The Identification of Oral Cariogenic Bacteria through Colorimetric Sensor Array Based on Single-Atom Nanozymes.

Effective identification of multiple cariogenic bacteria in saliva samples is important for oral disease prevention and treatment. Here, a simple colorimetric sensor array is developed for the identification of cariogenic bacteria using single-atom nanozymes (SANs) assisted by machine learning. Interestingly, cariogenic bacteria can increase oxidase-like activity of iron (Fe)─nitrogen (N)─carbon (C) SANs by accelerating electron transfer, and inversely reduce the activity of Fe─N─C further reconstruction with urea. Through machine-learning-assisted sensor array, colorimetric responses are developed as "fingerprints" of cariogenic bacteria. Multiple cariogenic bacteria can be well distinguished by linear discriminant analysis and bacteria at different genera can also be distinguished by hierarchical cluster analysis. Furthermore, colorimetric sensor array has demonstrated excellent performance for the identification of mixed cariogenic bacteria in artificial saliva samples. In view of convenience, precise, and high-throughput discrimination, the developed colorimetric sensor array based on SANs assisted by machine learning, has great potential for the identification of oral cariogenic bacteria so as to serve for oral disease prevention and treatment.

Transparent, flexible, and eco-friendly starch-based films for reversible optoelectronic noses for food spoilage monitoring in smart packaging.

A reversible optoelectronic nose is presented consisting of ten acid-base indicators incorporated into a starch-based film, covering a wide pH range. The starch substrate is odorless, biocompatible, flexible, and exhibits high tensile resistance. This optical artificial olfaction system was used to detect the early stages of food decomposition by exposing it to the volatile compounds produced during the spoialge process of three food products (beef, chicken, and pork). A smartphone was used to capture the color changes caused by intermolecular interactions between each dye and the emitted volatiles over time. Digital images were processed to generate a differential color map, which uses the observed color shifts to create a unique signature for each food product. To effectively discriminate among different samples and exposure times, we employed chemometric tools, including hierarchical cluster analysis (HCA) and principal component analysis (PCA). This approach detects food deterioration in a practical, cost-effective, and user-friendly manner, making it suitable for smart packaging. Additionally, the use of starch-based films in the food industry is preferable due to their biocompatibility and biodegradability characteristics.

Colorimetric identification of multiple terpenoids based on bimetallic FeCu/NPCs nanozymes.

Nanozymes have been relatively well explored, and bimetal-doped nanozymes have attracted much exploration due to their superior catalytic activity. We developed bimetallic FeCu/NPCs and Cu/NPCs nanozymes, which have good catalytic properties due to the coordination of Fe and Cu with N and P. The nanozymes acted as sensing elements in a cascade reaction system to effectively recognize seven terpenoids, including menthol (Men), paeoniflorin (Pae), camphor (Cam), paclitaxel (Pac), andrographolide (Andro), ginkgolide A (Gin A), and piperone (Pip). Terpenoids act as inhibitors of acetylcholinesterase (AChE) and reduce the hydrolysis of acetylcholine (ATCh), providing insight into establishing a simple and distinct assay for terpenoids. Notably, the sensor array distinguished seven terpenoids with concentrations as low as 10 ng/mL and achieved high-precision detection of mixed samples with different molar ratios and 21 unknown samples. Finally, the sensor array successfully distinguished and identified multiple terpenoids in herbal samples.

Enhanced peroxidase-like activity of 2(3), 9(10), 16(17), 23(24)-octamethoxyphthalocyanine modified CoFe LDH for a sensor array for reducing substances with catechol structure.

