Fluorimetric identification of sulfonamides by carbon dots embedded photonic crystal molecularly imprinted sensor array
Introduction
Currently, a large number of antibiotic drugs were used to combat bacterial diseases in food. Among them, sulfonamides (SAs) are broad-spectrum antimicrobial agents based on the structure of p-aminobenzenesulfonamide, which are widely used in food because of their broad antimicrobial spectrum, low price, and stable properties (Santos & Ramos, 2016). However, many studies have shown that long-term intake of SAs can lead to health problems such as liver toxicity, kidney failure and intestinal bacterial resistance (Hu, Li, Liu, Fu, Lin & Li, 2022). And the maximum residue standard for most SAs in food tissues is set at 100 µg kg−1, and even some SAs are non-detectable, such as sulfaquinoxaline, sulfaguanidine (SG) and sulfathiazole (ST) (Lu, Cheng, Liu & Cao, 2016). These phenomena have inspired keen research on the rapid and selective detection of SAs. Currently, methods for the detection of multiple SAs in food are mainly based on high-performance liquid chromatography coupled with mass spectrometry (HPLC-MS) (Dmitrienko, Kochuk, Apyari, Tolmacheva & Zolotov, 2014). However, this method requires complex laboratory equipment and professional operators, which are expensive and time-consuming. Subsequently, a large number of new techniques are being developed, such as enzyme-linked immunoassays (ELISA) (Li, Cui, Liu, Liu & Wang, 2020), photo-electrochemical sensors (Liu et al., 2021), and Surface Enhanced Raman Scattering (SERS) (Zhou et al., 2021). However, many of the emerging technologies mentioned above are still plagued by high costs or low selectivity and difficulties in characterizing and quantifying all SAs during testing. Therefore, it is essential to develop intelligent sensing identification devices for rapid and specific identification of multiple SAs.
In recent years, molecularly imprinted polymers (MIP), as bio-inspired synthetic materials which can selectively bind certain target molecules, have been broadly used in the food analytical field (Villa, Sánchez, Valencia, Ahmed & Gutiérrez, 2021). The prepared MIP sensors commonly used photonic crystals (PCs), fluorescence or chemiluminescent materials as signal sources, which are widely adopted for the detection of antibiotic drugs due to the self-report and high selectivity properties. Wang et al., (2019) fabricated photonic crystal molecularly imprinted polymers (PCMIP) to visualize the detection of oxytetracycline in milk. Jalili, Khataee, Rashidi & Razmjou, (2020) developed a ratiometric/fluorescent sensor that contained different colored carbon dots as dual fluorophores combined with molecularly imprinted polymers for the detection of penicillin-G in milk. Therefore, carbon dots (CDs) and PCs are attractive sensor platforms due to their low cost, simple design and visualization of detection results (Yadav et al., 2021). CDs are a class of zero-dimensional nanoparticles with sizes below 10 nm showing excellent photochemical stability and deft synthesis process as a fluorescent probe, which has been widely used in bio-labeling, optical sensing and fluorescent sensor (Sobiech et al., 2021, Yuan et al., 2016). Moreover, PCs are one photonic material with a certain periodical arrangement pattern of dielectric materials to generate a property called photonic bandgap (PBG) (Cersonsky, Antonaglia, Dice & Glotzer, 2021). According to Bragg’s law, the light with certain wavelengths within the PBG region is prohibited from propagating through PCs, which endows PCs with the structural color and the slow-photon effect (Hou et al., 2018, Li et al., 2019). The characteristics of PC enable the preparation of sensors with brilliant color changes and significant enhancement of fluorescence signal intensity. For example, Eftekhari et al., (2017) reported a fluorescent solid-state sensor with CDs embedded in PCs resulting in a 73-fold amplification of the CDs fluorescence emission, achieving a detection limit of 91 pM for Hg2+ in water. Therefore, the combination of CDs, PC and MIP will enable the visualization as well as trace detection of hazardous substances.
Nevertheless, MIP exhibits specificity at varying degrees for templates and their structural analogs. To compensate for this drawback, chemical sensor arrays based on pattern recognition techniques are proposed. Chemical sensor arrays are a form of sensor with high recognition and accuracy, capable of analyzing and identifying substances with similar chemical structures. The advantage is that despite the low selectivity of single sensor unit for the identification of substances, the identification results still show differentiation and multiple sensor units can be assembled into a sensor with high selectivity and high resolution (Shimizu & Stephenson, 2010). For example, Han et al., (2017) prepared a set of 3-channel sensor arrays labeled with fluorescent dyes to identify 30 types of wine by the difference in fluorescence intensity in age, origin and taste of whiskey, which offers the possibility of identifying counterfeit consumer products (e.g., perfumes and beverages) in the future. Li et al., (2017) reported a set of 4-channel sensor arrays composed of gold nanoparticles (Au NPs) with different surface charges, which could rapidly identify 15 microorganisms by the naked eye. It is clear from the above study that chemical sensor arrays can be well applied in food and biological identification.
