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[Display omitted] • This paper presents a sensitive and label-free SERS method that requires no pre-derivatization and adjustment of pH for L-Hyp detection. • The mechanism of this work provides inspiration for detecting molecules with small Raman cross-section. • The proposed SERS method has a low LOD of 8.18 ng/mL in aqueous solution and 0.13 μg/mL in milk. The detection of non-protein nitrogen adulterants is a major challenge in dairy testing. As a marker molecule of animal hydrolyzed protein, the presence of non-edible L-hydroxyproline (L-Hyp) molecules can be used to identify low-quality milk containing components of animal hydrolyzed protein. However, it is still difficult to detect L-Hyp directly in milk. The Ag@COF-COOH substrate in this paper can be used to realize label-free L-Hyp sensitive detection based on the hydrogen bond transition mechanism. To explore the mechanism, the binding sites of hydrogen bond interaction have been verified experimentally and computationally, and the charge transfer process was also explained in terms of HOMO/LOMO energy level. In conclusion, the quantitative models for L-Hyp in an aqueous environment and in milk were developed. The limit of detection (LOD) of L-Hyp in an aqueous environment could reach 8.18 ng/mL, with R2 of 0.982. The linear range of quantitative detection in milk was 0.5–1000 μg/mL and the LOD was as low as 0.13 μg/mL. In this work, a hydrogen bond interaction based Surface-enhanced Raman spectroscopy (SERS) method for the label-free detection of L-Hyp was proposed, which complemented the application of SERS technology in the detection of dairy products. [ABSTRACT FROM AUTHOR]

[Display omitted] • COF (covalent-organic framework) capped with sliver nanoparticles (Ag NPs) were first developed as SERS substrate for detection of benzoic acid in liquid milk. • This detection method has high sensitivity and reliability that allows us to achieve LOD of 0.13μg/mL in milk and 0.00372μg/mL in water. • The proposed method can be applied in the detection of real milk samples. Benzoic acid, which has a pivotal role in food additive, is prohibited to add as a preservative in dairy products. China, Brazil, and other countries have proposed standard methods to detect the addition of benzoic acid in food. Surface-enhanced Raman scattering (SERS) is an upcoming spectral detection technique, which has been widely used in the field of material analysis with the advantages of non-invasive, fast detection speed and complex environment with little interference. To detect the illegal use of benzoic acid in dairy industry, we developed Ag-COF (covalent-organic framework) material as SERS substrate to detect benzoic acid in liquid milk. The great enhancement ability of Ag-COF substrate is controlled by the addition of acetic acid and complex interplay between COF material and benzoic acid. This detection method has high sensitivity and reliability that allows us to achieve limit of detection (LOD) of 0.13 μg/mL in milk and 0.00372 μg/mL in water by applying this method. In experiment on recovery rate of real samples, the detection time is less than 15 minutes and the relative standard deviation (RSD) ranged from 2.82% to 5.69%. Therefore, this method has practical significance of the detection of benzoic acid in dairy products. [ABSTRACT FROM AUTHOR]

[Display omitted] • The AgNPs-4MPBA composite structure was first designed as SERS substrate for detection of fructose in urine. • This method achieve the LOD of fructose to be 0.535 µmol/L in urine. • This method realizes rapid fructose detection in less than 10 min. The accurate detection of fructose in human urine can help prevent and screen for diseases such as fructokinase deficiency and hereditary fructose intolerance. Surface-enhanced Raman spectroscopy (SERS) is an analytical technique with selectivity and high sensitivity, which has been widely applied to the detection of targets with complex backgrounds. In this work, 4-mercaptophenylboronic acid (4-MPBA) was modified on the surface of silver nanoparticles (AgNPs) under mild conditions to obtain a boronic acid-functionalized SERS substrate for the detection of fructose in artificial urine. The detection mechanism was based on the deboronization reaction of 4-MPBA on the surface of AgNPs, which was induced by fructose, and the Raman signal of the generated thiophenol (TP) molecules was significantly enhanced by the silver nanoparticles, with a linear increase in SERS peak intensity at 1570 cm−1. We achieved the detection limits of 0.084 µmol/L in water and 0.535 µmol/L in urine by this method. The relative standard deviation (RSD) in the recovery experiments of urine ranged from 1.01 % to 2.22 %, and the whole detection time was less than 10 min, which indicated that this method is highly reliable for fructose detection and has a good prospect in bioassay and clinical medicine. [ABSTRACT FROM AUTHOR]

