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.

Rapid detection of depression by volatile organic compounds from exhalation.

Abstract： Background: Depression is a pervasive and often undetected mental health condition, which poses significant challenges for early diagnosis due to its silent and subtle nature. OBJECTIVE: To evaluate exhaled volatile organic compounds (VOCs) as non-invasive biomarkers for the detection of depression using a virtual surface acoustic wave sensors array (VSAW-SA). METHODS: A total of 245 participants were recruited from the Hangzhou Community Health Service Center, including 38 individuals diagnosed with depression and 207 control subjects. Breath samples were collected from all participants and subjected to analysis using VSAW-SA. Univariate and multivariate analyses were employed to assess the relationship between volatile organic compounds (VOCs) and depression. The findings revealed that the responses of virtual sensor ID 14, 44, 59, and 176, which corresponded respectively to ethanol, trichloroethylene or isoleucine, octanoic acid or lysine, and an unidentified compound, were sensitive to depression. Taking into account potential confounders, these sensor responses were utilized to calculate a depression detection indicator. RESULTS: It has a sensitivity of 81.6% and a specificity of 81.6%, with an area under the curve (AUC) of 0.870 (95% CI=0.816-0.923). CONCLUSIONS: Exhaled VOCs as non-invasive biomarkers of depression could be detected by a VSAW-SA. Large-scale cohort studies should be conducted to confirm the potential ability of the VSAW-SA to diagnose depression. .

Olfactory analysis of oolong tea sensory quality using composite nano-colorimetric sensor array.

Chinese oolong tea is famous for its rich and diverse aromas, which is an important indicator for sensor quality evaluation. To accurately and rapidly evaluate sensory quality, a novel colorimetric sensor array (CSA) was developed to detect volatile organic compounds (VOCs) in oolong tea. We further explored the binding mechanism between colorimetric dyes that trigger changes in charge transfer and visible color changes. Based on this, we modified and optimized the CSA to improve the sensitivity by 17.1-234.9% and the stability by 8.7-33.3%. The study also assessed the effectiveness of this method by comparing two linear and two non-linear classification models, with the support vector machine (SVM) model achieving the highest accuracy, identifying different flavor intensity and grades with rates of 100% and 95.83%, respectively. These findings sufficiently demonstrated that the novel CSA, integrated with the SVM model, has promising potential for predicting the sensory quality of oolong tea.

LTP-assisted fabrication of laccase-like Cu-MOF nanozyme-encoded array sensor for identification and intelligent sensing of bioactive components in food.

Nanozymes are potential candidates for constructing sensors due to their adjustable activity, high stability, and high cost-effectiveness. However, due to the lack of reasonable means, designing and preparing efficient nanozymes remains challenging. Herein, inspired by the property of natural laccase, we applied the novel and facile low-temperature plasma (LTP) technology to fabricate a series of different base-ligand Cu metal organic framework (MOF) nanozymes (namely, A-Cu, G-Cu, C-Cu and T-Cu nanozymes) with laccase-like activity successfully. Owing to the different catalytic capacities of four types of base-Cu-MOF nanozymes in the response to five common effective bioactive substances, we constructed the nanozyme-encoded array sensor for the identification of different bioactive compounds. As a result, the four-channel colorimetric sensor array was constructed, in which four laccase-like nanozymes were utilized as the sensing units, achieving high-throughput, high-sensitivity and rapid detection/identification of five common bioactive compounds in the concentration range of 1.5-150 µg mL-1 through different color output patterns. It is worth noting that the as-prepared sensor array can successfully distinguish the natural bioactive compounds in a variety of real samples. Furthermore, with the assistance of smartphones, we also designed a portable smart sensing approach for detecting the bioactive compounds effectively in food. This study has therefore not only provided an effective way for preparation highly effectively nanozymes, but also established a new sensing platform for intelligent sensing of bioactive components in food.

CAWE-ACNN Algorithm for Coprime Sensor Array Adaptive Beamforming.

