“Turn-off” fluorescent data array sensor based on double quantum dots coupled with chemometrics for highly sensitive and selective detection of multicomponent pesticides

Elevating nanomaterial optical sensor arrays through the integration of advanced machine learning techniques for enhancing visual inspection of food quality and safety

Food quality and safety problems caused by inefficient control in the food chain have significant implications for human health, social stability, and economic progress and optical sensor arrays (OSAs) can effectively address these challenges. This review aims to summarize the recent applications of nanomaterials-based OSA for food quality and safety visual monitoring, including colourimetric sensor array (CSA) and fluorescent sensor array (FSA). First, the fundamental properties of various advanced nanomaterials, mainly including metal nanoparticles (MNPs) and nanoclusters (MNCs), quantum dots (QDs), upconversion nanoparticles (UCNPs), and others, were described. Besides, the diverse machine learning (ML) and deep learning (DL) methods of high-dimensional data obtained from the responses between different sensing elements and analytes were presented. Moreover, the recent and representative applications in pesticide residues, heavy metal ions, bacterial contamination, antioxidants, flavor matters, and food freshness detection were comprehensively summarized. Finally, the challenges and future perspectives for nanomaterials-based OSAs are discussed. It is believed that with the advancements in artificial intelligence (AI) techniques and integrated technology, nanomaterials-based OSAs are expected to be an intelligent, effective, and rapid tool for food quality assessment and safety control.

Detection methods, migration patterns, and health effects of pesticide residues in tea

Due to its rich health benefits and unique cultural charm, tea drinking is increasingly popular with the public in modern society. The safety of tea is the top priority that affects the development of tea industry and the health of consumers. During the process of tea growth, pesticides are used to prevent the invasion of pests and diseases with maintaining high quality and stable yield. Because hot water brewing is the traditional way of tea consumption, water is the main carrier for pesticide residues in tea into human body accompanied by potential risks. In this review, pesticides used in tea gardens are divided into two categories according to their solubility, among which water-soluble pesticides pose a greater risk. We summarized the methods of the sample pretreatment and detection of pesticide residues and expounded the migration patterns and influencing factors of tea throughout the process of growth, processing, storage, and consumption. Moreover, the toxicity and safety of pesticide residues and diseases caused by human intake were analyzed. The risk assessment and traceability of pesticide residues in tea were carried out, and potential eco-friendly improvement strategies were proposed. The review is expected to provide a valuable reference for reducing risks of pesticide residues in tea and ensuring the safety of tea consumption.

Non-biological fluorescent chemosensors for pesticides detection

The ongoing poisoning of agricultural products has pushed the security problem to become an important issue. Among them, exceeding the standard rate of pesticide residues is the main factor influencing the quality and security of agricultural products. Moreover, the abuse of pesticides has introduced a large amount of residues in soil and drinking water, which will enter the food chain to the human body, leading to neurological disorders and cancer. Therefore, great efforts have been devoted to developing fluorescent sensors for detecting pesticide in a facile, quickly, sensitive, selective, accurate manner, which exhibit greater advantages than some traditional methods. In this review, we mainly focus on summarizing the non-biological fluorescent probes for organic pesticides detection with the detection limit of micromole to nanomole, including organic functional small molecules, calixarenes and pillararenes, metal organic framework systems, and nanomaterials. Meanwhile, we described the different sensing mechanisms for pesticides detection of these mentioned fluorescent sensors, the detection limit of each pesticide, the application in detecting actual samples, as well as their respective advantages and development prospects associated with present non-biological fluorescent sensors.

Synthetic Approaches, Modification Strategies and the Application of Quantum Dots in the Sensing of Priority Pollutants

Polycyclic aromatic hydrocarbons (PAHs) and nitro-aromatic compounds (NACs) are two classifications of environmental pollutants that have become a source of health concerns. As a result, there have been several efforts towards the development of analytical methods that are efficient and affordable that can sense these pollutants. In recent decades, a wide range of techniques has been developed for the detection of pollutants present in the environment. Among these different techniques, the use of semiconductor nanomaterials, also known as quantum dots, has continued to gain more attention in sensing because of the optical properties that make them useful in the identification and differentiation of pollutants in water bodies. Reported studies have shown great improvement in the sensing of these pollutants. This review article starts with an introduction on two types of organic pollutants, namely polycyclic aromatic hydrocarbons and nitro-aromatic explosives. This is then followed by different quantum dots used in sensing applications. Then, a detailed discussion on different groups of quantum dots, such as carbon-based quantum dots, binary and ternary quantum dots and quantum dot composites, and their application in the sensing of organic pollutants is presented. Different studies on the comparison of water-soluble quantum dots and organic-soluble quantum dots of a fluorescence sensing mechanism are reviewed. Then, different approaches on the improvement of their sensitivity and selectivity in addition to challenges associated with some of these approaches are also discussed. The review is concluded by looking at different mechanisms in the sensing of polycyclic aromatic hydrocarbons and nitro-aromatic compounds.

