Investigation of nonlinear relationship of surface enhanced Raman scattering signal for robust prediction of thiabendazole in apple

Oliveira M.J., Rubira R.J., Furini L.N., Batagin-Neto A., Constantino C.J.

The application of surface-enhanced Raman scattering (SERS) as analytical tool remains a challenge due signal intensity fluctuations, which depends on experimental parameters such as size, shape, and aggregation of the metallic nanoparticles responsible for enhancing the Raman signal. Colloidal nanoparticles can overtake this difficulty by optimizing some experimental conditions for each analyte. Here, we applied SERS as analytical technique to detect thiabendazole (TBZ) at low concentrations using Ag colloid. TBZ stock solutions were added into Ag colloids and SERS spectra were recorded in triplicate. Within the TBZ concentration from 1.6 × 10−7 to 8.0 × 10−8 mol/L a linear regimen for SERS intensity was achieved, leading to a TBZ limit of detection of 13.8 ppb. Besides, the main enhanced bands of the TBZ SERS spectrum suggest the TBZ adsorption mechanism on Ag surface takes place by the thiazole moiety. Theoretical calculations support the experimental data and indicate the interaction is stablished by S atom. Complementary to analytical application and adsorption mechanism, the dependence of SERS intensity on TBZ concentration follows a sigmoidal adsorption isotherm, which has a direct relation with the extinction spectra of Ag colloid containing TBZ at different concentrations, revealing intermolecular TBZ interactions and formation of Ag nanoparticle aggregates with distinct morphologies.

Xu Y., Kutsanedzie F.Y., Hassan M., Zhu J., Ahmad W., Li H., Chen Q.

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 ( 0.05).

Li X., Gao K., Jinfeng B., Wu X., Li X., Guo C.

The effects of polyphenols on the chromatic characteristics of thermal processed apple slices during the Maillard reaction is unknown. Changes in the chromatic values (CIE L*, a*, b*) of the d -fructose/ l -lysine solution containing the apple polyphenol compounds chlorogenic acid, (+)-catechin, (−)-epicatechin and phlorizin on exposure to different temperatures, pH and incubation times were investigated. The results showed that (+)-catechin/(−)-epicatechin and phlorizin elevated the redness value (a*) and retarded 5-hydroxymethyl furfural (5-HMF) formation in d -fructose/ l -lysine solution at 90 °C and pH 4.0. Chlorogenic acid, on the other hand, boosted the yellowness (b*) value and accelerated 5-HMF formation. Browning was mainly observed in fructose/ l -lysine solutions after addition of catechin, the products of which were identified by Fourier transform ion cyclotron resonance mass spectrometry (FT-ICR-MS). Five compounds (C21H23NO8, C21H28N2O8, C21H26O12, C27H37N3O10, and C27H40N2O14) were observed and their formation pathways were proposed. This demonstrates that catechin reacts with lysine, fructose or both at the para position of its B ring to form red-colored adducts, while simultaneously inhibiting 5-HMF formation. These results provide insights to explain and control browning in apple products.

Chen X., Xu Y., Meng L., Chen X., Yuan L., Cai Q., Shi W., Huang G.

Identifying tea grades is crucial to providing consumers with tea and ensuring consumer rights. Partial least squares–discriminant analysis (PLS-DA) is a simple and traditional classification algorithm in analyzing e-tongue data. However, the number of latent variables (LVs) in a PLS-DA model needs to be determined, and cross-validation is the most common way to identify the optimal latent variables. To overcome this obstacle, sum of ranking difference (SRD) algorithm was applied to create a non-parametric PLS-DA-SRD model. The performance of PLS-DA and PLS-DA-SRD models were then compared, and significant improvement in term of accuracy, sensitivity, and specificity was obtained when SRD was combined with PLS-DA algorithm. Moreover, no training phase was needed to identify the optimal LVs for PLS-DA, making the calculation of classification rapid and concise. The PLS-DA-SRD method demonstrated its efficiency and capability by successfully identifying the tea sample grade.

Chen H., Tan C., Lin Z.

Inspired by the attractive features of extreme learning machine (ELM), a simple ensemble ELM algorithm, named EELM, is proposed for multivariate calibration of near-infrared spectroscopy. Such an algorithm takes full advantage of random initialization of the weights of the hidden layer in ELM for obtaining the diversity between member models. Also, by combining a large number of member models, the stability of the final prediction can be greatly improved and the ensemble model outperforms its best member model. Compared with partial least-squares (PLS), the superiority of EELM is attributed to its inherent characteristics of high learning speed, simple structure and excellent predictive performance. Three NIR spectral datasets concerning solid samples are used to verify the proposed algorithm in terms of both the accuracy and robustness. The results confirmed the superiority of EELM to classic PLS. Also, even if the experiment is done on NIR datasets, it provides a good reference for other spectral calibration.

