Raman spectroscopy is a powerful analytical method, but when the composition of the test sample is intricate, the original spectral data may contain noise and fluorescence background interference, making it more difficult to extract Raman spectral information from the original spectra. Especially the fluorescence background signal, which is typically several orders of magnitude stronger than the Raman signal, can even overwhelm or obscure the Raman signals, thereby impeding the qualitative or quantitative analysis of the Raman spectra. One effective method for removing the fluorescence background is shift excitation Raman differential spectroscopy (SERDS), which typically involves measuring two raw Raman spectra using slightly different excitation wavelengths, combined with reconstruction algorithms, to obtain Raman spectra free from fluorescence interference. For this purpose, a reconstruction method based on Tikhonov regularized least squares (TRLS) was developed in this study, which mitigated the oscillations caused by the direct unconstrained least squares (DULS) reconstruction method. The method was verified and optimized using four groups of artificial datasets with different characteristics. By selecting an appropriate value for parameter α, the relative standard deviation (RSD) of the reconstructed datasets was lower than that of the artificial datasets in most cases. Additionally, we evaluated the performance of the TRLS reconstruction algorithm based on a quantitative model of real Raman spectral datasets, assessing the algorithm's performance from three perspectives: the root mean square error (RMSE), the correlation coefficient (R), and the ratio of prediction to deviation (RPD). The quantitative results indicate that using the TRLS method for reconstruction enhances both prediction accuracy and practicality. In summary, findings from both simulated data and actual experiments demonstrate that the TRLS-based reconstruction method substantially improves the stability and reliability of differential Raman spectra reconstruction.

Carbofuran (CBF), which exhibit high toxicity, persistent residues, ease of accumulation, and resistance to degradation, pose serious threats to human health and harm the ecological environment. Therefore, there is an urgent need to develop a rapid and accurate method for detecting CBF. In this work, a low-cost, portable, and easy-to-use paper chip biosensor was developed, integrating smartphones for the detection of CBF pesticide residues. This biosensor facilitates rapid on-site testing, meeting the needs for immediate analysis. CBF has the ability to inhibit acetylcholinesterase (AChE) activity. In the presence of AChE, acetylthiocholine (ATCh) is hydrolyzed to produce thiocholine (TCh). TCh, in turn, can inhibit the catalytic activity of Ni-N-C single-atom nanozymes (SAzyme) synthesized using Ni(OH) nanochip as a metal precursor, which possess high peroxidase activity. Consequently, the concentration of CBF can be determined by observing the resultant color changes. The results showed that this sensor had a good linear response in the range of CBF concentration from 10 to 500 ng/mL, and the LOD was as low as 8.79 ng/mL. In testing three actual samples-Chinese cabbage, cabbage, and lettuce-the recoveries ranged from 81.09% to 125.27%. This demonstrated that the proposed smartphone-based colorimetric paper chip sensor, utilizing Ni-N-C SAzyme, offers an immediate, convenient, and rapid new strategy for detecting CBF.

Food-derived angiotensin-converting enzyme inhibitory peptide (ACEIP) has an effect in supportive therapeutic on hypertension. Bovine serum albumin (BSA) as a model transporter protein to explore the interaction mechanisms with casein-hydrolyzed ACEIP Val-Ala-Pro (VAP) by multi-spectroscopic, biolayer interferometry (BLI), isothermal titration calorimetry (ITC), molecular docking, and molecular dynamics simulations. Multi-spectroscopic analysis showed that the non-covalent complexes formed by VAP and BSA resulted in decreased hydrophobicity and α-helix contents on BSA, revealing the unfolding of the BSA structure. BLI revealed the reversible binding process of BSA to VAP. ITC confirmed that the combination of VAP to BSA was a spontaneous process mainly driven by entropy. Molecular docking and molecular dynamic simulations showed that VAP was primarily bound in site II of BSA by hydrogen bonding, hydrophobic interactions, van der Waals force, and electrostatic force. This study provides a systematic method to reveal the structure-activity relationship of ACEIPs.