Single gold nanocluster probe-based fluorescent sensor array for heavy metal ion discrimination

There is a continuing high demand to design effective sensors for the determination of heavy metal ions (HMIs) since they are hazardous to both human health and the environment. In this study, we reported a facile fluorescent sensor array for rapid discrimination of HMIs based on a single gold nanocluster (AuNC) probe. This AuNC probe was prepared by using 2-mercapto-1-methylimidazole (MMI) as a ligand and polyvinypyrrolidone (PVP) as a dispersing agent. The fluorescence emission of PVP/MMI-AuNC was observed to be closely related to the pH value of the aqueous solution, which displays yellow (λ_max = 512 nm) and red (λ_max = 700 nm) fluorescence at pH 12.0 and 6.0, respectively. Further experiments indicated that different HMIs can produce differential effects on the photoluminescence of PVP/MMI-AuNC and thus generate distinct fluorescent responses at 512 and 700 nm. On the basis of this phenomenon, a fluorescent sensor array based on the PVP/MMI-AuNC was then built by simply changing pH value in the sensor element. A total of seven HMIs had their unique response patterns and were successfully distinguished by hierarchical cluster analysis and linear discriminant analysis both in buffer solution and spiked water samples, achieving 100% identification accuracy. This study provides a simple and powerful fingerprinting sensing platform for multiple HMIs, showing broad application prospects in the field of environmental monitoring.

Biosensors for wastewater-based epidemiology for monitoring public health

Public health is attracting increasing attention due to the current global pandemic, and wastewater-based epidemiology (WBE) has emerged as a powerful tool for monitoring of public health by analysis of a variety of biomarkers (e.g., chemicals and pathogens) in wastewater. Rapid development of WBE requires rapid and on-site analytical tools for monitoring of sewage biomarkers to provide immediate decision and intervention. Biosensors have been demonstrated to be highly sensitive and selective tools for the analysis of sewage biomarkers due to their fast response, ease-to-use, low cost and the potential for field-testing. This paper presents biosensors as effective tools for wastewater analysis of potential biomarkers and monitoring of public health via WBE. In particular, we discuss the use of sewage sensors for rapid detection of a range of targets, including rapid monitoring of community-wide illicit drug consumption and pathogens for early warning of infectious diseases outbreaks. Finally, we provide a perspective on the future use of the biosensor technology for WBE to enable rapid on-site monitoring of sewage, which will provide nearly real-time data for public health assessment and effective intervention.

PARAFAC study of L-cys@CdTe QDs interaction to BSA, cytochrome c and trypsin: An approach through electrostatic and covalent bonds

Utilizing fluorescence spectroscopy, non-covalent and covalent interactions of L-cys@CdTe quantum dots to bovine serum albumin (BSA), cytochrome c and trypsin were investigated. L-cys@CdTe QDs with the emission maximum at 530 nm and an average diameter of 2.6 nm were synthesized in the aqueous medium. Formaldehyde, N-(3-dimethylaminopropyl)-N-ethylcarbodiimide hydrochloride (EDC) with N-hydroxysuccinimide (NHS), and glutaraldehyde was applied as cross-linkers. In the case of both electrostatic and covalent strategies PARAFAC, as a powerful multi-way chemometrics technique, was utilized to analyze fluorescence excitation-emission (EEM) spectra. For non-covalent and covalent bonding, two and three significant components composed the PARAFAC models. Resolved EEM shows that in the presence of formaldehyde, a new component with an emission peak similar to BSA was obtained. Using EDC-NHS cross-linker, the fluorescence peak of the newly formed component was in a distinct wavelength with similar emission intensity, compared to L-cys@CdTe QDs and BSA. Employing glutaraldehyde, a distinguished component was easily detected at emission wavelengths higher than that of L-cys@CdTe QDs and proteins. It was concluded that the choice of cross-linker is a critical step to create different emission spectra when dealing with nano-bio-conjugations. This study shows that glutaraldehyde cross-linker leads to increase sensitivity, selectivity, and accuracy of protein analysis.

