Gold-Nanoparticle-Based Colorimetric Sensor Array for Discrimination of Organophosphate Pesticides

Surface-Engineered Nanomaterials for Optical Array Based Sensing

Array based sensing governed by optical methods provides fast and economic way for detection of wide variety of analytes where the ideality of detection processes depends on the sensor element's versatile mode of interaction with multiple analytes in an unbiased manner. This can be achieved by either the receptor unit having multiple recognition moiety, or their surface property should possess tuning ability upon fabrication called surface engineering. Nanomaterials have a high surface to volume ratio, making them viable candidates for molecule recognition through surface adsorption phenomena, which makes it ideal to meet the above requirements. Most crucially, by engineering a nanomaterial's surface, one may produce cross-reactive responses for a variety of analytes while focusing solely on a single nanomaterial. Depending on the nature of receptor elements, in the last decade the array-based sensing has been considering as multimodal detection platform which operates through various pathway including single channel, multichannel, binding and indicator displacement assay, sequential ON-OFF sensing, enzyme amplified and nanozyme based sensing etc. In this review we will deliver the working principle for Array-based sensing by using various nanomaterials like nanoparticles, nanosheets, nanodots and self-assembled nanomaterials and their surface functionality for suitable molecular recognition.

Pesticides impacts on human health and the environment with their mechanisms of action and possible countermeasures

Pesticides are chemical constituents used to prevent or control pests, including insects, rodents, fungi, weeds, and other unwanted organisms. Despite their advantages in crop production and disease management, the use of pesticides poses significant hazards to the environment and public health. Pesticide elements have now perpetually entered our atmosphere and subsequently contaminated water, food, and soil, leading to health threats ranging from acute to chronic toxicities. Pesticides can cause acute toxicity if a high dose is inhaled, ingested, or comes into contact with the skin or eyes, while prolonged or recurrent exposure to pesticides leads to chronic toxicity. Pesticides produce different types of toxicity, for instance, neurotoxicity, mutagenicity, carcinogenicity, teratogenicity, and endocrine disruption. The toxicity of a pesticide formulation may depend on the specific active ingredient and the presence of synergistic or inert compounds that can enhance or modify its toxicity. Safety concerns are the need of the hour to control contemporary pesticide-induced health hazards. The effectiveness and implementation of the current legislature in providing ample protection for human health and the environment are key concerns. This review explored a comprehensive summary of pesticides regarding their updated impacts on human health and advanced safety concerns with legislation. Implementing regulations, proper training, and education can help mitigate the negative impacts of pesticide use and promote safer and more sustainable agricultural practices.

Machine-learning assisted multicolor platform for multiplex detection of antibiotics in environmental water samples

Antibiotic (AB) resistance is one of daunting challenges of our time, attributed to overuse of ABs and usage of AB-contaminated food resources. Due to their detrimental impact on human health, development of visual detection methods for multiplex sensing of ABs is a top priority. In present study, a colorimetric sensor array consisting of two types of gold nanoparticles (AuNPs) were designed for identification and determination of ABs. Design principle of the probe was based on aggregation of AuNPs in the presence of ABs at different buffer conditions. The utilization of machine learning algorithms in this design enables classification and quantification of ABs in various samples. The response profile of the array was analyzed using linear discriminant analysis algorithm for classification of ABs. This colorimetric sensor array is capable of accurate distinguishing between individual ABs and their combinations. Partial least squares regression was also applied for quantitation purposes. The obtained analytical figures of merit demonstrated the potential applicability of the developed sensor array in multiplex detection of ABs. The response profiles of the array were linearly correlated to the concentrations of ABs in a wide range of concentration with limit of detections of 0.05, 0.03, 0.04, 0.01, 0.06, 0.05 and 0.04 μg.mL-1 for azithromycin, amoxicillin, ciprofloxacin, clindamycin, cefixime, doxycycline and metronidazole respectively. The practical applicability of this method was further investigated by analysis of mixture samples of ABs and determination of ABs in river and underground water with successful verification.

Breaking boundaries: Artificial intelligence for pesticide detection and eco-friendly degradation

Pesticides are extensively used agrochemicals across the world to control pest populations. However, irrational application of pesticides leads to contamination of various components of the environment, like air, soil, water, and vegetation, all of which build up significant levels of pesticide residues. Further, these environmental contaminants fuel objectionable human toxicity and impose a greater risk to the ecosystem. Therefore, search of methodologies having potential to detect and degrade pesticides in different environmental media is currently receiving profound global attention. Beyond the conventional approaches, Artificial Intelligence (AI) coupled with machine learning and artificial neural networks are rapidly growing branches of science that enable quick data analysis and precise detection of pesticides in various environmental components. Interestingly, nanoparticle (NP)-mediated detection and degradation of pesticides could be linked to AI algorithms to achieve superior performance. NP-based sensors stand out for their operational simplicity as well as their high sensitivity and low detection limits when compared to conventional, time-consuming spectrophotometric assays. NPs coated with fluorophores or conjugated with antibody or enzyme-anchored sensors can be used through Surface-Enhanced Raman Spectrometry, fluorescence, or chemiluminescence methodologies for selective and more precise detection of pesticides. Moreover, NPs assist in the photocatalytic breakdown of various organic and inorganic pesticides. Here, AI models are ideal means to identify, classify, characterize, and even predict the data of pesticides obtained through NP sensors. The present study aims to discuss the environmental contamination and negative impacts of pesticides on the ecosystem. The article also elaborates the AI and NP-assisted approaches for detecting and degrading a wide range of pesticide residues in various environmental and agrecultural sources including fruits and vegetables. Finally, the prevailing limitations and future goals of AI-NP-assisted techniques have also been dissected.