Mekonnen M.L., Chen C., Osada M., Su W., Hwang B.

The interaction of plasmonic nanoparticles with a dielectric platform gives rise to unique optical behaviors and this can be maneuvered to improve the plasmonic/SERS performances of a substrate. Herein, dielectric modified plasmonic-paper SERS substrate is developed by assembling Ag@SiO2 nanocubes on Fe-TiO2 nanosheets (NS) modified paper. The Fe-TiO2 NS being visible light responsive significantly alters the optical property of the paper and serves as a dielectric underlay for the Ag nanocubes. Hence, the incident light reflected back from the dielectric nanosheets couples with the scattered light from the Ag nanocubes leading to spatially enhanced electromagnetic field improving the SERS enhancement. The prepared dielectric modified plasmonic-paper has an average enhancement factor (EF) of 1.49 × 107 using R6G as a probe molecule. This value is superior to unmodified plasmonic-paper highlighting the coupling effect of the dielectric nanosheets. The substrate shows robust detection performance for thiabendazole and achieves a limit of detection (LOD) of 19 μg/L, which is 4-fold more sensitive than unmodified plasmonic paper. Direct swabbing test of thiabendazole sprayed apple fruit shows a discernible Raman signal down to 15 ppb indicating the utility of the substrate for point-of-need applications in food safety.

Xuan T., Gao Y., Cai Y., Guo X., Wen Y., Yang H.

Thiabendazole is likely to cause thyroid hormone imbalances and liver damage at high doses. Conventional detection methods are time-consuming with cumbersome pretreatment or insufficient sensitivity. Herein, Metal-organic framework (MOF, Mil-101(Fe))modified with inositol hexaphosphate (IP6)is fabricated by a solvothermal method and thenAg-Au-IP6-Mil-101(Fe) is obtained by in original position of MOF surface with the aid of IP6. The as-prepared Ag-Au-IP6-Mil-101(Fe) substrate exhibits high stability. Certain target molecules containing atoms of N or S could access the proximity to the surface of Au-IP6-Mil-101(Fe) substrate by the strong interaction with Fe3+ in the MOF, leading to improvement of SERS detection sensitivity. As a real application, Ag-Au-IP6-Mil-101 based Raman strategy is developed to achieve rapid detection of thiabendazole in juice and has good linearity in the range of 1.5 ppm˜75 ppm with correlation coefficient (R2) of 0.986. The detection limit for TBZ reached 50 ppb in juice sample, which meets the requirement of the national standard. In addition, the Ag-Au-IP6-Mil-101(Fe)substrate possessing peroxidase-like activity could degrade organic dyes within a short time.

Wang J., Zareef M., He P., Sun H., Chen Q., Li H., Ouyang Q., Guo Z., Zhang Z., Xu D.

The study reports a portable near infrared (NIR) spectroscopy system coupled with chemometric algorithms for prediction of tea polyphenols and amino acids in order to index matcha tea quality.Spectral data were preprocessed by standard normal variate (SNV), mean center (MC) and first-order derivative (1st D) tests. The data were then subjected to full spectral partial least squares (PLS) and four variable selection algorithms, such as random frog partial least square (RF-PLS), synergy interval partial least square (Si-PLS), genetic algorithm-partial least square (GA-PLS) and competitive adaptive reweighted sampling partial least square (CARS-PLS). RF-PLS was established and identified as the optimum model based on the values of the correlation coefficients of prediction (RP ), root mean square error of prediction (RMSEP) and residual predictive deviation (RPD), which were 0.8625, 0.82% and 2.13, and 0.9662, 0.14% and 3.83, respectively, for tea polyphenols and amino acids. The content range of tea polyphenols and amino acids in matcha tea samples was 8.51-14.58% and 2.10-3.75%, respectively. The quality of matcha tea was successfully classified with an accuracy rate of 83.33% as qualified, unqualified and excellent grade.The proposed method can be used as a rapid, accurate and non-destructive platform to classify various matcha tea samples based on the ratio of tea polyphenols to amino acids. © 2019 Society of Chemical Industry.

Fu G., Sun D., Pu H., Wei Q.

Thiabendazole (TBZ) is a kind of pesticide that is widely used in agriculture, and its residue may pose a threat to human health. In order to measure TBZ residues in food samples, a surface-enhanced Raman spectroscopy (SERS) method combined with a homogeneous and reusable gold nanorods (GNR) array substrate was proposed. GNR with a high uniformity was synthesized and then applied to the self-assembly of a GNR vertically aligned array. The relative standard deviation (RSD) of the array for SERS could reach 15.4%, and the array could be reused for more than seven times through the treatment of plasma etching. A logarithmic correlation between TBZ concentration and Raman intensity was obtained, with the best determination coefficient (R2) and the corresponding limit of detection (LOD) of 0.991 and 0.037 mg/L in methanol solution, and 0.980 and 0.06 ppm in apple samples, respectively. The recoveries of TBZ in apple samples ranged from 76% to 107%. This study provided a rapid and sensitive approach for detecting TBZ in apples based on SERS coupled with GNR array substrate, showing great potential for analyzing other trace contaminants in food matrices.

