A Rapid and Sensitive Multi-Residue Colloidal Gold Lateral Flow Immunoassay for Simultaneous Detection of Pesticide Residues in Complex Food Matrices

A Rapid and Sensitive Multi-Residue Colloidal Gold Lateral Flow Immunoassay for Simultaneous Detection of Pesticide Residues in Complex Food Matrices | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article A Rapid and Sensitive Multi-Residue Colloidal Gold Lateral Flow Immunoassay for Simultaneous Detection of Pesticide Residues in Complex Food Matrices Zizhe Li, Xiaole Pan, Chen Xing, Yantong Pan, Zhanhui Wang, Jiancheng Li This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9350683/v1 This work is licensed under a CC BY 4.0 License Status: Under Revision Version 1 posted 12 You are reading this latest preprint version Abstract Multi-residue colloidal gold lateral flow immunoassays (CG-LFIAs) play a critical role in high-throughput, rapid, and customized food safety testing. In this study, a systematic strategy for constructing multi-residue CG-LFIAs was developed, using chlorantraniliprole (CTP), emamectin benzoate (EMB), and fipronil (FEN) as model analytes. A stepwise workflow, progressing from single-strip to multiple T lines, was employed to systematically optimize key factors affecting assay performance, including antibody labeling, T line spatial arrangement, membrane treatment, absorbent pad length, buffer composition, pH, reaction time, and chromatographic time, in order to balance multi-analyte interactions and reduce cross-interference. Matrix-specific sample pretreatment further enhanced assay robustness and reproducibility. The resulting multi-residue LFIA demonstrated satisfactory sensitivity, precision, and matrix tolerance, enabling simultaneous detection of multiple chemical pesticides in complex plant- and animal-derived matrices, including cowpea, chicken, egg, and milk. The proposed strategy provides a general framework for developing multi-residue LFIA platforms, improving analytical efficiency and offering a practical, on-site solution for rapid pesticide residue monitoring in complex food systems. Colloidal gold lateral flow immunoassay Multi-residue detection Pesticide residues Complex food matrices Figures Figure 1 Figure 2 Figure 3 Figure 4 Introduction The yield and quality of tropical crops are critically threatened by the persistent high temperature and humidity characteristic of tropical climates, which create ideal conditions for pest proliferation and disease outbreaks (Muhammad et al. 2015 ; Fallahi et al. 2025 ). These environmental stressors not only accelerate the metabolic rates of insect pests but also enhance the virulence of fungal pathogens, leading to severe crop damage and yield losses (Lee et al. 2024; Ahmad et al. 2024 ). The integrated application of highly effective insecticides, such as chlorantraniliprole (CTP), emamectin benzoate (EMB), and fipronil (FEN), has brought a marked advancement to the management of insect pests in tropical agriculture, enabling broad-spectrum and targeted control of economically important pests (Huang et al. 2025 ; Sun et al. 2023). These compounds belong to distinct chemical classes—anthranilic diamides, macrocyclic lactones, and phenylpyrazoles, which collectively reduce the risk of cross-resistance while ensuring optimal pest control efficacy (Mishra et al. 2016 ; Song et al. 2025a ). Nevertheless, despite their high efficacy, these insecticides are associated with potential cardiovascular, neurological, immunological, and genotoxic risks upon chronic or accidental exposure, and bioaccumulation along the food chain further exacerbates growing concerns regarding their residues in animal-derived food matrices (Sevgiler et al. 2010; Majumder al. 2023; Zhou et al. 2023 ; Liu et al. 2015 ). To ensure food safety, China, the European Union, the United States, and other jurisdictions have established stringent maximum residue limits (MRLs) for CTP, EMB, and FEN in food products (National Health Commission of the People's Republic of China. 2021). Therefore, there is an urgent need to develop rapid, sensitive, and reliable analytical strategies for the simultaneous detection of multiple insecticide residues. Immunoassay-based rapid detection technologies, particularly colloidal gold lateral flow immunoassays (CG-LFIAs), have attracted increasing attention owing to their operational simplicity, low cost, and suitability for on-site analysis. Numerous studies have reported LFIAs with satisfactory sensitivity and assay times of 10–15 min, providing practical tools for field screening (Meyer et al. 2013 ). However, most existing LFIAs are designed for a single analyte, which results in low detection efficiency and increased cost when multiple pesticides must be monitored simultaneously. Considering that agricultural products are often exposed to more than one pesticide during cultivation, single-analyte assays are insufficient for realistic monitoring scenarios (Xie et al. 2023 ; Wu et al. 2024 ; Song et al. 2025). Therefore, it is essential to develop CG-LFIA methods that can simultaneously detect pesticide residues in food. The development of LFIA for multi-residue detection has evolved into two primary models: the multi-strip format and the single-strip multi-T line format (Shu et al. 2024 ; Yin 2026; Pan et al. 2022 ). Multi-strip LFIA combination formats offer excellent multiplexing capability and modularity, making them suitable for high-throughput applications such as clinical diagnostics. Hong et al. (2023) established a nanozyme-based LFIA strip for rapid detection of blood evidence that showed high generality on 12 substrates and high specificity to human HGB among 13 animal blood samples. In application scenarios such as law enforcement systems and community-based detoxification programs, multiple single-analyte LFIA strips targeting different drugs of abuse can be flexibly combined to enable rapid and adaptable screening of illicit drugs in urine samples (Yan et al. 2019 ). However, the intrinsic working principles of multi-strip LFIA combination formats inevitably result in substantially increased costs, thereby limiting their applicability for routine testing of low-value-added agricultural products at the primary monitoring level. By contrast, the single-strip LFIA format with multi-T lines enables the simultaneous detection of multiple analytes within a single device. By eliminating the need for modular on-site reconfiguration and parallel strip assembly, this configuration offers a more cost-effective and operationally streamlined solution, making it particularly attractive for rapid on-site screening and large-scale trend monitoring of agricultural products (Chen et al. 2025). Despite these apparent advantages, the development of single-strip multi-T line LFIA remains technically challenging. The integration of multiple recognition elements within a confined and continuous flow path inevitably introduces complex interactions among immunoreactions. Specifically, competition for labeled probes, nonuniform flow dynamics along the strip, and signal interference between adjacent T lines may occur, collectively leading to compromised analytical sensitivity, reduced quantitative accuracy, and poor result reproducibility (Sun et al. 2018 ). Moreover, the simultaneous detection of chemically diverse analytes is further complicated by intrinsic differences in antigen–antibody affinity, reaction kinetics, and optimal assay conditions for individual targets, making it difficult to achieve balanced signal responses across all T lines. Furthermore, considering the challenges faced during the production, sales, storage, and use of LFIA products in tropical regions, where high humidity and temperature are prevalent, excessive moisture can accelerate membrane aging, alter capillary flow, and destabilize colloidal gold probes (Li et al., 2024 ). These factors make test strips more susceptible to signal fluctuations and performance deterioration. This issue becomes even more critical when the application involves both plant- and animal-derived matrices. Complex matrices, such as meat, eggs, and milk, contain high levels of proteins, lipids, and endogenous interferents, which can affect chromatographic behavior, nanoparticle stability, and antigen–antibody interactions, thereby compromising assay sensitivity and reproducibility (Wang et al., 2024 ; Li et al., 2024 ). However, research on the matrix effects in LFIA is limited. Some studies suggest that proteins and starches in fruits and vegetables are the primary contributors to matrix effects, while others argue that water-soluble proteins and polysaccharides in animal-derived foods may also interfere with the results of immunoassays to varying degrees. Therefore, establishing a well-designed multi-residue LFIA development process and conducting meticulous and rigorous optimization of the key factors influencing LFIA performance are essential to ensure the stability, robustness, and reliability of LFIA strips intended for use in such challenging environments. To address these challenges, the present study proposes a systematic optimization strategy for single-strip multi-T line LFIA. CTP, EMB, and FEN, representative pesticides for tropical crops, were selected as target analytes to develop CG-LFIA for the simultaneous detection of CTP, EMB, and FEN in cowpea, as well as in representative animal-derived foods, including chicken, eggs, and milk. Through the systematic optimization of antibody labeling conditions, membrane configuration, buffer composition, and chromatographic parameters, this study aims to establish a multi-residue LFIA that maintains acceptable sensitivity and stability across diverse and complex matrices, even under humid conditions. The proposed method seeks to enhance detection efficiency while retaining the simplicity and portability essential for field applications, providing a practical tool for rapid on-site screening of pesticide residues in tropical regions. Materials and Methods Reagents and Instruments Chlorantraniliprole (CTP), emamectin benzoate (EMB), and fipronil (FEN) standards (purity ≥ 98%, purchased from TMO Standard Reference Materials Center); Abamectin, acephate, bromophos, dichlorvos, isocarbophos, chlorthiophos, methamidophos, chlorpyrifos, dimethoate, trichlorfon, malathion, parathion, triazophos, phorate, fonofos, phoxim, and omethoate (purity ≥ 98%, purchased from Tianjin Altech Technology Co., Ltd.); CTP coating antigen, EMB coating antigen, FEN coating antigen, and corresponding monoclonal antibodies (prepared in-house, titer ≥ 1:10000); Goat anti-mouse polyclonal antibody (purity ≥ 95%, purchased from Shanghai Jieyi Biotechnology Co., Ltd.); Bovine serum albumin, Trehalose dihydrate, Proclin-300 (BSA, Sigma-Aldrich, purity ≥ 98%); Potassium carbonate (K 2 CO 3 ), sodium chloride (NaCl), sucrose, disodium hydrogen phosphate dodecahydrate (Na 2 HPO 4 ·12H 2 O), sodium dihydrogen phosphate dihydrate (NaH 2 PO 4 ·2H 2 O), polyvinylpyrrolidone K30 (PVP), hydrochloric acid (HCl), methanol, and acetonitrile (all analytical grade, purchased from Sinopharm Chemical Reagent Co., Ltd.). Experimental equipment used in this study included a vortex mixer (model HD-60-IV, Beijing Tongzheng Biotechnology Development Co., Ltd.), a colloidal gold reader (Beijing Weidvick Biotechnology Co., Ltd.), a water bath incubator (Shanghai Zhetu Scientific Instrument Co.), a digital high-speed chopper (Shanghai GoldBio Technology Co., Ltd.), a 3D gold spraying and membrane scribing instrument (model BIODOT, Shanghai GoldBio Technology Co., Ltd.), a double-door forced draft dry oven (model LD-6, Shanghai Shibei Instrument Equipment Factory), a magnetic stirrer hotplate (IKA, Germany), adjustable pipettes (Eppendorf, Germany), a high-speed refrigerated centrifuge (Eppendorf, Germany), an electronic balance (Sartorius Analytical, Germany; range: 3100 g, precision: 0.01 g), an ultra-pure water system (model Milli-Q, Millipore, USA), ELISA 96-well plates (Costar, USA), a micro-spectrophotometer (model Nanodrop, Thermo Scientific, USA), a nitrogen evaporator (Organomation Associates, USA), a pH meter (Mettler Toledo, Switzerland), and a mini benchtop centrifuge (model PMC-880, Tomy Kogyo, Japan). Establishment of the Single-Residue CG-LFIA Assembly of LFIA strips: The coating antigen and goat anti-mouse IgG (1 mg/mL) were dispensed onto the T line and C line of the NC membrane, respectively, using a three-dimensional spraying and membrane scribing system. The membrane was dried in an oven at 45°C overnight. Thereafter, the NC membrane, sample pad, and absorbent pad were sequentially laminated onto a polyvinyl chloride (PVC) backing card, with a 2.0 mm overlap between adjacent components to ensure continuous capillary flow. The assembled cards were cut into strips with a width of 3.2 mm and stored at room temperature in a dry environment until use (Li et al. 2024 ; Wang et al. 2024 ). Preparation of colloidal gold-labeled antibody (CG-mAb): CG nanoparticles with an