Rapid quantitative analysis of Hg2+ residue in dairy products using SERS coupled with ACO-BP-AdaBoost algorithm

Flexible electronics for heavy metal ion detection in water: a comprehensive review

Flexible electronics offer a versatile, rapid, cost-effective and portable solution to monitor water contamination, which poses serious threat to the environment and human health. This review paper presents a comprehensive exploration of the versatile platforms of flexible electronics in the context of heavy metal ion detection in water systems. The review overviews of the fundamental principles of heavy metal ion detection, surveys the state-of-the-art materials and fabrication techniques for flexible sensors, analyses key performance metrics and limitations, and discusses future opportunities and challenges. By highlighting recent advances in nanomaterials, polymers, wireless integration, and sustainability, this review aims to serve as an essential resource for researchers, engineers, and policy makers seeking to address the critical challenge of heavy metal contamination in water resources. The versatile promise of flexible electronics is thoroughly elucidated to inspire continued innovation in this emerging technology arena.

Rapid quantitative analysis of Rongalite adulteration in rice flour using autoencoder and residual-based model associated with portable Raman spectroscopy

Rice flour is a raw material for various foods and is used as a substitute for wheat flour. However, some merchants adulterate rice flour with the illegal additive Rongalite to extend the shelf life and earn illegal profits. Rongalite is highly carcinogenic, and ingestion of more than 10 g can even cause death. high-performance liquid chromatography (HPLC) and mass spectrometry (MS) are currently the main methods for detecting food adulteration, however, the existing methods have many limitations, complex operation, expensive instrumentation, etc. Raman spectroscopy has the advantages of convenience and non-destructive samples, but Raman spectroscopy can be affected by interference such as fluorescence background that affects detection, in addition to the problem of difficult quantitative analysis due to nonlinear bias. In this article, we used the preprocessing method of Savitzky-Golay smoothing filtering and VTPspline to improve the quality of the spectra and proposed the SARNet, which combines autoencoder and residual network to achieve the quantitative analysis of Rongalite content in rice flour. The new model combines a linear model with a nonlinear model, which can solve the nonlinear problem effectively. Experiments showed that the new SARNet model achieved state-of-the-art results, achieving the best R2 of 0.9703 and RMSEP of 0.0075. The lowest Rongalite concentration detected by the portable Raman spectrometer was 0.49%. In summary, the proposed method using portable Raman spectroscopy combined with machine learning has low detection bias and high accuracy, which can realize quantitative analyses of adulterated Rongalite in rice flour quickly. The method provides an accurate and nondestructive analytical tool in the field of food detection.

Quantitative detection of phenol red by surface enhanced Raman spectroscopy based on improved GA-BP

Phenol red (PR) is generally used as an acid-base indicator and a printing and dyeing colorant. When its content exceeds a certain concentration in water, it will cause great damage to the human body. Therefore, it is very important to detect the content of PR in water. The advantage of surface enhanced Raman scattering (SERS) is detecting samples quickly, non-destructive and high sensitivity without sample pre-treatment. SERS has attracted great attention in all fields of detection and analysis. In this paper, the method of attaching silver nanoparticles to metallic single-walled carbon nanotubes form carbon nanotubes/silver nanoparticles (CNTs/AgNPs) structure and then combining it with silica sheet is proposed. SERS substrate with silica/carbon nanotubes/silver nanoparticles (SiO₂/CNTs/AgNPs) composite structure has extremely high reinforcement effect. In the quantitative analysis of the detected substance, mathematical fitting or machine learning is used to find the relationship between the intensity of Raman signal and the concentration of the detected substance. The BP neural network optimized by genetic algorithm (GA-BP) is designed in this study. The weights of GA-BP to enhance the robustness of BP neural network, the method of adaptive learning rate and the number of hidden nodes is set to solve the problem that GA-BP is easy to fall into local optimum, thus establishing a quantitative analysis model of PR solution concentration. The model can detect different concentrations of PR solutions with high accuracy quickly, simply and sensitively. Finally, compared with other published quantitative models, GA-BP correlation coefficient R² determined by the training results of the model is 0.99996, and the root mean square error of the prediction is RMSEP = 0.002510, which is 0.0005 higher than the mathematical fitting method, it shows better performance. A reliable idea for the preparation of SERS substrate and online detection of PR concentration in water proposed in this study.