Nie M., Meng L., Chen X., Hu X., Li L., Yuan L., Shi W.

Zhu J., Ahmad W., Xu Y., Liu S., Chen Q., Hassan M.M., Ouyang Q.

A novel wavelength selection method named ICPA-mRMR coupled SERS was employed for the detection of CPS residues in tea samples.

Riaz A., Lei S., Akhtar H.M., Wan P., Chen D., Jabbar S., Abid M., Hashim M.M., Zeng X.

In the present study, apple peel polyphenols (APP) were incorporated into chitosan (CS) to develop a novel functional film. Scanning electron microscopy, Fourier transform-infrared spectroscopy and thermogravimetric analyses were performed to study the structure, potential interaction and thermal stability of the prepared films. Physical properties including moisture content, density, color, opacity, water solubility, swelling ration and water vapor permeability were measured. The results revealed that addition of APP into CS significantly improved the physical properties of the film by increasing its thickness, density, solubility, opacity and swelling ratio whereas moisture content and water vapor permeability were decreased. Tensile strength and elongation at break of the CS-APP film with 1% APP was 16.48MPa and 13.33%, respectively, significantly lower than those for CS control film. Thermal stability of the prepared films was decreased while antioxidant and antimicrobial activities of the CS-based APP film were significantly increased. CS-APP film with 0.50% APP concentration exhibited good mechanical and antimicrobial properties, indicating that it could be developed as bio-composite food packaging material for the food industry.

Wang C.M., Roy P.K., Juluri B.K., Chattopadhyay S.

We demonstrate a gold island film (GIF) coated tattoo paper as acid free ‘green’ fabrication of transferable plasmonic patterns for surface enhanced Raman spectroscopy (SERS) based screening of food toxins. A tattoo paper, with a water soluble release layer, was optimally sputter coated with a gold pattern which can be transferred onto any real fruit surface to enable in situ molecular detection (Thiabendazole, TBZ, used here) from the surface of the fruit (orange used here). The GIF loading and morphology is simply controlled by varying the sputtering time between 1 and 8 min. The integrated plasmonic field strength, calculated by finite difference time domain simulations, peaked for the GIF obtained with 3 min of sputtering time to match the experimental results. The SERS tattoo can also be transferred to a copper foil to enable conventional ex situ molecular detection (Di 2-ethylhexyl phthalate, DEHP, used here) in commercial sports drinks. The SERS tattoo could detect 0.1 μM (0.2 ppm) of TBZ on orange in situ, and 0.0009 vol.% DEHP in sports drink ex situ. Multiplexed SERS experiments were performed to detect specific signals of TBZ, and commercial soybean oil from their combination.

Kutsanedzie F.Y., Chen Q., Hassan M.M., Yang M., Sun H., Rahman M.H.

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

Feng J., Hu Y., Grant E., Lu X.

Thiabendazole, a systemic fungicide used to treat vegetables and fruits during postharvest process, persists as detrimental residue to consumers. We combine a molecularly imprinted polymers (MIPs) with surface enhanced Raman spectroscopy (SERS) to form a novel MISPE-SERS chemosensor and determined thiabendazole in orange juice. Kinetic and static adsorption tests validated the efficient and selective adsorption of thiabendazole using synthesized MIPs via precipitation polymerization. Synthesized MIPs were packed into solid phase extraction (SPE) cartridge to serve as tailor-made sorbents for the separation of thiabendazole in orange juice. Silver colloids synthesized by reduction of AgNO3 by trisodium citrate were used as SERS-active substrate to quantify the eluted thiabendazole from MISPE. The overall process including sample preparation and detection took 23min and the limit of detection of this chemosensor was 4ppm for thiabendazole in orange juice. This chemosensor can be used for rapid and sensitive detection of thiabendazole in agri-foods.

Wang J., Qin Y., Su R., Zhu W., Zhao S., Zhang S., Tan X., Huang K., Yan J.

Chang H., Wu H., Wang T., Wang X., Yu R.

