Search Results (2,763)

Real-time detection of chemical warfare agents (CWAs) and toxic industrial chemicals underpins military protection, counter-terrorism, and emergency response. Yet field instruments usually fail for a reason unrelated to sensitivity: they cannot identify agents that are not already in their reference libraries, such as novel analogs, mixtures, and degradation products. We argue that this unknown-agent problem is a structural limitation of single-modality sensing, because any one class of information (molecular bonds, ion mobility, elemental composition, or chemical reactivity) is rarely sufficient to resolve an unfamiliar threat. We review the dominant field modalities, including FTIR, Raman/SERS, ion mobility and field-asymmetric ion mobility spectrometry, laser- and spark-induced plasma spectroscopy, metal-oxide sensor arrays, and portable mass spectrometry, and show that their weaknesses are largely complementary. We then set out the principle of orthogonal multimodal sensing, in which complementary information axes are combined by machine learning with anomaly and open-set detection so that unfamiliar agents are recognized as such rather than misidentified. Four hybrid architectures are critically compared, and we examine spark-induced decomposition diagnostics, consumable-free self-decontaminating field systems with edge AI, and the open challenges of standardized datasets, calibration transfer, and validation, before outlining a roadmap toward field-relevant recognition of unidentified chemical threats. Full article

Tapered optical fiber-based surface-enhanced Raman scattering (SERS) probes have emerged as promising miniaturized platforms for chemical and molecular sensing by integrating optical excitation, plasmonic enhancement, and Raman signal collection within a single fiber architecture. Their enhanced light–matter interaction, compact geometry, and remote interrogation capability make them particularly attractive for in situ sensing in confined and complex environments. This review systematically examines recent advances in tapered optical fiber SERS probes, covering enhancement mechanisms, taper fabrication, plasmonic hotspot engineering, and analytical applications. Particular emphasis is placed on how taper geometry and plasmonic nanostructure organization jointly influence sensing performance. Fabrication and hotspot-engineering strategies are critically compared in terms of sensitivity, reproducibility, stability, fabrication complexity, and scalability. Representative applications in biomedical analysis, food safety, and environmental monitoring are further evaluated. Despite these advances, practical implementation remains constrained by insufficient hotspot reproducibility, quantitative reliability in complex matrices, long-term stability and antifouling performance, as well as the limited scalability of current fabrication protocols. Future progress will require balancing analytical sensitivity with reproducibility, robustness, and real-sample compatibility, while advancing deterministic hotspot engineering, selective recognition interfaces, standardized performance evaluation, intelligent spectral analysis, and Lab-on-Fiber integration. Together, these developments could accelerate the transition of tapered optical fiber SERS from laboratory-scale demonstrations to field-deployable platforms for remote and in situ molecular sensing. Full article

Molecularly imprinted polymers combined with carbon nanomaterials have proven effective in constructing electrochemical sensors with high selectivity, sensitivity, and robustness. Herein, a polypyrrole-based molecularly imprinted electrochemical sensor was developed on a multi-walled carbon nanotube-modified glassy carbon electrode for homocysteine detection in human serum. The sensor was fabricated via drop-coating of sodium dodecyl sulfate-dispersed multi-walled carbon nanotubes, followed by in situ electropolymerization of pyrrole using homocysteine as the template. The morphology, interfacial properties, and electrochemical behavior of the electrode were systematically characterized by scanning electron microscopy and electrochemical techniques. Under optimized conditions, the sensor showed a linear response to homocysteine in the range of 1.0 × 10⁻¹⁰ mol/L to 1.0 × 10⁻⁵ mol/L, with a detection limit of 7.12 × 10⁻¹¹ mol/L (S/N = 3). The sensor also exhibited good selectivity against common interferents, as well as acceptable reproducibility and stability. Recovery tests in human serum yielded recoveries of 91.00~110.50% (average: 100.73%), demonstrating its potential for practical homocysteine analysis in complex biological matrices. Full article