Improving the catalytic activity of artificial nanozymes to realize the real-time detection of small molecules becomes an important task. Herein, a highly active nanozyme, 2(3), 9(10), 16(17), 23(24)-octamethoxyphthalocyanine (Pc(OH)8) modified CoFe LDH microspheres (Pc(OH)8-CoFe LDH) have been prepared by the two-step hydrothermal method. The 3,3',5,5'-tetramylbenzidine (TMB), a chromogenic substrate, was fast oxidized into blue oxTMB by H2O2 in the presence of Pc(OH)8-CoFe LDH, indicating that Pc(OH)8-CoFe LDH possesses high peroxidase-like activity rather than pure CoFe LDH. The enhancement peroxidase-like activity of the Pc(OH)8-CoFe LDH is ascribed to the synergistic action between Pc(OH)8 and CoFe LDH. Experimental results of radical scavenger and fluorescence probe verify that superoxide radical (•O2-) plays an important role during the catalytic reaction. Interestingly, the absorption intensity of reaction system has been enhanced largely, due to adding of the reducing substances containing catechol structure. Based on this, the three reducing substances (dopamine, procyanidin B2, catechins) containing catechol structure were distinguished from other reducing substances without catechol structure. Thus, a colorimetric array has been constructed using reaction time as the sensing element to realize the sensitive and selective recognition of catechol structures at a certain concentration.

Application of colorimetric sensor array coupled with machine-learning approaches for the discrimination of grains based on freshness.

BACKGROUND: Volatile organic compounds (VOCs) in grain fluctuate depending on the degree of grain freshness. A new colorimetric sensor array (CSA) was developed as capture probes for the quantification of VOCs in grains in this work, and it was designed to monitor the variation of grain VOCs. CSA spectral data acquisition using visible-near-infrared spectroscopy and image processing of CSA's image imformation by computer were used comparatively. Then, machine-learning-based models - for example, synergistic interval partial least squares, genetic algorithm, competitive adaptive reweighted sampling (CARS) algorithm, and ant colony optimization (ACO) algorithm - were introduced to optimize variables. Moreover, principal component analysis, and linear discriminant analysis (LDA), and K-nearest neighbors (KNN) were used for the classification. Ultimately, quantitative models for detecting grain freshness are developed using various variable selection strategies. RESULTS: Compared with the pattern recognition results of image processing, visible-near-infrared spectroscopy could better separate the grains with different freshness from principal component analysis, and the prediction set of LDA models could correctly identify 100% of rice, 96.88% of paddy, and 97.9% of soybeans. In addition, compared with CARS and ACO, the LDA model and KNN model based on genetic algorithms show the best prediction performance. The prediction set could correctly identify 100% of rice and paddy samples and 95.83% of soybean samples. CONCLUSION: The method developed could be used for non-destructive detection of grain freshness. © 2023 Society of Chemical Industry.

Freshness Identification of Oysters Based on Colorimetric Sensor Array Combined with Image Processing and Visible Near-Infrared Spectroscopy.

Volatile organic compounds (VOCs) could be used as an indicator of the freshness of oysters. However, traditional characterization methods for VOCs have some disadvantages, such as having a high instrument cost, cumbersome pretreatment, and being time consuming. In this work, a fast and non-destructive method based on colorimetric sensor array (CSA) and visible near-infrared spectroscopy (VNIRS) was established to identify the freshness of oysters. Firstly, four color-sensitive dyes, which were sensitive to VOCs of oysters, were selected, and they were printed on a silica gel plate to obtain a CSA. Secondly, a charge coupled device (CCD) camera was used to obtain the "before" and "after" image of CSA. Thirdly, VNIS system obtained the reflected spectrum data of the CSA, which can not only obtain the color change information before and after the reaction of the CSA with the VOCs of oysters, but also reflect the changes in the internal structure of color-sensitive materials after the reaction of oysters' VOCs. The pattern recognition results of VNIS data showed that the fresh oysters and stale oysters could be separated directly from the principal component analysis (PCA) score plot, and linear discriminant analysis (LDA) model based on variables selection methods could obtain a good performance for the freshness detection of oysters, and the recognition rate of the calibration set was 100%, while the recognition rate of the prediction set was 97.22%. The result demonstrated that the CSA, combined with VNIRS, showed great potential for VOCS measurement, and this research result provided a fast and nondestructive identification method for the freshness identification of oysters.

