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

Application of benchtop NIR spectroscopy coupled with multivariate analysis for rapid prediction of antioxidant properties of walnut (Juglans regia)

Benchtop near-infrared (NIR) spectroscopy coupled with multivariate analysis was used for the classification and prediction of antioxidant properties of walnut. Total phenolic content (TPC), total flavonoid content (TFC), ABTS assay and FRAP assay were performed spectrophotometrically. The synergy interval partial least square coupled competitive adaptive reweighted sampling (Si-CARS-PLS) was used for the prediction. A decent discrimination using principal component analysis (PCA) was observed by mean of spectroscopic and antioxidant properties data with total cumulative variance of 99.26% (PC1 = 95.07%, PC2 = 2.98%, PC3 = 1.21%) and 96.60% (PC1 = 64.28%, PC2 = 32.32%) respectively. The Si-CARS-PLS yielded optimal performance, R_P = 0.9616, RPD = 3.807 for TPC, R_P = 0.9657, RPD = 3.367 for TFC, R_P = 0.9683, RPD = 2.728 for ABTS assay, and R_P = 0.914, RPD = 2.669 for FRAP assay. These findings revealed that NIR integrated with Si-CARS-PLS could be used for the prediction of antioxidant properties of walnut.

An overview of nanomaterial based biosensors for detection of Aflatoxin B1 toxicity in foods

Aflatoxin B1 (AFB1) is one of the most potent mycotoxin contaminating several foods and feeds. It suppresses immunity and consequently increases mutagenicity, carcinogenicity, teratogenicity, hepatotoxicity, embryonic toxicity and increasing morbidity and mortality. Continuous exposure of AFB1 causes liver damage and thus increases the prevalence of cirrhosis and hepatic cancer. This article was planned to provide understanding of AFB1 toxicity and provides future directions for fabrication of cost effective and user-friendly nanomaterials based analytical devices. In the present article various conventional (chromatographic & spectroscopic), modern (PCR & immunoassays) and nanomaterials based biosensing techniques (electrochemical, optical, piezoelectrical and microfluidic) are discussed alongwith their merits and demerits. Nanomaterials based amperometric biosensors are found to be more stable, selective and cost-effective analytical devices in comparison to other biosensors. But many unresolved issues about their stability, toxicity and metabolic fate needs further studies. In-depth studies are needed for development of advanced nanomaterials integrated biosensors for specific, sensitive and fast monitoring of AFB1 toxicity in foods. Integration of biosensing system with micro array technology for simultaneous and automated detection of multiple AFs in real samples is also needed. Concerted efforts are also required to reduce their possible hazardous consequences of nanomaterials based biosensors.

A study of starch content detection and the visualization of fresh-cut potato based on hyperspectral imaging

Fresh-cut potatoes are popular with consumers because of their healthiness, hygiene, and convenience. Currently, starch content is mainly detected using chemical methods, which are time-consuming and laborious. Moreover, these methods may cause some side effects in the human body. Therefore, suitable methods are required for the rapid and accurate detection of starch content. In this study, Zihuabai and Atlantic potatoes were used as experimental samples. The potatoes were sliced with stainless-steel blades, and images of these potatoes were obtained through hyperspectral imaging. The images were preprocessed using different methods. Competitive adaptive reweighed sampling (CARS) and the successive projection algorithm (SPA) were used to extract characteristic wavelengths. A partial least squares regression (PLSR) model was constructed to predict the starch content from the preprocessed full spectrum and the spectrum under the characteristic wavelength. The results indicate that the full spectrum model constructed through standard normal variable transformation (SNV) preprocessing had the best performance, with a correlation coefficient in the calibration set (R c) value of 0.9020, a root mean square error of correction (RMSEC) of 2.06, and a residual prediction deviation (RPD) of 2.33. The characteristic wavelength-based multivariate scattering correction (MSC)-CARS-PLSR model exhibited better performance than the PLSR model constructed using the full spectrum, with an R c value of 0.9276, RMSEC of 1.76, correlation coefficient in the prediction set (R p) value of 0.9467, root mean square error of prediction of 1.63, and RPD of 2.95. The starch content in fresh-cut potatoes was visualized using the best model in combination with pseudocolor technology. The results indicate that hyperspectral imaging is effective for mapping the spatial distribution of starch content; thus, a solid theoretical basis is obtained for the grading and online monitoring of fresh-cut potato slices.