This study evaluates calibration strategies for predicting soluble solids content (SSC) in Ambrosia apples using a low-cost, portable short-wave near-infrared (SW-NIR) spectrometer (640–1050 nm) under interactance acquisitions. Replicate sampling spectral curves exhibited notable variability due to asymmetric illumination, peel color heterogeneity, and probe contact inconsistencies. Regression models were comparatively built on averaged spectra compared with those trained directly on multiply sampled replicate spectra, applying piecewise Savitzky–Golay smoothing and detrending as pretreatment. Variable selection was performed via uninformative variable elimination (UVE) and backward interval partial least squares (BiPLS). Models calibrated on replicate spectra demonstrated superior generalization to unseen replicate measurements, despite slightly higher cross-validation errors. The BiPLS model on replicate spectra achieved the best predictive performance (mean RMSEP = 0.677 °Brix, Rp = 0.796, RPD = 1.656), with improved trueness (lower relative absolute bias) and precision (lower relative standard deviation). For comparison, the BiPLS model on averaged spectra yielded a mean RMSEP = 0.899 °Brix, Rp = 0.593, RPD = 1.25; the replicate-spectra strategy thus reduced the RMSEP by 24.7% and increased Rp and RPD accordingly. This suggests that for low-cost NIR instruments, using replicate sampling spectral modeling combined with interval variable selection can provide better prediction performance and achieve the purpose of on-site sorting in food quality analysis. Full article

During the experiments presented in this work, the electrochemical synthesis of ferrate ions was performed from high-purity iron electrode in a 45% (m/m) aqueous NaOH solution at different temperatures. The synthesis process was investigated using dual dynamic voltammetry (DDV), which involves applying independent potential–time waveforms (dynamic potential programs) simultaneously to the disk and ring electrodes of a rotating ring–disk electrode (RRDE, Pt-ring—Fe-disk) setup. This innovative technique facilitates the instantaneous measurement of the concentration of ferrate ions generated at the disk electrode. The effect of temperature on ferrate ion formation was examined, and the optimal potential range and applied current density at various temperatures were determined to maximize ferrate ion production and current efficiency. The results indicate that the rate of both ferrate ion production and oxygen evolution increases with temperature within the investigated temperature range (15–45 °C). It was found that there is an optimal potential range at each temperature where ferrate ion formation occurs at the highest rate (limited by other factors). The maximum current efficiency was determined at each temperature, with the highest value obtained at approximately 35 °C. Full article

The widespread use of thiamethoxam, a neonicotinoid pesticide, poses increasing risks to environmental and food safety, highlighting the urgent need for rapid, selective, and biomass-derived analytical platforms. In this study, fluorescent carbon dots (CDs) were synthesized from rice straw via a water-based hydrothermal approach and subsequently functionalized with a biopolymer-based molecularly imprinted polymer (MIP) for the selective detection of thiamethoxam. The synthesized CDs exhibited strong blue fluorescence with a maximum emission wavelength at 455 nm. Integration of the CDs with a thin MIP layer generated specific binding cavities complementary to thiamethoxam, significantly enhancing selectivity toward the target analyte over structurally related neonicotinoids. The resulting CD@MIP sensor demonstrated rapid and concentration-dependent fluorescence quenching, with a wide linear detection range from 1 to 500 nM and a limit of detection of 9.15 nM. An imprinting factor of 3.54 confirmed the selective recognition capability of the imprinted system. The developed sensing platform was successfully validated using real environmental water samples, achieving recovery values between 100% and 102% with relative standard deviations ranging from 0.9% to 2.5%, thereby demonstrating good accuracy and reproducibility. Overall, this work presents a biomass-derived and efficient fluorescent sensing platform for monitoring pesticide residues, with promising applications in environmental monitoring and food safety analysis. Full article