Evaluation of adulteration in distillate samples of Rosa damascena Mill using colorimetric sensor arrays, chemometric tools and dispersive liquid-liquid microextraction-GC-MS.

INTRODUCTION: Rosa damascena Mill distillate and its essential oil are widely used in cosmetics, perfumes and food industries. Therefore, the methods of detection for its authentication is an important issue. OBJECTIVES: We suggest colorimetric sensor array and chemometric methods to discriminate natural Rosa distillate from synthetic adulterates. MATERIAL AND METHODS: The colour responses of 20 indicators spotted on polyvinylidene fluoride (PVDF) substrate were monitored with a flatbed scanner; then their digital representation was analysed with principal component analysis (PCA), partial least squares-discriminant analysis (PLS-DA) and soft independent modelling of class analogy (SIMCA). RESULTS: Accurate discrimination of the diluted- and synthetic-mixture samples from the original ones was achieved by PLS-DA and SIMCA models with error rate of 0.01 and 0, specificity of 0.98 and 1, sensitivity of 1 and 1, and accuracy of 0.98 and 0.96, respectively. Discrimination of the synthetic adulterate from the original samples was achieved with error rate of 0.03 and 0.03, specificity of 0.94 and 0.93, sensitivity of 1 and 1, and accuracy of 0.93 and 0.71 with PLS-DA and SIMCA models, respectively. Moreover, the chemical constituents of the samples were analysed using dispersive liquid-liquid microextraction and gas chromatography-mass spectrometry (GC-MS). The main constituents of the distillate were geraniol, citronellol, and phenylethyl alcohol in different percentages, in both original and synthetic adulterate samples. CONCLUSION: These results point out the successful combination of colorimetric sensor array and PLS-DA and SIMCA as a fast, sensitive and inexpensive screening tool for discrimination of original samples of R. damascena Mill distillate from those prepared from synthetic Rosa essential oils.

A colorimetric sensor array based on natural pigments for the discrimination of saccharides.

A colorimetric sensor array based on natural pigments was developed to discriminate between various saccharides. Anthocyanins, pH-sensitive natural pigments, were extracted from fruits and flowers and used as components of the sensor array. Variation in pH, due to the reaction between saccharides and boronic acids, caused obvious colour changes in the natural pigments. Only by observing the difference map with the naked eye could 11 common saccharides be divided into independent individuals. In conjunction with pattern recognition, the sensor array clearly differentiated between sugar and sugar alcohol with highly accuracy and allowed rapid quantification of different concentrations of maltitol and fructose. This sensor array for saccharides is expected to become a promising alternative tool for food monitoring. The link between anthocyanin and saccharide detection opened a new guiding direction for the application of anthocyanins in foods.

Colorimetric aggregation assay based on array of gold and silver nanoparticles for simultaneous analysis of aflatoxins, ochratoxin and zearalenone by using chemometric analysis and paper based analytical devices.