This paper presents a robust adaptive beamforming algorithm based on an attention convolutional neural network (ACNN) for coprime sensor arrays, named the CAWE-ACNN algorithm. In the proposed algorithm, via a spatial and channel attention unit, an ACNN model is constructed to enhance the features contributing to beamforming weight vector estimation and to improve the signal-to-interference-plus-noise ratio (SINR) performance, respectively. Then, an interference-plus-noise covariance matrix reconstruction algorithm is used to obtain an appropriate label for the proposed ACNN model. By the calculated label and the sample signals received from the coprime sensor arrays, the ACNN is well-trained and capable of accurately and efficiently outputting the beamforming weight vector. The simulation results verify that the proposed algorithm achieves excellent SINR performance and high computation efficiency.

Identification of Beef Odors under Different Storage Day and Processing Temperature Conditions Using an Odor Sensing System.

This study used an odor sensing system with a 16-channel electrochemical sensor array to measure beef odors, aiming to distinguish odors under different storage days and processing temperatures for quality monitoring. Six storage days ranged from purchase (D0) to eight days (D8), with three temperature conditions: no heat (RT), boiling (100 °C), and frying (180 °C). Gas chromatography-mass spectrometry (GC-MS) analysis showed that odorants in the beef varied under different conditions. Compounds like acetoin and 1-hexanol changed significantly with the storage days, while pyrazines and furans were more detectable at higher temperatures. The odor sensing system data were visualized using principal component analysis (PCA) and uniform manifold approximation and projection (UMAP). PCA and unsupervised UMAP clustered beef odors by storage days but struggled with the processing temperatures. Supervised UMAP accurately clustered different temperatures and dates. Machine learning analysis using six classifiers, including support vector machine, achieved 57% accuracy for PCA-reduced data, while unsupervised UMAP reached 49.1% accuracy. Supervised UMAP significantly enhanced the classification accuracy, achieving over 99.5% with the dimensionality reduced to three or above. Results suggest that the odor sensing system can sufficiently enhance non-destructive beef quality and safety monitoring. This research advances electronic nose applications and explores data downscaling techniques, providing valuable insights for future studies.

Intelligent identification of picking periods of Lu'an Guapian tea by an indicator displacement colorimetric sensor array combined with machine learning.

Lu'an Gua Pian (LAGP) tea is one of the most famous green teas in China. The quality of green tea is related to its picking periods, especially the green tea before Qingming Festival (usually April 6th) is highly praised as precious in the market. In this work, a simple and cheap indicator displacement colorimetric sensor array combined with smartphone was developed to rapidly identify LAGP picked during different picking periods. First, the chemical component contents of LAGP picked before and after Qingming Festival were analyzed. Second, a well-designed colorimetric sensor array was proposed based on the tea component contents differences. Finally, machine learning was used to process the array data taken by a smartphone. By comparison, the accuracy of the best model for the prediction set was 97%. Meanwhile, the multi-channel advantages of the sensing array were demonstrated by an ablation experiment. In addition, the method achieved an AGREE analysis score of 0.88, indicating that it was environmental-friendly.

Machine learning-assisted melamine-Cu nanozyme and cholinesterase integrated array for multi-category pesticide intelligent recognition.

Expanding target pesticide species and intelligent pesticide recognition were formidable challenges for existing cholinesterase inhibition methods. To improve this status, multi-active Mel-Cu nanozyme with mimetic Cu-N sites was prepared for the first time. It exhibited excellent laccase-like and peroxidase-like activities, and can respond to some pesticides beyond the detected range of enzyme inhibition methods, such as glyphosate, carbendazim, fumonisulfuron, etc., through coordination and hydrogen bonding. Inspired by the signal complementarity of Mel-Cu and cholinesterase, an integrated sensor array based on the Mel-Cu laccase-like activity, Mel-Cu peroxidase-like activity, acetylcholinesterase, and butyrylcholinesterase was creatively constructed. And it could successfully discriminate 12 pesticides at 0.5-50 µg/mL, which was significantly superior to traditional enzyme inhibition methods. Moreover, on the basis of above array, a unified stepwise prediction model was built using classification and regression algorithms in machine learning, which enabled concentration-independent qualitative identification as well as precise quantitative determination of multiple pesticide targets, simultaneously. The sensing accuracy was verified by blind sample analysis, in which the species was correctly identified and the concentration was predicted within 10% error, suggesting great intelligent recognition ability. Further, the proposed method also demonstrated significant immunity to interference and practical application feasibility, providing powerful means for pesticide residue analysis.