average diameter of approximately 30 nm were synthesized and used for antibody labeling as previously described (Zhang et al. 2022 ). Briefly, 1 mL of the CG suspension was transferred into a 1.5 mL centrifuge tube, and its pH was adjusted by dropwise addition of 0.1 M K 2 CO 3 under gentle mixing, followed by incubation at room temperature for 3 min. Subsequently, 10 µg of mAb (1 mg/mL) was added and incubated for 10 min to allow adsorption onto the CG surface. Blocking was performed by adding 20 µL of 20% BSA and incubating for an additional 10 min. The mixture was then centrifuged at 10,000 rpm and 4°C for 10 min. After discarding the supernatant, the pellet was resuspended in 200 µL of reconstitution buffer, dispersed by ultrasonication, and stored at 4°C in a sealed conditions. For single-residue detection, CG-mAb and sample extracts were added to a microplate, after which the LFIA strips were inserted, and the results could be interpreted within several minutes. When both the C line and T line exhibited clear color development, the result was considered negative. As the concentration of the target analyte increased, a larger proportion of CG-mAb bound to the free analyte in solution, leading to a gradual decrease in T line intensity; complete disappearance of the T line was interpreted as a positive result (Luo et al. 2025 ). Optimization of key conditions for LFIA: Gradient experiments were conducted to optimize critical parameters, including the antibody dilution buffer (0.5% BSA-1), blocking buffer, NC membrane type, coating buffer, and coating antigen dilution ratio. The optimization criteria were based on the color intensity of the T line and the inhibition rate, which was calculated as: Inhibition rate (%) = [1 - (T/T 0 )] × 100% where T represents the T line intensity obtained with analyte-containing standards, and T₀ represents the T line intensity of the blank buffer. Conditions yielding high signal intensity for negative samples and strong inhibition in the presence of analytes were selected for subsequent experiments. Establishment of a Multi-Residue CG-LFIA The assembly and sample loading procedures of the multi-residue CG-LFIA were essentially identical to those of the single-analyte format, with the primary difference being the configuration of T lines on the NC membrane. In the multi-residue system, three independent competitive immunoreactions occur simultaneously on a single strip; therefore, its analytical performance is highly dependent on the rational design and fine-tuning of assay parameters. The three target analytes (CTP, EMB, and FEN) belong to distinct chemical classes (diamide, macrolide, and organophosphate, respectively), and their corresponding antigen–antibody systems exhibit different affinities and kinetic behaviors. Without proper coordination of spatial arrangement and reaction conditions, mutual interference among the three systems may occur, leading to signal imbalance, reduced sensitivity, or even misinterpretation of results. Consequently, balanced integration of the three detection modules is a prerequisite for reliable multi-residue analysis. To minimize cross-interference and ensure independent yet synchronous signal development, six spatial configurations of the three T lines (T1, T2, and T3) were designed and evaluated (Fig. 1 ). Each T line was immobilized with a specific coating antigen corresponding to one target pesticide, while the C line was coated with goat anti-mouse IgG to verify chromatographic validity. The optimized layout was selected based on signal uniformity, chromatographic stability, and discrimination ability among targets. According to the predefined interpretation criteria, when the C line exhibits clear color development, the disappearance or significant fading of any T line (T1–T3) indicates a positive result for the corresponding analyte (Nos. 1–8 in Fig. 1 ). Conversely, absence of color on the C line signifies strip failure, and any color pattern on the T lines under this condition is considered invalid. This design allows intuitive visual interpretation while maintaining consistency with conventional competitive LFIA principles. Following structural establishment, the analytical performance of the multi-residue LFIA was systematically evaluated. Standard solutions of CTP, EMB, and FEN were prepared at gradient concentrations to construct calibration curves. The same sample pretreatment procedures used in the single-residue assays were uniformly applied to ensure methodological consistency. Matrix-matched standard curves were generated for each food matrix to assess matrix effects and practical applicability. Visual limits of detection (vLODs) were determined by spiking blank samples with gradient concentrations of each analyte until a clear disappearance of the corresponding T line was observed. For accuracy and precision assessment, blank samples were fortified at three concentration levels, and the recovery rates and coefficients of variation (CVs) were calculated. All experiments were conducted in triplicate, and results were expressed as mean ± standard deviation (SD). Data processing, curve fitting, and graphical presentation were performed using Origin 2022 software. Sample preparation and the application of CG-LFIA Standard solutions of CTP, EMB, and FEN were prepared at gradient concentrations for the construction of calibration curves. To ensure compatibility with on-site screening while minimizing matrix interference, different pretreatment strategies were adopted according to matrix characteristics. Chicken and egg samples were extracted with acetonitrile, followed by nitrogen blow-down to concentrate the extracts, and subsequently diluted with reaction buffer prior to analysis. In contrast, milk and cowpea samples were directly diluted with reaction buffer to attenuate matrix effects, and matrix-matched standard curves were constructed accordingly. The visual limit of detection (vLOD) was defined as the lowest analyte concentration at which the color intensity of the corresponding T line was visually weaker than that of the C line. Specificity was evaluated using 17 commonly used pesticides at a concentration of 1000 ng/mL to assess potential cross-reactivity. For the evaluation of accuracy and precision, blank samples were fortified with standard analytes at three concentration levels. The spiked recovery was calculated as the ratio of the measured concentration to the spiked concentration multiplied by 100%, and precision was expressed as the coefficient of variation (CV), defined as the standard deviation divided by the mean value multiplied by 100%. The treated extracts were directly subjected to the multi-residue CG-LFIA, providing the basis for subsequent performance evaluation across different matrices. Results and Discussion Development of single-residue CG-LFIA Optimization results of single-residue CG-LFIA To optimize the antibody conjugation with CG and to enhance the stability, sensitivity, and specificity of the LFIA, systematic optimization was conducted for critical experimental parameters. This optimization strategy was applied to CTP, EMB, and FEN (Fig. S1 ). The optimization was evaluated based on inhibition rate, color intensity of the C and T lines, band morphology, and background clarity (Wang et al., 2024 ). The pH of the CG labeling system, which plays a pivotal role in regulating electrostatic interactions between antibodies and CG, was incrementally adjusted by the addition of 0.1 M K 2 CO 3 (Chen et al. 2025). The inhibition rate increased as the amount of potassium carbonate increased. For CTP, the CG-mAb reached peak performance at an addition volume of 26 µL, which resulted in optimal color intensity and overall assay performance. In contrast, the optimal addition volumes for EMB and FEN were determined to be 25 µL and 38 µL, respectively (Fig. S1 A). The appropriate antibody loading amount is one of the critical factors for the development of highly sensitive LFIAs (Cui et al., 2026). For all three LFIAs, 10 µg of antibody yielded satisfactory performance, providing high inhibition efficiency and sufficient color intensity (Fig. S1 B). To further enhance assay sensitivity, the antibody dilution buffer and NC membrane type were optimized to minimize nonspecific adsorption and increase the activity of the CG-mAb probes. The results for all three LFIAs indicated that 0.5% BSA-1 (solution B1) effectively stabilized the antibody conformation and reduced nonspecific binding (Fig. S2A). The S 2.5 NC membrane was selected for its ability to provide well-defined T lines, high color intensity, and high inhibition rates (Fig. S2B). Subsequently, the composition and dilution of the coating buffer were systematically optimized, as the ionic strength and pH of the buffer can significantly affect antigen adsorption and the uniformity of the T lines. For CTP, EMB, and FEN, 0.02 M phosphate buffer (PB, pH 7.4, containing 1% BSA), 0.02 M borate buffer (BB), and 0.02 M phosphate-buffered saline (PBS) were selected as the respective coating buffers, which effectively reduced T line diffusion and aggregation (Fig. S2C). Finally, the dilution ratios of the coating antigens were optimized. When the coating antigens for CTP and FEN were diluted 15-fold and that for EMB was diluted 10-fold, well-defined T lines were obtained, with a good balance between color intensity and inhibition efficiency (Fig. S2D). Overall, the comprehensive optimization of these key parameters resulted in a robust CG-LFIA with clear visual signals, low background interference, and high sensitivity and specificity, providing a reliable foundation for subsequent analytical applications. Performance evaluation of single-residue CG-LFIA Using the optimized conditions, standard calibration curves for CTP, EMB, and FEN were established (Fig. S3). The developed methods exhibited vLOD of 2, 8, and 27 ng/mL for CTP, EMB, and FEN, respectively. The corresponding IC 50 values were 1.51, 5.56, and 9.06 ng/mL, with linearity ranges determined as 0.99–2.29 ng/mL for CTP, 3.42–9.01 ng/mL for EMB, and 2.65–30.97 ng/mL for FEN. These results confirm the high sensitivity and robustness of the developed assays for detecting the target pesticides. To evaluate and mitigate matrix effects, different sample pretreatment and dilution strategies were optimized for CTP, EMB, and FEN in chicken, egg, milk, and cowpea matrices (Fig. S4). The strategies aimed to minimize interference from complex sample components that could affect assay accuracy and reproducibility. Milk samples were directly diluted 50-fold with assay buffer, while cowpea supernatants were diluted 2.5-fold. Similarly, for EMB, the reconstituted chicken and egg extracts were diluted 10-fold and 2-fold, respectively, while milk samples were diluted 50-fold, and cowpea extracts were diluted 2-fold. For FEN, dilution factors of 15-fold and 3-fold were applied to chicken and egg samples, respectively, whereas milk and cowpea samples were diluted 10-fold and 6-fold. Under these optimized conditions, the matrix-matched calibration curves for each pesticide showed excellent agreement with the standard curves prepared in assay buffer, indicating that matrix effects were largely mitigated. The calibration curves obtained from treated chicken, egg, and cowpea samples closely matched their corresponding standard curves, suggesting that these matrices exerted minimal interference on the detection system. In contrast, milk exhibited a pronounced matrix effect, particularly in the detection of CTP. This is likely due to the complex composition of milk, which contains high levels of proteins (such as casein and whey proteins), lipid globules, and lactose. These components can interfere with the assay through several mechanisms: Nonspecific Adsorption—Protein micelles and lipid globules in milk are prone to nonspecific adsorption of antibodies, which can lead to probe aggregation and an increase in background signals. This phenomenon reduces the sensitivity and clarity of T lines; Cross-reactivity—Certain whey protein components in milk may interact with the antibody-antigen recognition sites, leading to cross-reactivity and compromising the assay's specificity. This could result in false positives or diminished assay performance (Yonn et al. 2021). These matrix effects suggest the importance of considering the composition of complex matrices like milk when developing immunoassays. While the assay performed well in simpler matrices like chicken, egg, and cowpea, further optimization will be necessary for matrices with more complex compositions, such as milk, to enhance the detection performance and minimize interference. The vLOD values of the three pesticides were determined in different sample matrices (Fig. 2 ). In cowpea samples, the vLOD values were 20, 40, and 50 µg/kg for CTP, EMB, and FEN, respectively; in chicken, the vLOD values for CTP, EMB, and FEN were 4, 10, and 27 µg/kg, respectively; in egg samples, the corresponding vLOD values were 20, 10, and 9 µg/kg; in