Cellulose paper-based SERS sensor for sensitive detection of 2,4-D residue levels in tea coupled uninformative variable elimination-partial least squares

Food safety is a growing concern in recent years. This work presents the design of a simple and sensitive method for predicting 2,4-D (2,4-dichlorophenoxyacetic acid) residue levels in green tea extract employing surface-enhanced Raman spectroscopy (SERS) coupled uninformative variable elimination-partial least squares (UVE-PLS). Herein, SERS active citrate functionalized silver nanoparticles (AgNPs) with enhancement factor 1.51 × 10⁸ was used to prepare cellulose paper (common office) templated SERS sensor for acquiring SERS spectra of 2,4-D. The principle of the work was based on the interaction between 2,4-D and citrate group of AgNPs via chlorine atoms in the concentration range 1.0 × 10^-4 to 1.0 × 10³ µg/g. Three different wavenumber selection chemometric algorithms were studied comparatively to build an optimum calibration model, among them UVE-PLS showed enhanced performance as evident from the RPD value of 6.01 and Rp = 0.9864. Under optimized experimental condition proposed paper-based SERS sensor exhibited detection limit and RSD of 1.0 × 10^-4 µg/g and <5%, respectively. In addition, the validation results by HPLC method were satisfactory (p > 0.05).

Mathematical Modelling of Biosensing Platforms Applied for Environmental Monitoring

In recent years, mathematical modelling has known an overwhelming integration in different scientific fields. In general, modelling is used to obtain new insights and achieve more quantitative and qualitative information about systems by programming language, manipulating matrices, creating algorithms and tracing functions and data. Researchers have been inspired by these techniques to explore several methods to solve many problems with high precision. In this direction, simulation and modelling have been employed for the development of sensitive and selective detection tools in different fields including environmental control. Emerging pollutants such as pesticides, heavy metals and pharmaceuticals are contaminating water resources, thus threatening wildlife. As a consequence, various biosensors using modelling have been reported in the literature for efficient environmental monitoring. In this review paper, the recent biosensors inspired by modelling and applied for environmental monitoring will be overviewed. Moreover, the level of success and the analytical performances of each modelling-biosensor will be discussed. Finally, current challenges in this field will be highlighted.

Self-Cleaning-Mediated SERS Chip Coupled Chemometric Algorithms for Detection and Photocatalytic Degradation of Pesticides in Food

Pesticide residues in food have been a grave concern to consumers. Herein, we have developed a dual-mode SERS chip using Cu₂O mesoporous spheres decorated with Ag nanoparticles (MCu₂O@Ag NPs) as both sensing and degradation/clearing unit for rapid detection of pymetrozine and thiram pesticides in tea samples. Three kinds of chemometric algorithms were comparatively applied to analyze the collected SERS spectra of pesticides. In comparison, random frog-partial least squares achieved the best performance with root mean square error of prediction and residual predictive deviation values of 0.9871, 6.17, and 0.9873, 6.64 for pymetrozine and thiram, respectively. Additionally, the prepared SERS chip showed great photocatalytic activity to degrade pesticides under visible light irradiation. Through a facile method, this work presented a novel dual-functional SERS chip for the rapid detection and degradation of low-concentration pesticides in both environmental and food samples.

Rapid on-site identification of pesticide residues in tea by one-dimensional convolutional neural network coupled with surface-enhanced Raman scattering

In this study, a novel analytical approach is proposed for the identification of pesticide residues in tea by combining surface-enhanced Raman scattering (SERS) with a deep learning method one-dimensional convolutional neural network (1D CNN). First, a handheld Raman spectrometer was used for rapid on-site collection of SERS spectra. Second, the collected SERS spectra were augmented by a data augmentation strategy. Third, based on the augmented SERS spectra, the 1D CNN models were established on the cloud server, and then the trained 1D CNN models were used for subsequent pesticide residue identification analysis. In addition, to investigate the identification performance of the 1D CNN method, four conventional identification methods, including partial least square-discriminant analysis (PLS-DA), k-nearest neighbour (k-NN), support vector machine (SVM) and random forest (RF), were also developed on the basis of the augmented SERS spectra and applied for pesticide residue identification analysis. The comparative studies show that the 1D CNN method possesses better identification accuracy, stability and sensitivity than the other four conventional identification methods. In conclusion, the proposed novel analytical approach that exploits the advantages of SERS and a deep learning method (1D CNN) is a promising method for rapid on-site identification of pesticide residues in tea.