The nondestructive detection of sodium chloride in mural plaster layers is important for assessing salt-related deterioration in cultural heritage materials. However, the weak and indirect spectral response of sodium chloride makes accurate hyperspectral detection challenging. This study developed a hyperspectral regression framework centered on Sparrow Search Algorithm (SSA) optimization, in which continuous wavelet transform (CWT) was used to construct multiscale spectral representations and Pearson correlation analysis combined with the Successive Projections Algorithm (PCC-SPA) was used for compact variable selection. Partial least squares regression (PLSR), support vector regression (SVR), extreme gradient boosting (XGBoost), SSA-optimized SVR, and SSA-optimized XGBoost were evaluated under nested stratified specimen-grouped five-fold cross-validation. Feature selection and hyperparameter optimization were independently performed within each outer training fold, whereas the held-out specimens were reserved for performance evaluation. SVR-SSA maintained high predictive capability across both conventional and multiscale spectral representations. SG + SNV yielded an R² of 0.8167 ± 0.0690 and an RMSE of 0.3701 ± 0.0636 percentage points. Scale 6 CWT achieved closely comparable R² and RMSE values of 0.8100 ± 0.0812 and 0.3731 ± 0.0767 percentage points, respectively, together with a lower MAE of 0.2788 ± 0.0620 percentage points. Among the ten CWT scales, Scale 6 achieved the highest mean prediction accuracy, whereas Scale 2 provided the best comprehensive balance between predictive accuracy and fold-to-fold stability. These results demonstrate that the effectiveness of SSA optimization depends on the input feature representation and that CWT provides scale-resolved information beyond a single conventional spectral representation. The proposed framework provides methodological support for the nondestructive quantitative assessment of NaCl-related deterioration in mural plaster materials and establishes a basis for further application in mural conservation. Full article

High-voltage wiring harnesses in new energy vehicles (NEVs) are susceptible to thermal aging in high-temperature environments, whereas conventional assessment methods are difficult to deploy rapidly. This study combines laser-induced breakdown spectroscopy (LIBS) with random forest (RF) classification to assess thermal aging levels in cross-linked polyethylene (XLPE) insulation. Thirteen laboratory-aged XLPE wiring harness samples were prepared, and 100 single-shot spectra were acquired at fresh positions for each aging level. The specific methodological contribution is the use of class-wise median absolute deviation (MAD) at each wavelength as a variable-selection criterion before RF training. Three models were compared: RF, principal component analysis (PCA) combined with RF (PCA–RF), and MAD combined with RF (MAD–RF). RF achieved 100% internal hold-out accuracy for the non-aged versus 60-day comparison and 84.23% across all 13 aging levels. PCA–RF increased the multiclass accuracy to 87.31%, whereas MAD–RF reached 95.00% under the original exploratory hold-out workflow. These results indicate that wavelength-wise robust dispersion can provide a compact, discriminative representation of the present LIBS dataset. Full article

The detection of organic carbon in wastewater is essential for process monitoring and regulatory assessment. Yet conventional chemical oxygen demand (COD) and total organic carbon (TOC) methods remain reagent-dependent, slow, and unsuitable for inline operation. Photoelectrochemical (PEC) sensing based on TiO₂ offers a reagent-free alternative, but its response to wastewater-relevant dissolved organic matter (DOM) and real effluent matrices is still poorly understood. In this study, a TiO₂-based PEC system was systematically evaluated using four representative model compounds—glucose, potassium hydrogen phthalate, L-tryptophan, and urea—covering major fractions typically present in municipal wastewater. For the first time, representative wastewater-associated organic compound classes, conductivity effects, and the transferability of the PEC response to real wastewater effluent were systematically investigated. The photocurrent response showed distinct, highly linear concentration–signal relationships for each substance, suggesting a dominant contribution of surface-associated electronic effects. Conductivity variations across a relevant range had no measurable influence on sensitivity or photocurrent magnitude, indicating that the PEC response is not governed by bulk ionic transport but primarily is an interfacial process at the site of TiO₂. When applied to real wastewater effluent, the sensor exhibited an excellent linear correlation with dilution level (R² = 0.9954), demonstrating a linear response within a defined matrix and an LOD of 1.12 mg L⁻¹ COD. For the investigated model compounds, LOD values ranged from 1.06 to 3.00 mg L⁻¹ COD, while a linear response was maintained up to approximately 80–100 mg L⁻¹ COD. These findings establish TiO₂-based PEC sensing as a promising platform for the reagent-free, online monitoring of organic loads in wastewater treatment. Full article