milk, the vLOD values for CTP, EMB, and FEN were 50, 50, and 81 ng/mL, respectively. The single-residue test strips developed in this study generally met the detection requirements specified by national standards and were suitable for on-site screening at the primary level. For EMB in chicken and egg samples and for FEN in chicken, egg, and milk samples, no MRLs have been established in the Chinese national standard; therefore, the corresponding European Union MRLs were used as reference criteria. Development of Multi-residue CG-LFIA Optimization results of multi-residue CG-LFIA Building on the optimization results obtained from the single-residue CG-LFIAs, further refinements were made to the structural configuration and operational parameters to enable the successful development of a multi-residue CG-LFIA. Key parameters such as the coating positions on the NC membrane, membrane drying time, absorbent pad length, assay buffer composition and pH, reaction time, and chromatographic time were systematically optimized to facilitate high-throughput detection as follows: The spatial arrangement of the three T lines on the NC membrane was evaluated with respect to color intensity, visual distinguish ability, and inhibition efficiency. Six different configurations were tested to determine the optimal arrangement. The results showed that the position of the T lines did not significantly affect the assay performance. This outcome can be attributed to the high protein-binding capacity of the NC membrane chosen during preliminary experiments (Fig. 3 A). The membrane facilitates uniform immobilization of the coating antigens through hydrophobic interactions and covalent coupling, minimizing positional bias and ensuring even distribution of binding sites. Given the considerations of signal clarity and practical layout, the configuration with FEN at T1, EMB at T2, and CTP at T3 was selected for subsequent experiments. This layout offered optimal visibility of the T lines and ensured smooth chromatographic flow, which is essential for reliable performance in multi-residue detection; The composition of the reaction buffer was another critical factor affecting colloidal gold stability, analyte solubility, and antibody–antigen interactions. Deionized water, which lacks buffering capacity and electrolytes, caused aggregation of CGs and reduced their binding efficiency. Phosphate buffer (PB) and borate buffer (BB) demonstrated advantages for individual analytes; however, their limited universality, likely due to suboptimal ionic strength or charge-related effects, restricted their overall applicability in the multi-residue format. In contrast, phosphate-buffered saline (PBS) provided a moderate ionic strength and a stable pH environment, ensuring uniform migration of gold nanoparticles and consistent performance across all three pesticides. As such, PBS was selected as the optimal reaction buffer (Fig. 3 B); The drying time of the NC membrane was found to significantly influence assay stability and sensitivity. Inadequate drying resulted in incomplete immobilization of the coating antigens and secondary antibodies, leading to unstable or faint T line signals. On the other hand, excessive drying negatively affected chromatographic efficiency and signal sensitivity. Through comparative evaluation of various drying durations, it was determined that overnight drying (≥ 12 h at 45°C) provided consistent and stable color intensity and inhibition performance for all three pesticides. This drying time was subsequently adopted for further experiments (Fig. 3 C); The length of the absorbent pad was optimized to regulate capillary force and migration velocity. An excessively long absorbent pad generated strong suction, which led to rapid sample migration and insufficient antigen–antibody interaction, thereby reducing assay sensitivity. Conversely, a shorter absorbent pad resulted in slower liquid migration, which prolonged the assay time and sometimes prevented complete membrane wetting. Among the various lengths tested, 15 mm was found to provide the optimal balance between signal intensity, inhibition efficiency, and assay time for CTP, EMB, and FEN. This length enabled rapid and uniform sample flow, sufficient antigen–antibody interaction, and smooth chromatographic flow (Fig. 3 D); Buffer pH was evaluated for its impact on antibody conformation and analyte ionization, both of which are critical determinants of competitive binding in immunoassays. At pH values above 8.0, electrostatic interactions between antibodies and gold nanoparticles were weakened, resulting in partial desorption of labeled antibodies. On the other hand, acidic conditions (pH < 5.5) caused antibody denaturation, thereby reducing binding specificity. A pH of 7.0 effectively preserved antibody activity, stabilized CG-mAb conjugates, and maintained analytes in favorable binding states, resulting in maximal signal intensity and inhibition efficiency (Fig. 3 E). Incubation reaction time and chromatographic times were optimized to ensure sufficient competitive interaction while minimizing signal diffusion. A incubation reaction time of 5 minutes was found to produce strong, uniform T line signals with high inhibition efficiency. Shorter reaction times resulted in incomplete binding, while prolonged reaction times led to reduced visual contrast and compromised signal clarity (Fig. 3 F). For chromatographic time, a duration of 7 minutes was optimal, allowing adequate interaction between the gold-labeled antibodies and immobilized antigens. Shorter chromatographic times limited binding efficiency, while excessive migration resulted in signal attenuation due to over-diffusion of the analyte within the membrane matrix (Fig. 3 G). Through the systematic optimization of these parameters, balanced multi-analyte detection was achieved, and a robust multi-residue CG-LFIA was developed for the simultaneous detection of CTP, EMB, and FEN. The resulting assay demonstrated improved sensitivity, stable performance, and reduced interference, making it suitable for high-throughput detection in complex matrices. Performance evaluation of multi-residue CG-LFIA Under the optimized conditions, the multi-residue LFIA demonstrated reliable analytical performance for the simultaneous detection of CTP, EMB, and FEN, as shown in Fig. 4 A. The calibration curves for the three pesticides exhibited high sensitivity, with vLOD of 0.5, 8, and 16 ng/mL for CTP, EMB, and FEN, respectively. The corresponding IC₅₀ values, which represent the concentration at which 50% inhibition occurs, were 0.31, 3.68, and 4.00 ng/mL for CTP, EMB, and FEN. The linear detection ranges were 0.18–0.55 ng/mL for CTP, 1.76–7.69 ng/mL for EMB, and 1.12–14.35 ng/mL for FEN, with correlation coefficients close to 0.99. These results indicate excellent linearity and quantitative reliability across the multi-residue assay format. Notably, the simultaneous configuration showed no evident loss in sensitivity compared to the single-residue LFIA systems, suggesting that the competitive interactions among multiple targets and probes were effectively balanced through careful optimization of experimental parameters. To assess matrix effects, matrix-matched calibration curves were established using processed extracts from chicken, egg, milk, and cowpea. For all matrices, appropriate sample dilution was applied to mitigate matrix interference. Chicken and egg samples were subjected to nitrogen evaporation, followed by reconstitution and dilution 10-fold and 4-fold, respectively. Milk samples were diluted 50-fold with assay buffer, and cowpea supernatants were diluted 2.5-fold. These adjustments helped to minimize matrix effects, as evidenced by the close overlap of matrix-matched calibration curves with the standard curves prepared in assay buffer (Fig. 4 B-E). The results indicated that matrix effects were largely eliminated in the treated chicken, egg, and cowpea samples, with the matrix curves showing good agreement with the corresponding standard curves. However, milk samples exhibited a pronounced matrix effect, which could be attributed to the complex composition of milk, including high levels of proteins (casein and whey proteins), lipids, and lactose. The vLODs for the real samples ranged from 10 to 50 µg/kg in cowpea, 1.6 to 16 µg/kg in chicken, 4 to 10 µg/kg in egg, and 15 to 40 ng/mL in milk (Fig. 4 B-E). With the exception of EMB and FEN in milk, all vLOD values met the relevant regulatory requirements, demonstrating that the proposed assay is suitable for rapid screening of pesticide residues in a wide range of sample matrices. Notably, the relatively higher detection limits in milk emphasize the strong inhibitory effects of complex animal-derived matrices, suggesting that further optimization is needed to address these challenges. Table 1 Summary of spike-and-recovery results for three target analytes in different matrices Sample CTP EMB FEN Recovery (%) CV (%) Recovery (%) CV (%) Recovery (%) CV (%) Cowpea 62.5–87.2 5.1–13.3 60.1–83.1 5.9–9.6 55.5–64.8 6.3–8.5 Chicken 111.0–116.3 6.7–10.5 86.7–97.8 7.4–12.1 101.2–110.6 7.1–13.4 Egg 79.1–93.7 8.6–12.1 63.3–75.5 6.2–8.6 71.4–81.4 9.2–14.5 Milk 105.4–126.8 6.9–15.1 77.6–80.7 7.8–14.4 114.2–123.6 5.3–13.3 The accuracy and precision of the multi-residue CG-LFIA were evaluated through spike-and-recovery experiments conducted at three concentration levels for each matrix. The recovery rates ranged from 86.7% to 116.3% in chicken, 63.3% to 93.7% in egg, 77.6% to 126.8% in milk, and 55.5% to 87.2% in cowpea, with coefficients of variation (CVs) below 15% for all analytes and matrices. These results indicate that the developed assay provides acceptable analytical reliability for on-site screening applications. The relatively lower recoveries observed in cowpea samples are likely due to the adsorption of pesticides by plant cell wall components, as well as interference from pigments and polyphenols, which reduce the bioavailable fraction of the target analytes (Li et al. 2024 ; Lin et al. 2019). Variability observed in milk samples was mainly attributed to protein–lipid interactions, competitive binding effects, and partial obstruction of membrane pores (Burkin et al. 2018 ). These factors can influence chromatographic behavior and antigen–antibody recognition. Despite these challenges associated with complex matrices, the assay maintained stable performance within acceptable tolerance ranges for rapid qualitative and semi-quantitative analysis. Table 2 Comparison of CG-LFIA methods References Sample Analysts vLOD(µg/kg) Wu et al. 2024 Brown rice, apples, soil CTP 50 Chinese cabbage 100 Chen et al. 2023 Cabbage, tomatoes FEN 50 This work Chicken, Egg, Milk, Cowpea CTP 1.6–15 EMB 8–50 FEN 6–40 Currently, most studies focus on the detection of single analytes, which limits analytical throughput and increases costs. In contrast, reports on multi-residue LFIAs for the simultaneous detection of CTP, EMB, and FEN remain scarce (Table 2 ). Wu et al. developed a highly sensitive single-residue LFIA for CTP, achieving a vLOD of 1.25 ng/mL and matrix vLODs of 0.05–0.1 mg/kg in rice, apple, soil, and cabbage, all of which were below the corresponding MRLs, demonstrating its suitability for rapid field screening. Similarly, Chen et al. established immunoenzymatic assays (icELISA) and LFIAs for six compounds, including FEN, reporting a vLOD of 50 ng/g for FEN in cabbage and tomato. Moreover, these assays were limited to single-analyte formats. In this study, a multi-residue CG-LFIA was successfully developed for the simultaneous detection of CTP, EMB, and FEN in both plant- and animal-derived matrices, including chicken, egg, milk, and cowpea. Through systematic optimization of key parameters (such as antibody labeling conditions, T line spatial arrangement, membrane treatment, absorbent pad length, buffer composition and pH, as well as reaction times) balanced multi-analyte detection was achieved with minimal interference. The assay demonstrated satisfactory sensitivity, reproducibility, and matrix tolerance, with visual limits of detection meeting regulatory requirements across most tested samples. This systematic optimization strategy provides a generalizable framework for the construction of multi-residue LFIA platforms, enhancing analytical efficiency and offering a practical tool for rapid, on-site screening of pesticide residues in complex food matrices. Conclusion In this study, a robust multi-residue CG-LFIA was successfully established for the simultaneous detection of CTP, EMB, and FEN in both plant- and animal-derived foods. Systematic optimization of antibody labeling, T line layout, membrane processing, absorbent pad length, buffer conditions, and reaction parameters enabled balanced detection of multiple analytes with high sensitivity, reproducibility, and matrix tolerance. The assay achieved visual detection limits meeting regulatory requirements for most matrices and demonstrated consistent performance in spike-and-recovery experiments. The proposed strategy provides a general framework for developing multi-residue LFIA platforms, improving analytical efficiency and offering a practical, on-site solution for rapid pesticide residue monitoring in complex food systems. Declarations CRediT authorship contribution statement Zizhe Li: Writing - original draft, Investigation, Software, Data curation, Visualization. Xiaole Pan: Methodology, Software, Formal analysis, Validation, Visualization, Writing - original draft. Chen Xing: Investigation, Software. Yantong Pan: Supervision, Conceptualization. Zhanhui Wang: Funding acquisition, Investigation, Project administration, Supervision. Jiancheng Li: Conceptualization, Funding acquisition, Supervision, Writing - review & editing, Resources. Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. Acknowledgments This work was supported by the financial support of the Natural Nature Science Foundation of China (31972738) References Ahmad F, Kusumiyati K, Soleh MA, Khan MR, Sundari RS (2024) Chili cultivars vulnerability: a multi-factorial examination of disease and pest-induced yield decline across different growing microclimates and watering regimens. 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Drug and Chemical Toxicology 33(4): 348–356. https://doi.org/10.3109/01480541003734048 Song XY, Xie WR, Yuan D (2025a) Application status, problems and countermeasures of colloidal gold rapid detection technology in pesticide residue analysis of cowpea. China Food Safety Magazine (07): 11–13. https://doi.org/10.16043/j.cnki.cfs.2025.07.040 Song AN, Wang M, She YX, Jin MJ, Cao Z, Abd El-Aty AM, Wang J (2025b) Evaluation and validation of colloidal gold immunochromatographic qualitative testing products for the detection of emamectin benzoate, isocarbophos, and fipronil in cowpea samples. Foods 14(3): 478. https://doi.org/10.3390/foods14030478 Sun Y, Yang J, Yang S, Sang Q, Teng M, Li Q, et al (2018) Development of an immunochromatographic lateral flow strip for the simultaneous detection of aminoglycoside residues in milk. RSC Advances 8(17): 9580-9586. https://doi.org/10.1039/C8RA01116H Shu L, Liu W, Wang Z, Zhang Z, Wang Y, Darwish A, Wang J, Zhang H (2024) Dual-plasmonic CuS@Au heterojunctions synergistic enhanced photothermal and colorimetric dual signal for sensitive multiplexed LFIA. Biosensors & Bioelectronics 255: 116235. https://doi.org/10.1016/j.bios.2024.116235 Wang J, Bu T, Cui Q, Li Z, Xu Q, Jiang ZX, et al (2024) A broad-spectrum antibody-based lateral flow immunoassay for detection of carbofuran and 3-hydroxy-carbofuran. Food Chemistry 465(2): 142062. https://doi.org/10.1016/j.foodchem.2024.142062 Wu Y, Li J, Zhu J, Zhang ZX, Zhang SG, Wamh MH, et al (2024) A rapid and sensitive gold nanoparticle-based lateral flow immunoassay for chlorantraniliprole in agricultural and environmental samples. Foods 13(2): 205. https://doi.org/10.3390/foods13020205 Xie GF, Lai F, Wang YR, Zhou XL, Wang XL (2023) The progress of the application of colloidal gold immunochromatography in rapid food detection. China Food Safety Magazine (32): 181–185. https://doi.org/10.16043/j.cnki.cfs.2023.32.047. Yan W, Wang K, Xu H, Huo X, Jin Q, Cui D (2019) Machine learning approach to enhance the performance of MNP-labeled lateral flow immunoassay. Nano-Micro Letters 11,7. https://doi.org/10.1007/s40820-019-0239-3 Yin Y, Ma M, Jia J, Gong C, Cai Y (2026) Portable SERS-based lateral flow strip for simultaneous rapid detection of three typical corn mycotoxins. Food Chemistry 502: 147677. https://doi.org/10.1016/j.foodchem.2025.147677 Yoon BK, Sut TN, Yoo KY, Lee SH, Hwang YK, Jackman JA, et al (2021) Lipid bilayer coatings for rapid enzyme-linked immunosorbent assay. Applied Materials Today 24: 101128. https://doi.org/10.1016/j.apmt.2021.101128 Zhang X, Ding M, Mao Y, Huang X, Xie X, Song L, et al. (2022) A comparative study of “turn-off” and “turn-on” mode lateral flow immunoassays for T-2 toxin detection. Sensors and Actuators B: Chemical 359: 131545. https://doi.org/10.1016/j.snb.2022.131545 Zhou XY, Wen X, Jia XF, et al. (2023) Determination of 314 pesticide residues in animal-derived foods by liquid chromatography–tandem mass spectrometry. Journal of Zhejiang Agricultural Sciences 64(04): 940–944. https://doi.org/10.16178/j.issn.0528-9017.20220669 Additional Declarations No competing interests reported. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-9350683","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":635534214,"identity":"42ace664-33d1-4d3e-8915-5a834fa60d3b","order_by":0,"name":"Zizhe Li","email":"","orcid":"","institution":"Sanya Institute of China Agricultural University","correspondingAuthor":false,"prefix":"","firstName":"Zizhe","middleName":"","lastName":"Li","suffix":""},{"id":635534215,"identity":"f664b5dd-c8dc-44d6-8928-868d488025a6","order_by":1,"name":"Xiaole Pan","email":"","orcid":"","institution":"Sanya Institute of China Agricultural University","correspondingAuthor":false,"prefix":"","firstName":"Xiaole","middleName":"","lastName":"Pan","suffix":""},{"id":635534216,"identity":"743af66d-d375-4a2f-877a-8c405ce96efd","order_by":2,"name":"Chen Xing","email":"","orcid":"","institution":"Sanya Institute of China Agricultural University","correspondingAuthor":false,"prefix":"","firstName":"Chen","middleName":"","lastName":"Xing","suffix":""},{"id":635534217,"identity":"5e35186e-2954-4018-a823-0cc46439c81a","order_by":3,"name":"Yantong Pan","email":"","orcid":"","institution":"Sanya Institute of China Agricultural University","correspondingAuthor":false,"prefix":"","firstName":"Yantong","middleName":"","lastName":"Pan","suffix":""},{"id":635534218,"identity":"bad4fd1e-5396-43e6-b9d3-bda9bcf70bbd","order_by":4,"name":"Zhanhui Wang","email":"","orcid":"","institution":"Sanya Institute of China Agricultural University","correspondingAuthor":false,"prefix":"","firstName":"Zhanhui","middleName":"","lastName":"Wang","suffix":""},{"id":635534219,"identity":"5fe8d481-fbe8-4b24-ab55-c3817500be48","order_by":5,"name":"Jiancheng Li","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAyUlEQVRIiWNgGAWjYBACAzBZISEH4bIRreWMjTGJWhjb0hIbiNZizt77+MMHtsPp/e1nDBg+lB1m4J/dgF+LZc9xM8kZPIdzZ5zJMWCcce4wg8SdAwQcdiONjZlH4nDuBoYcA2betsMMBhIJBLUwf/5jcDjdgP+NAfNfIrUwSDMkpCUYSABtYSRKy5ljbJI9B2wMZ9x4VnCw51w6j8QNQlqOtzF/+PlPQp6/P3njgx9l1nL8MwhoQQEHgJiHBPWjYBSMglEwCnABAGjzQbioMmhyAAAAAElFTkSuQmCC","orcid":"","institution":"Sanya Institute of China Agricultural University","correspondingAuthor":true,"prefix":"","firstName":"Jiancheng","middleName":"","lastName":"Li","suffix":""}],"badges":[],"createdAt":"2026-04-08 02:53:43","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9350683/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9350683/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":108680427,"identity":"7ef7e8dd-5c14-4e14-94cf-5873aae2f187","added_by":"auto","created_at":"2026-05-07 09:13:08","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":89021,"visible":true,"origin":"","legend":"\u003cp\u003eSchematic Diagram of Multi-Residue Detection and Reaction Result (T1, T2, and T3 represent the original spraying positions for different pesticides)\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-9350683/v1/9eff4112df41bfe48a1d5044.png"},{"id":108680352,"identity":"eaa19b95-def7-46ba-aef6-8cb689c1de7f","added_by":"auto","created_at":"2026-05-07 09:12:50","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":1339399,"visible":true,"origin":"","legend":"\u003cp\u003eSingle-residue CG-LFIA detection results (A) and vLOD in cowpea, chicken, egg and milk sample matrices (B).\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-9350683/v1/78697a58337e1fe96e72d195.png"},{"id":108680429,"identity":"cb341a9f-4de1-4c1d-bf7d-e1a714302b3f","added_by":"auto","created_at":"2026-05-07 09:13:09","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":2153833,"visible":true,"origin":"","legend":"\u003cp\u003eOptimization of key parameters for the multi-residue LFIA. Optimization results of (A) coating position of T line configuration; (B) coating buffer composition; (C) NC membrane drying time; (D) absorbent pad length; (E) reaction buffer pH; (F) Incubation reaction time; (G) chromatography time.\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-9350683/v1/c50ec09145435aae3e767bd3.png"},{"id":108680434,"identity":"3164ebe8-4d3d-4e52-8e2b-af063cc2dc49","added_by":"auto","created_at":"2026-05-07 09:13:10","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":1667702,"visible":true,"origin":"","legend":"\u003cp\u003e(A) The standard curves and corresponding LFIA diagrams of CTP EMB and FEN; (B-E) Matrix curves for the multi-residue detection of CTP, EMB, and FEN in chicken, egg, milk, and cowpea samples, together with the corresponding vLOD.\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-9350683/v1/745373b0ef312e12fb920fa5.png"},{"id":108680505,"identity":"c39b8884-89f9-4250-a76f-8f50bc930e15","added_by":"auto","created_at":"2026-05-07 09:13:25","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":5495106,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9350683/v1/bb19c538-0f53-43c7-a3cb-a9e514dd0e04.pdf"},{"id":108680428,"identity":"e0979321-a7fe-4717-a312-6c92c91e9f75","added_by":"auto","created_at":"2026-05-07 09:13:08","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":6637567,"visible":true,"origin":"","legend":"","description":"","filename":"Supportinginformation1.docx","url":"https://assets-eu.researchsquare.com/files/rs-9350683/v1/be5e876b7f017ce5f7df231d.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"A Rapid and Sensitive Multi-Residue Colloidal Gold Lateral Flow Immunoassay for Simultaneous Detection of Pesticide Residues in Complex Food Matrices","fulltext":[{"header":"Introduction","content":"\u003cp\u003eThe yield and quality of tropical crops are critically threatened by the persistent high temperature and humidity characteristic of tropical climates, which create ideal conditions for pest proliferation and disease outbreaks (Muhammad et al. \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Fallahi et al. \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). These environmental stressors not only accelerate the metabolic rates of insect pests but also enhance the virulence of fungal pathogens, leading to severe crop damage and yield losses (Lee et al. 2024; Ahmad et al. \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). The integrated application of highly effective insecticides, such as chlorantraniliprole (CTP), emamectin benzoate (EMB), and fipronil (FEN), has brought a marked advancement to the management of insect pests in tropical agriculture, enabling broad-spectrum and targeted control of economically important pests (Huang et al. \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Sun et al. 2023). These compounds belong to distinct chemical classes\u0026mdash;anthranilic diamides, macrocyclic lactones, and phenylpyrazoles, which collectively reduce the risk of cross-resistance while ensuring optimal pest control efficacy (Mishra et al. \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Song et al. \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2025a\u003c/span\u003e). Nevertheless, despite their high efficacy, these insecticides are associated with potential cardiovascular, neurological, immunological, and genotoxic risks upon chronic or accidental exposure, and bioaccumulation along the food chain further exacerbates growing concerns regarding their residues in animal-derived food matrices (Sevgiler et al. 2010; Majumder al. 2023; Zhou et al. \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Liu et al. \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). To ensure food safety, China, the European Union, the United States, and other jurisdictions have established stringent maximum residue limits (MRLs) for CTP, EMB, and FEN in food products (National Health Commission of the People's Republic of China. 2021). Therefore, there is an urgent need to develop rapid, sensitive, and reliable analytical strategies for the simultaneous detection of multiple insecticide residues.