Photoluminescence-Electrochemiluminescence Dual-Mode Sensor Arrays for Histidine and Its Metabolite Discrimination and Disease Identification

Histidine (His) and its metabolite analysis is significant due to their vital roles in the diagnosis of diseases. In practical applications, simple and effective detection and discrimination of these metabolic species are still a great challenge due to their highly similar structures. Herein, photoluminescence (PL)-electrochemiluminescence (ECL) dual-mode sensor arrays consisting of a series of sensing elements were proposed for simultaneous quantitation and accurate discrimination of His and its four key metabolites (including histamine, imidazole-4-acetic acid, N-acetylhistamine, and imidazole propionate). The sensing elements of these sensor arrays were constructed by employing two solvent iridium(III) complexes ([Ir(pbz)2(DMSO)Cl] and [Ir(ppy)2(DMSO)Cl], pbz = 3-(2-pyridyl)benzoic acid, ppy = 2-phenylpyridine) with excellent PL and ECL performances as cross-responsive sensing units. Based on diverse coordination abilities of the two complexes with the imidazole group of the five targets, PL and ECL responses of each sensing unit can be enhanced to various degrees, which generate unique fingerprint patterns for the corresponding targets. Through principal component analysis, the multifarious patterns (two-, three-, and four-element sensor arrays) can be transformed into simple visualization modes, from which His and its four key metabolites can be effectively discriminated against each other. Moreover, the quantitation of an individual metabolic species at different concentrations and the recognition of the mixtures with different ratios were also accurately achieved. Notably, His and its four key metabolites in urine can also be successfully discriminated by the as-fabricated sensor arrays, and the patients with kidney diseases can be identified clearly, providing a promising way for disease diagnosis.

A deep learning-enabled smartphone platform for rapid and sensitive colorimetric detection of dimethoate pesticide

A novel deep learning-enabled smartphone platform is developed to assist a colorimetric aptamer biosensor for fast and highly sensitive detection of dimethoate. The colorimetric determination of dimethoate is based on the specific binding of dimethoate and aptamer, which leads to the aggregation of AuNPs in high-concentration NaCl solution, resulting in an obvious color change from red to blue. This color change provides sufficient data for self-learning enabled by a convolutional neural network (CNN) model, which is established to predict dimethoate concentration based on images acquired from a smartphone. To enhance user-friendliness for non-experts, the CNN model is then embedded into a smartphone app, enabling offline detection of dimethoate pesticide in real environments within just 15 min using a pre-configured colorimetric probe. The developed platform exhibits superior performance, achieving a regression coefficient of 0.9992 in the concentration range of 0-10 μM. Moreover, the app's performance is found to be consistent with the ELISA kit. These remarkable findings demonstrate the potential of combining colorimetric biosensors with smartphone-based deep learning methods for the development of portable and affordable tools for pesticide detection.

Highly-oxidizing Au@MnO2-X nanozymes mediated homogeneous electrochemical detection of organophosphorus independent of dissolved oxygen

Traditional oxidase-like (OXD) nanozymes rely primarily on O2-mediated superoxide anion (O2·-) process for catalytic oxidation and organophosphorus (Ops) detection. While during the actual detection process, the concentration of O2 is inconstant that can be easily changed with the external environment, distorting detection results. Herein, highly-oxidizing Au@MnO2-X nanozymes with core-shell nanostructure are designed which trigger substantial electron transfer from inner Au core to outer ultrathin MnO2-X layer. According to experimental and theoretical calculations, the core-shell nanostructure and ultrathin MnO2-X of Au@MnO2-X result in the large surface defects, high oxygen vacancies and MnIII ratios. The specially structured Au@MnO2-X nanozymes are therefore highly-oxidizing and the catalytic oxidation can be completed merely through electrons transferring instead of the O2-mediated O2·- process. Based on this, an oxygen independent and ultrasensitive nanozyme-based sensor is established using homogeneous electrochemistry (HEC), its Ops is detected at a LOD of 0.039 ng mL-1. Combined with the UV-vis spectrum of 3,3',5,5'-tetramethylbenzidine (TMB), the linear discriminant analysis of five Ops i.e., Ethion, Omethoate, Diazinon, Chlorpyrifos methyl and Dipterex has achieved superior discrimination results. Therefore, HEC based on strong oxidizing nanozymes provide a new avenue for the development of high-performance electrochemical sensors and demonstrate potential applicability to pesticide residue determination in real samples.

A novel strategy for identification of pesticides in different categories by concentration-independent model based on a nanozyme with multienzyme-like activities

Conventional rapid detection methods are difficult to identify or distinguish various pesticide residues at the same time. And sensor arrays are also limited by the complexity of preparing multiple receptors and high cost. To address this challenge, a single material with multiple properties is considered. Herein, we first found that different categories of pesticides have diverse regulatory behaviors on the multiple catalytic activities of Asp-Cu nanozyme. Thus, a three-channel sensor array based on the laccase-like, peroxidase-like, and superoxide dismutase-like activities of Asp-Cu nanozyme was constructed and successfully used for the discrimination of eight kinds of pesticides (glyphosate, phosmet, isocarbophos, carbaryl, pentachloronitrobenzene, metsulfuron-methyl, etoxazole, and 2-methyl-4-chlorophenoxyacetic acid). In addition, a concentration-independent model for qualitative identification of pesticides has been established, and 100% correctness was achieved in the recognition of unknown samples. Then, the sensor array also exhibited excellent interference immunity and was reliable for real sample analysis. It provided a reference for pesticide efficient detection and food quality supervision.

Multiplex Detection of Biogenic Amines for Meat Freshness Monitoring Using Nanoplasmonic Colorimetric Sensor Array

Biogenic amines (BAs) were presented as significant markers for the evaluation of the spoilage of meat and meat products. In this work, a colorimetric sensor array was developed for the discrimination and detection of spermine (SP), spermidine (SD), histamine (HS), and tryptamine (TP) as important BAs in food assessment. For this aim, two important spherical plasmonic nanoparticles, namely gold nanoparticles (AuNPs) and silver nanoparticles (AgNPs), were utilized as the sensing elements of the probes. The cross-reactive interaction of the target biogenic amines and the plasmonic nanoparticles caused the aggregation-induced UV-Vis spectra changes, which were accompanied by visual color variation in the solution. The collected responses were analyzed by principal component analysis-linear discrimination analysis (PCA-LDA) to classify the four BAs. This colorimetric sensor array can also discriminate between the individual BAs and their mixture accurately. Partial least squares regression (PLS-R) was also utilized for quantitative analysis of the BAs. The wide linear concentration ranges of 0.1-10.0 µM for the four BAs and desirable figures of merits (FOMs) showed the potential of the developed sensor for quantitative detection of the BAs. Finally, the practical ability of the developed probe was studied by the determination of the BAs in the meat samples, which successfully proved the potential of the colorimetric sensor array in a food sample.