Nanocomposite Multimodal Sensor Array Integrated with Auxetic Structure for an Intelligent Biometrics System.

A multimodal sensor array, combining pressure and proximity sensing, has attracted considerable interest due to its importance in ubiquitous monitoring of cardiopulmonary health- and sleep-related biometrics. However, the sensitivity and dynamic range of prevalent sensors are often insufficient to detect subtle body signals. This study introduces a novel capacitive nanocomposite proximity-pressure sensor (NPPS) for detecting multiple human biometrics. NPPS consists of a carbon nanotube-paper composite (CPC) electrode and a percolating multiwalled carbon nanotube (MWCNT) foam enclosed in a MWCNT-coated auxetic frame. The fractured fibers in the CPC electrode intensify an electric field, enabling highly sensitive detection of proximity and pressure. When pressure is applied to the sensor, the synergic effect of MWCNT foam and auxetic deformation amplifies the sensitivity. The simple and mass-producible fabrication protocol allows for building an array of highly sensitive sensors to monitor human presence, sleep posture, and vital signs, including ballistocardiography (BCG). With the aid of a machine learning algorithm, the sensor array accurately detects blood pressure (BP) without intervention. This advancement holds promise for unrestricted vital sign monitoring during sleep or driving.

Machine learning supported single-stranded DNA sensor array for multiple foodborne pathogenic and spoilage bacteria identification in milk.

Ensuring food safety through rapid and accurate detection of pathogenic bacteria in food products is a critical challenge in the food supply chain. In this study, a non-specific optical sensor array was proposed for the identification of multiple pathogenic bacteria in contaminated milk samples. Fluorescence-labeled single-stranded DNA was efficiently quenched by two-dimensional nanoparticles and subsequently recovered by foreign biomolecules. The recovered fluorescence generated a unique fingerprint for each bacterial species, enabling the sensor array to identify eight bacteria (pathogenic and spoilage) within a few hours. Four traditional machine learning models and two artificial neural networks were applied for classification. The neural network showed a 93.8 % accuracy with a 30-min incubation. Extending the incubation to 120 min increased the accuracy of the multiplayer perceptron to 98.4 %. This sensor array is a novel, low-cost, and high-accuracy approach for the identification of multiple bacteria, providing an alternative to plate counting and ELISA methods.

Flexible Artificial Tactility with Excellent Robustness and Temperature Tolerance Based on Organohydrogel Sensor Array for Robot Motion Detection and Object Shape Recognition.

Hydrogel-based flexible artificial tactility is equipped to intelligent robots to mimic human mechanosensory perception. However, it remains a great challenge for hydrogel sensors to maintain flexibility and sensory performances during cyclic loadings at high or low temperatures due to water loss or freezing. Here, a flexible robot tactility is developed with high robustness based on organohydrogel sensor arrays with negligent hysteresis and temperature tolerance. Conductive polyaniline chains are interpenetrated through a poly(acrylamide-co-acrylic acid) network with glycerin/water mixture with interchain electrostatic interactions and hydrogen bonds, yielding a high dissipated energy of 1.58 MJ m-3, and ultralow hysteresis during 1000 cyclic loadings. Moreover, the binary solvent provides the gels with outstanding tolerance from -100 to 60 °C and the organohydrogel sensors remain flexible, fatigue resistant, conductive (0.27 S m-1), highly strain sensitive (GF of 3.88) and pressure sensitive (35.8 MPa-1). The organohydrogel sensor arrays are equipped on manipulator finger dorsa and pads to simultaneously monitor the finger motions and detect the pressure distribution exerted by grasped objects. A machine learning model is used to train the system to recognize the shape of grasped objects with 100% accuracy. The flexible robot tactility based on organohydrogels is promising for novel intelligent robots.

Smartphone-Assisted Nanozyme Colorimetric Sensor Array Combined "Image Segmentation-Feature Extraction" Deep Learning for Detecting Unsaturated Fatty Acids.