\u003c/p\u003e \u003cp\u003eImmunoassay-based rapid detection technologies, particularly colloidal gold lateral flow immunoassays (CG-LFIAs), have attracted increasing attention owing to their operational simplicity, low cost, and suitability for on-site analysis. Numerous studies have reported LFIAs with satisfactory sensitivity and assay times of 10\u0026ndash;15 min, providing practical tools for field screening (Meyer et al. \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). However, most existing LFIAs are designed for a single analyte, which results in low detection efficiency and increased cost when multiple pesticides must be monitored simultaneously. Considering that agricultural products are often exposed to more than one pesticide during cultivation, single-analyte assays are insufficient for realistic monitoring scenarios (Xie et al. \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Wu et al. \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Song et al. 2025). Therefore, it is essential to develop CG-LFIA methods that can simultaneously detect pesticide residues in food. The development of LFIA for multi-residue detection has evolved into two primary models: the multi-strip format and the single-strip multi-T line format (Shu et al. \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Yin 2026; Pan et al. \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Multi-strip LFIA combination formats offer excellent multiplexing capability and modularity, making them suitable for high-throughput applications such as clinical diagnostics. Hong et al. (2023) established a nanozyme-based LFIA strip for rapid detection of blood evidence that showed high generality on 12 substrates and high specificity to human HGB among 13 animal blood samples. In application scenarios such as law enforcement systems and community-based detoxification programs, multiple single-analyte LFIA strips targeting different drugs of abuse can be flexibly combined to enable rapid and adaptable screening of illicit drugs in urine samples (Yan et al. \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). However, the intrinsic working principles of multi-strip LFIA combination formats inevitably result in substantially increased costs, thereby limiting their applicability for routine testing of low-value-added agricultural products at the primary monitoring level.\u003c/p\u003e \u003cp\u003eBy contrast, the single-strip LFIA format with multi-T lines enables the simultaneous detection of multiple analytes within a single device. By eliminating the need for modular on-site reconfiguration and parallel strip assembly, this configuration offers a more cost-effective and operationally streamlined solution, making it particularly attractive for rapid on-site screening and large-scale trend monitoring of agricultural products (Chen et al. 2025). Despite these apparent advantages, the development of single-strip multi-T line LFIA remains technically challenging. The integration of multiple recognition elements within a confined and continuous flow path inevitably introduces complex interactions among immunoreactions. Specifically, competition for labeled probes, nonuniform flow dynamics along the strip, and signal interference between adjacent T lines may occur, collectively leading to compromised analytical sensitivity, reduced quantitative accuracy, and poor result reproducibility (Sun et al. \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Moreover, the simultaneous detection of chemically diverse analytes is further complicated by intrinsic differences in antigen\u0026ndash;antibody affinity, reaction kinetics, and optimal assay conditions for individual targets, making it difficult to achieve balanced signal responses across all T lines.\u003c/p\u003e \u003cp\u003eFurthermore, considering the challenges faced during the production, sales, storage, and use of LFIA products in tropical regions, where high humidity and temperature are prevalent, excessive moisture can accelerate membrane aging, alter capillary flow, and destabilize colloidal gold probes (Li et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). These factors make test strips more susceptible to signal fluctuations and performance deterioration. This issue becomes even more critical when the application involves both plant- and animal-derived matrices. Complex matrices, such as meat, eggs, and milk, contain high levels of proteins, lipids, and endogenous interferents, which can affect chromatographic behavior, nanoparticle stability, and antigen\u0026ndash;antibody interactions, thereby compromising assay sensitivity and reproducibility (Wang et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Li et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). However, research on the matrix effects in LFIA is limited. Some studies suggest that proteins and starches in fruits and vegetables are the primary contributors to matrix effects, while others argue that water-soluble proteins and polysaccharides in animal-derived foods may also interfere with the results of immunoassays to varying degrees. Therefore, establishing a well-designed multi-residue LFIA development process and conducting meticulous and rigorous optimization of the key factors influencing LFIA performance are essential to ensure the stability, robustness, and reliability of LFIA strips intended for use in such challenging environments.\u003c/p\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003eTo address these challenges, the present study proposes a systematic optimization strategy for single-strip multi-T line LFIA. CTP, EMB, and FEN, representative pesticides for tropical crops, were selected as target analytes to develop CG-LFIA for the simultaneous detection of CTP, EMB, and FEN in cowpea, as well as in representative animal-derived foods, including chicken, eggs, and milk. Through the systematic optimization of antibody labeling conditions, membrane configuration, buffer composition, and chromatographic parameters, this study aims to establish a multi-residue LFIA that maintains acceptable sensitivity and stability across diverse and complex matrices, even under humid conditions. The proposed method seeks to enhance detection efficiency while retaining the simplicity and portability essential for field applications, providing a practical tool for rapid on-site screening of pesticide residues in tropical regions.\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eReagents and Instruments\u003c/h2\u003e \u003cp\u003eChlorantraniliprole (CTP), emamectin benzoate (EMB), and fipronil (FEN) standards (purity\u0026thinsp;\u0026ge;\u0026thinsp;98%, purchased from TMO Standard Reference Materials Center); Abamectin, acephate, bromophos, dichlorvos, isocarbophos, chlorthiophos, methamidophos, chlorpyrifos, dimethoate, trichlorfon, malathion, parathion, triazophos, phorate, fonofos, phoxim, and omethoate (purity\u0026thinsp;\u0026ge;\u0026thinsp;98%, purchased from Tianjin Altech Technology Co., Ltd.); CTP coating antigen, EMB coating antigen, FEN coating antigen, and corresponding monoclonal antibodies (prepared in-house, titer\u0026thinsp;\u0026ge;\u0026thinsp;1:10000); Goat anti-mouse polyclonal antibody (purity\u0026thinsp;\u0026ge;\u0026thinsp;95%, purchased from Shanghai Jieyi Biotechnology Co., Ltd.); Bovine serum albumin, Trehalose dihydrate, Proclin-300 (BSA, Sigma-Aldrich, purity\u0026thinsp;\u0026ge;\u0026thinsp;98%); Potassium carbonate (K\u003csub\u003e2\u003c/sub\u003eCO\u003csub\u003e3\u003c/sub\u003e), sodium chloride (NaCl), sucrose, disodium hydrogen phosphate dodecahydrate (Na\u003csub\u003e2\u003c/sub\u003eHPO\u003csub\u003e4\u003c/sub\u003e\u0026middot;12H\u003csub\u003e2\u003c/sub\u003eO), sodium dihydrogen phosphate dihydrate (NaH\u003csub\u003e2\u003c/sub\u003ePO\u003csub\u003e4\u003c/sub\u003e\u0026middot;2H\u003csub\u003e2\u003c/sub\u003eO), polyvinylpyrrolidone K30 (PVP), hydrochloric acid (HCl), methanol, and acetonitrile (all analytical grade, purchased from Sinopharm Chemical Reagent Co., Ltd.).\u003c/p\u003e \u003cp\u003eExperimental equipment used in this study included a vortex mixer (model HD-60-IV, Beijing Tongzheng Biotechnology Development Co., Ltd.), a colloidal gold reader (Beijing Weidvick Biotechnology Co., Ltd.), a water bath incubator (Shanghai Zhetu Scientific Instrument Co.), a digital high-speed chopper (Shanghai GoldBio Technology Co., Ltd.), a 3D gold spraying and membrane scribing instrument (model BIODOT, Shanghai GoldBio Technology Co., Ltd.), a double-door forced draft dry oven (model LD-6, Shanghai Shibei Instrument Equipment Factory), a magnetic stirrer hotplate (IKA, Germany), adjustable pipettes (Eppendorf, Germany), a high-speed refrigerated centrifuge (Eppendorf, Germany), an electronic balance (Sartorius Analytical, Germany; range: 3100 g, precision: 0.01 g), an ultra-pure water system (model Milli-Q, Millipore, USA), ELISA 96-well plates (Costar, USA), a micro-spectrophotometer (model Nanodrop, Thermo Scientific, USA), a nitrogen evaporator (Organomation Associates, USA), a pH meter (Mettler Toledo, Switzerland), and a mini benchtop centrifuge (model PMC-880, Tomy Kogyo, Japan).\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eEstablishment of the Single-Residue CG-LFIA\u003c/h3\u003e\n\u003cp\u003eAssembly of LFIA strips: The coating antigen and goat anti-mouse IgG (1 mg/mL) were dispensed onto the T line and C line of the NC membrane, respectively, using a three-dimensional spraying and membrane scribing system. The membrane was dried in an oven at 45\u0026deg;C overnight. Thereafter, the NC membrane, sample pad, and absorbent pad were sequentially laminated onto a polyvinyl chloride (PVC) backing card, with a 2.0 mm overlap between adjacent components to ensure continuous capillary flow. The assembled cards were cut into strips with a width of 3.2 mm and stored at room temperature in a dry environment until use (Li et al. \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Wang et al. \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e \u003cp\u003ePreparation of colloidal gold-labeled antibody (CG-mAb): CG nanoparticles with an average diameter of approximately 30 nm were synthesized and used for antibody labeling as previously described (Zhang et al. \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Briefly, 1 mL of the CG suspension was transferred into a 1.5 mL centrifuge tube, and its pH was adjusted by dropwise addition of 0.1 M K\u003csub\u003e2\u003c/sub\u003eCO\u003csub\u003e3\u003c/sub\u003e under gentle mixing, followed by incubation at room temperature for 3 min. Subsequently, 10 \u0026micro;g of mAb (1 mg/mL) was added and incubated for 10 min to allow adsorption onto the CG surface. Blocking was performed by adding 20 \u0026micro;L of 20% BSA and incubating for an additional 10 min. The mixture was then centrifuged at 10,000 rpm and 4\u0026deg;C for 10 min. After discarding the supernatant, the pellet was resuspended in 200 \u0026micro;L of reconstitution buffer, dispersed by ultrasonication, and stored at 4\u0026deg;C in a sealed conditions. For single-residue detection, CG-mAb and sample extracts were added to a microplate, after which the LFIA strips were inserted, and the results could be interpreted within several minutes. When both the C line and T line exhibited clear color development, the result was considered negative. As the concentration of the target analyte increased, a larger proportion of CG-mAb bound to the free analyte in solution, leading to a gradual decrease in T line intensity; complete disappearance of the T line was interpreted as a positive result (Luo et al. \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2025\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eOptimization of key conditions for LFIA: Gradient experiments were conducted to optimize critical parameters, including the antibody dilution buffer (0.5% BSA-1), blocking buffer, NC membrane type, coating buffer, and coating antigen dilution ratio. The optimization criteria were based on the color intensity of the T line and the inhibition rate, which was calculated as:\u003c/p\u003e \u003cp\u003eInhibition rate (%) = [1 - (T/T\u003csub\u003e0\u003c/sub\u003e)] \u0026times; 100%\u003c/p\u003e \u003cp\u003ewhere T represents the T line intensity obtained with analyte-containing standards, and T₀ represents the T line intensity of the blank buffer. Conditions yielding high signal intensity for negative samples and strong inhibition in the presence of analytes were selected for subsequent experiments.\u003c/p\u003e\n\u003ch3\u003eEstablishment of a Multi-Residue CG-LFIA\u003c/h3\u003e\n\u003cp\u003eThe assembly and sample loading procedures of the multi-residue CG-LFIA were essentially identical to those of the single-analyte format, with the primary difference being the configuration of T lines on the NC membrane. In the multi-residue system, three independent competitive immunoreactions occur simultaneously on a single strip; therefore, its analytical performance is highly dependent on the rational design and fine-tuning of assay parameters. The three target analytes (CTP, EMB, and FEN) belong to distinct chemical classes (diamide, macrolide, and organophosphate, respectively), and their corresponding antigen\u0026ndash;antibody systems exhibit different affinities and kinetic behaviors. Without proper coordination of spatial arrangement and reaction conditions, mutual interference among the three systems may occur, leading to signal imbalance, reduced sensitivity, or even misinterpretation of results. Consequently, balanced integration of the three detection modules is a prerequisite for reliable multi-residue analysis.