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Bimetallic Core Shelled Nanoparticles (Au@AgNPs) for Rapid Detection of Thiram and Dicyandiamide Contaminants in Liquid Milk Using SERS
摘要： Existing methods for contaminants detection in liquid milk are complex, requires chemicals and time-consuming experimental procedure. In this study, SERS based on bimetallic core shelled nanoparticles was employed for simultaneous and fast detection of thiram and dicyandiamide (DCD) in the milk. Spectra ranging from 400 to 1700 cm-1 were selected to examine thiram (0.5, 1, 2, 5 and 10 ppm) and DCD (20, 40, 80,160 and 320 ppm), by employing 28 nm gold cores and silver-shell thickness of 8 nm. A strong peak at 1379 cm-1 was ascribed to thiram with LOD of 0.21 ppm and R2 of 0.9896, whereas a band at 929 cm-1 was associated with DCD, delivering LOD of 14.88 ppm and R2 of 0.9956. The proposed method could achieve results within 34 min and this ecofriendly method can be further employed for simultaneous and rapid screening of other accidental contaminants in milk.
关键词： SERS,dicyandiamide,milk adulteration,thiram
更新于2025-09-23 15:19:57
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Robust Fourier transformed infrared spectroscopy coupled with multivariate methods for detection and quantification of urea adulteration in fresh milk samples
摘要： Urea is added as an adulterant to give milk whiteness and increase its consistency for improving the solid not fat percentage, but the excessive amount of urea in milk causes overburden and kidney damages. Here, an innovative sensitive methodology based on near‐infrared spectroscopy coupled with multivariate analysis has been proposed for the robust detection and quantification of urea adulteration in fresh milk samples. In this study, 162 fresh milk samples were used, those consisting 20 nonadulterated samples (without urea) and 142 with urea adulterant. Eight different percentage levels of urea adulterant, that is, 0.10%, 0.30%, 0.50%, 0.70%, 0.90%, 1.10%, 1.30%, and 1.70%, were prepared, each of them prepared in triplicates. A Frontier NIR spectrophotometer (BSEN60825‐1:2007) by Perkin Elmer was used for scanning the absorption of each sample in the wavenumber range of 10,000–4,000 cm-1, using 0.2 mm path length CaF2 sealed cell at resolution of 2 cm-1. Principal components analysis (PCA), partial least‐squares discriminant analysis (PLS‐DA), and partial least‐squares regressions (PLSR) methods were applied for the multivariate analysis of the NIR spectral data collected. PCA was used to reduce the dimensionality of the spectral data and to explore the similarities and differences among the fresh milk samples and the adulterated ones. PLS‐DA also showed the discrimination between the nonadulterated and adulterated milk samples. The R‐square and root mean square error (RMSE) values obtained for the PLS‐DA model were 0.9680 and 0.08%, respectively. Furthermore, PLSR model was also built using the training set of NIR spectral data to make a regression model. For this PLSR model, leave‐one‐out cross‐validation procedure was used as an internal cross‐validation criteria and the R‐square and the root mean square error (RMSE) values for the PLSR model were found as 0.9800 and 0.56%, respectively. The PLSR model was then externally validated using a test set. The root means square error of prediction (RMSEP) obtained was 0.48%. The present proposed study was intended to contribute toward the development of a robust, sensitive, and reproducible method to detect and determine the urea adulterant concentration in fresh milk samples.
关键词： urea,principal components analysis,partial least‐squares regressions,milk adulteration,NIR spectroscopy,partial least‐squares discriminant analysis
更新于2025-09-19 17:13:59
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SERS detection of sodium thiocyanate and benzoic acid preservatives in liquid milk using cysteamine functionalized core-shelled nanoparticles
摘要： A cysteamine functionalized core shelled nanoparticles (Au@Ag-CysNPs) was presented for simultaneous and rapid detection of sodium thiocyanate (STC) and benzoic acid (BA) preservatives in liquid milk using surface-enhanced Raman spectroscopy (SERS) technique. A spectrum covering 350-2350 cm-1 region was selected to detect STC with concentrations ranging from 0.5 to 10 mg/L and BA with concentrations ranging from 15 to 240 mg/L in milk samples. Characterization of nanoparticles using high-resolution TEM confirmed that the successful synthesis of Au@AgNPs with core (gold) size of 28 nm and shell (silver) thickness of about 5 nm was grafted with 120 μL of 0.1 nM cysteamine hydrochloride. Results showed that Au@Ag-CysNPs could be used to detect STC up to 0.03 mg/L with a limit of quantification (LOQ) of 0.039 mg/L and a coefficient of determination (R2) of 0.9833 in the milk sample. For detecting BA, it could be screened up to 9.8 mg/L with LOQ of 10.2 mg/L and R2 of 0.9903. The proposed substrate was also highly sensitive and the employed method involved only minor sample pretreatment steps. It is thus hoped that the new substrate could be used in the screening of prohibited chemicals in complex food matrices in future studies.
关键词： SERS,milk adulteration,sodium thiocyanate,nanoparticles,benzoic acid
更新于2025-09-16 10:30:52