Enzyme-targeted near-infrared fluorescent probe for organophosphorus pesticide residue detection

Pesticide residues significantly affect food safety and harm human health. In this work, a series of near-infrared fluorescent probes were designed and developed by acylating the hydroxyl group of the hemicyanine skeleton with a quenching moiety for monitoring the presence of organophosphorus pesticides in food and live cells. The carboxylic ester bond on the probe was hydrolyzed catalytically in the presence of carboxylesterase and thereby the fluorophore was released with near-infrared emission. Notably, the proposed probe 1 exhibited excellent sensitivity against organophosphorus based on the carboxylesterase inhibition mechanism and the detection limit for isocarbophos achieved 0.1734 μg/L in the fresh vegetable sample. More importantly, probe 1 allowed for situ visualization of organophosphorus in live cells and bacteria, meaning great potential for tracking the organophosphorus in biological systems. Consequently, this study presents a promising strategy for tracking pesticide residues in food and biological systems.

Aptamer Sensors for the Detection of Antibiotic Residues- A Mini-Review

Food security is a global issue, since it is closely related to human health. Antibiotics play a significant role in animal husbandry owing to their desirable broad-spectrum antibacterial activity. However, irrational use of antibiotics has caused serious environmental pollution and food safety problems; thus, the on-site detection of antibiotics is in high demand in environmental analysis and food safety assessment. Aptamer-based sensors are simple to use, accurate, inexpensive, selective, and are suitable for detecting antibiotics for environmental and food safety analysis. This review summarizes the recent advances in aptamer-based electrochemical, fluorescent, and colorimetric sensors for antibiotics detection. The review focuses on the detection principles of different aptamer sensors and recent achievements in developing electrochemical, fluorescent, and colorimetric aptamer sensors. The advantages and disadvantages of different sensors, current challenges, and future trends of aptamer-based sensors are also discussed.

An enzyme-free, ultrasensitive strategy for simultaneous screening of the p-nitrophenyl substituent organophosphorus pesticides

An enzyme-free, sensitive, and convenient approach was reported for the P-nitrophenyl substituent organophosphorus pesticides (NSOPs) of paraoxon-methyl (PM), paraoxon-ethyl (PE), parathion-methyl (PTM) and parathion-ethyl (PTE)) by indirectly quantification of the 4-nitrophenol (4-NP, hydrolysis product of the NSOPs). NaOH instead of hydrolase/nanozyme was applied, and temperature, pH, ultrasound was investigated to improve the NSOPs hydrolysis. Under the optimized conditions, the hydrolysis efficiencies were up to 99.9 %, 99.9 %, 99.6 %, 96.0 % for PM (10 min), PE (30 min), PTM (90 min) and PTE (120 min), based on which a low detection limits of 0.06 (PM), 0.07 (PE), 0.06 (PTM) and 0.07 (PTE) ppb were calculated with the 4-NP detection limit (0.03 ppb). Furthermore, the method exhibited good performance for the NSOPs with recoveries from 88.87 % to 100.33 % in real samples. This indirect approach offered an ultrasensitive alternative for the NSOPs detection, which holds great potential in practical application for the assessment of food safety and environmental risks.

Deep eutectic solvent-based adhesive tape extraction combined with enzyme inhibition assay for the determination and distinction of dithiocarbamate pesticides in food samples

A simple, green extraction method of dithiocarbamate (DTC) pesticides in food samples was developed using adhesive tapes and a green deep eutectic solvent (DES). A rapid and convenient determination and distinction method of DTC pesticides was established using tyrosinase inhibition assay. First, DTC pesticides were extracted by pasting and peeling off the adhesive tape, then eluted by the DES synthesized from xylitol and ethylene glycol. Second, determination of DTC pesticides was conducted by inhibiting the activity of tyrosinase which can catalyze the oxidation of catechol. Less colored products were generated in the reaction system (tyrosinase, catechol, and 4-aminoantipyrine), leading to weak absorbance. In addition, different DTC pesticides (ziram, propineb, zineb, mancozeb, thiram, metiram, and ferbam) were successfully distinguished by sensor arrays (tyrosinase, phenolic compounds, and 4-aminoantipyrine) through principal component analysis. The limit of detection was found to be 0.2 μg kg^-1, and the limit of quantification was 0.6 μg kg^-1. The recoveries ranging from 89.4% to 103.8% were obtained in vegetable, fruit, and cereal, with a relative standard deviation of less than 4.2%. The method is simple, rapid, and convenient and shows good application prospects in the determination of pesticides in a variety of food samples.

Colorimetric Sensing of Gram-Negative and Gram-Positive Bacteria Using 4-Mercaptophenylboronic Acid-Functionalized Gold Nanoparticles in the Presence of Polyethylene Glycol

Gold nanoparticles (GNPs) have been used as detection probes for rapid and sensitive detection of various analytes, including bacteria. Here, we demonstrate a simple strategy for bacterial detection using GNPs functionalized with 4-mercaptophenylboronic acid (4-MPBA). 4-MPBA can interact with peptidoglycan or lipopolysaccharides present in bacterial organelles. After the addition of a high concentration of sodium hydroxide (NaOH), the functionalization of the surface of 50 nm GNPs with 4-MPBA (4-MPBA@GNPs) in the presence of polyethylene glycol results in a color change because of the aggregation of 4-MPBA@GNPs. This color change is dependent on the amount of bacteria present in the tested samples. Escherichia coli (E. coli) K-12 and Staphylococcus aureus (S. aureus) are used as Gram-negative and Gram-positive bacterial models, respectively. The color change can be detected within an hour by the naked eye. A linear relationship is observed between bacterial concentrations and the absorbance intensity at 533 nm; R ² values of 0.9152 and 0.8185 are obtained for E. coli K-12 and S. aureus, respectively. The limit of detection of E. coli K-12 is ∼2.38 × 10² CFU mL^-1 and that of S. aureus is ∼4.77 × 10³ CFU mL^-1. This study provides a promising approach for the rapid detection of target Gram-negative and Gram-positive bacteria.