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eTo minimize cross-interference and ensure independent yet synchronous signal development, six spatial configurations of the three T lines (T1, T2, and T3) were designed and evaluated (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Each T line was immobilized with a specific coating antigen corresponding to one target pesticide, while the C line was coated with goat anti-mouse IgG to verify chromatographic validity. The optimized layout was selected based on signal uniformity, chromatographic stability, and discrimination ability among targets. According to the predefined interpretation criteria, when the C line exhibits clear color development, the disappearance or significant fading of any T line (T1\u0026ndash;T3) indicates a positive result for the corresponding analyte (Nos. 1\u0026ndash;8 in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Conversely, absence of color on the C line signifies strip failure, and any color pattern on the T lines under this condition is considered invalid. This design allows intuitive visual interpretation while maintaining consistency with conventional competitive LFIA principles.\u003c/p\u003e \u003cp\u003eFollowing structural establishment, the analytical performance of the multi-residue LFIA was systematically evaluated. Standard solutions of CTP, EMB, and FEN were prepared at gradient concentrations to construct calibration curves. The same sample pretreatment procedures used in the single-residue assays were uniformly applied to ensure methodological consistency. Matrix-matched standard curves were generated for each food matrix to assess matrix effects and practical applicability. Visual limits of detection (vLODs) were determined by spiking blank samples with gradient concentrations of each analyte until a clear disappearance of the corresponding T line was observed. For accuracy and precision assessment, blank samples were fortified at three concentration levels, and the recovery rates and coefficients of variation (CVs) were calculated. All experiments were conducted in triplicate, and results were expressed as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation (SD). Data processing, curve fitting, and graphical presentation were performed using Origin 2022 software.\u003c/p\u003e\n\u003ch3\u003eSample preparation and the application of CG-LFIA\u003c/h3\u003e\n\u003cp\u003eStandard solutions of CTP, EMB, and FEN were prepared at gradient concentrations for the construction of calibration curves. To ensure compatibility with on-site screening while minimizing matrix interference, different pretreatment strategies were adopted according to matrix characteristics. Chicken and egg samples were extracted with acetonitrile, followed by nitrogen blow-down to concentrate the extracts, and subsequently diluted with reaction buffer prior to analysis. In contrast, milk and cowpea samples were directly diluted with reaction buffer to attenuate matrix effects, and matrix-matched standard curves were constructed accordingly. The visual limit of detection (vLOD) was defined as the lowest analyte concentration at which the color intensity of the corresponding T line was visually weaker than that of the C line. Specificity was evaluated using 17 commonly used pesticides at a concentration of 1000 ng/mL to assess potential cross-reactivity.\u003c/p\u003e \u003cp\u003eFor the evaluation of accuracy and precision, blank samples were fortified with standard analytes at three concentration levels. The spiked recovery was calculated as the ratio of the measured concentration to the spiked concentration multiplied by 100%, and precision was expressed as the coefficient of variation (CV), defined as the standard deviation divided by the mean value multiplied by 100%. The treated extracts were directly subjected to the multi-residue CG-LFIA, providing the basis for subsequent performance evaluation across different matrices.\u003c/p\u003e"},{"header":"Results and Discussion","content":"\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eDevelopment of single-residue CG-LFIA\u003c/h2\u003e \u003cdiv id=\"Sec9\" class=\"Section3\"\u003e \u003ch2\u003eOptimization results of single-residue CG-LFIA\u003c/h2\u003e \u003cp\u003eTo optimize the antibody conjugation with CG and to enhance the stability, sensitivity, and specificity of the LFIA, systematic optimization was conducted for critical experimental parameters. This optimization strategy was applied to CTP, EMB, and FEN (Fig. \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e). The optimization was evaluated based on inhibition rate, color intensity of the C and T lines, band morphology, and background clarity (Wang et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe pH of the CG labeling system, which plays a pivotal role in regulating electrostatic interactions between antibodies and CG, was incrementally adjusted by the addition of 0.1 M K\u003csub\u003e2\u003c/sub\u003eCO\u003csub\u003e3\u003c/sub\u003e (Chen et al. 2025). The inhibition rate increased as the amount of potassium carbonate increased. For CTP, the CG-mAb reached peak performance at an addition volume of 26 \u0026micro;L, which resulted in optimal color intensity and overall assay performance. In contrast, the optimal addition volumes for EMB and FEN were determined to be 25 \u0026micro;L and 38 \u0026micro;L, respectively (Fig. \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003eA). The appropriate antibody loading amount is one of the critical factors for the development of highly sensitive LFIAs (Cui et al., 2026). For all three LFIAs, 10 \u0026micro;g of antibody yielded satisfactory performance, providing high inhibition efficiency and sufficient color intensity (Fig. \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003eB).\u003c/p\u003e \u003cp\u003eTo further enhance assay sensitivity, the antibody dilution buffer and NC membrane type were optimized to minimize nonspecific adsorption and increase the activity of the CG-mAb probes. The results for all three LFIAs indicated that 0.5% BSA-1 (solution B1) effectively stabilized the antibody conformation and reduced nonspecific binding (Fig. S2A). The S 2.5 NC membrane was selected for its ability to provide well-defined T lines, high color intensity, and high inhibition rates (Fig. S2B). Subsequently, the composition and dilution of the coating buffer were systematically optimized, as the ionic strength and pH of the buffer can significantly affect antigen adsorption and the uniformity of the T lines. For CTP, EMB, and FEN, 0.02 M phosphate buffer (PB, pH 7.4, containing 1% BSA), 0.02 M borate buffer (BB), and 0.02 M phosphate-buffered saline (PBS) were selected as the respective coating buffers, which effectively reduced T line diffusion and aggregation (Fig. S2C). Finally, the dilution ratios of the coating antigens were optimized. When the coating antigens for CTP and FEN were diluted 15-fold and that for EMB was diluted 10-fold, well-defined T lines were obtained, with a good balance between color intensity and inhibition efficiency (Fig. S2D). Overall, the comprehensive optimization of these key parameters resulted in a robust CG-LFIA with clear visual signals, low background interference, and high sensitivity and specificity, providing a reliable foundation for subsequent analytical applications.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e\n\u003ch3\u003ePerformance evaluation of single-residue CG-LFIA\u003c/h3\u003e\n\u003cp\u003eUsing the optimized conditions, standard calibration curves for CTP, EMB, and FEN were established (Fig. S3). The developed methods exhibited vLOD of 2, 8, and 27 ng/mL for CTP, EMB, and FEN, respectively. The corresponding IC\u003csub\u003e50\u003c/sub\u003e values were 1.51, 5.56, and 9.06 ng/mL, with linearity ranges determined as 0.99\u0026ndash;2.29 ng/mL for CTP, 3.42\u0026ndash;9.01 ng/mL for EMB, and 2.65\u0026ndash;30.97 ng/mL for FEN. These results confirm the high sensitivity and robustness of the developed assays for detecting the target pesticides.\u003c/p\u003e \u003cp\u003eTo evaluate and mitigate matrix effects, different sample pretreatment and dilution strategies were optimized for CTP, EMB, and FEN in chicken, egg, milk, and cowpea matrices (Fig. S4). The strategies aimed to minimize interference from complex sample components that could affect assay accuracy and reproducibility. Milk samples were directly diluted 50-fold with assay buffer, while cowpea supernatants were diluted 2.5-fold. Similarly, for EMB, the reconstituted chicken and egg extracts were diluted 10-fold and 2-fold, respectively, while milk samples were diluted 50-fold, and cowpea extracts were diluted 2-fold. For FEN, dilution factors of 15-fold and 3-fold were applied to chicken and egg samples, respectively, whereas milk and cowpea samples were diluted 10-fold and 6-fold.\u003c/p\u003e \u003cp\u003eUnder these optimized conditions, the matrix-matched calibration curves for each pesticide showed excellent agreement with the standard curves prepared in assay buffer, indicating that matrix effects were largely mitigated. The calibration curves obtained from treated chicken, egg, and cowpea samples closely matched their corresponding standard curves, suggesting that these matrices exerted minimal interference on the detection system. In contrast, milk exhibited a pronounced matrix effect, particularly in the detection of CTP. This is likely due to the complex composition of milk, which contains high levels of proteins (such as casein and whey proteins), lipid globules, and lactose. These components can interfere with the assay through several mechanisms: Nonspecific Adsorption\u0026mdash;Protein micelles and lipid globules in milk are prone to nonspecific adsorption of antibodies, which can lead to probe aggregation and an increase in background signals. This phenomenon reduces the sensitivity and clarity of T lines; Cross-reactivity\u0026mdash;Certain whey protein components in milk may interact with the antibody-antigen recognition sites, leading to cross-reactivity and compromising the assay's specificity. This could result in false positives or diminished assay performance (Yonn et al. 2021).\u003c/p\u003e \u003cp\u003eThese matrix effects suggest the importance of considering the composition of complex matrices like milk when developing immunoassays. While the assay performed well in simpler matrices like chicken, egg, and cowpea, further optimization will be necessary for matrices with more complex compositions, such as milk, to enhance the detection performance and minimize interference.\u003c/p\u003e \u003cp\u003eThe vLOD values of the three pesticides were determined in different sample matrices (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). In cowpea samples, the vLOD values were 20, 40, and 50 \u0026micro;g/kg for CTP, EMB, and FEN, respectively; in chicken, the vLOD values for CTP, EMB, and FEN were 4, 10, and 27 \u0026micro;g/kg, respectively; in egg samples, the corresponding vLOD values were 20, 10, and 9 \u0026micro;g/kg; in milk, the vLOD values for CTP, EMB, and FEN were 50, 50, and 81 ng/mL, respectively. The single-residue test strips developed in this study generally met the detection requirements specified by national standards and were suitable for on-site screening at the primary level. For EMB in chicken and egg samples and for FEN in chicken, egg, and milk samples, no MRLs have been established in the Chinese national standard; therefore, the corresponding European Union MRLs were used as reference criteria.