Conventional methods for detecting unsaturated fatty acids (UFAs) pose challenges for rapid analyses due to the need for complex pretreatment and expensive instruments. Here, we developed an intelligent platform for facile and low-cost analysis of UFAs by combining a smartphone-assisted colorimetric sensor array (CSA) based on MnO2 nanozymes with "image segmentation-feature extraction" deep learning (ISFE-DL). Density functional theory predictions were validated by doping experiments using Ag, Pd, and Pt, which enhanced the catalytic activity of the MnO2 nanozymes. A CSA mimicking mammalian olfactory system was constructed with the principle that UFAs competitively inhibit the oxidization of the enzyme substrate, resulting in color changes in the nanozyme-ABTS substrate system. Through linear discriminant analysis coupled with the smartphone App "Quick Viewer" that utilizes multihole parallel acquisition technology, oleic acid (OA), linoleic acid (LA), α-linolenic acid (ALA), and their mixtures were clearly discriminated; various edible vegetable oils, different camellia oils (CAO), and adulterated CAOs were also successfully distinguished. Furthermore, the ISFE-DL method was combined in multicomponent quantitative analysis. The sensing elements of the CSA (3 × 4) were individually segmented for single-hole feature extraction containing information from 38,868 images of three UFAs, thereby allowing for the extraction of more features and augmenting sample size. After training with the MobileNetV3 small model, the determination coefficients of OA, LA, and ALA were 0.9969, 0.9668, and 0.7393, respectively. The model was embedded in the smartphone App "Intelligent Analysis Master" for one-click quantification. We provide an innovative approach for intelligent and efficient qualitative and quantitative analysis of UFAs and other compounds with similar characteristics.

Development of Highly Sensitive and Thermostable Microelectromechanical System Pressure Sensor Based on Array-Type Aluminum-Silicon Hybrid Structures.

In order to meet the better performance requirements of pressure detection, a microelectromechanical system (MEMS) piezoresistive pressure sensor utilizing an array-type aluminum-silicon hybrid structure with high sensitivity and low temperature drift is designed, fabricated, and characterized. Each element of the 3 × 3 sensor array has one stress-sensitive aluminum-silicon hybrid structure on the strain membrane for measuring pressure and another temperature-dependent structure outside the strain membrane for measuring temperature and temperature drift compensation. Finite-element numerical simulation has been adopted to verify that the array-type pressure sensor has an enhanced piezoresistive effect and high sensitivity, and then this sensor is fabricated based on the standard MEMS process. In order to further reduce the temperature drift, a thermodynamic control system whose heating feedback temperature is measured by the temperature-dependent structure is adopted to keep the working temperature of the sensor constant by using the PID algorithm. The experiment test results show that the average sensitivity of the proposed sensor after temperature compensation reaches 0.25 mV/ (V kPa) in the range of 0-370 kPa, the average nonlinear error is about 1.7%, and the thermal sensitivity drift coefficient (TCS) is reduced to 0.0152%FS/°C when the ambient temperature ranges from -20 °C to 50 °C. The research results may provide a useful reference for the development of a high-performance MEMS array-type pressure sensor.

A Diboronic Acid-Based Fluorescent Sensor Array for Rapid Identification of Lonicerae Japonicae Flos and Lonicerae Flos.

Lonicerae japonicae flos (LJF) and Lonicerae flos (LF) are traditional Chinese herbs that are commonly used and widely known for their medicinal properties and edibility. Although they may have a similar appearance and vary slightly in chemical composition, their effectiveness as medicine and their use in clinical settings vary significantly, making them unsuitable for substitution. In this study, a novel 2 × 3 six-channel fluorescent sensor array is proposed that uses machine learning algorithms in combination with the indicator displacement assay (IDA) method to quickly identify LJF and LF. This array comprises two coumarin-based fluorescent indicators (ES and MS) and three diboronic acid-substituted 4,4'-bipyridinium cation quenchers (Q1-Q3), forming six dynamic complexes (C1-C6). When these complexes react with the ortho-dihydroxy groups of phenolic acid compounds in LJF and LF, they release different fluorescent indicators, which in turn causes distinct fluorescence recovery. By optimizing eight machine learning algorithms, the model achieved 100% and 98.21% accuracy rates in the testing set and the cross-validation predictions, respectively, in distinguishing between LJF and LF using Linear Discriminant Analysis (LDA). The integration of machine learning with this fluorescent sensor array shows great potential in analyzing and detecting foods and pharmaceuticals that contain polyphenols.