In this study, an excitation–emission–pH multi-way fluorescence technique coupled with a third-order calibration method based on an alternating quadrilinear decomposition (AQLD) algorithm was proposed for the simultaneous determination of thiabendazole (TBZ) and carbaryl (CAR) in apples. AQLD can be considered a “mathematical separation” technique that extracts the pure signal of the target analyte from complex mixed signals, thereby effectively addressing fluorescence peak overlap and unknown interference. The average spiked recoveries of the target analytes ranged from 98.4% to 101.9%, and the relative standard deviation was less than 5.6%. To evaluate the performance of the method, a number of parameters were calculated, including sensitivity (SEN), selectivity (SEL), limit of detection (LOD), limit of quantification (LOQ), and intra-day and inter-day precision. The results of the third-order calibration method were compared with those of the second-order calibration method (based on excitation–emission matrix fluorescence). These results showed that the former was superior. In short, the proposed strategy is simple, cost-effective, and anti-interference, providing a valuable reference for accurate quantification of TBZ and CAR in complex food matrices with uncalibrated interferences.

Shen Y., Ou Q., Yang Y., Zhu W., Zhao S., Tan X., Huang K., Yan J.

Peng S., Zhang Z., Guo J., Ma T., Liu D.

Yin T., Peng Y., Li Y., Chao K., Nie S., Tao F., Zuo J.

Li H., Zhang W., Nunekpeku X., Sheng W., Chen Q.

Wang Y., Yang X., Zhang Y., Zhu Z., Wang R., Li X., Guan A., Tian F., Teng P., Gao S., Jonesc A., Zhang B., Sivanathan S., Li K.

Li H., Nunekpeku X., Zhang W., Adade S.Y., Ahmad W., Sheng W., Chen Q.

Achieving the ideal gel strength is essential for desired texture in minced chicken products. This study developed a rapid, non-destructive method using near-infrared (NIR) spectroscopy and nonlinear chemometric modeling to predict minced chicken gel strength under ultrasonic treatment. Initially, minced chicken samples were subjected to high-intensity ultrasound for 0-50 min. This was followed by heat-induced gelation. Gel strength was conventionally measured, and NIR spectra were collected. Nonlinearity between gel strength and spectral data was confirmed using augmented partial residual plots (APaRPs). Subsequently, nonlinear support vector machine (SVM) and extreme learning machine (ELM) models were developed using full NIR spectra and variable selection methods, including uninformative variable elimination (UVE), competitive adaptive reweighting (CARS), and genetic algorithms (GA). GA proved most effective for enhancing model performance, achieving the highest predicted coefficient of determination (Rp

Zhou J., Gao W., Wu J., Xiang Z., Zeng J., Wang B., Xu J.

Shi T., Pan T., Lu P.

Dong Y., Hu Y., Jin J., Zhou H., Jin S., Yang D.

Skvortsova A., Trelin A., Guselnikova O., Pershina A., Vokata B., Svorcik V., Lyutakov O.

Sun H., Xiong S., Shi B., Zhou Y., Bi C., Li J., Li L., Liu B., Dai C., Wang Y., Wang C., Wang D., Liu W.

Li H., Sheng W., Hassan M., Geng W., Chen Q.

The abuse of antibiotics has caused gradually increases drug-resistant bacterial strains that pose health risks. Herein, a sensitive SERS sensor coupled multivariate calibration was proposed for quantification of antibiotics in milk. Initially, octahedral gold-silver nanocages (Au@Ag MCs) were synthesized by Cu

Wan F., Li S., Lei Y., Wang M., Liu R., Hu K., Xia Y., Chen W.

Accurate detection of dissolved furfural in transformer oil is crucial for real-time monitoring of the aging state of transformer oil-paper insulation. While label-free surface-enhanced Raman spectroscopy (SERS) has demonstrated high sensitivity for dissolved furfural in transformer oil, challenges persist due to poor substrate consistency and low quantitative reliability. Herein, machine learning (ML) algorithms were employed in both substrate fabrication and spectral analysis of label-free SERS. Initially, a high-consistency Ag@Au substrate was prepared through a combination of experiments, particle swarm optimization-neural network (PSO-NN), and a hybrid strategy of particle swarm optimization and genetic algorithm (Hybrid PSO-GA). Notably, a two-step ML framework was proposed, whose operational mechanism is classification followed by quantification. The framework adopts a hierarchical modeling strategy, incorporating simple algorithms such as kernel support vector machine (Kernel-SVM), k-nearest neighbors (KNN), etc., to independently establish lightweight regression models on each cluster, which allows each model to focus more effectively on fitting the data within its cluster. The classification model achieved an accuracy of 100%, while the regression models exhibited an average correlation coefficient (R2) of 0.9953 and the root mean square errors (RMSE) consistently below 10-2. Thus, this ML framework emerges as a rapid and reliable method for detecting dissolved furfural in transformer oil, even in the presence of different interfering substances, which may also have potentiality for other complex mixture monitoring systems.