A paper based sensor array is presented to discriminate and determine five mycotoxins classified into three categories, namely aflatoxins, ochratoxins and zearalenone. The gold and silver nanoparticles, synthesized by three different reducing or capping agents, were employed as sensing elements of the fabricated device. These nanoparticles were poured onto hydrophilic circular zones embedded on the hydrophobic substrate. The response of the assay is dependent on the aggregation of nanoparticles for interaction with mycotoxins. Due to aggregation, the gold and silver nanoparticles changed to purple and brown, respectively. Color changes provide unique colorimetric signatures conducive to recognizing the type of mycotoxin, identifying its chemical structure, and finding the fungi that produce it. The discrimination ability of the assay was investigated by both supervised (linear discriminate analysis) and unsupervised (principle component analysis and hierarchical cluster analysis) pattern recognition methods. The assay was applied to the point of need determination of aflatoxin B1, aflatoxin G1, aflatoxin M1, ochratoxin A and zearalenone with a detection limit of 2.7, 7.3, 2.1, 3.3 and 7.0 ng.mL-1, respectively. The fabricated device has high potential of simultaneously determining the mycotoxins in pistachio, wheat, coffee and milk with the help of partial least square method. The root mean square errors for prediction of PLS model were 5.7, 5.2, 1.5, 7.2 and 2.9 for aflatoxin B1, aflatoxin G1, aflatoxin M1, ochratoxin A and zearalenone, respectively. Graphical abstractSchematic representation of paper based colorimetric sensor array based on gold and silver nanoparticles for both qualitative and quantitative analysis of aflatoxins, ochratoxin and zearalenone.

Comparative Chemometric Analysis for Classification of Acids and Bases via a Colorimetric Sensor Array.

With the increasing availability of digital imaging devices, colorimetric sensor arrays are rapidly becoming a simple, yet effective tool for the identification and quantification of various analytes. Colorimetric arrays utilize colorimetric data from many colorimetric sensors, with the multidimensional nature of the resulting data necessitating the use of chemometric analysis. Herein, an 8 sensor colorimetric array was used to analyze select acid and basic samples (0.5 - 10 M) to determine which chemometric methods are best suited for classification quantification of analytes within clusters. PCA, HCA, and LDA were used to visualize the data set. All three methods showed well-separated clusters for each of the acid or base analytes and moderate separation between analyte concentrations, indicating that the sensor array can be used to identify and quantify samples. Furthermore, PCA could be used to determine which sensors showed the most effective analyte identification. LDA, KNN, and HQI were used for identification of analyte and concentration. HQI and KNN could be used to correctly identify the analytes in all cases, while LDA correctly identified 95 of 96 analytes correctly. Additional studies demonstrated that controlling for solvent and image effects was unnecessary for all chemometric methods utilized in this study.

An optoelectronic tongue based on an array of gold and silver nanoparticles for analysis of natural, synthetic and biological antioxidants.

A colorimetric array, which can discriminate 20 food antioxidants of natural, synthetic and biological groups, is described. It consists of gold and silver nanoparticles that were synthesized using six different reducing and/or capping agents. The function of the array relies on the interaction of the antioxidants with the nanoparticles which causes aggregation or morphological changes. This, in turn, causes a change in the sensors' colors. The array produces a unique combination of colors for each antioxidant. The resulting colorations are determined by recording the absorbances of the arrays at wavelengths of 405, 450, 490 and 630 nm, or by capturing the images with a digital camera. The discriminatory ability of the array is investigated by principle component analysis and hierarchical cluster analysis. The method was applied to quantitative assay of gallic acid, caffeic acid, catechin, dopamine, citric acid, butylated hydroxytoluene and ascorbic acid. The respective limits of detection are 4.2, 13, 53, 6.9, 47, 3.5 and 43 nM, respectively. The simultaneous determination of 5 different antioxidants is achieved utilizing partial least square regression. The root mean square errors for prediction of the test set are 0.0650, 0.0782, 0.811, 0.0206 and 0.135 nM for gallic acid, catechin, butylated hydroxytoluene, dopamine, and ascorbic acid, respectively. This method demonstrates excellent potential for analysis of antioxidants in beverages such as tea and lemon juice. Graphical abstract Schematic of a method for the simultaneous determination of several antioxidants based on changes in the color of gold and silver nanoparticles. The antioxidants cause aggregation and/or morphological changes which can be detected by using both image analysis or by colorimetry.

Esters-targeted colorimetric sensor array for the authenticity discrimination of strong-aroma baijiu with different origins.