Detection of Mycotoxins in Food Using Surface-Enhanced Raman Spectroscopy: A Review

Mycotoxins are toxic metabolites produced by fungi that contaminate many important crops worldwide. Humans are commonly exposed to mycotoxins through the consumption of contaminated food products. Mycotoxin contamination is unpredictable and unavoidable; it occurs at any point in the food production system under favorable conditions, and they cannot be destroyed by common heat treatments, because of their high thermal stability. Early and fast detection plays an essential role in this unique challenge to monitor the presence of these compounds in the food chain. Surface-enhanced Raman spectroscopy (SERS) is an advanced spectroscopic technique that integrates Raman spectroscopic molecular fingerprinting and enhanced sensitivity based on nanotechnology to meet the requirement of sensitivity and selectivity, but that can also be performed in a cost-effective and straightforward manner. This Review focuses on the SERS methodologies applied to date for qualitative and quantitative analysis of mycotoxins based on a variety of SERS substrates, as well as our perspectives on current limitations and future trends for applying this technique to mycotoxin analyses.

Recent progress in mycotoxins detection based on surface-enhanced Raman spectroscopy

Mycotoxins are toxic compounds naturally produced by certain types of fungi. The contamination of mycotoxins can occur on numerous foodstuffs, including cereals, nuts, fruits, and spices, and pose a major threat to humans and animals by causing acute and chronic toxic effects. In this regard, reliable techniques for accurate and sensitive detection of mycotoxins in agricultural products and food samples are urgently needed. As an advanced analytical tool, surface-enhanced Raman spectroscopy (SERS), presents several major advantages, such as ultrahigh sensitivity, rapid detection, fingerprint-type information, and miniaturized equipment. Benefiting from these merits, rapid growth has been observed under the topic of SERS-based mycotoxin detection. This review provides a comprehensive overview of the recent achievements in this area. The progress of SERS-based label-free detection, aptasensor, and immunosensor, as well as SERS combined with other techniques, has been summarized, and in-depth discussion of the remaining challenges has been provided, in order to inspire future development of translating the techniques invented in scientific laboratories into easy-to-operate analytic platforms for rapid detection of mycotoxins.

A novel electrochemical aflatoxin B1 immunosensor based on gold nanoparticle-decorated porous graphene nanoribbon and Ag nanocube-incorporated MoS2 nanosheets

The accurate and precisive monitoring of aflatoxin B1 (AFB1), which is one of the most hazardous mycotoxins, especially in agricultural products, is significant for human and environmental health.

Nondestructive monitoring, kinetics and antimicrobial properties of ultrasound technology applied for surface decontamination of bacterial foodborne pathogen in pork

In this study, electronic nose (E-nose) and Hyperspectral Imaging (HSI) was employed for nondestructive monitoring of ultrasound efficiency (20KHZ) in the inactivation of Salmonella Typhimurium, and Escherichia coli in inoculated pork samples treated for 10, 20 and 30 min. Weibull, and Log-linear model fitted well (R2 ≥ 0.9) for both Salmonella Typhimurium, and Escherichia coli inactivation kinetics. The study also revealed that ultrasound has antimicrobial effects on the pathogens. For qualitative analysis, unsupervised (PCA) and supervised (LDA) chemometric algorithms were applied. PCA was used for successful sample clustering and LDA approach was used to construct statistical models for the classification of ultrasound treated and untreated samples. LDA showed classification accuracies of 99.26%,99.63%,99.70%, 99.43% for E-nose - S. Typhimurium, E-nose -E. coli, HSI - S. Typhimurium and HSI -E. coli respectively. PLSR quantitative models showed robust models for S. Typhimurium- (E-nose Rp2 = 0.9375, RMSEP = 0.2107 log CFU/g and RPD = 9.7240 and (HSI Rp2 = 0.9687 RMSEP = 0.1985 log CFU/g and RPD = 10.3217) and E. coli -(E-nose -Rp2 = 0.9531, RMSEP = 0.2057 log CFU/g and RPD = 9.9604) and (HIS- Rp2 = 0.9687, RMSEP = 0.2014 log CFU/g and RPD = 10.1731). This novel study shows the overall effectiveness of applying E-nose and HSI for in-situ and nondestructive detection, discrimination and quantification of bacterial foodborne pathogens during the application of food processing technologies like ultrasound for pathogen inactivation.

