Rapid and simultaneous detection of multiple illegal additives in feed and food by SERS with reusable Cu2O-Ag/AF-C3N4 substrate.

Illegal additives can bring the economic benefit, resulting in the continuous irregularities in the use of illegal additives. In this study, a method for rapid, sensitive, and simultaneous detection of multiple illegal additives including enrofloxacin, malachite green, nitrofurazone, and Sudan Ⅰ in feed and food samples by surface-enhanced Raman spectroscopy (SERS) with Cu2O-Ag/AF-C3N4 composite substrate was developed. A Cu2O-Ag/AF-C3N4 composite substrate was prepared by reacting Cu2O modified by AF-C3N4 nanosheets with AgNO3 solution. The substrate has a limit of detection (LOD) of 1.29 × 10-6 mg/L, a good linear relationship of between 10-6 and 10-2 mg/L, and an R2 value of 0.95 for Rhodamine B detection. Furthermore, the substrate showed high uniformity and reproducibility, with relative standard deviations (RSD) of 6.74% and 4.85%, respectively. Adding AF-C3N4 nanosheets not only increased the enhancement effect of the substrate, which was 4.4 times of that before addition, but also endowed it with good self-cleaning characteristics owing to its excellent photocatalytic activity. The substrate can be reused, with over 80% of the original Raman signal strength remaining after four repeat uses. The SERS based on the above substrate was used to detect the illegal additives, the LOD of enrofloxacin, malachite green, nitrofurazone, and Sudan Ⅰ can reach 4.67 × 10-4 mg/L, 2.57 × 10-5 mg/L, 5.7 × 10-7 mg/L and 6.92 × 10-5 mg/L. The results reveal that this substrate has great application potential in feed and food safety.

A novel analytical strategy for discriminating antibiotic mycelial residue adulteration in feed based on ATR-IR and microscopic infrared imaging.

The Antibiotic mycelial residue (AMR) contains antibiotic residue, there are safety risks if it is used illegally in feed. This study investigated the feasibility of qualitative identification of AMR in protein feed and self-prepared feed based on attenuated total reflection mid-infrared spectrum (ATR-IR) and microscopic infrared imaging. Cottonseed meal (CM), soybean meal (SM), distillers dried grains with solubles (DDGS), nucleotide residue (NR), oxytetracycline residue (OR) and streptomycin sulfate residue (SR) and two self-prepared feed (broiler and pig) were used as research objects. The results showed that there were characteristic peaks at 1614 cm-1, 1315 cm-1, 779 cm-1, 514 cm-1 in the ATR-IR spectra of AMR, which were related to calcium oxalate hydrate. After detection, the content of total calcium and calcium oxalate in AMR were higher than those in protein feed. ATR-IR can quickly realize the qualitative discrimination of pure material samples. The combination of ATR-IR and partial least squares discriminant analysis (PLSDA) was effective in discriminating AMR from CM and SM with a single component (the classification errors were 0), but it cannot meet the discrimination of AMR from the fermented protein feed (such as DDGS and NR, the classification errors were 0.10 and 0.12) and self-prepared feed with complex components. Compared with ATR-IR, microscopic infrared imaging was less affected by the sample complexity. Multi-component samples belong to physical mixing and will not affect the infrared spectra of each component. Therefore, microscopic infrared imaging combined with effective information extraction algorithms such as cosine similarity can distinguish OR in the fermented protein feed and self-prepared feed. The above results showed that the advantages of ATR-IR and microscopic infrared imaging were complementary, which provided a new idea for the discrimination analysis of illegal feed additives.

Identification of antibiotic mycelia residues in cottonseed meal using Fourier transform near-infrared microspectroscopic imaging.

Near-infrared microscopy (NIRM) technology can analyze different components within a sample while also obtaining spatial information about the sample. No rapid detection methods are available for effectively identifying antibiotic mycelia residues (AMRs) in protein feeds materials to date. In this study, the feasibility of using NIRM to identify AMRs (oxytetracycline residue, streptomycin sulfate residue and clay colysin sulfate residue) mixed in cottonseed meals was studied. The samples were scanned by NIRM, then the spectra of images were analyzed by principal component analysis (PCA) to select characteristic bands for further identification with one-class partial least squares analysis (OCPLS). The results showed that: a) AMRs were effectively identified in cottonseed meal; b) screening characteristic bands and increasing the spectral number of the calibration set improved the identification results of the model; and c) the sensitivity, specificity, accuracy and class error of the method were 100%, 95.93%, 99.01% and 2.03%, respectively.

Identification of antibiotic mycelia residue in protein rich feed using on near-infrared microscopy imaging.

Antibiotic mycelial residues (AMRs) added to animal feeds easily lead to drug resistance that affects human health and environment. However, there is a lack of effective detection methods, especially a fast and convenient detection technology, to distinguish AMRs from other components in animal feeds. To develop effective detection methods, two types of global Mahalanobis distance (GH) algorithms based on near-infrared microscopy (NIRM) imaging are proposed. The aim of this study is to investigate the feasibility of using NIRM imaging to identify AMRs in soybean meals. We prepared 15 mixed samples containing 5% AMRs using three types of soybean meals and four types of AMRs. The GH algorithm was used to identify non-soybean meals among the mixed samples. The hierarchical cluster analysis was employed to verify the recognition accuracy. The results indicate that use of the GH algorithm could identify soybean meals with AMR at a level as low as 5%.