An ultrasensitive NIR-IIa' fluorescence-based multiplex immunochromatographic strip test platform for antibiotic residues detection in milk samples

Sulfadiazine detection in aquatic products using upconversion nanosensor based on photo-induced electron transfer with imidazole ligands and copper ions

Contamination of aquatic products with sulfonamide antibiotics poses a threat to consumer health and can lead to the emergence of drug-resistant bacteria. Common methods to detect such compounds are slow and require expensive instruments. We developed a sensitive sulfadiazine (SDZ) detection method based on the photoinduced electron transfer between UCNPs and Cu2+. The surface-modified upconversion nanoparticles bind to Cu2+ by electrostatic adsorption, causing fluorescence quenching. The quenched fluorescence was subsequently recovered by the addition of imidazole and SDZ to the detection system, which formed a complex with Cu2+. The sensor showed excellent linearity over a wide concentration range (0.05-1000 ng/mL), had a low limit of detection (0.04 ng/mL), was selective, and was not affected by common substances present in aquatic media. This indicates that the sensor has great potential for application in the detection of SDZ residues in aquatic products.

High-Performance Fluorescent Microspheres Based on Fluorescence Resonance Energy Transfer Mode for Lateral Flow Immunoassays

The label with a large Stokes shift and strong fluorescence properties could improve the sensitivity of the lateral flow immunoassay (LFIA). Herein, two aggregation-induced emission (AIE) luminogens with spectral overlap were encapsulated in polymers by using the microemulsion method as a label, and the construction of a fluorescence resonance energy transfer mode was further verified via theoretical calculation and spectral analysis. Satisfactorily, the doped AIE polymer microspheres (DAIEPMs) exhibited a large Stokes shift of 285 nm and a 10.8-fold fluorescence enhancement compared to those of the AIEPMs loaded with acceptors. Benefiting from the excellent optical performance, DAIEPMs were applied to the LFIA for sensitive detection of chlorothalonil, which is an organochlorine pesticide. The limit of detection of the proposed DAIEPMs-LFIA was 1.2 pg/mL, which was 4.8-fold and 11.6-fold lower than those of quantum dot bead LFIA and gold nanoparticle LFIA, respectively. This work provides a new strategy to improve the optical properties of fluorescent materials and construct a sensitive and reliable detection platform.

Indocyanine green-based fluorescence imaging improved by deep learning

Intraoperative identification of malignancies using indocyanine green (ICG)-based fluorescence imaging could provide real-time guidance for surgeons. Existing ICG-based fluorescence imaging mostly operates in the near-infrared (NIR)-I (700-1000 nm) or the NIR-IIa' windows (1000-1300 nm), which is not optimal in terms of spatial resolution and contrast as their light scattering is higher than the NIR-IIb window (1500-1700 nm). It is highly desired to achieve ICG-based fluorescence imaging in the NIR-IIb window, but it is hindered by its ultra-low NIR-IIb emission tail of ICG. Herein, we employ a generative adversarial network to generate NIR-IIb ICG images directly from the acquired NIR-I ICG images. This approach was investigated by in vivo imaging of sub-surface vascular, intestine structure, and tumors, and their results demonstrated significant improvement in spatial resolution and contrast for ICG-based fluorescence imaging. It is potential for deep learning to improve ICG-based fluorescence imaging in clinical diagnostics and image-guided surgery in clinics.

Application of Near-Infrared Spectroscopy and Fuzzy Improved Null Linear Discriminant Analysis for Rapid Discrimination of Milk Brands

The quality of milk is tightly linked to its brand. A famous brand of milk always has good quality. Therefore, this study seeks to design a new fuzzy feature extraction method, called fuzzy improved null linear discriminant analysis (FiNLDA), to cluster the spectra of collected milk for identifying milk brands. To elevate the classification accuracy, FiNLDA was applied to process the near-infrared (NIR) spectra of milk acquired by the portable near-infrared spectrometer. The principal component analysis and Savitzky-Golay (SG) filtering algorithm were employed to lower dimensionality and eliminate noise in this system, respectively. Thereafter, improved null linear discriminant analysis (iNLDA) and FiNLDA were applied to attain the discriminant information of the NIR spectra. At last, the K-nearest neighbor classifier was utilized for assessing the performance of the identification system. The results indicated that the maximum classification accuracies of LDA, iNLDA and FiNLDA were 74.7%, 88% and 94.67%, respectively. Accordingly, the portable NIR spectrometer in combination with FiNLDA can classify milk brands correctly and effectively.

Dual-mode immunochromatographic assay based on dendritic gold nanoparticles with superior fluorescence quenching for ultrasensitive detection of E. coli O157:H7

We presented a colorimetric/fluorescent dual-mode immunochromatographic assay (ICA) for the sensitive detection of Escherichia coli O157:H7. The use of polydopamine (PDA)-modified gold nanoparticles (AuNPs) with broadband absorption allowed for excellent colorimetry signals for the ICA detection. Moreover, the absorption spectrum of PDA-AuNPs significantly overlaps with the excitation and emission spectra of ZnCdSe/ZnS quantum dots (QDs), resulting in effective quenching of the QDs fluorescence due to the inner filter effect. The fluorescence intensity changes induced by PDA-AuNPs were utilized for the sensitive detection of E. coli O157:H7, achieving a detection limit of 9.06 × 10¹ CFU/mL, which was 46-fold lower than that of traditional AuNPs-based immunoassay. The proposed immunosensor exhibited the recovery rate between 80.12% and 114.69% in detecting actual samples, indicating its reliability and satisfactory accuracy. This study provides insights into dual-mode signal outputs and the ICA development for food safety applications.