Mesoporous silica supported orderly-spaced gold nanoparticles SERS-based sensor for pesticides detection in food.

In this study, a novel sensor fabricated with compactly arranged gold nanoparticles (AuNPs) templated from mesoporous silica film (MSF) via air-water interface has been confirmed as a promising surface-enhanced Raman scattering (SERS) substrate for detecting trace levels of 2,4-dichlorophenoxyacetic acid (2,4-D), pymetrozine and thiamethoxam. The densely arranged AuNPs@MSF had an average AuNPs size of 5.15 nm with small nanogaps (<2nm) between AuNPs, and exhibited a high SERS performance. SERS spectra of pesticides were collected after their adsorption on the AuNPs@MSF. The results showed that the concentration of 2,4-D, pymetrozine and thiamethoxam gave a good linear relationship with SERS intensity. Moreover, the designed SERS-based sensor (AuNPs@MSF) was stable for 3 months with ca. 3% relative standard deviation (RSD) and was applied successfully for the analysis of 2,4-D extraction from both environmental and food samples. The proposed SERS-based sensor was further validated by HPLC and showed satisfactory result (p > 0.05).

Signal-enhanced SERS-sensors of CAR-PLS and GA-PLS coupled AgNPs for ochratoxin A and aflatoxin B1 detection.

Ochratoxin-A (OTA) and aflatoxin-B1 (AFT-B1) pose debilitating health threats to consumers and therefore require rapid monitoring with sensors. This work synthesized silver nanoparticles (AgNPs) within (4 ≤ pH ≤ 11) ± 0.2 to attain different enhancement-factors (EF). AgNP@pH-11 which gave the highest SERS-EF (1.45 × 108) was selected to fabricate SERS-sensor; and coupled to two chemometric algorithms for the prediction of OTA and AFT-B1 in prepared standard solutions (SS) and spiked-cocoa-beans samples (SCBS). The LOD for OTA (2.63 pg/mL) and AFT-B1 (4.15 pg/mL) in the SCBS were lower compared with 0.002 µg/mL. The built-models recorded residual-predictive-deviations above 3. Obtained recovery rates of 96-110%; and the low coefficients of variation (2.12-8.07%) realized for both toxins suggest the predicted results are reproducible. The SERS-sensor holds promise for the rapid quantification of OTA and AFT-B1 at pg/mL level in cocoa beans to enable safety assurance in the cocoa beans industry.

rGO-NS SERS-based coupled chemometric prediction of acetamiprid residue in green tea.

Pesticide residue in food is of grave concern in recent years. In this paper, a rapid, sensitive, SERS (Surface-enhanced Raman scattering) active reduced-graphene-oxide-gold-nano-star (rGO-NS) nano-composite nanosensor was developed for the detection of acetamiprid (AC) residue in green tea. Different concentrations of AC combined with rGO-NS nano-composite electro-statically, yielded a strong SERS signal linearly with increasing concentration of AC ranging from 1.0 × 10-4 to 1.0 × 103 µg/mL indicating the potential of rGO-NS nano-composite to detect AC in green tea. Genetic algorithm-partial least squares regression (GA-PLS) algorithm was used to develop a quantitative model for AC residue prediction. The GA-PLS model achieved a correlation coefficient (Rc) of 0.9772 and recovery of the real sample of 97.06%-115.88% and RSD of 5.98% using the developed method. The overall results demonstrated that Raman spectroscopy combined with SERS active rGO-NS nano-composite could be utilized to determine AC residue in green tea to achieve quality and safety.

Synthesized Au NPs@silica composite as surface-enhanced Raman spectroscopy (SERS) substrate for fast sensing trace contaminant in milk.

