colorimetric-assisted chemometrics methods based on the localized gold nanoparticles surface plasmon resonance for fast simultaneous estimation of anti-hepatitis C virus drugs in their combined dosage form: A comparative study with HPLC method

The present work describes a developed analytical method based on a colorimetric assay using gold nanoparticles (AuNPs) along with chemometric techniques for the simultaneous estimation of sofosbuvir (SOF) and ledipasvir (LED) in their synthetic mixtures and tablet dosage form. The applied chemometric approaches were continuous wavelet transform (CWT) and least squares support vector machine (LS-SVM). Characterization of AuNPs and AuNPs in combination with the drug was performed by UV-vis spectrophotometer, transmission electron microscopy (TEM), dynamic light scattering (DLS), and Fourier transform infrared (FTIR) spectroscopy. In the CWT method, the zero amplitudes were determined at 427 nm with Daubechies wavelet family for SOF (zero crossing point of LED) and 440 nm with Symlet wavelet family for LED (zero crossing point of SOF) over the concentration range of 7.5-90.0 μg/L and 40.0-100.0 μg/L with coefficients of determination (R2) of 0.9974 and 0.9907 for SOF and LED, respectively. The limit of detection (LOD) and limit of quantification (LOQ) of this method were found to be 7.92, 9.96 μg/L and 12.02, 30.2 μg/L for SOF and LED, respectively. In the LS-SVM model, the mean percentage recovery of SOF and LED in synthetic mixtures was 98.29 % and 99.25 % with root mean square error of 2.392 and 1.034, which were obtained by the optimization of regularization parameter (γ) and width of the function (σ) based on the cross-validation method. The proposed methods were also applied for the determination concentration of SOF and LED in the combined dosage form, recoveries were higher than 95 %, and relative standard deviation (RSD) values were lower than 0.4 %. The achieved results were statistically compared with those obtained from the high-performance liquid chromatography (HPLC) technique for the concurrent estimation of components through one-way analysis of variance (ANOVA), and no significant difference was found between the suggested approaches and the reference one. According to these results, simplicity, high speed, lack of time-consuming process, and cost savings are considerable benefits of colorimetry along with chemometrics methods compared to other ways.

Machine learning-enabled colorimetric sensors for foodborne pathogen detection

In the past decade, there have been various advancements to colorimetric sensors to improve their potential applications in food and agriculture. One application of growing interest is sensing foodborne pathogens. There are unique considerations for sensing in the food industry, including food sample destruction, specificity amidst a complex food matrix, and high sensitivity requirements. Incorporating novel technology, such as nanotechnology, microfluidics, and smartphone app development, into colorimetric sensing methodology can enhance sensor performance. Nonetheless, there remain challenges to integrating sensors with existing food safety infrastructure. Recently, increasingly advanced machine learning techniques have been employed to facilitate nondestructive, multiplex detection for feasible assimilation of sensors into the food industry. With its ability to analyze and make predictions from highly complex data, machine learning holds potential for advanced yet practical colorimetric sensing of foodborne pathogens. This article summarizes recent developments and hurdles of machine learning-enabled colorimetric foodborne pathogen sensing. These advancements underscore the potential of interdisciplinary, cutting-edge technology in providing safer and more efficient food systems.

Hymenaea courbaril resin-mediated gold nanoparticles as catalysts in organic dyes degradation and sensors in pharmaceutical pollutants

In this study, green synthesis of gold nanoparticles (AuNPs) using aqueous extract from Hymenaea courbaril resin (HCR) is reported. The successful formation, functional group involvement, size, and morphology of the subject H. courbaril resin mediated gold nanoparticles (HCRAuNPs) were confirmed by Ultra Violet-Visible (UV-vis) spectroscopy, Fourier-Transform Infrared spectroscopy (FTIR), and Transmission Electron Microscopy (TEM) techniques. Stable and high yield of HCRAuNPs was formed in 1:15 (aqueous solution: salt solution) reacted in sunlight as indicated by the visual colour change and appearance of surface Plasmon resonance (SPR) at 560 nm. From the FT-IR results, the phenolic hydroxyl (-OH) functional group was found to be involved in synthesis and stabilization of nanoparticles. The TEM analysis showed that the particles are highly dispersed and spherical in shape with average size of 17.5 nm. The synthesized HCRAuNPs showed significant degradation potential against organic dyes, including methylene blue (MB, 85 %), methyl orange (MO, 90 %), congo red (CR, 83 %), and para nitrophenol (PNP, 76 %) up to 180 min. The nanoparticles also demonstrated the effective detection of pharmaceutical pollutants, including amoxicillin, levofloxacin, and azithromycin in aqueous environment as observable changes in color and UV-Vis spectral graph.