Chrono-colorimetric sensor array for detection and discrimination of halide ions using an all-in-one plasmonic sensor element

Most nanoparticle based colorimetric sensor array utilize several sensor elements and static response for discrimination of target analytes. This approach can be complicated and costly to synthesize or functionalize different nanoparticles for providing wide color variation. Herein, triangular silver nanoparticles (TSNPs) were used to develop a colorimetric sensor array by time-dimension responses. The principle of this sensor array is based on the diverse etching process of TSNPs in the presence of three halide ions, including bromide (Br^-), iodide (I^-) and chloride (Cl^-). Various etchings of TSNPs induced color changes at different reaction time intervals, which produced a colorimetric pattern for each ion. Therefore, using time dependent etching responses of TSNPs as a single sensing component can produce a wide color variation which can be distinguished by naked eyes. The colorimetric responses of TSNPs upon the addition of different concentrations of halide ions have been analyzed by PLS regression (PLS-R) and PLS discriminant analysis (PLS-DA). The analytical figures of merit confirmed that the developed chrono-colorimetric TSNPs -based sensor array is successful in both the discrimination and quantitative detection of halide ions. At the final step, the three halide ions were accurately determined in a real water sample, which verified the potential of the developed sensor in a real sample.

Recent Progress in Sensor Arrays: From Construction Principles of Sensing Elements to Applications

The traditional sensors are designed based on the "lock-and-key" strategy with high selectivity and specificity for detecting specific analytes, which however are not suitable for detecting multiple analytes simultaneously. With the help of pattern recognition technologies, the sensor arrays excel in distinguishing subtle changes caused by multitarget analytes with similar structures in a complex system. To construct a sensor array, the multiple sensing elements are undoubtedly indispensable units that will selectively interact with targets to generate the unique "fingerprints" based on the distinct responses, enabling the identification among various analytes through pattern recognition methods. This comprehensive review mainly focuses on the construction strategies and principles of sensing elements, as well as the applications of sensor array for identification and detection of target analytes in a wide range of fields. Furthermore, the present challenges and further perspectives of sensor arrays are discussed in detail.

Direct and Specific Detection of Glyphosate Using a Phosphatase-like Nanozyme-Mediated Chemiluminescence Strategy

Most organophosphorus pesticide (OP) sensors reported in the literature rely on the inhibition effect of OPs on the activity of acetylcholinesterase (AChE), which suffer from the drawbacks of lack of selective recognition of OPs, high cost, and poor stability. Herein, we proposed a novel chemiluminescence (CL) strategy for the direct detection of glyphosate (an organophosphorus herbicide) with high sensitivity and specificity, which is based on the porous hydroxy zirconium oxide nanozyme (ZrO_X-OH) obtained via a facile alkali solution treatment of UIO-66. ZrO_X-OH displayed excellent phosphatase-like activity, which could catalyze the dephosphorylation of 3-(2'-spiroadamantyl)-4-methoxy-4-(3'-phosphoryloxyphenyl)-1,2-dioxetane (AMPPD) to generate strong CL. The experimental results showed that the phosphatase-like activity of ZrO_X-OH is closely related to the content of hydroxyl groups on their surface. Interestingly, ZrO_X-OH with phosphatase-like properties exhibited a unique response to glyphosate because of the consumption of the surface hydroxyl group by the unique carboxyl group of glyphosates and was thus employed to develop a CL sensor for direct and selective detection of glyphosate without using bio-enzymes. The recovery for glyphosate detection of cabbage juice ranged from 96.8 to 103.0%. We believe that the as-proposed CL sensor based on ZrO_X-OH with phosphatase-like properties supplies a simpler and more highly selective approach for OP assay and provides a new method for the development of CL sensors for the direct analysis of OPs in real samples.

Machine-learning assisted multiplex detection of catecholamine neurotransmitters with a colorimetric sensor array

Catecholamine neurotransmitters (CNs), such as dopamine (DA), epinephrine (EP), norepinephrine (NEP), and levodopa (LD), are recognized as the primary biomarkers of a variety of neurological illnesses. Therefore, simultaneous monitoring of these biomarkers is highly recommended for clinical diagnosis and treatment. In this study, a high-performance colorimetric artificial tongue has been proposed for the multiplex detection of CNs. Different aggregation behaviors of gold nanoparticles in the presence of CNs under various buffering conditions generate unique fingerprint response patterns. Under various buffering conditions, the distinct acidity constants of CNs, and consequently their predominant species at a given pH, drive the aggregation of gold nanoparticles (AuNPs). The utilization of machine learning algorithms in this design enables classification and quantification of CNs in various samples. The response profile of the array was analyzed using the linear discriminant analysis algorithm for classification of CNs. This colorimetric sensor array is capable of accurately distinguishing between individual neurotransmitters and their combinations. Partial least squares regression was also applied for quantitation purposes. The obtained analytical figures of merit (FOMs) and linear ranges of 0.6-9 μM (R² = 0.99) for DA, 0.1-10 μM (R² = 0.99) for EP, 0.1-9 μM (R² = 0.99) for NEP and 1-70 μM (R² = 0.99) for LD demonstrated the potential applicability of the developed sensor array in precise and accurate determination of CNs. Finally, the feasibility of the array was validated in human urine samples as a complex biological fluid with LODs of 0.3, 0.5, 0.2, and 1.9 μM for DA, EP, NEP, and LD, respectively.