Ensuring food safety by artificial intelligence-enhanced nanosensor arrays.

Current analytical methods utilized for food safety inspection requires improvement in terms of their cost-efficiency, speed of detection, and ease of use. Sensor array technology has emerged as a food safety assessment method that applies multiple cross-reactive sensors to identify specific targets via pattern recognition. When the sensor arrays are fabricated with nanomaterials, the binding affinity of analytes to the sensors and the response of sensor arrays can be remarkably enhanced, thereby making the detection process more rapid, sensitive, and accurate. Data analysis is vital in converting the signals from sensor arrays into meaningful information regarding the analytes. As the sensor arrays can generate complex, high-dimensional data in response to analytes, they require the use of machine learning algorithms to reduce the dimensionality of the data to gain more reliable outcomes. Moreover, the advances in handheld smart devices have made it easier to read and analyze the sensor array signals, with the advantages of convenience, portability, and efficiency. While facing some challenges, the integration of artificial intelligence with nanosensor arrays holds promise for enhancing food safety monitoring.

Battery-Free, Wireless Multi-Modal Sensor, and Actuator Array System for Pressure Injury Prevention.

Simultaneous monitoring of critical parameters (e.g., pressure, shear, and temperature) at bony prominences is essential for the prevention of pressure injuries in a systematic manner. However, the development of wireless sensor array for accurate mapping of risk factors has been limited due to the challenges in the convergence of wireless technologies and wearable sensor arrays with a thin and small form factor. Herein, a battery-free, wireless, miniaturized multi-modal sensor array is introduced for continuous mapping of pressure, shear, and temperature at skin interfaces. The sensor array includes an integrated pressure and shear sensor consisting of 3D strain gauges and micromachined components. The mechanically decoupled design of the integrated sensor enables reliable data acquisition of pressure and shear at skin interfaces without the need for additional data processing. The sensor platform enables the analysis of interplay among localized pressure, shear, and temperature in response to changes in the patient's movement, posture, and bed inclination. The validation trials using a novel combination of wireless sensor arrays and customized pneumatic actuator demonstrate the efficacy of the platform in continuous monitoring and efficient redistribution of pressure and shear without repositioning, thereby improving the patient's quality of life.

Junction Piezotronic Transistor Arrays Based on Patterned ZnO Nanowires for High-Resolution Tactile and Photo Mapping.

Recently, a great deal of interest has been focused on developing sensors that can measure both pressure and light. However, traditional sensors are difficult to integrate into silicon (Si)-based integrated circuits. Therefore, it is particularly important to design a sensor that operates on a new principle. In this paper, junction piezotronic transistor (JPT) arrays based on zinc oxide (ZnO) nanowire are demonstrated. And the JPT arrays show high spatial resolution pressure and light mapping with 195 dpi. Because ZnO nanowires are arranged vertically above the p-type Si channel's center of the transistor, the width of the heterojunction depletion region is constricted by the positive piezoelectric potential generated by strained ZnO. In addition, photogenerated charge carriers can be created in the Si channel when JPT is stimulated by light, which increases its electrical conductivity. Consequently, the external pressure and light distribution information can be obtained from the variation in the output current of the device. The prepared JPT arrays can be compatible with Si transistors, which make them highly competitive and make it possible to incorporate both pressure and light sensors into large integrated circuits. This work will contribute to many applications, such as intelligent clothing, human-computer interaction, and electronic skin.

Multicolorimetric Sensor Array Based on Silver Metallization of Gold Nanorods for Discriminating Dopaminergic Agents.