Rapid detection of five pesticide residues using complexes of gold nanoparticle and porphyrin combined with ultraviolet visible spectrum

BACKGROUD

Pesticides are widely used to control insect infestation and weeds in agriculture. However, concerns about the pesticide residues in agricultural products have been raised in recent years because of public interest in health and food quality and safety. Thus, rapid, convenient, and accurate analytical methods for the detection and quantification of pesticides are urgently required.

RESULTS

A nanohybrid system composed of gold nanoparticles (AuNPs) and tetrakis(N-methyl-4-pyridiniumyl) porphyrin (TMPyP) was used as an optical probe for the detection and quantification of five pesticides (Paraquat, Dipterex, Dursban, methyl thiophanate and Cartap). The method is based on the aggregation effect of pesticides on the carboxyl group modified by AuNPs. Subsequently, with the help of particle swarm optimization-optimized sample weighted least squares-support vector machine (PSO-OSWLS-SVM), all the pesticides could be successfully quantified. In addition, partial least squares discriminant analysis (PLS-DA) was applied and the five pesticides were satisfactorily recognized based on data array obtained from the ultraviolet visible (UV-visible) spectra of AuNP-TMPyP complex. Furthermore, the quantitative and qualitative analysis of the five pesticides could be also achieved in the complex real samples, in which all the relative standard deviations (RSDs) were less than 0.3‰ and all the linear absolute correlation coefficients were more than 0.9990. Furthermore, recognition rate of the training set and the prediction set based on multiplicative scatter correction (MSC), or second-order derivative (2nd derivative) UV-visible spectra in PLS-DA model could reach 100%.

CONCLUSION

This method was successfully applied for the rapid and accurate determination of multicomponent pesticide residues in real food samples. © 2020 Society of Chemical Industry.

Machine learning assisted dual-channel carbon quantum dots-based fluorescence sensor array for detection of tetracyclines

The detection and differentiation of tetracyclines (TCs) has received increasing attention due to the severe threat they pose to human health and the ecological balance. A dual-channel fluorescence sensor array based on two carbon quantum dots (CDs) was fabricated to distinguish between four TCs, including tetracycline (TC), oxytetracycline (OTC), doxycycline (DOX), and metacycline (MTC). A distinct fluorescence variation pattern (I/I₀) was produced when CDs interacted with the four TCs. This pattern was analyzed by LDA and SVM. This was the first time that SVM was used for data processing of fluorescence sensor arrays. LDA and SVM showed that the array has the capacity for parallel and accurate determination of TCs at concentrations between 1.0 μM and 150 μM. In addition, the interference experiment using metal ions and antibiotics as possible coexisting interference substances proves that the sensor array has excellent selectivity and anti-interference ability. The array was also used for the accurate detection and identification of TCs in binary mixtures, and furthermore, the four TCs were successfully identified in river water and milk samples. Besides, the sensor array successfully identified the four TCs in 72 unknown samples with a 100% accuracy. The results proved that SVM can achieve the same accurate classification and prediction as LDA, and considering its additional advantages, it can be used as an optional supplementary method for data processing, thereby expanding the data processing field.

Dual-emission CdTe/AgInS2 photoluminescence probe coupled to neural network data processing for the simultaneous determination of folic acid and iron (II)

This work focused on the combination of CdTe and AgInS₂ quantum dots in a dual-emission nanoprobe for the simultaneous determination of folic acid and Fe(II) in pharmaceutical formulations. The surface chemistry of the used QDs was amended with suitable capping ligands to obtain appropriate reactivity in terms of selectivity and sensitivity towards the target analytes. The implementation of PL-based sensing schemes combining multiple QDs of different nature, excited at the same wavelength and emitting at different ones, allowed to obtain a specific analyte-response profile. The first-order fluorescence data obtained from the whole emission spectra of the CdTe/AgInS₂ combined nanoprobe upon interaction with folic acid and Fe(II) were processed by using chemometric tools, namely partial least-squares (PLS) and artificial neural network (ANN). This enabled to circumvent the selectivity issues commonly associated with the use of QDs prone to indiscriminate interaction with multiple species, which impair reliable and accurate quantification in complex matrices samples. ANN demonstrated to be the most efficient chemometric model for the simultaneous determination of both analytes in binary mixtures and pharmaceutical formulations due to the non-linear relationship between analyte concentration and fluorescence data that it could handle. The R²_P and SEP% obtained for both analytes quantification in pharmaceutical formulations through ANN modelling ranged from 0.92 to 0.99 and 5.7-9.1%, respectively. The obtained results revealed that the developed approach is able to quantify, with high reliability and accuracy, more than one analyte in complex mixtures and real samples with pharmaceutical interest.