\u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eDevelopment of Multi-residue CG-LFIA\u003c/h2\u003e \u003cdiv id=\"Sec12\" class=\"Section3\"\u003e \u003ch2\u003eOptimization results of multi-residue CG-LFIA\u003c/h2\u003e \u003cp\u003eBuilding on the optimization results obtained from the single-residue CG-LFIAs, further refinements were made to the structural configuration and operational parameters to enable the successful development of a multi-residue CG-LFIA. Key parameters such as the coating positions on the NC membrane, membrane drying time, absorbent pad length, assay buffer composition and pH, reaction time, and chromatographic time were systematically optimized to facilitate high-throughput detection as follows:\u003c/p\u003e \u003cp\u003eThe spatial arrangement of the three T lines on the NC membrane was evaluated with respect to color intensity, visual distinguish ability, and inhibition efficiency. Six different configurations were tested to determine the optimal arrangement. The results showed that the position of the T lines did not significantly affect the assay performance. This outcome can be attributed to the high protein-binding capacity of the NC membrane chosen during preliminary experiments (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA). The membrane facilitates uniform immobilization of the coating antigens through hydrophobic interactions and covalent coupling, minimizing positional bias and ensuring even distribution of binding sites. Given the considerations of signal clarity and practical layout, the configuration with FEN at T1, EMB at T2, and CTP at T3 was selected for subsequent experiments. This layout offered optimal visibility of the T lines and ensured smooth chromatographic flow, which is essential for reliable performance in multi-residue detection; The composition of the reaction buffer was another critical factor affecting colloidal gold stability, analyte solubility, and antibody\u0026ndash;antigen interactions. Deionized water, which lacks buffering capacity and electrolytes, caused aggregation of CGs and reduced their binding efficiency. Phosphate buffer (PB) and borate buffer (BB) demonstrated advantages for individual analytes; however, their limited universality, likely due to suboptimal ionic strength or charge-related effects, restricted their overall applicability in the multi-residue format. In contrast, phosphate-buffered saline (PBS) provided a moderate ionic strength and a stable pH environment, ensuring uniform migration of gold nanoparticles and consistent performance across all three pesticides. As such, PBS was selected as the optimal reaction buffer (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB); The drying time of the NC membrane was found to significantly influence assay stability and sensitivity. Inadequate drying resulted in incomplete immobilization of the coating antigens and secondary antibodies, leading to unstable or faint T line signals. On the other hand, excessive drying negatively affected chromatographic efficiency and signal sensitivity. Through comparative evaluation of various drying durations, it was determined that overnight drying (\u0026ge;\u0026thinsp;12 h at 45\u0026deg;C) provided consistent and stable color intensity and inhibition performance for all three pesticides. This drying time was subsequently adopted for further experiments (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eC); The length of the absorbent pad was optimized to regulate capillary force and migration velocity. An excessively long absorbent pad generated strong suction, which led to rapid sample migration and insufficient antigen\u0026ndash;antibody interaction, thereby reducing assay sensitivity. Conversely, a shorter absorbent pad resulted in slower liquid migration, which prolonged the assay time and sometimes prevented complete membrane wetting. Among the various lengths tested, 15 mm was found to provide the optimal balance between signal intensity, inhibition efficiency, and assay time for CTP, EMB, and FEN. This length enabled rapid and uniform sample flow, sufficient antigen\u0026ndash;antibody interaction, and smooth chromatographic flow (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eD); Buffer pH was evaluated for its impact on antibody conformation and analyte ionization, both of which are critical determinants of competitive binding in immunoassays. At pH values above 8.0, electrostatic interactions between antibodies and gold nanoparticles were weakened, resulting in partial desorption of labeled antibodies. On the other hand, acidic conditions (pH\u0026thinsp;\u0026lt;\u0026thinsp;5.5) caused antibody denaturation, thereby reducing binding specificity. A pH of 7.0 effectively preserved antibody activity, stabilized CG-mAb conjugates, and maintained analytes in favorable binding states, resulting in maximal signal intensity and inhibition efficiency (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eE). Incubation reaction time and chromatographic times were optimized to ensure sufficient competitive interaction while minimizing signal diffusion. A incubation reaction time of 5 minutes was found to produce strong, uniform T line signals with high inhibition efficiency. Shorter reaction times resulted in incomplete binding, while prolonged reaction times led to reduced visual contrast and compromised signal clarity (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eF). For chromatographic time, a duration of 7 minutes was optimal, allowing adequate interaction between the gold-labeled antibodies and immobilized antigens. Shorter chromatographic times limited binding efficiency, while excessive migration resulted in signal attenuation due to over-diffusion of the analyte within the membrane matrix (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eG).\u003c/p\u003e \u003cp\u003eThrough the systematic optimization of these parameters, balanced multi-analyte detection was achieved, and a robust multi-residue CG-LFIA was developed for the simultaneous detection of CTP, EMB, and FEN. The resulting assay demonstrated improved sensitivity, stable performance, and reduced interference, making it suitable for high-throughput detection in complex matrices.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003ePerformance evaluation of multi-residue CG-LFIA\u003c/h2\u003e \u003cp\u003eUnder the optimized conditions, the multi-residue LFIA demonstrated reliable analytical performance for the simultaneous detection of CTP, EMB, and FEN, as shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA. The calibration curves for the three pesticides exhibited high sensitivity, with vLOD of 0.5, 8, and 16 ng/mL for CTP, EMB, and FEN, respectively. The corresponding IC₅₀ values, which represent the concentration at which 50% inhibition occurs, were 0.31, 3.68, and 4.00 ng/mL for CTP, EMB, and FEN. The linear detection ranges were 0.18\u0026ndash;0.55 ng/mL for CTP, 1.76\u0026ndash;7.69 ng/mL for EMB, and 1.12\u0026ndash;14.35 ng/mL for FEN, with correlation coefficients close to 0.99. These results indicate excellent linearity and quantitative reliability across the multi-residue assay format. Notably, the simultaneous configuration showed no evident loss in sensitivity compared to the single-residue LFIA systems, suggesting that the competitive interactions among multiple targets and probes were effectively balanced through careful optimization of experimental parameters.\u003c/p\u003e \u003cp\u003eTo assess matrix effects, matrix-matched calibration curves were established using processed extracts from chicken, egg, milk, and cowpea. For all matrices, appropriate sample dilution was applied to mitigate matrix interference. Chicken and egg samples were subjected to nitrogen evaporation, followed by reconstitution and dilution 10-fold and 4-fold, respectively. Milk samples were diluted 50-fold with assay buffer, and cowpea supernatants were diluted 2.5-fold. These adjustments helped to minimize matrix effects, as evidenced by the close overlap of matrix-matched calibration curves with the standard curves prepared in assay buffer (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eB-E). The results indicated that matrix effects were largely eliminated in the treated chicken, egg, and cowpea samples, with the matrix curves showing good agreement with the corresponding standard curves. However, milk samples exhibited a pronounced matrix effect, which could be attributed to the complex composition of milk, including high levels of proteins (casein and whey proteins), lipids, and lactose. The vLODs for the real samples ranged from 10 to 50 \u0026micro;g/kg in cowpea, 1.6 to 16 \u0026micro;g/kg in chicken, 4 to 10 \u0026micro;g/kg in egg, and 15 to 40 ng/mL in milk (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eB-E). With the exception of EMB and FEN in milk, all vLOD values met the relevant regulatory requirements, demonstrating that the proposed assay is suitable for rapid screening of pesticide residues in a wide range of sample matrices. Notably, the relatively higher detection limits in milk emphasize the strong inhibitory effects of complex animal-derived matrices, suggesting that further optimization is needed to address these challenges.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eSummary of spike-and-recovery results for three target analytes in different matrices\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eSample\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eCTP\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003eEMB\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003eFEN\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRecovery (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCV (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRecovery (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eCV (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eRecovery (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eCV (%)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCowpea\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e62.5\u0026ndash;87.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5.1\u0026ndash;13.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e60.1\u0026ndash;83.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e5.9\u0026ndash;9.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e55.5\u0026ndash;64.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e6.3\u0026ndash;8.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eChicken\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e111.0\u0026ndash;116.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e6.7\u0026ndash;10.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e86.7\u0026ndash;97.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e7.4\u0026ndash;12.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e101.2\u0026ndash;110.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e7.1\u0026ndash;13.4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEgg\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e79.1\u0026ndash;93.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e8.6\u0026ndash;12.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e63.3\u0026ndash;75.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e6.2\u0026ndash;8.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e71.4\u0026ndash;81.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e9.2\u0026ndash;14.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMilk\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e105.4\u0026ndash;126.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e6.9\u0026ndash;15.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e77.6\u0026ndash;80.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e7.8\u0026ndash;14.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e114.2\u0026ndash;123.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e5.3\u0026ndash;13.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe accuracy and precision of the multi-residue CG-LFIA were evaluated through spike-and-recovery experiments conducted at three concentration levels for each matrix. The recovery rates ranged from 86.7% to 116.3% in chicken, 63.3% to 93.7% in egg, 77.6% to 126.8% in milk, and 55.5% to 87.2% in cowpea, with coefficients of variation (CVs) below 15% for all analytes and matrices. These results indicate that the developed assay provides acceptable analytical reliability for on-site screening applications. The relatively lower recoveries observed in cowpea samples are likely due to the adsorption of pesticides by plant cell wall components, as well as interference from pigments and polyphenols, which reduce the bioavailable fraction of the target analytes (Li et al. \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Lin et al. 2019). Variability observed in milk samples was mainly attributed to protein\u0026ndash;lipid interactions, competitive binding effects, and partial obstruction of membrane pores (Burkin et al. \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). These factors can influence chromatographic behavior and antigen\u0026ndash;antibody recognition. Despite these challenges associated with complex matrices, the assay maintained stable performance within acceptable tolerance ranges for rapid qualitative and semi-quantitative analysis.