Dopaminergic agents are compounds that modulate dopamine-related activity in the brain and peripheral nerves within the pathways on both sides of the blood-brain barrier. Atypical levels of them can precipitate a multitude of neurological disorders, whose timely diagnosis signifies not only stopping the advancement of the illness but also surmounting it. A silver metallized gold nanorod (AuNRs) conditional sensor array, designed to detect dopaminergic agents for assessing nervous system disorders, yielded significant results in simultaneous detection and discrimination of Benserazide (Benz), Levodopa (L-DOPA), and Carbidopa (Carb). The array was composed of two different concentrations of silver ions as sensor elements (SEs), which generated unique signatures indicative of the presence of reductive target analytes, triggered by the incongruent formation of the Au@Ag core-shell, causing visual and fingerprint colorimetric patterns. Generating diverse responses is the key to the functionality of array-based sensing, which facilitated achieving spectral and color variation originating from the blue shift of AuNRs longitudinal localized surface plasmon resonance (LLSPR) in the extinction spectrum. Also, employing a smartphone camera enables clear visual discrimination across an extensive concentration span. Pattern recognition through linear discriminant analysis (LDA) underscored the robust discrimination accuracies of this sensor, along with quantification by means of partial least-squares regression (PLSR), affirming its potential for practical applications. Notably, the array demonstrated high sensitivity in detecting varied concentrations of target analytes, even in commercial drug samples. The sensor responses exhibited a linear correlation with the concentrations of Benz, L-DOPA, and Carb ranging from 1.59 to 100.0, 5.26 to 100.0, and 5.32 to 100.0 µmol L-1, respectively, and the minimum detectable concentrations for Benz, L-DOPA, and Carb were measured at 0.53, 1.75, and 1.77 µmol L-1, respectively. The implemented machine-learning-empowered array-based sensor represents advancements in dopaminergic agent tracing and naked eye detection.

Fluorescence array sensor based on lanthanide complex for pattern recognition detection of fluoroquinolone antibiotics.

Fluoroquinolone antibiotics, a class of animal and human useful antibiotics, are widely utilized in numerous fields including biomedical science, animal husbandry, and aquatic finfish farming. Its high demand and wide application have directly or indirectly led to substantial consumption and discharge of antibiotics, affecting not only the environment but also endangering human health through bioaccumulation. Hence, rapid and precise detection of trace antibiotics in water, food, and biological samples is critically important. This research synthesized Tb3+/Eu3+ complexes with dual emission centers, and a fluorescence sensor array was constructed with the fluorescence intensity ratio F1/F2 of the two emission centers as a signal. Different sensitization effect of fluoroquinolone antibiotics towards lanthanide complexes aided in differentiating five fluoroquinolone antibiotics from two others. Additionally, the sensor array can effectively detect fluoroquinolone antibiotics in real samples, suggesting its reliability and practicality of complex sample analysis. The excellent qualitative and quantitative analysis ability of this strategy for fluoroquinolone antibiotics offers a novel perspective for antibiotic residue detection, showcasing a new opportunity for lanthanide complex application in sensor arrays.

Deep convolutional neural network-based 3D fluorescence sensor array for sugar identification in serum based on the oxidase-mimicking property of CuO nanoparticles.

Developing sensor arrays capturing comprehensive fluorescence (FL) spectra from a single probe is crucial for understanding sugar structures with very high similarity in biofluids. Therefore, the analysis of highly similar sugar' structures in biofluids based on the entire FL of a single nanozyme probe needs more concern, which makes the development of novel alternative approaches highly wanted for biomedical and other applications. Herein, a well-designed deep learning model with intrinsic information of 3D FL of CuO nanoparticles (NPs)' oxidase-like activity was developed to classify and predict the concentration of a group of sugars with very similar chemical structures in different media. The findings presented that the overall accuracy of the developed model in classifying the nine selected sugars was (99-100 %), which prompted us to transfer the developed model to predict the concentration of the selected sugars at a concentration range of (1-100 µM). The transferred model also gave excellent results (R2 = 97-100 %). Therefore, the model was extended to other more complex applications, namely the identification of mixtures of sugars in serum and the detection of polysaccharides in different media such as serum and lake water. Notably, LOD for fructose was determined at 4.23 nM, marking a 120-fold decrease compared to previous studies. Our developed model was also compared with other deep learning-based models, and the results have demonstrated remarkable progress. Moreover, the identification of other possible coexisting interference substances in lake water samples was considered. This work marks a significant advancement, opening avenues for the widespread application of sensor arrays integrating nanozymes and deep learning techniques in biomedical and other diverse fields.