A high-performance Calix@ZnO based bifunctional nanomaterial for selective detection and degradation of toxic azinphos methyl in environmental samples

One of the key tenets of sustainable agriculture and food safety is the removal of toxic pesticides from the environment. However, developing reliable, affordable, and efficient methods for detecting and degrading pesticides into non-toxic degradable products remains an immediate matter of concern. Herein, we attempt to develop a strategy for the detection as well as degradation of highly toxic phosphorodithioate pesticide, Azinphos methyl (AZM), using hybrid zinc oxide nanoparticles (ZnO NPs). Considering the non-selectivity of bare ZnO and receptor R1, we have fabricated the heterocalixarene-based Calix (R1) over zinc oxide (ZnO) surface in situ via the sol-gel process. The synthesized heterocaliaxrene-modified ZnO (R1@ZnO) NPs show an excellent affinity for the selective and sensitive detection of AZM with a tremendously low limit of detection (68 mg L^-1) and no interference from other pesticides. Degradation of AZM was fully supported by fluorescence spectroscopy, scanning electron microscopy (SEM), ¹H NMR titrations, FTIR spectroscopy, cyclic voltammetry, and mass spectroscopy, which unequivocally confirmed the formation of non-toxic products. According to our findings, R1@ZnO NPs are sustainable nanomaterials that can be employed for environmental remediation since they operate in an aqueous medium.

Advanced visual sensing techniques for on-site detection of pesticide residue in water environments

Pesticide usage has increased to fulfil agricultural demand. Pesticides such as organophosphorus pesticides (OPPs) are ubiquitous in world food production. Their widespread usage has unavoidable detrimental consequences for humans, wildlife, water, and soil environments. Hence, the development of more convenient and efficient pesticide residue (PR) detection methods is of paramount importance. Visual detecting approaches have acquired a lot of interest among different sensing systems due to inherent advantages in terms of simplicity, speed, sensitivity, and eco-friendliness. Furthermore, various detections have been proven to enable real-life PR surveillance in environment water. Fluorometric (FL), colourimetric (CL), and enzyme-inhibition (EI) techniques have emerged as viable options. These sensing technologies do not need complex operating processes or specialist equipment, and the simple colour change allows for visual monitoring of the sensing result. Visual sensing techniques for on-site detection of PR in water environments are discussed in this paper. This paper further reviews prior research on the integration of CL, FL, and EI-based techniques with nanoparticles (NPs), quantum dots (QDs), and metal-organic frameworks (MOFs). Smartphone detection technologies for PRs are also reviewed. Finally, conventional methods and nanoparticle (NPs) based strategies for the detection of PRs are compared.

Machine Learning-Based Analytical Systems: Food Forensics

Despite a large amount of money being spent on both food analyses and control measures, various food-borne illnesses associated with pathogens, toxins, pesticides, adulterants, colorants, and other contaminants pose a serious threat to human health, and thus food safety draws considerable attention in the modern pace of the world. The presence of various biogenic amines in processed food have been frequently considered as the primary quality parameter in order to check food freshness and spoilage of protein-rich food. Various conventional detection methods for detecting hazardous analytes including microscopy, nucleic acid, and immunoassay-based techniques have been employed; however, recently, array-based sensing strategies are becoming popular for the development of a highly accurate and precise analytical method. Array-based sensing is majorly facilitated by the advancements in multivariate analytical techniques as well as machine learning-based approaches. These techniques allow one to solve the typical problem associated with the interpretation of the complex response patterns generated in array-based strategies. Consequently, the machine learning-based neural networks enable the fast, robust, and accurate detection of analytes using sensor arrays. Thus, for commercial applications, most of the focus has shifted toward the development of analytical methods based on electrical and chemical sensor arrays. Therefore, herein, we briefly highlight and review the recently reported array-based sensor systems supported by machine learning and multivariate analytics to monitor food safety and quality in the field of food forensics.

Colorimetric sensor arrays for the differentiation of baijiu based on amino-acid-modified gold nanoparticles

It is of great significance for quality control to realize the discrimination for baijiu from different brands and origins. Strong-aroma-type baijiu (SAB), one of the most important Chinese aroma-type baijiu, exhibits the largest variety and market share. In this study, we proposed colorimetric sensor arrays based on gold nanoparticles (AuNPs) modified with different amino acids (AAs) to recognize the organic acids, and further distinguish different SABs. Three representative AAs, namely methionine (Met), tryptophan (Trp), and histidine (His), were selected to modify the AuNPs surface. The investigation of the effect of the main ingredients of SAB on AA@AuNPs aggregation confirmed that this aggregation mainly resulted from organic acids. Moreover, this aggregation was successfully used for differentiating 11 organic acids. Different pH conditions can not only cause changes of the content of organic acids in baijiu, but also disrupt the balance among flavor substances of baijiu to some extent. Consequently, the AA@AuNPs arrays under two pH conditions have been successfully applied to distinguish 14 kinds of SABs from different brands and origins. The proposed colorimetric sensor method is simple, rapid, and visualized and provides a potential application prospect for the quality control of baijiu and other alcoholic beverages.

Mixed-Ligand gold nanoparticles based optical sensor array for the recognition and quantification of seven toxic metals

Sensor arrays use pattern recognition for the identification and quantification of analytes. In the presented work, a gold nanoparticle (GNP) based optical sensor array was employed to classify and quantify seven toxic metals (arsenic, barium, cadmium, cerium, chromium, lead, and mercury). The sensor array receptors were GNPs functionalized by mercaptoundecanoic acid, 2-mercaptoethanesulfonate, and a 1:1 mixture of the two ligands. The mixed-ligand particle responds to the same analytes as the mono-ligand particles but in a distinctive way. This behavior demonstrates the high potential of mixed-ligand particles in the fabrication of sensor array receptors. The responses of the GNPs to different concentrations of the seven metal ions were analyzed, and a unique "classification trajectory" was produced for every metal. Samples of different metal concentrations were then measured and identified using the "classification trajectories". Once sample composition has been identified, a PLSR model, produced from the concatenated sensor array spectra of four calibration samples for each nanoparticle, was used to determine the metal concentration.