Pattern recognition is a data processing technique for detecting and identifying the constructed cross-response sensor arrays, mainly used in fields such as face recognition and electronic nose (Granato et al., 2018), and now is gradually used in sensors to improve selectivity and reduce detection errors (Gale and Gunnlaugsson, 2010, Shi et al., 2017). Among them, principal component analysis (PCA) and linear discrimination analysis (LDA) are among the statistical methods commonly used to analyze data in the sensor array. A 4-channel colorimetric sensor array with PCMIP was constructed by Lu et al., (2017) for the differentiation of nitro-aromatics. And the PCA method was utilized to analyze the color data matrix in the sensor array, which induced a great improvement in the discrimination ability of the sensor. Moreover, fluorescent sensor arrays based on six kinds of near-infrared fluorescent dual ligand functionalized Au NPs had been exploited and could analyze various proteins with high precision by the LDA method (Xu et al., 2017). In sensor arrays, the choice of identification signal and conversion method is an essential consideration. The PCA method is an unsupervised dimensionality reduction technique, which can ensure the minimum loss of original data and reduce the dimensionality of the initial high-dimensional linear combination with the help of the matrix operation method, so as to achieve a comprehensive evaluation of the objects under study (Li, Askim & Suslick, 2019). Like PCA, LDA is a dimensionality reduction technique that constructs a set of orthogonal dimensions used to describe the data. Unlike for PCA, however, the output space of LDA is optimized so that the vector connecting the two class-means provides the highest possible separability between these two classes, thus maximizing the signal-to-noise ratio of discriminative power (Askim et al., 2013, Li et al., 2019). Thereby, multidimensional data are transformed into low-dimensional data by PCA and LDA methods to obtain visualized statistical analysis plots and are used to distinguish between chemical classes.
Based on the above strategy, we constructed a set of 4-channel sensor array based on CDs embedded in photonic crystal molecularly imprinted (PCMIP@CDs) film for identifying five SAs. This is the first case of PCMIP@CDs array-based fluorimetry for the identification of SAs in fish samples to achieve rapid identification of sulfonamides. The sensor array achieved 100 % accuracy in classifying five SAs in water and fish samples when the LDA method was adopted, which had a basis for the practical application of the sensor array.
Section snippets
Reagents and materials
Sulfadimethoxine (SDM), sulfathiazole (ST), sulfaguanidine (SG), sulfamethazine (SM2), sulfadiazine (SDZ) and azobisisobutyronitrile (AIBN) were obtained from Shanghai Maclin Biochemical Co., ltd. (Shanghai, China). Tetraethoxysilane (TEOS), ethylene glycol dimethacrylate (EGDMA), and methacrylic acid (MAA) were purchased from Aladdin Reagent Co., ltd. (Shanghai, China). Anhydrous citric acid, urea, methanol, ethanol and acetic acid were purchased from Xilong Chemical Co., ltd. (Guangdong,
Principle
The principle of the proposed approach is shown in Fig. 1. Firstly, three PCMIP@CDs sensor units and one PCNIP@CDs unit were prepared by using sulfadimethoxine (SDM), SG and ST as templates, and the recognition time of the sensor array was only 200 s. There were specific and non-specific adsorptions of each sensor unit to the five SAs, which resulted in differential fluorescence signals of the sensor unit for SAs. The “radar” mode diagrams were used to provide qualitative and semi-quantitative
Conclusions
In this chapter, CDs were used as the fluorescence source, combined with photonic crystal and molecularly imprinted techniques to construct a set of 4-channel sensor arrays. The sensor array was prepared by using 0.06 mg mL−1 CDs and the molar ratio of 1:60:24 between SAs, MAA and EGDMA to achieve the optimal differentiation capability with a response time of only 200 s. The data matrix of the sensor array for the five SAs was analyzed using “radar” plots and a combination of PCA and LDA
CRediT authorship contribution statement
Shishun Zhang: Methodology, Software, Investigation, Writing – original draft, Visualization. Keman Shao: Data curation, Investigation. Chengyi Hong: Formal analysis. Suyan Chen: Resources. Zhengzhong Lin: Conceptualization, Supervision, Writing – review & editing. Zhiyong Huang: Validation, Project administration. Zhuzhi Lai: Data curation.
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.
Acknowledgements
This research was supported by the Foundation from the Natural Science Foundation of China (21804050), the Open Foundation from Key Laboratory of Food Microbiology and Enzyme Engineering of Fujian Province (Z820224-3) and the Cultivation Project in Jimei University for National Foundation (202010390021).
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