Unraveling surface-enhanced Raman spectroscopy results through chemometrics and machine learning: principles, progress, and trends

Surface-enhanced Raman spectroscopy (SERS) has gained increasing attention because it provides rich chemical information and high sensitivity, being applicable in many scientific fields including medical diagnosis, forensic analysis, food control, and microbiology. Although SERS is often limited by the lack of selectivity in the analysis of samples with complex matrices, the use of multivariate statistics and mathematical tools has been demonstrated to be an efficient strategy to circumvent this issue. Importantly, since the rapid development of artificial intelligence has been promoting the implementation of a wide variety of advanced multivariate methods in SERS, a discussion about the extent of their synergy and possible standardization becomes necessary. This critical review comprises the principles, advantages, and limitations of coupling SERS with chemometrics and machine learning for both qualitative and quantitative analytical applications. Recent advances and trends in combining SERS with uncommonly used but powerful data analysis tools are also discussed. Finally, a section on benchmarking and tips for selecting the suitable chemometric/machine learning method is included. We believe this will help to move SERS from an alternative detection strategy to a general analytical technique for real-life applications.

Incorporation of perovskite nanocrystals into lanthanide metal-organic frameworks with enhanced stability for ratiometric and visual sensing of mercury in aqueous solution

In-situ growth of CsPbBr₃ nanocrystal into Eu-BTC was realized for synthesis of dual-emission CsPbBr₃@Eu-BTC by a facile solvothermal method, and a novel ratiometric fluorescence sensor based on the CsPbBr₃@Eu-BTC was prepared for rapid, sensitive and visual detection of Hg²⁺ in aqueous solution. The transmission electron microscopy (TEM), X-ray diffraction pattern (XRD), X-ray photoelectron spectroscopy (XPS) and Brunauer-Emmett-Teller (BET) analysis were used to verify the successful incorporation of CsPbBr₃ into the Eu-BTC. Meanwhile, the CsPbBr₃@Eu-BTC nanocomposite maintained high fluorescence performance and stability in aqueous solution. After adding Hg²⁺, the green fluorescence of CsPbBr₃ was quenched and the red fluorescence of Eu³⁺ remained unchanged, while the color changed from green to red obviously. The occurrence of dynamic quenching and electron transfer were verified by fluorescence lifetime, Stern-Volmer quenching constant and XPS analysis. The ratiometric fluorescence sensor shows high analytical performance for Hg²⁺ detection with a wide linear range of 0-1 μM and a low detection limit of 0.116 nM. In addition, it also shows high selectivity for the detection of Hg²⁺ and can be successfully applied to detect Hg²⁺ in environmental water samples. More importantly, a novel paper-based sensor based on the CsPbBr₃@Eu-BTC ratiometric probe was successfully manufactured for the visual detection of Hg²⁺ by naked eyes. This new type of ratiometric fluorescent sensor shows great potential for applications in point-of-care diagnostics.

Applied surface enhanced Raman Spectroscopy in plant hormones detection, annexation of advanced technologies: A review

Plant hormones are the molecules that control the vigorous development of plants and help to cope with the stress conditions efficiently due to vital and mechanized physiochemical regulations. Biologists and analytical chemists, both endorsed the extreme problems to quantify plant hormones due to their low level existence in plants and the technological support is devastatingly required to established reliable and efficient detection methods of plant hormones. Surface Enhanced Raman Spectroscopy (SERS) technology is becoming vigorously favored and can be used to accurately and specifically identify biological and chemical molecules. Subsistence molecular properties with varying excitation wavelength require the pertinent substrate to detect SERS signals from plant hormones. Three typical mechanisms of Raman signal enhancement have been discovered, electromagnetic, chemical and Tip-enhanced Raman spectroscopy (TERS). Though, complex detection samples hinder in consistent and reproducible results of SERS-based technology. However, different algorithmic models applied on preprocessed data enhanced the prediction performances of Raman spectra by many folds and decreased the fluorescence value. By incorporating SERS measurements into the microfluidic platform, further highly repeatable SERS results can be obtained. This review paper tends to study the fundamental working principles, methods, applications of SERS systems and their execution in experiments of rapid determination of plant hormones as well as several ways of integrated SERS substrates. The challenges to develop an SERS-microfluidic framework with reproducible and accurate results for plant hormone detection are discussed comprehensively and highlighted the key areas for future investigation briefly.

