Highly selective and sensitive colorimetric detection for glyphosate based on ß-CD@DNA-CuNCs enzyme mimics.

A colorimetric assay based on an enzyme-inhibition strategy is promising for the on-site detection of pesticide residues. However, very few works of pesticide detection were reported based on the inhibition toward nanozymes although nanozymes have demonstrated many advantages in sensing various targets. Herein, a facile colorimetric detection for Glyp was developed based on ß-CD@DNA-CuNCs enzyme mimics. The ß-CD@DNA-CuNCs with high peroxidase-like activity was synthesized using random DNA double strands as template and ß-CD as surface ligand. ß-CD@DNA-CuNCs could catalyze the H2O2-3,3',5,5'-tetramethylbenzidine (TMB) system. The oxidation product OxTMB with a blue color and presented a large absorption signal at 652 nm. However, Glyp could destroy the synergic effect between redox doublet (Cu2+/Cu+) on the ß-CD@DNA-CuNCs surface, resulting in the inhibition of the peroxidase-like activity. Colorimetric detection for Glyp could be established by detecting the changes of absorption signal at 652 nm. The linear range was 0.02-2 µg/mL and the detection limit was 0.85 ng/mL (3δ/s). The method was applied in measuring Glyp spiked in lake water and various food samples. This method had rapidness, high sensitivity, and selectivity advantages, indicating the high application potential in monitoring Glyp residue in food.

Determination of perchlorate in tea using SERS with a superhydrophobically treated cysteine modified silver film/polydimethylsiloxane substrate.

Perchlorate is a new type of persistent pollutant, which interferes with the synthesis and secretion of thyroxine and affects human health. The EU's limit for perchlorate in tea is 750 µg kg-1. The surface-enhanced Raman scattering (SERS) technique has the characteristics of a simple pretreatment method, rapid detection, high sensitivity, high specificity and great stability in the detection of perchlorate. This study proposed a novel superhydrophobic SERS substrate, which can be used to detect perchlorate in tea. Firstly, a chemical deposition method was used to deposit a silver film on the surface of a thin layer of polydimethylsiloxane. After drying, the substrate was immersed in 1H,1H,2H,2H-perfluorodecyltriethoxysilane aqueous solution for 15 hours to make the surface of the substrate superhydrophobic. Then cysteine molecules were deposited on the surface of the silver film/polydimethylsiloxane by incubation. The superhydrophobic surface has a unique enrichment effect on the highly diluted solution, and perchlorate has a strong affinity for the amino group of cysteine. We collected the Raman spectra of 9 gradient concentrations (1-100 µmol L-1) of perchlorate-spiked tea samples on the hydrophobic substrate, and a linear model of the relationship between the SERS spectral intensity and the concentrations of perchlorate in tea was established. This method reached a good limit of detection of 0.0067 µmol L-1 (0.82 µg kg-1) in tea, which showed that the developed sensor has high sensitivity and could be used as a fast and simple technique for quantitative detection of perchlorate based on SERS technology.

Intelligent evaluation of taste constituents and polyphenols-to-amino acids ratio in matcha tea powder using near infrared spectroscopy.

Matcha tea is rich in taste and bioactive constituents, quality evaluation of matcha tea is important to ensure flavor and efficacy. Near-infrared spectroscopy (NIR) in combination with variable selection algorithms was proposed as a fast and non-destructive method for the quality evaluation of matcha tea. Total polyphenols (TP), free amino acids (FAA), and polyphenols-to-amino acids ratio (TP/FAA) were assessed as the taste quality indicators. Successive projections algorithm (SPA), genetic algorithm (GA), and simulated annealing (SA) were subsequently developed from the synergy interval partial least squares (SiPLS). The overall results revealed that SiPLS-SPA and SiPLS-SA models combined with NIR exhibited higher predictive capabilities for the effective determination of TP, FAA and TP/FAA with correlation coefficient in the prediction set (Rp) of Rp > 0.97, Rp > 0.98 and Rp > 0.98 respectively. Therefore, this simple and efficient technique could be practically exploited for tea quality control assessment.

Label-free surface enhanced Raman scattering spectroscopy for discrimination and detection of dominant apple spoilage fungus.

Fungal infection is one of the main causes of apple corruption. The main dominant spoilage fungi in causing apple spoilage are storage mainly include Penicillium Paecilomyces paecilomyces (P. paecilomyces), penicillium chrysanthemum (P. chrysogenum), expanded Penicillium expansum (P. expansum), Aspergillus niger (Asp. niger) and Alternaria. In this study, surface-enhanced Raman spectroscopy (SERS) based on gold nanorod (AuNRs) substrate method was developed to collect and examine the Raman fingerprints of dominant apple spoilage fungus spores. Standard normal variable (SNV) was used to pretreat the obtained spectra to improve signal-to-noise ratio. Principal component analysis (PCA) was applied to extract useful spectral information. Linear discriminant analysis (LDA) and non-linear pattern recognition methods including K nearest neighbor (KNN), Support vector machine (SVM) and back propagation artificial neural networks (BPANN) were used to identify fungal species. As the comparison of modeling results shown, the BPANN model established based on the characteristic spectra variables have achieved the satisfactory result with discrimination accuracy of 98.23%; while the PCA-LDA model built using principal component variables achieved the best distinguish result with discrimination accuracy of 98.31%. It was concluded that SERS has the potential to be an inexpensive, rapid and effective method to detect and identify fungal species.

Chemometrics coupled 4-Aminothiophenol labelled Ag-Au alloy SERS off-signal nanosensor for quantitative detection of mercury in black tea.

Black tea like other food crops is prone to mercury ion (Hg2+) contamination right from cultivation to industrial processing. Due to the dangerous health effects posed even in trace contents, sensitive detection and quantification sensors are required. This study employed the surface-enhanced Raman scattering (SERS) enhancement property of 4-aminothiophenol (4-ATP) as a signal turn off approach functionalized on Ag-Au alloyed nanoparticle to firstly detect Hg2+ in standard solutions and spiked tea samples. Different chemometric algorithms were applied on the acquired SERS and inductively coupled plasma-mass spectrometry (ICP-MS) chemical reference data to select effective wavelengths and spectral variables in order to develop models to predict the Hg2+. Results indicated that Ag-Au/4-ATP SERS sensor combined with ant colony optimization partial least squares (ACO-PLS) exhibited the best correlation efficient and minimum errors for Hg2+ standard solutions (Rc = 0.984, Rp = 0.974, RMSEC = 0.157 µg/mL, RMSEP = 0.211 µg/mL) and spiked tea samples (Rc = 0.979, Rp = 0.963, RMSEC = 0.181 µg/g and RMSEP = 0.210 µg/g). The limit of detection of the proposed sensor was 4.12 × 10-7 µg/mL for Hg2+ standard solutions and 2.83 × 10-5 µg/g for Hg2+ spiked tea samples. High stability and reproducibility with relative standard deviation of 1.14% and 0.84% were detected. The potent strong relationship between the SERS sensor and the chemical reference method encourages the application of the developed chemometrics coupled SERS system for future monitoring and evaluation of Hg2+ in tea.