Thermal-controlled active sensor module using enzyme-regulated UiO-66-NH2/MnO2 fluorescence probe for total organophosphorus pesticide determination

An enzyme-regulated UiO-66-NH₂/MnO₂ fluorescence sensor, fully functionalized with spectrometric capacities, is developed for budget-friendly total organophosphorus pesticides (OPs) determination. The fluorescence probe, UiO-66-NH₂/MnO₂, is hydrothermally synthesized and morphologically examined. A specialized enzyme-catalyzed reaction, which can be gradually inhibited by OPs, is designed with participations of alkaline phosphatase (ALP) and sodium L-ascorbyl-2-phosphate (AAP). The reaction product of ascorbic acid (AA) decomposes MnO₂ and restores UiO-66-NH₂ fluorescence, establishing a relationship between OPs level and fluorescence intensity. Interactions among UiO-66-NH₂, MnO₂, OPs, and AA are clarified. Stepwise optimizations are performed to the UiO-66-NH₂/MnO₂ probe, ensuring considerable advantages as OPs affinity and fluorescence quenching behavior over rival nanomaterials. Analytical advances are magnified by fabricating an active sensor module, with self-acting thermal regulation for optimal enzyme activity. Under 4 and 20 °C environment, regulation period is less than 40 and 100 s. In total OPs determination for laboratorial and real-vegetable samples, this method exhibits uniform and log-linear responses to common species of OPs in a range as 1.0 × 10^-7~10 mg L^-1, and limit of detection is established as 8.9 × 10^-8 mg L^-1. Proposed readouts are validated with certified HPLC and recovery test. Relative errors and recovery rates are found as 2.7-6.4% and 95.8-102.6%, respectively.

A Gold Nanoparticle-Based Molecular Self-Assembled Colorimetric Chemosensor Array for Monitoring Multiple Organic Oxyanions

Determination of oxyanions is of paramount importance because of the essential role they play in metabolic processes involved in various aquatic environmental problems. In this investigation, a novel chemical sensor array has been developed by using gold nanoparticles modified with different chain lengths of aminothiols (AET-AuNPs) as sensing elements. The proposed sensor array provides a fingerprint-like response pattern originating from cross-reactive binding events and capable of targeting various anions, including the herbicide glyphosate. In addition, chemometric techniques, linear discrimination analysis (LDA) and the support vector machine (SVM) algorithm were employed for analyte classification and regression/prediction. The obtained sensor array demonstrates a remarkable ability to determine multiple oxyanions in both qualitative and quantitative analysis. The described methodology could be used as a simple, sensitive and fast routine analysis for oxyanions in both laboratory and field settings.

A nanozyme-based colorimetric sensor array as electronic tongue for thiols discrimination and disease identification

Thiol analysis is of vital significance due to the essential roles in disease diagnosis, while the highly similar structures of thiols are a major challenge in practical determination. Herein, a nanozyme-based colorimetric sensor array has been proposed as electronic tongue for excellent discrimination and sensitive quantitation of thiols. The sensing units are fabricated by integrating the terephthalic acid modified graphene quantum dots (TPA@GQDs) with three transition metal ions (Fe²⁺, Cu²⁺ and Zn²⁺) via coordination, respectively, which not only provide sufficient substrate binding sites but also form the metal ion-regulated catalytic active centers. In this way, disparate promotion degrees on the peroxidase-like catalytic activity have been achieved in different metal ion-TPA@GQD ensembles. Based on the strong binding affinity between metal ions and thiols, the catalytic active centers are removed from TPA@GQDs, which inhibits the catalytic activity of sensing unit to diverse degrees. Accordingly, using 3, 3', 5, 5'-tetramethylbenzidine (TMB) as chromogenic substrate in the presence of hydrogen peroxide (H₂O₂), each sensing unit can generate differential colorimetric signals (fingerprints) for six thiol analytes, which can be accurately discriminated through linear discriminant analysis (LDA) with a detection limit of 50 nM. In addition, the discrimination of the same thiol with different concentrations and thiol mixtures have also been achieved. Furthermore, inspired by the distinct levels of thiols in practical samples, the proposed sensor array enables the identification of thiol-associated diseases by means of machine learning algorithm, which makes a positive contribution to medical diagnosis.

A Novel Quantum Dot-Based pH Probe for Long-Term Fluorescence Lifetime Imaging Microscopy Experiments in Living Cells

The use of two nanoparticles for quantitative pH measurements in live cells by means of fluorescence lifetime imaging microscopy (FLIM) is investigated here. These nanoparticles are based on CdSe/ZnS quantum dots (QDs), functionalized with N-acetylcysteine (CdSe/ZnS-A) and with a small peptide containing D-penicillamine and histidine (CdSe/ZnS-PH). CdSe/ZnS-A has tendency to aggregate and nonlinear pH sensitivity in a complex medium containing salts and macromolecules. On the contrary, CdSe/ZnS-PH shows chemical stability, low toxicity, efficient uptake in C3H10T1/2 cells, and good performance as an FLIM probe. CdSe/ZnS-PH also has key advantages over a recently reported probe based on a CdSe/ZnS QD functionalized with D-penicillamine (longer lifetimes and higher pH-sensitivity). A pH(±2σ) of 6.97 ± 0.14 was determined for C3H10T1/2 cells by FLIM employing this nanoprobe. In addition, the fluorescence lifetime signal remains nearly constant for C3H10T1/2 cells treated with CdSe/ZnS-PH for 24 h. These results show the promising applications of this nanoprobe to monitor the intracellular pH and cell state employing the FLIM technique.

Localized Surface Plasmon Resonance-Based Colorimetric Assay Featuring Thiol-Capped Au Nanoparticles Combined with a Mobile Application for On-Site Parathion Organophosphate Pesticide Detection

In this study, we employed a dual strategy for parathion organophosphate pesticide (parathion) detection; first, we used a localized surface plasmon resonance (LSPR)-based colorimetric sensor featuring thiol-capped Au NPs, namely cysteine (Cys)@Au NPs, 11-mercaptoundecanoic acid (11-MUA)@Au NPs, and glutathione (GSH)@Au NPs, via acetylcholinesterase (ACHE) and acetylthiocholine (ATCH) enzyme-mediated hydrolysis reactions; second, we developed a color analysis toxicity-sensing app (Toxin APP). Positively charged thiocholine (TCH) molecules, which were continuously generated via hydrolysis, subsequently conjugated with thiol-capped Au NPs, causing Au NP aggregation through electrostatic attractions. The degree of aggregation of the thiol-capped Au NPs was influenced by parathion concentrations in the range 0 to 10⁸ ppt, because parathion acted as an ACHE inhibitor by controlling the amount of TCH generated. Based on the values of LSPR absorbance ratio, the limits of detection (LODs) of three types thiol-capped Au NPs were determined to be 100 ppt using ultraviolet-visible spectroscopy measurements. However, the aggregation efficiency of GSH@Au NPs was lower than that of the others regarding gradual changes in their color and LSPR absorbance band. Furthermore, we designed Toxin APP for color analysis which consists of three modules: processing, database collection, and communication. Toxin APP could on-site and precisely detect the color changes of GSH@Au NPs at parathion concentrations in the ranges of 100 ppt to 1, 10, and 100 ppm and could distinguish between OP and non-OP pesticides (e.g., fipronil) in tap water samples with high sensitivity and selectivity. Moreover, the concentration of residual parathion in real samples (tomato and strawberry) was quantified based on the color changes of GSH@Au NPs detected using Toxin APP. Therefore, the combination of an LSPR-based colorimetric assay and Toxin APP can be a reliable method for the facile and rapid detection of parathion in food and water samples.