SERS Sensors Based on Aptamer-Gated Mesoporous Silica Nanoparticles for Quantitative Detection of Staphylococcus aureus with Signal Molecular Release

This work describes a simple and novel biosensor for the quantitative determination of Staphylococcus aureus (S. aureus) based on target-induced release of signal molecules from aptamer-gated aminated mesoporous silica nanoparticles (MSNs) coupled with surface-enhanced Raman scattering (SERS) technology. MSNs were synthesized and then modified with amino groups by (3-aminopropyl) triethoxysilane to make them positively charged. Next, signal molecules (4-aminothiophenol, 4-ATP) were loaded into the pores of MSNs. Then, negatively charged aptamers of S. aureus were assembled on the surface of MSNs through electrostatic interactions. Upon the addition of S. aureus, the assembled aptamers were specifically bound to the bacteria. Consequently, the "gates" were opened, resulting in the release of 4-ATP from the pores of MSNs. The released molecules were measured by a Raman spectrometer, and the intensity of 4-ATP at 1071 cm^-1 was linearly related to the S. aureus concentration. A silver nanoflower silica core-shell structure (Ag NFs@SiO₂) was prepared and it served as a SERS substrate. Under optimized experimental conditions, a good linear relationship (y = 2107.93 + 1536.30x, R² = 0.9956) in the range from 4.7 × 10 to 4.7 × 10⁸ cfu/mL was observed with a limit of detection of 17 cfu/mL. The method was successfully applied for the analysis of S. aureus in fish samples and the recovery rate was 91.3-109%.

A Novel Method to Determine Basic Probability Assignment Based on Adaboost and Its Application in Classification

In the framework of evidence theory, one of the open and crucial issues is how to determine the basic probability assignment (BPA), which is directly related to whether the decision result is correct. This paper proposes a novel method for obtaining BPA based on Adaboost. The method uses training data to generate multiple strong classifiers for each attribute model, which is used to determine the BPA of the singleton proposition since the weights of classification provide necessary information for fundamental hypotheses. The BPA of the composite proposition is quantified by calculating the area ratio of the singleton proposition’s intersection region. The recursive formula of the area ratio of the intersection region is proposed, which is very useful for computer calculation. Finally, BPAs are combined by Dempster’s rule of combination. Using the proposed method to classify the Iris dataset, the experiment concludes that the total recognition rate is 96.53% and the classification accuracy is 90% when the training percentage is 10%. For the other datasets, the experiment results also show that the proposed method is reasonable and effective, and the proposed method performs well in the case of insufficient samples.

Quantitative Detection of Chromium Pollution in Biochar Based on Matrix Effect Classification Regression Model

Returning biochar to farmland has become one of the nationally promoted technologies for soil remediation and improvement in China. Rapid detection of heavy metals in biochar derived from varied materials can provide a guarantee for contaminated soil, avoiding secondary pollution. This work aims first to apply laser-induced breakdown spectroscopy (LIBS) for the quantitative detection of Cr in biochar. Learning from the principles of traditional matrix effect correction methods, calibration samples were divided into 1–3 classifications by an unsupervised hierarchical clustering method based on the main elemental LIBS data in biochar. The prediction samples were then divided into diverse classifications of calibration samples by a supervised K-nearest neighbor (KNN) algorithm. By comparing the effects of multiple partial least squares regression (PLSR) models, the results show that larger numbered classifications have a lower averaged relative standard deviations of cross-validation (ARSDCV) value, signifying a better calibration performance. Therefore, the 3 classification regression model was employed in this study, which had a better prediction performance with a lower averaged relative standard deviations of prediction (ARSDP) value of 8.13%, in comparison with our previous research and related literature results. The LIBS technology combined with matrix effect classification regression model can weaken the influence of the complex matrix effect of biochar and achieve accurate quantification of contaminated metal Cr in biochar.

Chemometrics coupled 4-Aminothiophenol labelled Ag-Au alloy SERS off-signal nanosensor for quantitative detection of mercury in black tea

Black tea like other food crops is prone to mercury ion (Hg²⁺) contamination right from cultivation to industrial processing. Due to the dangerous health effects posed even in trace contents, sensitive detection and quantification sensors are required. This study employed the surface-enhanced Raman scattering (SERS) enhancement property of 4-aminothiophenol (4-ATP) as a signal turn off approach functionalized on Ag-Au alloyed nanoparticle to firstly detect Hg²⁺ in standard solutions and spiked tea samples. Different chemometric algorithms were applied on the acquired SERS and inductively coupled plasma-mass spectrometry (ICP-MS) chemical reference data to select effective wavelengths and spectral variables in order to develop models to predict the Hg²⁺. Results indicated that Ag-Au/4-ATP SERS sensor combined with ant colony optimization partial least squares (ACO-PLS) exhibited the best correlation efficient and minimum errors for Hg²⁺ standard solutions (R_c = 0.984, R_p = 0.974, RMSEC = 0.157 μg/mL, RMSEP = 0.211 μg/mL) and spiked tea samples (R_c = 0.979, R_p = 0.963, RMSEC = 0.181 μg/g and RMSEP = 0.210 μg/g). The limit of detection of the proposed sensor was 4.12 × 10^-7 μg/mL for Hg²⁺ standard solutions and 2.83 × 10^-5 μg/g for Hg²⁺ spiked tea samples. High stability and reproducibility with relative standard deviation of 1.14% and 0.84% were detected. The potent strong relationship between the SERS sensor and the chemical reference method encourages the application of the developed chemometrics coupled SERS system for future monitoring and evaluation of Hg²⁺ in tea.