Baijiu authenticity has been a frequent problem driven by economic interests in recent years, so it is important to discriminate against baijiu with different origins. Herein, we proposed a simple and efficient esters-targeted colorimetric sensor array mediated by hydroxylamine hydrochloride. Esters undergo a nucleophilic addition reaction with hydroxylamine hydrochloride to form hydroxamic acid, which rapidly forms a purplish red ferric hydroxamate under FeCl3·6H2O. Bromophenol blue and rhodamine B enrich the color effects. The array detected 12 esters with a detection limit on the order of 10-5 of most esters and 16 mixed esters with R2 > 0.999 and recoveries close to 100%. Otherwise, for discriminating 34 strong-aroma baijius (SABs), the array has an accuracy of 98% according to the origin, and 95% according to the grades, with a response time of 1 min. This study provides a new strategy for authenticity determination and quality control of baijiu.

Colorimetric sensor array for identifying antioxidants based on pyrolysis-free synthesis of Fe-N/C single-atom nanozymes.

Iron-anchored nitrogen/doped carbon single-atom nanozymes (Fe-N/C), which possess homogeneous active sites and adjustable catalytic environment, represent an exemplary model for investigating the structure-function relationship and catalytic activity. However, the development of pyrolysis-free synthesis technique for Fe-N/C with adjustable enzyme-mimicking activity still presents a significant challenge. Herein, Fe-N/C anchored three carrier morphologies were created via a pyrolysis-free approach by covalent organic polymers. The peroxidase-like activity of these Fe-N/C nanozymes was regulated via the pores of the anchored carrier, resulting in varying electron transfer efficiency due to disparities in contact efficacy between substrates and catalytic sites within diverse microenvironments. Additionally, a colorimetric sensor array for identifying antioxidants was developed: (1) the Fe-N/C catalytically oxidized two substrates TMB and ABTS, respectively; (2) the development of a colorimetric sensor array utilizing oxTMB and oxABTS as sensing channels enabled accurate discrimination of antioxidants such as ascorbic acid (AsA), glutathione (GSH), cysteine (Cys), gallic acid (GA), and caffeic acid (CA). Subsequently, the sensor array underwent rigorous testing to validate its performance, including assessment of antioxidant mixtures and individual antioxidants at varying concentrations, as well as target antioxidants and interfering substances. In general, the present study offered valuable insights into the active origin and rational design of nanozyme materials, and highlighting their potential applications in food analysis.

Machine learning-enabled attapulgite/polyimide nanofiber composite aerogels-based colorimetric sensor array for real-time monitoring of balsa fish freshness.

This paper presents the development and application of attapulgite/polyimide nanofiber composite aerogels (ATP/PI NFAs) integrated with a range of acid-base indicators, fabricated using electrospinning and freeze-drying technologies. A detailed characterization of the ATP/PI NFAs revealed a 3D multi-level pore structure that enhanced the mass transfer of target gas molecules and their interaction with probe molecules. Utilizing machine learning approaches, we designed an ATP/PI NFAs-based colorimetric sensor array capable of real-time evaluation of balsa fish freshness. Color features sensitive to changes in freshness were selected using principal component analysis and random forest. Partial least squares regression and random forest regression models were established, achieving the prediction of total volatile basic nitrogen content in balsa fish. The system was validated using a national standard method to demonstrate its accuracy and practicality. The combination of advanced ATP/PI NFAs-based colorimetric sensor array with robust machine learning models paves the way for food safety monitoring.

Cu2Cl(OH)3 nanozyme-based colorimetric sensor array for phosphates discrimination and disease identification.