Tailoring plasmonic sensing strategies for the rapid and sensitive detection of hypochlorite in swimming water samples

A tunable plasmonic sensor has been developed by varying the dextran content in the initially synthesized dextran-gold nanoparticle (dAuNPs) solution. A colloidal nanogold solution (dAuNPs-Sol) was initially prepared using dextran and gold salt in alkaline media by a one-pot green synthetic route. The dAuNPs-Sol was combined with varying amounts of dextran (ranging from 0.01 to 30.01%) to create a tunable probe, along with different solid formats, including tablet (dAuNPs-Tab), powder (dAuNPs-Powder), and composite (dAuNPs-Comp). Both the liquid and solid phase plasmonic probes were characterized using UV-vis spectroscopy, transmission electron microscopy (TEM) dynamic light scattering (DLS), and zeta potential analysis. The impact of dextran content in the dAuNP solution is studied in terms of surface charge and hydrodynamic size. The influence of operational treatments used to achieve solid dAuNPs probes is also explored. All plasmonic probes were employed to detect a broad range of OCl¯ concentrations (ranging from µM to mM) in water through aggregation followed by calculating a lower and upper limit of detection (LLoD, ULoD) of the proposed colorimetric sensors. Results indicate that the most sensitive detection is achieved with a lower dextran content (0.01%), which exhibits an LLoD of 50 µM. The dAuNPs-Sol sensor is selective and demonstrates real-world applicability, as confirmed by interference analysis and successful testing with various water samples. Additionally, it is found that a 20 × concentration of dextran-coated gold nanoparticles could be attained without any changes in the particle morphology. This concentration is achieved through a straightforward process that does not require the use of a centrifuge machine. This finding highlights the practicality and simplicity of the method, indicating its potential for scalable and cost-effective production of concentrated dAuNPs without compromising their structural integrity.

Explainable Deep Learning-Assisted Self-Calibrating Colorimetric Patches for In Situ Sweat Analysis

Sweat has emerged as a compelling analyte for noninvasive biosensing technology because it contains a wealth of important biomarkers in hormones, organic biomacromolecules, and various ionic mixtures. These components offer valuable insights and can reflect an individual's physiological conditions. Here, we introduced an explainable deep learning (DL)-assisted wearable self-calibrating colorimetric biosensing analysis platform to efficiently and precisely detect the biomarker's concentration in sweat. Specifically, we have integrated the advantages of the colorimetric sensing method, adsorbing-swelling hydrogel, and explainable DL algorithms to develop an enzyme/indicator-immobilized colorimetric patch, which has reliable colorimetric sensing ability and excellent adsorbing-swelling function. A total of 5625 colorimetric images were collected as the analysis data set and assessed two DL algorithms and seven machine learning (ML) algorithms. Zn2+, glucose, and Ca2+ in human sweats could be facilely classified and quantified with 100% accuracy via the convolutional neural network (CNN) model, and the testing results of actual sweats via the DL-assisted colorimetric approach are 91.7-97.2% matching with the classical UV-vis spectrum. Class activation mapping (CAM) was utilized to visualize the inner working mechanism of CNN operation, which contributes to verify and explicate the design rationality of the noninvasive biosensing technology. An "end-to-end" model was established to ascertain the black box of the DL algorithm, promoted software design or principium optimization, and contributed facile indicators for health monitoring, disease prevention, and clinical diagnosis.

Gold Nanoparticle-Based Colorimetric Sensors: Properties and Application in Detection of Heavy Metals and Biological Molecules

During the last decade, advances have been made in nanotechnology using nanomaterials, leading to improvements in their performance. Gold nanoparticles (AuNPs) have been widely used in the field of sensor analysis and are also combined with certain materials to obtain the desired characteristics. AuNPs are commonly used as colorimetric sensors in detection methods. In developing an ideal sensor, there are certain characteristics that must be met such as selectivity, sensitivity, accuracy, precision, and linearity, among others. Various methods for the synthesis of AuNPs and conjugation with other components have been carried out in order to obtain good characteristics for their application. AuNPs can be applied in the detection of both heavy metals and biological molecules. This review aimed at observing the role of AuNPs in its application. The synthesis of AuNPs for sensors will also be revealed, along with their characteristics suitable for this role. In the application method, the size and shape of the particles must be considered. AuNPs used in heavy metal detection have a particle size of around 15-50 nm; in the detection of biological molecules, the particle size of AuNPs used is 6-35 nm whereas in pharmaceutical compounds for cancer treatment and the detection of other drugs, the particle size used is 12-30 nm. The particle sizes did not correlate with the type of molecules regardless of whether it was a heavy metal, biological molecule, or pharmaceutical compound but depended on the properties of the molecule itself. In general, the best morphology for application in the detection process is a spherical shape to obtain good sensitivity and selectivity based on previous studies. Functionalization of AuNPs with conjugates/receptors can be carried out to increase the stability, sensitivity, selectivity, solubility, and plays a role in detecting biological compounds through conjugating AuNPs with biological molecules.

Overview on the Development of Alkaline-Phosphatase-Linked Optical Immunoassays

The drive to achieve ultrasensitive target detection with exceptional efficiency and accuracy requires the advancement of immunoassays. Optical immunoassays have demonstrated significant potential in clinical diagnosis, food safety, environmental protection, and other fields. Through the innovative and feasible combination of enzyme catalysis and optical immunoassays, notable progress has been made in enhancing analytical performances. Among the kinds of reporter enzymes, alkaline phosphatase (ALP) stands out due to its high catalytic activity, elevated turnover number, and broad substrate specificity, rendering it an excellent candidate for the development of various immunoassays. This review provides a systematic evaluation of the advancements in optical immunoassays by employing ALP as the signal label, encompassing fluorescence, colorimetry, chemiluminescence, and surface-enhanced Raman scattering. Particular emphasis is placed on the fundamental signal amplification strategies employed in ALP-linked immunoassays. Furthermore, this work briefly discusses the proposed solutions and challenges that need to be addressed to further enhance the performances of ALP-linked immunoassays.