Trajectory Tracking of a Flexible Robot Manipulator by a New Optimized Fuzzy Adaptive Sliding Mode-Based Feedback Linearization Controller

This work presents a novel fuzzy adaptive sliding mode-based feedback linearization controller for trajectory tracking of a flexible robot manipulator. To reach this goal, after deriving the dynamical equations of the robot, the feedback linearization approach is utilized to change the nonlinear dynamics to a linear one and find the control law. Then, the sliding mode control strategy is implemented to design a stabilizer for trajectory tracking of the flexible robot. In order to adaptively tune the parameters of the designed controller, the gradient descent approach and the chain derivative rule are employed. Moreover, the Takagi–Sugeno–Kang fuzzy system is applied to regulate the controller gains. Finally, a multiobjective particle swarm optimization algorithm is used to find the optimum fuzzy rules. The conflicting objective functions considered as the integrals of the absolute values of the state error and the control effort should be minimized, simultaneously. The simulation results illustrate the effectiveness and capability of the introduced scenario in comparison with other methods.

Highly identification of keemun black tea rank based on cognitive spectroscopy: Near infrared spectroscopy combined with feature variable selection

From the perspective of combating fraud issues and examining keemun black tea properties, there was a contemporary urgent demand for a keemun black tea rankings identification system. Current rapid evaluation systems had been mainly developed for green tea grade evaluation, but there was space for improvement to establish a highly robust model. The present study proposed cognitive spectroscopy that combined near infrared spectroscopy (NIRS) with multivariate calibration and feature variable selection methods. We defined "cognitive spectroscopy" as a protocol that selects characteristic information from complex spectral data and showed optimal results without human intervention. 700 samples representing keemun black tea from seven quality levels were scanned applying an NIR sensor. To differentiate which wavelength variables of the acquired NIRS data carry key and feature information regarding keemun black tea grades, there were four different variables screening approaches, namely genetic algorithm (GA), successive projections algorithm (SPA), competitive adaptive reweighted sampling (CARS), and shuffled frog leaping algorithm (SFLA), were compared in this study. Cognitive models were developed using least squares support vector machine (LSSVM), back propagation neural network (BPNN) and random forest (RF) methods combined with the optimized characteristic variables from the above variables selection algorithms for the identification of keemun black tea rank quality. Experimental results showed that all cognitive models utilizing the SFLA approach achieved steady predictive results based on eight latent variables and selected thirteen characteristic wavelength variables. The CARS-LSSVM model with the best predictive performance was proposed based on selecting ten characteristic latent variables, and the best performance indicators of the model were as follows: the root mean square error of prediction (RMSEP) was 0.0413, the correlation coefficients of prediction set (R_p) was 0.9884, and the correct discriminant rate (CDR) was 99.01% in the validation process. This study demonstrated that cognitive spectroscopy represented a proper strategy for the highly identification of quality rankings of keemun black tea.

Rapid determination of marbofloxacin by surface-enhanced Raman spectroscopy of silver nanoparticles modified by β-cyclodextrin

A novel surface enhanced-Raman spectroscopy (SERS) assay for marbofloxacin was developed based on β-cyclodextrin-modified silver nanoparticles (β-CD-AgNPs). The marbofloxacin could interact with β-CD-AgNPs and a new assembly was formed by AgN covalent bond. This assembly was characterized by the spectra of FT-IR and UV-vis. The optimal measurement conditions were studied in detail. In 0.033 mol L^-1 HCl solution, marbofloxacin had a sensitive SERS signal at 806 cm^-1. The enhancement factor (EF) was 2.11 × 10⁷. There was a good linear correlation between the concentration of marbofloxacin and SERS intensity: the linear range was 0.003-0.03 μmol L^-1 (r² = 0.996). The limit of detection (LOD) (S/N = 3) was 1.7 nmol L^-1 (S/N = 3). Moreover, the influence of some interferences including Cu²⁺, K⁺, Zn²⁺, Ca²⁺, Na⁺, Mg²⁺, glucose and tiamulin on the determination were studied. The developed SERS method was used to detect the content of marbofloxacin in chicken and duck, the recovery was 101.3%-103.1% with RSD 4.07%-6.83%.