Serum biochemical markers are widely used in clinical practice but often require expensive, specific reagents, complex instruments, and prolonged result waiting times. Infrared spectroscopy offers multiple advantages for serum analysis, such as reagent-free testing and the ability to quickly and directly measure multiple parameters simultaneously. This study collected serum samples from 66 healthy subjects to explore the relationship between dried serum infrared spectra and biochemical parameters, and to investigate the feasibility of simultaneously quantifying nine major serum components using dried serum infrared spectra. Initially, correlation analysis was conducted between spectral data and biochemical parameters, and the correlation spectral bands of glucose, protein and lipid were determined according to the correlation results. Subsequently, the interval random frog (IRF) algorithm was utilized to select the optimal characteristic wavenumbers of the correlated spectral bands, extracting the most informative spectral variables and constructing partial least squares (PLS) quantitative models. This method successfully achieved rapid and accurate quantification of nine major components in serum, including glucose, total protein, albumin, apolipoprotein A1, apolipoprotein B, total cholesterol, triglycerides, low-density lipoprotein cholesterol, and high-density lipoprotein cholesterol. The experimental results showed that the correlation coefficient (Rp) range in the test set was 0.8892-0.9941. Among them, the quantification of total cholesterol yielded the highest Rp, corresponding to a root mean square error (RMSEP) of 7.2425 mg/dL in the test set, while the quantification of glucose yielded the lowest Rp, with an associated RMSEP of 2.3683 mg/dL. The Correlation Analysis (CA)-IRF-PLS method developed in this study outperformed the conventional PLS method, the direct use of the successive projection algorithm (SPA)-PLS quantitative method and other reported quantitative techniques, providing a novel approach for the real-time determination of clinical parameters in serum.

This study synthesized a 4,4-difluoro-2,6-di (1-octyl, 5-esteryluridine)-8-(3,4,5-tri (ethanoxy) phenyl) fluoroboron dipyrrole (BODIPY-A) near-infrared fluorescent probe based on 4,4-difluoro-2,6-diiodio-8-(3,4,5-tri (ethanoxy) phenyl) fluoroboron dipyrrole and 1-octanyl, 5-carboxyuracil. The emission wavelength of BODIPY-A probe is 672 nm, located in the near-infrared region, and it possesses benefits such as deep tissue penetration, low background self-fluorescence, and minimal light damage. The BODIPY-A probe exhibits a good turn-on fluorescence response to Cys through nucleophilic substitution reaction with cysteine (Cys), and can eliminate interference from homocysteine (Hcy) and glutathione (GSH). The BODIPY-A probe has been applied to the detection of Cys, with a linear range and detection limit of 0-90 μM and 0.3 μM, respectively. The BODIPY-A probe was applied to analyze the serum samples, achieving an absolute recovery rate ranging from 95 %-99 % and a relative standard deviation (RSD) of 0.031 %-0.371 %. Research has shown that the BODIPY-A probe has the hope to be used for sensitive detection of Cys in the human body.

In this study, UV/visible absorption maxima of organic compounds are predicted with the help of machine learning (ML). Four ML models are evaluated, the gradient boosting model has performed best. We also analyzed feature importance. Using Python-based tools, we generated and visualized a new set of 5,000 organic compounds. These compounds were screened based on their predicted UV/visible absorption maxima, selecting those with red-shifted absorption. The assessment of synthetic accessibility indicated that most of the chosen compounds are relatively easy to synthesize.

By integrating Rhodamine B and 4-phenylmorpholine moieties, a novel fluorescent probe named RhPy is synthesized for detecting Hg. Its recognition mechanism involves the reaction of Hg with dithiooxamide, ultimately triggering the opening of the Rhodamine spirolactam and forming a new molecule RhPy-S with strong emission. The probe exhibits impressive limit of detection (0.015 μM) and short response time (<10 s). Importantly, RhPy shows almost none-cytotoxicity and RhPy-S has the emission spectrum peaking at 596 nm, which endow the probe with a good tissue penetration ability and practical utility in living cells, zebrafish and in vivo mice models. This work advances the field by providing a highly sensitive chemosensor for both environmental and biological applications.

The human gut microbiota plays an integral role in the management of human health. Effective detection of gut-derived bacteria and their metabolites, as well as assessing antibiotic susceptibility, are crucial for the treatment of intestinal bacteria-related diseases. Herein, we designed and developed a dual-site (nitrophenyl sulfide group and aldehyde group) fluorescent probe DNO-HC, which could rapidly (∼1 min) respond to glutathione (GSH) with low background fluorescence, high selectivity, and low detection limits (45 nM). Moreover, the probe can be used to evaluate the metabolic levels (GSH) in different gut-derived bacteria and discriminate their Gram status. Remarkably, the assessment of antimicrobial susceptibility to a variety of antibiotics has been successfully accomplished utilizing this probe. It offers a promising strategy for the treatment of diseases associated with bacterial infections.