Electronic nose (EN) technology, based on electrochemical sensor arrays, has emerged as a rapid and objective tool for aroma characterization in food systems. This study investigated factors influencing cat food preference using physicochemical analysis, texture measurement, preference testing, and EN technology. Nine (9) commercial cat foods from three brands (A: premium; B and C: medium price) with different flavors were evaluated. Proximate analysis revealed no significant differences (p > 0.05) in most nutrients, except crude fiber (CF) (p < 0.05), which was higher in lower-priced cat foods. Shear force showed a positive correlation with consumption (r = 0.72), while CF was negatively correlated (r = −0.52). Preference tests indicated that Brand A was most preferred, followed by Brand C, while Brand B was least preferred. Principal component analysis (PCA) identified variability in individual cat preferences, and one outlier cat was excluded. Discriminant analysis of EN data showed clear separation by brand rather than flavor, suggesting brand-related aroma profiles can be monitored rapidly through the digital odor fingerprint. Cat food acceptance is mainly driven by texture, CF content, and aroma rather than macronutrient composition. The results of the EN evaluation of cat food samples revealed the same group similarities and differences as the 8-month preference test performed with cats. Similar to this study, EN classification models can be developed using food preference data and the digital aroma fingerprints of foods. After validations, the EN models can be used as an effective tool to monitor the quality and assess new diets according to the known preferred odor fingerprint. Full article

Raman spectroscopy combined with machine learning offers a rapid, label-free approach for bacterial identification, but robust translation remains challenged by spectral variability, biological heterogeneity, and limited model interpretability. Here, we present an integrated evaluation of an optimized Spectral Transformer (ST) framework for Raman-based bacterial classification benchmarked against a systematically optimized one-dimensional convolutional neural network (1D-CNN). The comparison was performed using a curated 36-class dataset comprising 15 Gram-negative bacterial entries, 15 Gram-positive bacterial entries, one non-bacterial microorganism, and five background/reference classes, enabling evaluation of both species-level and fine-grained bacterial classification. Under 15 dB noise-augmented evaluation, the ST achieved 80.6% ± 0.3% accuracy and a Matthews correlation coefficient (MCC) of 0.801 ± 0.003, outperforming the 1D-CNN baseline with 72.9% ± 0.3% accuracy and an MCC of 0.721 ± 0.003. Integrated Gradients analysis combined with attention map visualization enabled multi-level model interpretation, revealing that the ST’s improved robustness correlates with more bounded attribution patterns during misclassification, whereas the 1D-CNN’s feature attribution becomes scattered under noise perturbation. Importantly, this interpretability-driven analysis identified model-specific failure modes in the baseline architecture, including an over-reliance on non-specific spectral regions under noise, which can inform future data collection strategies and guide refinements to experimental protocols. These results demonstrate that attention-based spectral modeling improves Raman-based bacterial classification under noise-perturbed conditions while enabling multi-level interpretability that bridges model understanding with actionable feedback on experimental design and data quality requirements. Full article

Curdlan is an industrially important β-1,3-glucan with applications in the food, pharmaceutical, and biomaterial industries. However, the identification of high-yielding curdlan-producing strains is hindered by the absence of rapid and efficient screening methods. To address this limitation, we developed a systematically optimized integrated microscale workflow combining 48-well plate fermentation with a quantitative aniline blue-based colorimetric assay in 96-well plates for high-throughput screening of curdlan-producing strains. Fermentation was miniaturized using 48-well plates, while curdlan quantification was performed in 96-well plates through formation of a curdlan–aniline blue complex. Key parameters were systematically optimized. Under optimal conditions, curdlan dissolved in 1.0 mol/L NaOH was reacted with 2.0 mg/mL aniline blue in 0.5 mol/L phosphate buffer (pH 7.0) for 90 min, and absorbance was measured at 550 nm. The assay demonstrated excellent linearity between curdlan concentration and absorbance (y = 1.0008x + 0.1536, R² = 0.9957, p < 0.001). To validate the method, curdlan yields from nine mutants derived from ATCC31749 were determined using both gravimetric and colorimetric approaches, revealing a strong correlation (R² = 0.8683, p < 0.001), that confirmed the assay’s reliability for rapid screening. Application of this platform to 132 UV-mutagenized strains identified nine mutants with enhanced curdlan production. The best-performing strain, UV150824-02, produced 46.8 ± 0.08 g/L curdlan, an 11.4% increase over the wild-type strain ATCC31749 (41.6 ± 1.54 g/L), and maintained stable curdlan production over nine laboratory passages (coefficient of variation = 2.72%). This method significantly improves the efficiency of mutagenesis-based strain screening and provides a practical and efficient tool for accelerating strain improvement in industrial polysaccharide fermentation. Full article