Sensors Applied for the Detection of Pesticides and Heavy Metals in Freshwaters

Water is essential for every life living on the planet. However, we are facing a more serious situation such as water pollution since the industrial revolution. Fortunately, many efforts have been done to alleviate/restore water quality in freshwaters. Numerous sensors have been developed to monitor the dynamic change of water quality for ecological, early warning, and protection reasons. In the present review, we briefly introduced the pollution status of two major pollutants, i.e., pesticides and heavy metals, in freshwaters worldwide. Then, we collected data on the sensors applied to detect the two categories of pollutants in freshwaters. Special focuses were given on the sensitivity of sensors indicated by the limit of detection (LOD), sensor types, and applied waterbodies. Our results showed that most of the sensors can be applied for stream and river water. The average LOD was72.53±12.69 ng/ml (n=180) for all pesticides, which is significantly higher than that for heavy metals (65.36±47.51 ng/ml,n=117). However, the LODs of a considerable part of pesticides and heavy metal sensors were higher than the criterion maximum concentration for aquatic life or the maximum contaminant limit concentration for drinking water. For pesticide sensors, the average LODs did not differ among insecticides (63.83±17.42 ng/ml,n=87), herbicides (98.06±23.39 ng/ml,n=71), and fungicides (24.60±14.41 ng/ml,n=22). The LODs that differed among sensor types with biosensors had the highest sensitivity, while electrochemical optical and biooptical sensors showed the lowest sensitivity. The sensitivity of heavy metal sensors varied among heavy metals and sensor types. Most of the sensors were targeted on lead, cadmium, mercury, and copper using electrochemical methods. These results imply that future development of pesticides and heavy metal sensors should (1) enhance the sensitivity to meet the requirements for the protection of aquatic ecosystems and human health and (2) cover more diverse pesticides and heavy metals especially those toxic pollutants that are widely used and frequently been detected in freshwaters (e.g., glyphosate, fungicides, zinc, chromium, and arsenic).

Zirconia nanocomposites with carbon and iron(iii) oxide for voltammetric detection of sub-nanomolar levels of methyl parathion

This study reports the synthesis of zirconia nanoparticles loaded on various carbon substrates, namely, reduced graphene oxide (Zr-r-GO), carbon nanotubes (Zr-CNT), and activated carbon (Zr-AC). In addition, a composite of zirconia-iron mixed oxide loaded on activated carbon (FeZr-AC) was also synthesized. The materials were characterized using SEM-EDX, HRTEM, FTIR, Raman spectroscopy, TGA and XRD. The FeZr-AC sample was found to have a nanorod like morphology. The samples were evaluated for their sensing potential towards methyl parathion (MP) using differential pulse voltammetry in a range of 0.0 V to -0.9 V (vs. Ag/AgCl) by drop casting on a glassy carbon electrode (GCE). All the modified GCEs best operated at a working potential of 0.4-0.9 V vs. Ag/AgCl/Cl-. FeZr-AC was found have the best limit of detection followed by Zr-AC, Zr-CNT and Zr-r-GO with their detection limits being 1.7 × 10-9 M, 17.2 ×10-9 M, 243.3 × 10-9 M and 534.0 × 10-9 M respectively. These materials were then used to detect MP in spiked sewage samples and showed good recoveries.

Simultaneous Recognition of Species, Quality Grades, and Multivariate Calibration of Antioxidant Activities for 12 Famous Green Teas Using Mid- and Near-Infrared Spectroscopy Coupled with Chemometrics

In this paper, mid- and near-infrared spectroscopy fingerprints were combined to simultaneously discriminate 12 famous green teas and quantitatively characterize their antioxidant activities using chemometrics. A supervised pattern recognition method based on partial least square discriminant analysis (PLSDA) was adopted to classify the 12 famous green teas with different species and quality grades, and then optimized sample-weighted least-squares support vector machine (OSWLS-SVM) based on particle swarm optimization was employed to investigate the quantitative relationship between their antioxidant activities and the spectral fingerprints. As a result, 12 famous green teas can be discriminated with a recognition rate of 100% by MIR or NIR data. However, compared with individual instrumental data, data fusion was more adequate for modeling the antioxidant activities of samples with RMSEP of 0.0065. Finally, the performance of the proposed method was evaluated and validated by some statistical parameters and the elliptical joint confidence region (EJCR) test. The results indicate that fusion of mid- and near-infrared spectroscopy suggests a new avenue to discriminate the species and grades of green teas. Moreover, the proposed method also implies other promising applications with more accurate multivariate calibration of antioxidant activities.