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 (R_c = 0.984, R_p = 0.974, RMSEC = 0.157 μg/mL, RMSEP = 0.211 μg/mL) and spiked tea samples (R_c = 0.979, R_p = 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.

A SERS aptasensor based on AuNPs functionalized PDMS film for selective and sensitive detection of Staphylococcus aureus

In this study, a sensitive biosensor was developed based on aptamer functionalized polydimethylsiloxane (PDMS) film for the detection of Staphylococcus aureus (S. aureus) using surface-enhanced Raman scattering (SERS) technology. Initially, the surface of PDMS film was chemically modified by piranha solution and 3-Aminopropyltriethoxysilane (APTES), and then AuNPs-PDMS film was prepared by coating gold nanoparticles (AuNPs) through electrostatic interaction. Next, the aptamers were immobilized on the AuNPs-PDMS membrane via gold-sulfur bond to form the capture substrate. Meanwhile, gold-silver core-shell nanoflowers (Au@Ag NFs) modified with mercaptobenzoic acid (4-MBA) and aptamers were applied as a signal probe. In the presence of the target, the signal molecular probe and the capturing substrate specifically combined with the target and resulted in a sandwich structure "capture substrate-target-signal molecular probe". Under the optimized experimental condition, the signal of 4-MBA at 1085 cm^-1 was linearly related to the S. aureus concentration in the range of 4.3 × 10 cfu mL^-1-4.3 × 10⁷ cfu mL^-1 (y = 326.91x-117.62, R² = 0.9932) with a detection limit of 13 cfu mL^-1. The method was successfully applied to spiked actual samples and a 92.5-110% recovery rate was achieved.

A competitive fluorescence assay based on free-complementary DNA for ochratoxin A detection

An ultrasensitive, rapid, and specific method for Ochratoxin A (OTA) detection was designed using complementary sequence to aptamer as a target of molecular beacon (MB). The designed loop structure of the MB has the same sequence as the aptamer with a complementary DNA (cDNA) which translates the level of the target into a measurable response. The presence of the target holds aptamer at the corresponding amount and the additional cDNAs are consumed by unbound aptamers which avails free cDNAs that resulting in fluorescence rising due to unfolding of MBs. Under the optimized conditions, the fluorescence intensity increased linearly with OTA concentration over the range of 10 pg mL^-1-1 µg mL^-1 with the detection limit of 0.247 pg mL^-1. The application of this assay in wheat sample in comparison with HPLC-MS/MS method, demonstrated that the new assay could be a potential sensing platform for OTA detection.

Pre etched Ag nanocluster as SERS substrate for the rapid quantification of AFB1 in peanut oil via DFT coupled multivariate calibration

The current study extends the use of surface-enhanced Raman spectroscopy (SERS) combined with density functional theory (DFT) and multivariate calibration towards the rapid quantification of aflatoxin B1 (AFB1) in peanut oil samples. It reports the design of pre etched Ag nanocluster as an active SERS substrate for quantifying AFB1, after being impregnated on its surface. The SERS spectra of AFB1@pre etched Ag nanocluster was recorded and its respective theoretical spectrum was calculated by density functional theory (DFT) to assign the characteristic peaks. The baseline drift and rotation effects were masked by the first-order derivative preprocessing method followed by multivariate calibration. The BP-AdaBoost model exhibited optimum prediction (Rp = 0.9283 and 0.9332) ability over the concentration range 5-100 and 100-1000 ngmL^-1, respectively. The limit of detection calculated was 5.0 ngmL^-1 and the obtained recoveries were in the range from 90.4% to 113.1% in spiked peanut oil samples. Additionally, precision analysis revealed an RSD ca. 5%, suggesting the applicability of the pre etched Ag nanocluster SERS substrate towards AFB1 detection. Thus, the proposed SERS platform exploiting DFT and BP-AdaBoost model was found reproducible for the quantification of AFB1 in peanut oil.