[Display omitted] • In this study, a stable, uniform, and sensitive SERS substrate was successfully synthesized by combining COFs and AgNPs. • We conducted a detailed analysis of the specific role of COFs in the substrate through their characterization and DFT simulation. • The detection limits achieved in amoxicillin aqueous solution, lake water, and honey were found to be satisfactory. Specifically, the detection limits were 0.279 μg/L in the aqueous solution, 8.246 μg/L in lake water, and 2.47 ng/g in honey. This study presents the design of a Surface-enhanced Raman scattering (SERS) substrate, COF@Ag, for the sensitive detection of Amoxicillin (AMX) in lake water and honey. Furthermore, the study investigates the role of covalent organic frameworks (COFs) in SERS detection. The characterization results demonstrate the capability of COFs to efficiently enrich Ag nanoparticles (AgNPs), resulting in a more concentrated distribution of hotspots and an enhanced electromagnetic field on the substrate. By employing density functional theory (DFT) simulation, the frontier electronic orbitals of COFs and AMX were analyzed, and the chemical bonds and weak interactions in the system were examined using the Interaction Region Indicator (IRI) method to propose potential enhancement mechanisms. In aqueous solutions, the linear range is 1 μg/L-30 μg/L, with a limit of detection (LOD) 0.279 μg/L. In lake water, the linear range span from 100 μg/L to 500 μg/L, with a detection limit of 8.244 μg/L. For honey, the linear range extend from 20 ng/g to 100 ng/g, with a detection limit of 2.917 ng/g. This method holds key significance in facilitating the rapid detection of amoxicillin and advancing the application of COFs in SERS. [ABSTRACT FROM AUTHOR]

[Display omitted] • The β-CD-AgNPs substrate can selectively combine with NFX. And the proposed method can be applied in the detection of real milk samples. • The "host–guest" interaction of NFX and β-CD-AgNPs was verified by the UV–Vis absorption spectroscopy, fluorescence spectroscopy, Zeta potential and DLS. • This developed SERS method is simple and sensitive, the LOD of NFX was 3.214 pmol/L in standard solution and 5.327 nmol/L in milk. The norfloxacin (NFX) residue in milk will increase human resistance to drugs and pose a threat to public health. In this work, a highly sensitive method for detection of NFX was developed based on surface enhanced Raman spectroscopy (SERS) using β-cyclodextrin functionalized silver nanoparticles (β-CD-AgNPs) as substrate. The unique spatial size and hydrophilicity of β-CD on the surface of AgNPs could selectively capture the target molecule (NFX) through some weak interactions, including hydrogen-bond interaction, electrostatic interaction, etc. The interactions were characterized by the UV–Vis absorption spectroscopy, fluorescence spectroscopy, Zeta potential and DLS. The Raman signal of NFX is largely enhanced when anchored by β-CD on the surface of AgNPs due to SERS effect. Through a series of experiments and analysis, the limit of detection (LOD) in standard solution and spiked milk were calculated to be 3.214 pmol/L and 5.327 nmol/L. The correlation coefficients (R2) were 0.986 and 0.984, respectively. For milk sample determination of NFX, the recovery was 101.29% to 104.00% with the relative standard deviation (RSD) from 2.986% to 9.136%. To sum up, this developed SERS strategy is sensitive and specific to detect NFX in milk, it has practical application value and prospects. [ABSTRACT FROM AUTHOR]