Freshness monitoring of raw fish by detecting biogenic amines using a gold nanoparticle-based colorimetric sensor array

We herein report the quantitative detection of biogenic amines using a gold nanoparticle-based colorimetric chemosensor array for food analysis. The gold nanoparticles are functionalized with carboxylate derivatives, which capture target amines through hydrogen bonds and electrostatic interactions. The simultaneous discrimination of 10 amine derivatives was achieved by a linear discriminant analysis with a 100% correct classification based on the multi-colorimetric response pattern of structural differences. Furthermore, a real sample analysis for raw fish (i.e., tuna) demonstrated highly accurate determination of histamine concentrations by a support vector machine, the result of which was matched with high-performance liquid chromatography. Most importantly, the chemosensor array succeeded in detecting the time-dependent concentration change of histamine in the raw fish, meaning that the decomposition of the fish could be monitored by the colorimetric changes. Hence, the proposed chemosensor array combined with pattern recognition techniques can be a user-friendly analytical method for food freshness monitoring.

A gold nanoparticle-based chemosensor array functionalized with carboxylate derivatives performed freshness monitoring of amines in a fish sample.

Hybridizing aggregated gold nanoparticles with a hydrogel to prepare a flexible SERS chip for detecting organophosphorus pesticides

A simple method has been developed to hybridize aggregated gold nanoparticles with a hydrogel for novel hydrogel SERS chips with high sensitivity, good repeatability, long-term stability, and strong anti-interference ability.

Lab-on-a-ZnO-Submicron-Particle Sensor Array for Monitoring AD upon Cd2+ Exposure with CSF Tau441% as an Effective Hallmark

In this study, based on the posttreatment strategy, blue-color-emissive ZnO submicron particles (B-ZnO SMPs) and red-color-emissive ZnO submicron particles (R-ZnO SMPs) were obtained from rationally designed Zn-infinite coordination polymer (ICP) precursors. After modification of thiol-containing aptamers, diverse spectral changes in the ultraviolet and visible regions of B- and R-ZnO SMPs toward different tau species were explored to construct a lab-on-a-ZnO-submicron-particle sensor array. Assisted by principal component analysis (PCA), the unique fingerprints of the sensor array enabled the simultaneous differentiation and quantitative detection of different tau species (tau381, tau410, and tau441) for the first time. Furthermore, the dynamic changes of tau441% (the ratio of the two most reported representative 4R isoform (full-length tau441) and 3R isoform (tau381)) in cerebrospinal fluid (CSF) during the Alzheimer's disease (AD) onset of Cd²⁺-exposed rats could also be monitored by the lab-on-a-ZnO-submicron-particle sensor array, which was supposed to be an effective hallmark and highly correlated with the formation of neurofibrillary tangles (NFTs). This study not only provides a further insight into the involvement of subchronic Cd²⁺ exposure in the tau etiology of AD but also offers more comprehensive and effective information about the asymptomatic stage of AD upon environmental risk, which has potential applications in the early diagnosis and therapy.

Recent advancements in the detection of organophosphate pesticides: a review

Organophosphorus pesticides (OPPs) are generally utilized for the protection of crops from pests. Because the use of OPPs in various agricultural operations has expanded dramatically, precise monitoring of their concentration levels has become the critical issue, which will help in the protection of ecological systems and food supply. However, the World Health Organization (WHO) has classified them as extremely dangerous chemical compounds. Taking their immense use and toxicity into consideration, the development of easy, rapid and highly sensitive techniques is necessary. Despite the fact that there are numerous conventional ways for detecting OPPs, the development of portable sensors is required to make routine analysis considerably more convenient. Some of these advanced techniques include colorimetric sensors, fluorescence sensors, molecular imprinted polymer-based sensors, and surface plasmon resonance-based sensors. This review article specifically focuses on the colorimetric, fluorescence and electrochemical sensors. In this article, the sensing strategies of these developed sensors, analytical conditions and their respective limit of detection are compiled.

Interaction of borohydride stabilized silver nanoparticles with sulfur-containing organophosphates

Understanding the interactions between nanoparticles and organophosphates is the key to developing cost-effective colorimetric pesticide detection. We have studied the interaction between three different organophosphates containing the P[double bond, length as m-dash]S group and borohydride stabilized silver nanoparticles. Three different organophosphates, namely phorate, chlorpyrifos, and malathion, have been used. The colorimetric changes are corroborated with UV-visible absorption studies along with the change in particle size and zeta potential. This effect persists in the presence of NaCl solution also. The chlorpyrifos and malathion do not show significant interactions with uncapped nanoparticles over time, while phorate undergoes degradation due to the scission of the S-CH2 linkage. A reaction mechanism, wherein a silver and sulfur (Ag→S) complex is formed, which is in agreement with Raman spectroscopic studies is proposed. The orientations of phorate near Ag nanoparticles are discussed from the adsorption energy calculation using density functional theory.