Micro-NIR spectrometer for quality assessment of tea: Comparison of local and global models

Near-infrared (NIR) spectroscopy is an effective tool for analyzing components relevant to tea quality, especially catechins and caffeine. In this study, we predicted catechins and caffeine content in green and black tea, the main consumed tea types worldwide, by using a micro-NIR spectrometer connected to a smartphone. Local models were established separately for green and black tea samples, and these samples were combined to create global models. Different spectral preprocessing methods were combined with linear partial-least squares regression and nonlinear support vector machine regression (SVR) to obtain accurate models. Standard normal variate (SNV)-based SNV-SVR models exhibited accurate predictive performance for both catechins and caffeine. For the prediction of quality components of tea, the global models obtained results comparable to those of the local models. The optimal global models for catechins and caffeine were SNV-SVR and particle swarm optimization (PSO)-simplified SNV-PSO-SVR, which achieved the best predictive performance with correlation coefficients in prediction (Rp) of 0.98 and 0.93, root mean square errors in prediction of 9.83 and 2.71, and residual predictive deviations of 4.44 and 2.60, respectively. Therefore, the proposed low-price, compact, and portable micro-NIR spectrometer connected to smartphones is an effective tool for analyzing tea quality.

Quantification of deltamethrin residues in wheat by Ag@ZnO NFs-based surface-enhanced Raman spectroscopy coupling chemometric models

Deltamethrin, one of the most toxic pyrethroids, is commonly used to inhibit pests in wheat. However, the trace levels of deltamethrin in wheat is alarming to human health. In this study, surface-enhanced Raman spectroscopy (SERS)-active silver nanoparticles-plated-zinc oxide nanoflowers (Ag@ZnO NFs) nano-sensor were employed for rapid and sensitive quantification of deltamethrin in wheat. To sufficiently utilize the chemical-related information in SERS spectra, various spectral pretreatment and chemometric models were studied. The mean centering (MC) coupling successive projection algorithm-partial least squares regression (SPA-PLS) provided optimal predictive performance (correlation coefficient of prediction (Rp) = 0.9736 and residual predictive deviation (RPD) = 4.75). The proposed method achieved the limit of detection (LOD) = 0.16 μg·kg^-1, the recovery of predicted results was in the range of 96.33-109.17% and the relative standard deviation (RSD) was < 5%. The overall results suggested that SERS based Ag@ZnO NFs combined with MC-SPA-PLS could be an easy and efficient method to quantify deltamethrin residue levels in wheat.

SERS-based rapid detection of 2,4-dichlorophenoxyacetic acid in food matrices using molecularly imprinted magnetic polymers

In order to remove the limitations of natural antibodies or enzymes, a nano-magnetic biomimetic platform based on a surface-enhanced Raman scattering (SERS) sensor has been developed for highly sensitive capture and detection of 2,4-dichlorophenoxyacetic acid (2,4-D) in food and water samples. Magnetic-based molecular imprinted polymer nanoparticles (Mag@MIP NPs) were constructed to capture the target 2,4-D molecule via biomimetic recognition, and gold nanoparticles (Au NPs) served as SERS-based probes, which are bound to the Mag@MIP NPs by electrostatic adsorption. The as-prepared SERS-MIP sensor for sensing of 2,4-D achieved a good linear relationship with a low detection limit (LOD) of 0.00147 ng/mL within 2 h and exhibited high sensitivity. The sensor was successfully applied to detect 2,4-D in milk and tap water and achieved good recoveries ranging from 93.5 to 102.2%. Moreover, the designed sensor system exhibited satisfactory results (p > 0.05) compared to HPLC by validation analysis. Hence, the findings demonstrated that the proposed method has significant potential for practical application in food safety and environmental protection. Graphical abstract .

Landing microextraction sediment phase onto surface enhanced Raman scattering to enhance sensitivity and selectivity for chromium speciation in food and environmental samples

The current study explores the first full mode liquid microextraction technique coupled with surface-enhanced Raman spectroscopy (SERS), and has been successfully applied for chromium speciation in food and environmental matrices. Herein, chromium as chlorochromate anion [CrO3Cl]- and the cationic rhodamine 6G [RG]+ dye has been extracted in organic phase as a complex ion associate [RG+.CrO3Cl-.nS]org at pH ≤ 1.0. Afterwards, the extracted phase was deposited on the surface of the nano-flower shaped silver nanoparticles substrate and the SERS response was monitored against the reagent blank at 1505 cm-1. Substrate characterizations, reaction mechanism assignment, stoichiometry, speciation, analytical applications, selectivity and validation were performed. The analytical procedure exhibits a detection limit of 0.03 µg L-1 under the optimized experimental conditions. The accuracy of the proposed strategy was validated by inductively coupled plasma optical emission spectrometry method using student's t- and F tests at 95% confidence.

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).