With increased concerns on milk safety issues, the development of a simple and sensitive method to detect 2,4-dichlorophenoxyacetic acid (2,4-D), a common contaminant in milk, becomes relevant in safeguarding human health threats that results from its consumption. Surface-enhanced Raman spectroscopy (SERS) shows excellent ability for various targets analysis but its usage for rapid and accurate determination of analyte via SERS presents challenges. This study attempted the quantification of 2,4-dichlorophenoxyacetic acid (2,4-D) residue in milk using a novel SERS active substrate- decorated silica films with Au nanoparticles (Au NPs@ silica) coupled to chemometric algorithms. Au NPs@ silica composite was synthesized as a SERS sensor through self-assembly. Thereafter, the SERS spectrum of 2,4-D extract from milk with different concentrations based on the developed SERS sensor was collected and the spectra were analyzed by partial least squares (PLS), and variable selection algorithms - genetic algorithm-PLS (GA-PLS), competitive-adaptive reweighted sampling-PLS (CARS-PLS) and ant colony optimization-PLS (ACO-PLS), to develop quantitative models for 2,4-D prediction. The results obtained showed that the CARS-PLS model gave the optimum result with LOD of 0.01 ng/mL realized and a determination coefficient in the prediction set of (RP) = 0.9836 within a linear range of 10-2 to 106 ng/mL was achieved. Au NPs@ silica SERS sensor combined with CARS-PLS may be employed for rapid quantification of 2,4-D extract from milk towards its quality and safety monitoring.

Fast sensing of imidacloprid residue in tea using surface-enhanced Raman scattering by comparative multivariate calibration.

This study focused on the fabrication of a rapid, highly sensitive and inexpensive technique for the quantification of imidacloprid residue in green tea, based on surface-enhanced Raman scattering (SERS) using highly roughned surface flower shaped silver nanostructure (as SERS substrate) coupled with the chemometrics algorithm. The basic principle of this method is imidacloprid yielded SERS signal after adsorption on Ag-NF under laser excitation by the electromagnetic enhancement and the intensity of the peak is proportional to the concentration ranging from 1.0 × 103 to 1.0 × 10-4 µg/mL. Among the models used, the GA-PLS (Genetic algorithm-partial least square) exhibited superiority to quantify imidacloprid residue in green tea. The model achieved Rp (correlation coefficient) of 0.9702 with RPD of 4.95% in the test set and RSD for precision recorded up to 4.50%. Therefore, the proposed sensor could be employed to quantify imidacloprid residue in green tea for the safeguarding of quality and human health.

A nanosystem composed of upconversion nanoparticles and N, N-diethyl-p-phenylenediamine for fluorimetric determination of ferric ion.

A system composed of upconversion nanoparticles (UCNPs) and N,N-diethyl-p-phenylenediamine (EPA) is shown to be a useful probe for highly sensitive and selective fluorometric determination of ferric ion. The fluorescence of the UCNPs (under the 980 nm excitation) has peaks at 546, 657, 758 and 812 nm. EPA is readily oxidized by Fe(III) to generate a dye with a peak at 552 nm. This causes an inner filter effect on the fluorescence peaks at 546 nm, whereas the emissions at 657, 758 and 812 nm remained unchanged. Therefore, the iron concentration can be quantified by measurement of the ratio of fluorescence at 546 and 758. Under optimal condition, the ratio drops linearly in the 0.25 to 50 µM. Fe(III) concentration ranges, with a detection limit of 0.25 µM. The method is highly selective and was applied to the analysis of spiked samples (wastewater) where it gave recoveries of between 100.9 and 107.3%; and RSD values between 0.8 and 1.4%. Results are approximately the same as those obtained by AAS. Graphical abstract A method is presented for fluorimetric determination of Fe(III). Fe(III) reacts with N,N-diethyl-p-phenylenediamine (EPA) to generate EPA oxide. The fluorescence peaking at 546 nm is reduced in presence of oxidized EPA via an inner filter.

Near infrared system coupled chemometric algorithms for enumeration of total fungi count in cocoa beans neat solution.

Total fungi count (TFC) is a quality indicator of cocoa beans when unmonitored leads to quality and safety problems. Fourier transform near infrared spectroscopy (FT-NIRS) combined with chemometric algorithms like partial least square (PLS); synergy interval-PLS (Si-PLS); synergy interval-genetic algorithm-PLS (Si-GAPLS); Ant colony optimization - PLS (ACO-PLS) and competitive-adaptive reweighted sampling-PLS (CARS-PLS) was employed to predict TFC in cocoa beans neat solution. Model results were evaluated using the correlation coefficients of the prediction (Rp) and calibration (Rc); root mean square error of prediction (RMSEP), and the ratio of sample standard deviation to RMSEP (RPD). The developed models performance yielded 0.951≤Rp≤0.975; and 3.15≤RPD≤4.32. The models' prediction stability improved in the order of PLS