Chlorpyrifos in environment and food: a critical review of detection methods and degradation pathways

Chlorpyrifos (CP) is a class of organophosphorus (OP) pesticides, which find extensive applications as acaricide, insecticide and termiticide. The use of CP has been indicated in environmental contamination and disturbance in the biogeochemical cycles. CP has been reported to be neurotoxic and has a detrimental effect on immunological and psychological health. Therefore, it is necessary to design and develop effective degradation methods for the removal of CP from the environment. In the past few years, physicochemical (advanced oxidation process) and biological treatment approaches have been widely employed for the pesticide removal. However, the byproducts of this process are more toxic than the parent compound and along with an incomplete degradation of CP. This review focuses on the toxicity of CP, the sources of contamination, degradation pathways, physicochemical, biological, and nano-technology based methods employed for the degradation of CP. In addition, consolidated information on various detection methods and materials used for the detection have been provided in this review.

3D-Printed, Portable, Fluorescent-Sensing Platform for Smartphone-Capable Detection of Organophosphorus Residue Using Reaction-Based Aggregation Induced Emission Luminogens

Development of an easy-to-use, low-cost, household device can help the consumer quickly identify an organophosphorus (OP) residue concentration level. In this work, we demonstrate a 3D-printed, portable, fluorescent-sensing platform for smartphone-capable detection of OPs in vegetables. For development of the proposed device, we utilize the smartphone for capturing the strong thiol-activated fluorescence, which was produced by hydrolysis of OPs in the presence of alkali. The thiol-responsive AIEgen (maleimide-functionalized tetraphenylethylene) was non-emissive in both solution and the solid state but could be readily lighted up by the click addition of thiol to its MI pendant. An android application "Detection" has been developed on the basis of the gray value to analyze the different concentration levels of OPs in vegetable samples. The gray value was linearly related with the concentration of five kinds of organophosphorus residue, ranging from 0 to 20 μg/mL. It was also applied for determination of OPs residue in the leaves of cowpea, celery, and Chinese cabbage. Different from acetylcholinesterase enzyme-based sensors for poor stability under high temperature, the proposed method was a direct detection method for OPs and can be used for rapid monitoring of OPs residue concentration levels before LC-MS analysis.

Porous polymers from octa(amino-phenyl)silsesquioxane and metalloporphyrin as peroxidase-mimicking enzyme for malathion colorimetric sensor

Rapid and efficient pesticide detection methods are particularly important due to the growing problems of pesticide residues. Here, a new azo-based porous organic polymer, Azo(Fe)PPOP, was prepared from octa(amino-phenyl)silsesquioxane (OAPS) and iron(III) 5,10,15,20-tetrakis(4-nitrophenyl)porphyrin (FeTPP(NO₂)₄) via a simple coupling reaction without the participation of metal catalysts. The inorganic cage units of OAPS endowed Azo(Fe)PPOP a porous framework, high surface area, favorably thermal and chemical stability. In Azo(Fe)PPOP, iron(III) porphyrin units were individually isolated in a fixed location, which could effectively avoid dimerization or self-oxidation as happens as in the case of porphyrin monomers. Such a unique structure made Azo(Fe)PPOP exhibit an excellent peroxidase-like catalytic performance in the presence of H₂O₂ and 3,3',5,5'-tetramethylbenzidine (TMB). Because of these advantages, we established a selective, facile, and sensitive colorimetric platform for direct detection of malathion within a very short time (3 min) with a low detection limit (8.5 nM). In addition, the recognition mechanism between Azo(Fe)PPOP and malathion was verified using X-ray photoelectron spectroscopy spectra. The practicality of the constructed platform was further executed by the detection of the pesticide in soil and food samples.

An ionic liquid-assisted quantum dot-grafted covalent organic framework-based multi-dimensional sensing array for discrimination of insecticides using principal component analysis and clustered heat map

A robust multi-dimensional sensing array based on VBimBF₄B/MAA-anchored quantum dot (QD)-grafted covalent organic frameworks (COFs) [(V-M)/QD-grafted COFs] was established via one-pot strategy. The multi-dimensional sensing array has the outstanding advantages of physicochemical and thermal stability, large specific surface area, and regular pore structures. The assistance of ionic liquid VBimBF₄B enhanced the transduction efficiency, and the synergistic effect of COFs enhanced detection efficiency. The improved multi-dimensional sensing array by COFs and ionic liquid VBimBF₄B served to identify seven insecticides by non-specific interactions via hydrogen bonding, and the differences in the kinetics of the binding to the insecticides resulted in variation of the three-output channel (fluorescence, phosphorescence, and light scattering) signals, thus generating a distinct optical fingerprint. The unique fingerprint patterns of seven kinds of common insecticides at 200 μg L^-1 were successfully discriminated using principal component analysis and clustered heat map analysis. The multi-dimensional sensing array showed a response to seven insecticides based on three spectral channels over the range of 0.001-0.4 μg mL^-1 with a limit of detection of 1.08-18.68 μg L^-1. The spiked recovery of tap water was 79.86-134.22%, with RSD ranging from 0.89-14.9%. This study broadens the applications of sensing arrays technology and provides a promising building block for insecticide determination.

Porous chitosan/partially reduced graphene oxide/diatomite composite as an efficient adsorbent for quantitative colorimetric detection of pesticides in a complex matrix

On-site, instrument free quantitative analysis of pesticides is of significant importance for food safety control. However, it is still a great challenge for pesticide detection in food via the current visual detection methods due to the presence of interferents in a complex matrix. In this study, a complex tea matrix had a strong effect on a gold nanoparticles (Au NPs) based colorimetric sensor for the detection of pesticides. Here, a porous chitosan/partially reduced graphene oxide/diatomite (CS/prGO/DM) composite was successfully synthesized via a facile hydrothermal treatment. It could act as an efficient adsorbent for removing different types of tea interferents. A colorimetric sensing platform for the quantitative detection of pesticides in a complex matrix was successfully established. The color changes of the aggregation of Au NPs induced by pesticides were captured using the camera of a smartphone and the images were processed with average RGB (red, green, and blue) values obtained using self-developed software. The G/R values and A700/525 values obtained from UV-vis spectra could be used for quantitative analysis of pesticides. The limits of detection of phosalone and thiram in tea were 90 nM and 13.8 nM, respectively. It is expected that graphene-based materials are attractive for wide application of on-site colorimetric quantitative detection in a variety of fields like environmental protection, food safety and bioanalysis.