"Turn-off" fluorescent sensor based on double quantum dots coupled with chemometrics for highly sensitive and specific recognition of 53 famous green teas

Fluorescent "turn-off" sensors based on double quantum dots (QDs) has attracted increasing attention in the detection of many materials due to their properties such as more useful information, higher fluorescence efficiency and stability compared with the fluorescent "turn-off" sensors based on single QDs. In this work, highly sensitive and specific method for recognition of 53 different famous green teas was developed based on the fluorescent "turn-off" model with water-soluble ZnCdSe-CdTe double QDs. The fluorescence of the two QDs can be quenched by different teas with varying degrees, which results in the differences in positions and intensities of two peaks. By the combination of classic partial least square discriminant analysis (PLSDA), all the green teas can be discriminated with high sensitivity, specificity and a satisfactory recognition rate of 100% for training set and 100% for prediction set, respectively. The fluorescent "turn-off" sensors based on the single QDs (either ZnCdSe QDs or CdTe QDs) coupled with PLSDA were also employed to recognize the 53 famous green teas with unsatisfactory results. Therefore, the fluorescent "turn-off" sensors based on the double QDs is more appropriate for the large-class-number classification (LCNC) of green teas. Herein, we have demonstrated, for the first time, that so many kinds of famous green teas can be discriminated by the "turn-off" model of double QDs combined with chemometrics, which has largely extended the capability of traditional fluorescence and chemometrics, as well as exhibits great potential to perform LCNC in other practical applications.

A comprehensive quality evaluation method by FT-NIR spectroscopy and chemometric: Fine classification and untargeted authentication against multiple frauds for Chinese Ganoderma lucidum

The origins and authenticity against frauds are two essential aspects of food quality. In this work, a comprehensive quality evaluation method by FT-NIR spectroscopy and chemometrics were suggested to address the geographical origins and authentication of Chinese Ganoderma lucidum (GL). Classification for 25 groups of GL samples (7 common species from 15 producing areas) was performed using near-infrared spectroscopy and interval-combination One-Versus-One least squares support vector machine (IC-OVO-LS-SVM). Untargeted analysis of 4 adulterants of cheaper mushrooms was performed by one-class partial least squares (OCPLS) modeling for each of the 7 GL species. After outlier diagnosis and comparing the influences of different preprocessing methods and spectral intervals on classification, IC-OVO-LS-SVM with standard normal variate (SNV) spectra obtained a total classification accuracy of 0.9317, an average sensitivity and specificity of 0.9306 and 0.9971, respectively. With SNV or second-order derivative (D2) spectra, OCPLS could detect at least 2% or more doping levels of adulterants for 5 of the 7 GL species and 5% or more doping levels for the other 2 GL species. This study demonstrates the feasibility of using new chemometrics and NIR spectroscopy for fine classification of GL geographical origins and species as well as for untargeted analysis of multiple adulterants.

"Turn-off" fluorescent sensor for highly sensitive and specific simultaneous recognition of 29 famous green teas based on quantum dots combined with chemometrics

Fluorescent "turn-off" sensors based on water-soluble quantum dots (QDs) have drawn increasing attention owing to their unique properties such as high fluorescence quantum yields, chemical stability and low toxicity. In this work, a novel method based on the fluorescence "turn-off" model with water-soluble CdTe QDs as the fluorescent probes for differentiation of 29 different famous green teas is established. The fluorescence of the QDs can be quenched in different degrees in light of positions and intensities of the fluorescent peaks for the green teas. Subsequently, with aid of classic partial least square discriminant analysis (PLSDA), all the green teas can be discriminated with high sensitivity, specificity and a satisfactory recognition rate of 100% for training set and 98.3% for prediction set, respectively. Especially, the "turn-off" fluorescence PLSDA model based on second-order derivatives (2nd der) with reduced least complexity (LVs = 3) was the most effective one for modeling. Most importantly, we further demonstrated the established "turn-off" fluorescent sensor mode has several significant advantages and appealing properties over the conventional fluorescent method for large-class-number classification (LCNC) of green teas. This work is, to the best of our knowledge, the first report on the rapid and effective identification of so many kinds of famous green teas based on the "turn-off" model of QDs combined with chemometrics, which also implies other potential applications on complex LCNC classification system with weak fluorescence or even without fluorescence to achieve higher detective response and specificity.