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eComparison of CG-LFIA methods\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eReferences\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSample\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAnalysts\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003evLOD(\u0026micro;g/kg)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eWu et al. \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2024\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBrown rice, apples, soil\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eCTP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e50\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eChinese cabbage\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e100\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eChen et al. 2023\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCabbage, tomatoes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eFEN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e50\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eThis work\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eChicken, Egg, Milk, Cowpea\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCTP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.6\u0026ndash;15\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eEMB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8\u0026ndash;50\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eFEN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6\u0026ndash;40\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eCurrently, most studies focus on the detection of single analytes, which limits analytical throughput and increases costs. In contrast, reports on multi-residue LFIAs for the simultaneous detection of CTP, EMB, and FEN remain scarce (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Wu et al. developed a highly sensitive single-residue LFIA for CTP, achieving a vLOD of 1.25 ng/mL and matrix vLODs of 0.05\u0026ndash;0.1 mg/kg in rice, apple, soil, and cabbage, all of which were below the corresponding MRLs, demonstrating its suitability for rapid field screening. Similarly, Chen et al. established immunoenzymatic assays (icELISA) and LFIAs for six compounds, including FEN, reporting a vLOD of 50 ng/g for FEN in cabbage and tomato. Moreover, these assays were limited to single-analyte formats. In this study, a multi-residue CG-LFIA was successfully developed for the simultaneous detection of CTP, EMB, and FEN in both plant- and animal-derived matrices, including chicken, egg, milk, and cowpea. Through systematic optimization of key parameters (such as antibody labeling conditions, T line spatial arrangement, membrane treatment, absorbent pad length, buffer composition and pH, as well as reaction times) balanced multi-analyte detection was achieved with minimal interference. The assay demonstrated satisfactory sensitivity, reproducibility, and matrix tolerance, with visual limits of detection meeting regulatory requirements across most tested samples. This systematic optimization strategy provides a generalizable framework for the construction of multi-residue LFIA platforms, enhancing analytical efficiency and offering a practical tool for rapid, on-site screening of pesticide residues in complex food matrices.\u003c/p\u003e \u003c/div\u003e"},{"header":"Conclusion","content":"\u003cp\u003eIn this study, a robust multi-residue CG-LFIA was successfully established for the simultaneous detection of CTP, EMB, and FEN in both plant- and animal-derived foods. Systematic optimization of antibody labeling, T line layout, membrane processing, absorbent pad length, buffer conditions, and reaction parameters enabled balanced detection of multiple analytes with high sensitivity, reproducibility, and matrix tolerance. The assay achieved visual detection limits meeting regulatory requirements for most matrices and demonstrated consistent performance in spike-and-recovery experiments. The proposed strategy provides a general framework for developing multi-residue LFIA platforms, improving analytical efficiency and offering a practical, on-site solution for rapid pesticide residue monitoring in complex food systems.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eCRediT authorship contribution statement\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eZizhe Li: Writing - original draft, Investigation, Software, Data curation, Visualization. Xiaole Pan: Methodology, Software, Formal analysis, Validation, Visualization, Writing - original draft. Chen Xing: Investigation, Software. Yantong Pan: Supervision, Conceptualization. Zhanhui Wang: Funding acquisition, Investigation, Project administration, Supervision. Jiancheng Li: Conceptualization, Funding acquisition, Supervision, Writing - review \u0026amp; editing, Resources.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDeclaration of Competing Interest\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgments\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by the financial support of the Natural Nature Science Foundation of China (31972738)\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAhmad F, Kusumiyati K, Soleh MA, Khan MR, Sundari RS (2024) Chili cultivars vulnerability: a multi-factorial examination of disease and pest-induced yield decline across different growing microclimates and watering regimens. BMC Plant Biology 24(1). https://doi.org/10.21203/rs.3.rs-4619942/v1 \u003c/li\u003e\n\u003cli\u003eBurkin MA, Lapa GB, Galvidis IA, Burkin KM, Zubkov AV, Eremin SA (2018) Three steps improving the sensitivity of sulfonamide immunodetection in milk. Analytical Methods 10(48): 5773-5782. https://doi.org/10.1039/C8AY01904E\u003c/li\u003e\n\u003cli\u003eChen M, Yuan M, Bu T, Shi Y, Wang Y, Huang X, et al. (2010) The mechanism of weakening fat-induced matrix interference by deep eutectic solvents promoting fat phase transfer: a case of aflatoxin B1 detection by lateral flow immunoassay in 26 food matrices. Food Chemistry 493(2): 145886. https://doi.org/10.1016/j.foodchem.2025.145886\u003c/li\u003e\n\u003cli\u003eChen YH (2023) Rapid immunoassays for six chemical residues in food samples. Jiangnan University, MA thesis. https://doi.org/10.27169/d.cnki.gwqgu.2023.002030.\u003c/li\u003e\n\u003cli\u003eFallahi M, Armand A, Al-Otibi F, Hyde KD, Jayawardena RS (2025) Pathogenic fungi (Sordariomycetes) associated with annual and perennial crops in Northern Thailand. MycoKeys (117). https://doi.org/10.3897/mycokeys.117.137112\u003c/li\u003e\n\u003cli\u003eHuang WK, Qin S, Jia JJ, et al. (2025) Field Control Effects of 6 Pesticides on Cowpea Thrip and Impact on Quality and Safety of Cowpea. Journal of Changjiang Vegetables (02): 71\u0026ndash;76. https://doi.org/10.3865/j.issn.1001-3547.2025.02.020\u003c/li\u003e\n\u003cli\u003eLee DW (2024) Comparative metabolic profiling in Drosophila suzukii by combined treatment of fumigant phosphine and low temperature. Metabolites 14. https://doi.org/10.3390/metabo14100526\u003c/li\u003e\n\u003cli\u003eLi Z, Cui Q, Li Q, et al. 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Toxicological \u0026amp; Environmental Chemistry 106: 1\u0026ndash;10. https://doi.org/10.1080/02772248.2024.2430292\u003c/li\u003e\n\u003cli\u003eMeyer C, Fredriksson-Ahomaa M, Thiel S, et al. (2013) Detection of Salmonella in poultry meat using culture method, enzyme-linked fluorescent immunoassay and immunochromatography. Archiv f\u0026uuml;r Lebensmittelhygiene 64: 4\u0026ndash;7. https://doi.org/10.2376/0003-925X-64-4\u003c/li\u003e\n\u003cli\u003eMishra AK, Chandiraseharan VK, Jose N, et al. (2016) Chlorantraniliprole: an unusual insecticide poisoning in humans. Indian Journal of Critical Care Medicine : Peer-reviewed, Official Publication of Indian Society of Critical Care Medicine 20(12): 742\u0026ndash;744. https://doi.org/10.4103/0972-5229.195718\u003c/li\u003e\n\u003cli\u003eMuhammad M, Mansoor M, Afzal M, Abu et al. 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TRAC-Trends in Analytical Chemistry 157. https://doi.org/10.1016/j.trac.2022.116726\u003c/li\u003e\n\u003cli\u003eSevgiler Y, \u0026Uuml;ner N (2010) Tissue-specific effects of fenthion on glutathione metabolism modulated by NAC and BSO in Oreochromis niloticus. Drug and Chemical Toxicology 33(4): 348\u0026ndash;356. https://doi.org/10.3109/01480541003734048\u003c/li\u003e\n\u003cli\u003eSong XY, Xie WR, Yuan D (2025a) Application status, problems and countermeasures of colloidal gold rapid detection technology in pesticide residue analysis of cowpea. China Food Safety Magazine (07): 11\u0026ndash;13. https://doi.org/10.16043/j.cnki.cfs.2025.07.040\u003c/li\u003e\n\u003cli\u003eSong AN, Wang M, She YX, Jin MJ, Cao Z, Abd El-Aty AM, Wang J (2025b) Evaluation and validation of colloidal gold immunochromatographic qualitative testing products for the detection of emamectin benzoate, isocarbophos, and fipronil in cowpea samples. 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(2023) Determination of 314 pesticide residues in animal-derived foods by liquid chromatography\u0026ndash;tandem mass spectrometry. Journal of Zhejiang Agricultural Sciences 64(04): 940\u0026ndash;944. https://doi.org/10.16178/j.issn.0528-9017.20220669\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"food-analytical-methods","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"Learn more about [Food Analytical Methods](https://www.springer.com/journal/12161)","snPcode":"12161","submissionUrl":"https://submission.nature.com/new-submission/12161/3","title":"Food Analytical Methods","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Colloidal gold lateral flow immunoassay, Multi-residue detection, Pesticide residues, Complex food matrices","lastPublishedDoi":"10.21203/rs.3.rs-9350683/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9350683/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eMulti-residue colloidal gold lateral flow immunoassays (CG-LFIAs) play a critical role in high-throughput, rapid, and customized food safety testing. In this study, a systematic strategy for constructing multi-residue CG-LFIAs was developed, using chlorantraniliprole (CTP), emamectin benzoate (EMB), and fipronil (FEN) as model analytes. A stepwise workflow, progressing from single-strip to multiple T lines, was employed to systematically optimize key factors affecting assay performance, including antibody labeling, T line spatial arrangement, membrane treatment, absorbent pad length, buffer composition, pH, reaction time, and chromatographic time, in order to balance multi-analyte interactions and reduce cross-interference. Matrix-specific sample pretreatment further enhanced assay robustness and reproducibility. The resulting multi-residue LFIA demonstrated satisfactory sensitivity, precision, and matrix tolerance, enabling simultaneous detection of multiple chemical pesticides in complex plant- and animal-derived matrices, including cowpea, chicken, egg, and milk. The proposed strategy provides a general framework for developing multi-residue LFIA platforms, improving analytical efficiency and offering a practical, on-site solution for rapid pesticide residue monitoring in complex food systems.\u003c/p\u003e","manuscriptTitle":"A Rapid and Sensitive Multi-Residue Colloidal Gold Lateral Flow Immunoassay for Simultaneous Detection of Pesticide Residues in Complex Food Matrices","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-05-07 09:11:32","doi":"10.21203/rs.3.rs-9350683/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-05-10T14:44:11+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-05-06T12:55:49+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-05-06T09:02:18+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"125278184082161519549517707566109302411","date":"2026-05-01T03:40:11+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-04-29T09:21:48+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"113782099844273740178018960529413974296","date":"2026-04-29T06:25:51+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"220989781124081313339729155754084506468","date":"2026-04-29T00:09:44+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"240878032663400088284463842033307975478","date":"2026-04-28T20:13:02+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-04-28T15:41:10+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-04-08T05:22:22+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-04-08T05:21:24+00:00","index":"","fulltext":""},{"type":"submitted","content":"Food Analytical Methods","date":"2026-04-08T02:38:43+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"food-analytical-methods","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"Learn more about [Food Analytical Methods](https://www.springer.com/journal/12161)","snPcode":"12161","submissionUrl":"https://submission.nature.com/new-submission/12161/3","title":"Food Analytical Methods","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"e39888a3-c309-49b0-827e-1d131b83dae8","owner":[],"postedDate":"May 7th, 2026","published":true,"recentEditorialEvents":[{"type":"decision","content":"Revision requested","date":"2026-05-10T14:44:11+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-05-06T12:55:49+00:00","index":22,"fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-05-06T09:02:18+00:00","index":21,"fulltext":""},{"type":"reviewerAgreed","content":"125278184082161519549517707566109302411","date":"2026-05-01T03:40:11+00:00","index":19,"fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-04-29T09:21:48+00:00","index":18,"fulltext":""},{"type":"reviewerAgreed","content":"113782099844273740178018960529413974296","date":"2026-04-29T06:25:51+00:00","index":17,"fulltext":""},{"type":"reviewerAgreed","content":"220989781124081313339729155754084506468","date":"2026-04-29T00:09:44+00:00","index":15,"fulltext":""}],"rejectedJournal":[],"revision":"","amendment":"","status":"in-revision","subjectAreas":[],"tags":[],"updatedAt":"2026-05-10T14:54:08+00:00","versionOfRecord":[],"versionCreatedAt":"2026-05-07 09:11:32","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9350683","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9350683","identity":"rs-9350683","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}