The identification of phosphates holds significant importance in many physiological processes and disease diagnosis, and traditional detection techniques struggle to simultaneously detect and distinguish phosphates. The complexity of synthesizing sensing units restricts the construction of sensor arrays as well. In this study, a bifunctional dicopper chloride trihydroxide (Cu2Cl(OH)3) nanozyme with conspicuous laccase- and peroxidase-like activities has been synthesized in basic deep eutectic solvents (DES). Exploiting the various regulatory impacts of multiple phosphates on the dual-enzyme mimicking activities, the sensor array based on the laccase mimic and peroxidase mimic properties of Cu2Cl(OH)3 was designed, which has been successfully harnessed for the identification of eight phosphates (ATP, ADP, AMP, PPi, Pi, GTP, GDP, and GMP). This approach streamlines the creation of sensor arrays. Besides, the three simulated actual samples (healthy individuals, moderately ill patients, and severely ill patients) have been accurately distinguished. This work makes a substantial contribution to enhancing the highly effective construction of array channels and promoting discrimination of phosphates in intricate samples.

Nanozyme-based colorimetric sensor arrays coupling with smartphone for discrimination and "segmentation-extraction-regression" deep learning assisted quantification of flavonoids.

Achieving rapid, cost effective, and intelligent identification and quantification of flavonoids is challenging. For fast and uncomplicated flavonoid determination, a sensing platform of smartphone-coupled colorimetric sensor arrays (electronic noses) was developed, relying on the differential competitive inhibition of hesperidin, nobiletin, and tangeretin on the oxidation reactions of nanozymes with a 3,3',5,5'-tetramethylbenzidine substrate. First, density functional theory calculations predicted the enhanced peroxidase-like activities of CeO2 nanozymes after doping with Mn, Co, and Fe, which was then confirmed by experiments. The self-designed mobile application, Quick Viewer, enabled a rapid evaluation of the red, green, and blue values of colorimetric images using a multi-hole parallel acquisition strategy. The sensor array based on three channels of CeMn, CeFe, and CeCo was able to discriminate between different flavonoids from various categories, concentrations, mixtures, and the various storage durations of flavonoid-rich Citri Reticulatae Pericarpium through a linear discriminant analysis. Furthermore, the integration of a "segmentation-extraction-regression" deep learning algorithm enabled single-hole images to be obtained by segmenting from a 3 × 4 sensing array to augment the featured information of array images. The MobileNetV3-small neural network was trained on 37,488 single-well images and achieved an excellent predictive capability for flavonoid concentrations (R2 = 0.97). Finally, MobileNetV3-small was integrated into a smartphone as an application (Intelligent Analysis Master), to achieve the one-click output of three concentrations. This study developed an innovative approach for the qualitative and simultaneous multi-ingredient quantitative analysis of flavonoids.

Algae-derived biochar nanozyme array for discrimination and detection of multiple pesticides in soil, water and food.

Despite the potential of nanozymes combined with sensor arrays for discriminating multiple pesticides simultaneously, they have few practical pesticide sensing uses due to the limited performance of existing nanozymes and the complexity of their preparation. Here, agricultural waste is utilized for the facile synthesis of high-performance biochar nanozymes and the fabrication of biochar nanozyme sensor arrays. The production of autogenous N-doped biochars with abundant surface functional groups and good peroxidase-like activities is achieved with different types of algae. High-performance biochar nanozyme sensor arrays can discriminate pesticides in a concentration range from 1 to 500 µM and in real samples from soil, lake water, seawater, apples, cucumbers, peaches, tomatoes and cabbages. Furthermore, pesticides can be quantified down to 1 µM. The development of high-performance nanozyme sensor arrays based on waste conversion could be a step toward pesticide discrimination and detection, which would improve human and environmental safety.

FeMo2Ox(OH)y-based mineral hydrogels as a novel POD nanozyme for sensitive and selective detection of aromatic amines contaminants via a colorimetric sensor array.