Tea quality is fundamentally determined by the complex biochemical transformations occurring during manufacturing. Traditional sensory evaluation, while essential, suffers from inherent subjectivity and cannot meet the demands of modern, industrial-scale production monitoring. This review critically examines the application of electronic nose (E-nose) technology throughout the tea-processing pipeline, covering multiple transducer technologies including metal oxide semiconductor (MOS) sensors, quartz crystal microbalance (QCM) sensors, conducting polymer (CP) sensors, and emerging chemiresistive platforms. Particular emphasis is placed on the E-nose’s capacity for real-time, non-destructive monitoring of key processing stages, including withering, rolling, fermentation, and drying. We analyze how optimized sensor arrays capture dynamic volatile organic compound (VOC) evolution, enabling the precise identification of optimal processing endpoints, especially in black tea fermentation control. Furthermore, this review evaluates how advanced pattern recognition algorithms (such as deep learning models) and multi-sensor data fusion strategies enhance the robustness and accuracy of process monitoring. By correlating E-nose response patterns with critical biochemical markers and traditional taster metrics, this paper demonstrates the technology’s pivotal role in transitioning tea manufacturing from experience-based craftsmanship to data-driven automation, ultimately ensuring superior product consistency and efficiency. Full article

In this study, an electrochemical immunosensor based on polyaniline/nano-Fe₃O₄ (PANI/Nano-Fe₃O₄) nanocomposite (PANI/Nano-Fe₃O₄/Anti-FLA/BSA/GCE) was developed for the highly sensitive and selective detection of trace levels of fluoranthene (FLA) in marine environments. Fluoranthene antibodies (Anti-FLA) were covalently immobilized on a glassy carbon electrode (GCE) modified with PANI/Nano-Fe₃O₄ via an EDC/NHS activation strategy, enabling specific recognition of FLA based on the antigen–antibody binding mechanism. The performance of the sensor was systematically optimized using cyclic voltammetry (CV), electrochemical impedance spectroscopy (EIS), linear sweep voltammetry (LSV), and differential pulse voltammetry (DPV). The results demonstrated a linear inverse relationship between peak current (I_p) and FLA concentration in the range of 0.5~80 ng/mL, with a regression equation of I = −1.55C + 174.602 (R² = 0.996). The limit of detection (LOD) was as low as 0.354 ng/mL (S/N = 3). In real seawater sample analysis, spiked recovery tests at three representative sites in the Maowei Sea, Guangxi, yielded recoveries of 95.44%~97.51%, with RSDs below 3%, confirming the sensor’s resistance to matrix interference. The synergistic effect of the porous conductive network of PANI and the high specific surface area of Nano-Fe₃O₄ significantly amplified the electrochemical signal, while the molecular specificity of the antibody ensured targeted recognition. This sensor provides a novel and effective approach for the on-site rapid detection of polycyclic aromatic hydrocarbon (PAH) pollutants in complex marine environments, offering both high sensitivity and selectivity. Full article

The widespread use of tetracycline (TC) has resulted in its frequent occurrence in aquatic environments because of incomplete metabolism and continuous release, raising increasing concerns regarding water quality and environmental safety. Reliable determination of trace TC in real water samples remains challenging owing to its low concentration and the complex sample matrix. In this work, an aptamer-functionalized extended-gate ion-sensitive field-effect transistor (EG-ISFET) was developed for sensitive TC detection. The sensing interface was constructed by immobilizing a TC-specific aptamer on a Ta₂O₅ extended gate. Upon target recognition, aptamer folding redistributes the interfacial charge, thereby modulating the surface potential of the extended gate and producing a measurable field-effect response. The proposed sensor exhibits a broad linear detection range from 1 pM to 1 μM with a detection limit of 0.33 pM. To facilitate practical applications, a compact plug-and-play point-of-care testing (POCT) platform integrating the EG-ISFET sensor was further developed for rapid on-site analysis. The portable system demonstrated satisfactory accuracy and reliability for the determination of TC in river water samples, highlighting its potential for environmental monitoring and point-of-use water quality assessment. Full article