Rapid quantitative characterization of protein-rich agricultural materials remains challenging due to the molecular and elemental complexity of plant-derived matrices. In this study, external-cavity quantum cascade laser-based mid-infrared (QCL-MIR) spectroscopy and laser-induced breakdown spectroscopy (LIBS) were investigated as complementary approaches for predicting protein and elemental composition in pea flour (Pisum sativum L.) samples. QCL-MIR spectroscopy primarily probed molecular vibrational information, whereas LIBS directly monitored elemental composition through plasma-generated atomic and ionic emission signatures. To address the high dimensionality and nonlinear relationships present in both spectroscopic datasets, advanced chemometric strategies combining Uninformative Variable Elimination (UVE), Partial Least Squares (PLS) regression, and Extreme Learning Machine (ELM) modeling were implemented. Given the limited dataset size (N = 20), predictive performance was assessed using a concentration-stratified five-fold prediction strategy. The optimized UVE–PLS–ELM approach improved predictive performance compared with conventional linear PLS models for both techniques. QCL-MIR spectroscopy showed strong predictive performance within the present dataset for total protein content (R2 = 0.97) and also provided promising predictions of phosphorus and potassium through indirect correlations with sample composition. LIBS also demonstrated strong calibration and predictive capability for total protein content (R2 = 0.93) and good predictive performance for Na, Mg, Ca, Mn, and Fe. However, LIBS performance decreased at concentration extremes, particularly for high-concentration elements (K and P) and trace elements such as Zn and Cu. These results demonstrate the complementary strengths of QCL-MIR and LIBS and highlight the potential of nonlinear chemometric approaches for accurate compositional analysis of protein-rich agricultural materials within the present limited dataset.
Food allergies pose a serious and increasing public health challenge as many allergens can trigger potential life-threatening reactions in affected individuals. Reliable detection of allergens such as soy proteins is therefore a critical and essential tool to detect cross-contaminations, particularly in food processing and production environments. To enable monitoring directly at these sites, portable biosensing platforms are required to provide rapid point-of-need detection. However, the development of such systems requires the combination of biochemistry, material science, and precision engineering. In this article we present a centrifugal microfluidic platform integrating automated sample preparation, clean-up, and immunological detection in a single-use disposable cartridge. Targeting soybean protein as a model allergen, the system combines optimized immunoassay chemistry with advanced polymer microfabrication. Regioselective antibody coupling strategies were investigated for stable and oriented bead conjugation. N-glycan click chemistry was used to produce monodisperse, highly functional fluorescent microbeads. The microfluidic LabDisk was first manufactured by thermoforming and later transferred to injection molding with integrated optical elements to increase sensitivity. The fully integrated LabDisk detected soy protein at the lowest concentrations tested, namely 1 ppm in extraction buffer and 4 ppm in incurred complex matrices. This work shows the important steps from biochemical concept to scalable manufacturing, paving the way for mass-produced, portable allergen biosensors.
Accurate quantification of trace metal ions in complex biological matrices remains challenging because of severe matrix interference and sluggish metal–ligand coordination at ultralow concentrations. Herein, we report an active coordination sensitization strategy for surface-enhanced Raman scattering (SERS)-based metal-ion analysis by modulating molecular recognition kinetics and interfacial chemical equilibria. Unlike conventional approaches relying primarily on plasmonic substrate engineering, this strategy directly tunes coordination chemistry through temperature and pH control. For Cu2+ detection, thermal activation at 50 °C accelerates tetrakis(4-carboxyphenyl) porphyrin (TCPP) metalation, yielding limits of detection (LODs) of 2.94 × 10−11 M in water and 2.01 × 10−10 M in serum, representing 28- and 12-fold improvements, respectively. For Zn2+ sensing, pH regulation promotes thiol-functionalized terpyridine (TPY)–Zn2+ coordination, giving LODs of 2.86 × 10−13 M in water and 1.0 × 10−11 M in serum, with 35- and 18-fold improvements, respectively. Density functional theory (DFT) calculations provide insights into temperature- and protonation-dependent coordination behaviors. Integrated with a microfluidic SERS platform, the method achieved serum recoveries of 97.9–102.1% with relative standard deviations (RSDs) below 4.20%, demonstrating accurate and reproducible analysis while advancing SERS sensing from passive plasmonic signal amplification toward active regulation of molecular recognition chemistry.
Ensuring the microbiological safety of drinking water is critical for public health and food production. However, the conventional plate counting method is time-consuming and poorly suited for rapid on-site microbial detection. Here, we propose a filtration-enhanced dual-mode surface-enhanced Raman scattering (SERS) biosensor for the rapid, culture-free detection of waterborne pathogens, which can be directly integrated into household faucets. The biosensor combines a porous filter membrane with Au nanoparticles (AuNPs), achieving simultaneous enrichment of waterborne bacteria and significant amplification of SERS signals. The dual-mode design provides a visible colorimetric response, in which the substrate color gradually changes with increasing bacterial load, enabling preliminary semi-quantitative assessment of water contamination. In addition, the biosensor achieved the lowest experimentally detectable bacterial load of 103 CFU/platform. Support Vector Machine (SVM) algorithm was further applied to discriminate different types of pathogens, yielding a classification accuracy of 96%. Practical tests on household water purifiers revealed that microbial contamination accumulated progressively with service time, which was highly consistent with the results obtained by traditional plate counting. The developed biosensing platform provides a rapid (within 10 min), sensitive, and user-friendly tool for on-site monitoring of drinking water safety in household scenarios and food-related environments.
Sensitive label-free detection of low-abundance proteins requires both effective on-chip preconcentration and a compact, low-noise optical readout for use outside conventional laboratories. Here, we present a portable UV-LED dual-Nafion microfluidic platform that integrates a 280 nm UV-LED, a chromium-coated slot collimator, a dual-Nafion ion concentration polarization (ICP) preconcentrator, and a miniature spectrometer within a 3D-printed enclosure. The symmetric dual-Nafion geometry on a UV-transparent quartz substrate confines the ICP enrichment zone to a fixed 120 × 500 µm interrogation window, enabling spatially stable, label-free on-chip absorbance monitoring at 280 nm. Using bovine serum albumin (BSA) as a model protein, the platform showed a linear absorbance response over 0–60 µM (R2 = 0.9933) and a static-loading limit of detection of 0.91 µM. Under a 30 V bias in 0.3 mM phosphate-buffered saline, representative 60 min experiments produced apparent enrichment factors of approximately 41, 4.5 × 103, and 1.6 × 104 at initial BSA concentrations of 1 µM, 10 nM, and 1 nM, respectively. At 10 pM, a reproducible, voltage-dependent absorbance increase was detected with three independent chips, whereas the 0 V and BSA-free controls remained below the detection threshold. The platform provides a practical approach for label-free trace-protein analysis and a potential upstream module for biomarker-specific assays.
