A rapid and convenient screening method for detection of restricted monensin, decoquinate, and lasalocid in animal feed by applying SERS and chemometrics

Lee, Kyung-Min; Yarbrough, Danielle; Kozman, Mena Medhat; Herrman, Timothy J.; Park, Jinhyuk; Wang, Rui; Kurouski, Dmitry

doi:10.1016/j.fct.2020.111633

Cited by 11 publications

(2 citation statements)

References 68 publications

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“…Many types of models are used in SERS quantitative analysis, such as multiple linear regression (MLR) [92], principal component regression [93], partial least squares [94], artificial neural networks [95], and least squares-support vector machine [96]. Lee et al [97] intercepted SERS spectral signals of 400-1,800 cm −1 in the quantitative detection of monensin, decoquinate, and lasalocid. ey adopted multiple linear regression (MLR), partial least squares regression (PLSR), and principal component regression (PCR) to establish a quantitative prediction model of antibiotics in samples.…”

Section: Role Of Chemometricsmentioning

confidence: 99%

A Review of Detection of Antibiotic Residues in Food by Surface-Enhanced Raman Spectroscopy

Liang

Peng

et al. 2021

Bioinorganic Chemistry and Applications

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Antibiotics, as veterinary drugs, have made extremely important contributions to disease prevention and treatment in the animal breeding industry. However, the accumulation of antibiotics in animal food due to their overuse during animal feeding is a frequent occurrence, which in turn would cause serious harm to public health when they are consumed by humans. Antibiotic residues in food have become one of the central issues in global food safety. As a safety measure, rapid and effective analytical approaches for detecting these residues must be implemented to prevent contaminated products from reaching the consumers. Traditional analytical methods, such as liquid chromatography, liquid chromatography mass spectrometry, and capillary electrophoresis, involve time-consuming sample preparation and complicated operation and require expensive instrumentation. By comparison, surface-enhanced Raman spectroscopy (SERS) has excellent sensitivity and remarkably enhanced target recognition. Thus, SERS has become a promising alternative analytical method for detecting antibiotic residues, as it can provide an ultrasensitive fingerprint spectrum for the rapid and noninvasive detection of trace analytes. In this study, we comprehensively review the recent progress and advances that have been achieved in the use of SERS in antibiotic residue detection. We introduce and discuss the basic principles of SERS. We then present the prospects and challenges in the use of SERS in the detection of antibiotics in food. Finally, we summarize and discuss the current problems and future trends in the detection of antibiotics in food.

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Section: Role Of Chemometricsmentioning

confidence: 99%

A Review of Detection of Antibiotic Residues in Food by Surface-Enhanced Raman Spectroscopy

Liang

Peng

et al. 2021

Bioinorganic Chemistry and Applications

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“…At present, decoquinate residues are measured through HPLC-UVD (high-performance-liquid-chromatography-ultraviolet detection) [ 18 ], HPLC-DAD (diode array detection) [ 19 ], HPLC-FLD (fluorescence detection) [ 20 ], LC-MS/MS (liquid-chromatography-tandem mass spectrometry) [ 21 , 22 , 23 ], and surface-enhanced Raman spectroscopy [ 24 ]. One of the most widely used detection methods is LC-MS/MS, but the instruments and equipment are expensive and unsuitable for most laboratories.…”

Section: Introductionmentioning

confidence: 99%

Quantitative Analysis of Decoquinate Residues in Hen Eggs through Derivatization-Gas Chromatography Tandem Mass Spectrometry

Zhu,

Chen,

Guo

et al. 2023

Foods

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A novel precolumn derivatization-gas chromatography tandem mass spectrometry (GC-MS/MS) method was developed to detect and confirm the presence of decoquinate residues in eggs (whole egg, albumen and yolk). Liquid-liquid extraction (LLE) and solid phase extraction (SPE) were used to extract and purify samples. The derivatization reagents were pyridine and acetic anhydride, and the derivatives were subjected to GC-MS/MS detection. After the experimental conditions were optimized, satisfactory sensitivity was obtained. The limits of detection (LODs) and limits of quantification (LOQs) for the decoquinate in eggs (whole egg, albumen and yolk) were 1.4–2.4 μg/kg and 2.1–4.9 μg/kg, respectively. At four spiked concentration levels, the average recoveries were 74.3–89.8%, the intraday RSDs ranged from 1.22% to 4.78%, and the inter-day RSDs ranged from 1.61% to 7.54%. The feasibility and practicality of the method were confirmed by testing egg samples from a local supermarket.

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Machine learning using convolutional neural networks for SERS analysis of biomarkers in medical diagnostics

Dukes

Lee

et al. 2022

J Raman Spectroscopy

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Surface-enhanced Raman spectroscopy (SERS) has wide diagnostic applications because of narrow spectral features that allow multiplexed analysis. Machine learning (ML) has been used for non-dye-labeled SERS spectra but has not been applied to SERS dye-labeled materials with known spectral shapes. Here, we compare the performances of spectral decomposition, support vector regression, random forest regression, partial least squares regression, and convolutional neural network (CNN) for SERS "spectral unmixing" from a multiplexed mixture of 7 SERS-active "nanorattles" loaded with different dyes for mRNA biomarker detection. We showed that CNN most accurately determined relative contributions of each distinct dye-loaded nanorattle. CNN and comparative models were then used to analyze SERS spectra from a singleplexed, point-of-care assay detecting an mRNA biomarker for head and neck cancer in 20 samples. The CNN, trained on simulated multiplexed data, determined the correct dye contributions from the singleplex assay with RMSE label = 6.42 Â 10 À2 . These results demonstrate the potential of CNNbased ML to advance SERS-based diagnostics. | INTRODUCTIONRaman spectroscopy, particularly surface-enhanced Raman scattering (SERS), has widespread applications in chemical, [1] biological sensing, [2][3][4] and imaging. [5] The advantages of SERS include high sensitivity and very narrow Raman peaks, enabling single molecule detection [6] and high degrees of multiplexing, respectively. These narrow peaks are an important improvement over common fluorescence techniques, which have broad emission spectra without distinct maximums, precluding multiplex analysis. These unique characteristics make the development of highly multiplexed SERS-based technology an appealing target for the advancement of molecular diagnostics.Molecular diagnostics are powerful medical tools for the diagnosis and monitoring of a variety of diseases, including cancer, [7] infectious disease, [8] and molecular

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A rapid and convenient screening method for detection of restricted monensin, decoquinate, and lasalocid in animal feed by applying SERS and chemometrics

Cited by 11 publications

References 68 publications

A Review of Detection of Antibiotic Residues in Food by Surface-Enhanced Raman Spectroscopy

A Review of Detection of Antibiotic Residues in Food by Surface-Enhanced Raman Spectroscopy

Quantitative Analysis of Decoquinate Residues in Hen Eggs through Derivatization-Gas Chromatography Tandem Mass Spectrometry

Machine learning using convolutional neural networks for SERS analysis of biomarkers in medical diagnostics

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