Developing convenient pathways to discriminate and identify multiple aromatic amines (AAs) remains fascinating and critical. Here, a novel three-channel colorimetric sensor array based on FeMo2Ox(OH)y-based mineral (FM) hydrogels is successfully constructed to monitor AAs in tap water. Benefiting from the substantial oxygen vacancies (VO), FM nanozymes exhibit extraordinary peroxidase (POD)-like activities with Km of 0.133 mM and Vmax of 2.518 × 10-2 mM·s-1 toward 3,3',5,5'-tetramethylbenzidine (TMB), which are much better than horseradish peroxidase and most of POD mimics. This reveals that doping Cu and Co into FM (FM-Cu and FM-Co) can change POD activity. Based on various POD activities, TMB and H2O2 are used to generate fingerprint colorimetry signals from the colorimetry sensor array. The analytes can accurately discriminate through linear discriminant analysis, with a detection limit as low as 2.12 × 10-2-0.14 µM. The sensor array can effectively identify and discriminate AA contaminants and their mixtures and has performed well in real sample tests.

Detection of Maize Mold Based on a Nanocomposite Colorimetric Sensor Array under Different Substrates.

This study developed a novel nanocomposite colorimetric sensor array (CSA) to distinguish between fresh and moldy maize. First, the headspace solid-phase microextraction gas chromatography-mass spectrometry (HS-SPME-GC/MS) method was used to analyze volatile organic compounds (VOCs) in fresh and moldy maize samples. Then, principal component analysis and orthogonal partial least-squares discriminant analysis (OPLS-DA) were used to identify 2-methylbutyric acid and undecane as key VOCs associated with moldy maize. Furthermore, colorimetric sensitive dyes modified with different nanoparticles were employed to enhance the dye properties used in the nanocomposite CSA analysis of key VOCs. This study focused on synthesizing four types of nanoparticles: polystyrene acrylic (PSA), porous silica nanospheres (PSNs), zeolitic imidazolate framework-8 (ZIF-8), and ZIF-8 after etching. Additionally, three types of substrates, qualitative filter paper, polyvinylidene fluoride film, and thin-layer chromatography silica gel, were comparatively used to fabricate nanocomposite CSA combining with linear discriminant analysis (LDA) and K-nearest neighbor (KNN) models for real sample detection. All moldy maize samples were correctly identified and prepared to characterize the properties of the CSA. Through initial testing and nanoenhancement of the chosen dyes, four nanocomposite colorimetric sensitive dyes were confirmed. The accuracy rates for LDA and KNN models in this study reached 100%. This work shows great potential for grain quality control using CSA methods.

Accelerated and precise identification of antioxidants and pesticides using a smartphone-based colorimetric sensor array.

The integration of smartphones with conventional analytical approaches plays a crucial role in enhancing on-site detection platforms for point-of-care testing. Here, we developed a simple, rapid, and efficient three-channel colorimetric sensor array, leveraging the peroxidase (POD)-like activity of polydopamine-decorated FeNi foam (PDFeNi foam), to identify antioxidants using both microplate readers and smartphones for signal readouts. The exceptional catalytic capacity of PDFeNi foam enabled the quick catalytic oxidation of three typical peroxidase substrates (TMB, OPD and 4-AT) within 3 min. Consequently, we constructed a colorimetric sensor array with cross-reactive responses, which was successfully applied to differentiate five antioxidants (i.e., glycine (GLY), glutathione (GSH), citric acid (CA), ascorbic acid (AA), and tannic acid (TAN)) within the concentration range of 0.1-10 µM, quantitatively analyze individual antioxidants (with AA and CA as model analytes), and assess binary mixtures of AA and GSH. The practical application was further validated by discriminating antioxidants in serum samples with a smartphone for signal readout. In addition, since pesticides could be absorbed on the surface of PDFeNi foam through π-π stacking and hydrogen bonding, the active sites were differentially masked, leading to featured modulation on POD-like activity of PDFeNi foam, thereby forming the basis for pesticides discrimination on the sensor array. The nanozyme-based sensor array provides a simple, rapid, visual and high-throughput strategy for precise identification of various analytes with a versatile platform, highlighting its potential application in point-care-of diagnostic, food safety and environmental surveillance.