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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]

Quinolone antibiotics have good antibacterial properties and are commonly used antibiotics in the dairy industry. Currently, the problem of excessive antibiotics in dairy products is very serious. As an ultra-sensitive detection technology, Surface-Enhanced Raman Scattering (SERS) was applied to the detection of quinolone antibiotics in this work. In order to classify and quantify three antibiotics (Ciprofloxacin, Norfloxacin, Levofloxacin) with highly similar molecular structures, a combination of magnetic COF-based SERS substrate and machine learning algorithms (PCA-k-NN, PCA-SVM, PCA-Decision Tree) was used. The classification accuracy of the spectral dataset could reach 100% and the results of LOD calculation were: CIP: 5.61 × 10−9M, LEV: 1.44 × 10−8M, NFX: 1.56 × 10−8M. This provides a new method for the detection of antibiotics in dairy products. [Display omitted] • This method realized the detection of three quinolone antibiotics with one SERS substrate. • This method can classify three quinolone antibiotics with highly similar molecular structures with 100% accuracy. • The proposed SERS method has low LODs in milk: CIP: 5.61 × 10−9M, LEV: 1.44 × 10−8M, NFX: 1.56 × 10−8M. [ABSTRACT FROM AUTHOR]

• The novel Ag-COF-COOH substrates provide huge SERS hot spots, EF = 9.72 × 106. • The SERS method in this work is sensitive and the LOD is 0.68 μg/L in milk. • The SERS method in this work requires only one step of pretreatment. Melamine, a nitrogen-containing organic molecule, has received widespread attention as it had been illegally added in dairy products to increase the content of nitrogen, leading to kidney stones in healthy people after long-term ingestion. Based on SERS technology and Covalent Organic Framework (COF) materials, we developed carboxyl-functionalized Ag-COF-COOH materials as SERS substrates for the detection of melamine adulteration. Using COF material as a ligand can effectively reduce the influence of interferents in milk. In addition, we investigated two causes of melamine SERS enhancement: the ordered arrangement of Ag NPs and the strong interaction between the substrate and the melamine. The linear range was 1–20 μg/L and the limit of detection (LOD) was 0.68 μg/L in liquid milk due to the high sensitivity and reliability of this method. The results show that this new SERS substrate has great potential for applications in the food surveillance industry. [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]