In this study, we present a dual-modal fluorometric and visualized detection method for Cu ion, leveraging the synergistic properties of graphene quantum dots (GQDs) and Cu ion catalyzed Fenton-like reaction. The Fenton-like reaction of Cu ions and ascorbate generates highly reactive hydroxyl radical (·OH), which effectively disrupt the structure of GQDs, leading to fluorescence quenching. Under optimized conditions, the fluorescence quenching degree exhibited a linear correlation with Cu concentration within the range of 40 to 2000 nM, enabling the detection of Cu ions as low as 40 nM. Furthermore, we demonstrated the feasibility of semi-quantitative visual detection of Cu ion concentrations in water using a portable ultraviolet instrument. The method achieved a minimum detectable concentration of Cu ion as low as 10 μM, surpassing the maximum contaminant level goals of 20.47 μM set by the EPA and the guideline value of 31.47 μM recommended by the WHO. As such, this approach holds promise as a point-of-care testing (POCT) method for Cu ion detection during copper pollution emergency events in water. Additionally, this method can be adapted for the detection of ascorbic acid. Our findings showcase the potential of this dual-modal detection approach, offering a sensitive, rapid, and efficient means for detecting Cu ion, thereby contributing to environmental monitoring and public health applications.

Precise determination of veterinary pharmaceutical concentrations represents a critical foundation for delivering safe and efficacious animal healthcare interventions. Two synthetic glucocorticoids - dexamethasone sodium phosphate (DXM) and prednisolone acetate (PRD) - are extensively employed in veterinary medicine due to their potent anti-inflammatory capabilities. Our research presents a novel, cost-effective, and environmentally sustainable analytical methodology that enables simultaneous quantification of DXM and PRD within binary veterinary formulations. The method synergistically combines UV spectroscopy with dimension reduction algorithms (DRAs), representing a significant advancement in pharmaceutical analysis. A comprehensive evaluation of seventeen DRAs was conducted using four distinct performance metrics: mean squared error (MSE), mean absolute error (MAE), median absolute error (MedAE), and coefficient of determination (R). Among the assessed algorithms, mini-batch sparse principal component analysis demonstrated superior predictive accuracy for this specific analytical challenge. The developed method was validated using the accuracy profile approach, yielding results that confirm its satisfactory accuracy. An ecological impact assessment was conducted using five greenness evaluation tools: the Green Solvent Selection Tool (GSST), National Environmental Methods Index (NEMI), Green Certificate modified Eco-Scale, carbon footprint analysis, and the Modified GAPI (MoGAPI). In addition, whiteness was evaluated with Red-Green-Blue 12 (RGB 12) algorithms. The proposed method showed elevated GSST scores and a greener profile according to NEMI. The calculated carbon footprint was 0.0006 kg CO equivalent per sample, with a Green Certificate modified Eco-Scale score of 84, a MoGAPI score of 81, and a whiteness assessment of 90.1 by the RGB12 algorithm. Statistical comparison between the proposed spectrophotometric method and a previously reported HPLC method for pharmaceutical dosage form analysis revealed no statistically significant differences at the 95 % confidence level. This study underscores the innovative combination of UV spectroscopy with dimension reduction algorithms, presenting substantial improvements over traditional UV techniques for drug analysis. This method enhances both the efficiency and accuracy of active ingredient determination in pharmaceutical dosage forms while also supporting environmental sustainability.

In this paper, we developed a simple, rapid and sensitive method for detection of thiram on apple surfaces by surface enhance Raman spectroscopy (SERS) combined with chemometric methods. Ag NCs (Ag nanocubes) were firstly prepared by a sulfide-mediated polyol method. Then the flexible and adhesive Ag NCs@PDMS substrates were obtained by combining Ag NCs self-assembled films with PDMS films. Thiram residues on apple surfaces were transferred to the substrate using adhesion properties of Ag NCs@PDMS. And the SERS spectra were obtained by Raman microscopy and analyzed with chemometric methods. The results were analyzed by principal component analysis (PCA), for the limit of detection (LOD) of thriam on apple surfaces was 0.01 ppm. Principal component regression (PCR) and partial least squares regression (PLSR) were explored to develop quantitative models. Both models represented higher correlation coefficients (close to 1), but PLSR models exhibited better predictive performance, with the correlation coefficient was 0.99282 with a low root mean squared error of calibration (RMSEC = 0.438) and root mean squared error of validation (RMSECV = 0.597). The developed SERS method based on Ag NCs@PDMS substrate provide a simpler and more sensitive way to monitor thiram on apple surfaces.

Non-alcoholic fatty liver disease (NAFLD) becomes a world health issue due to its rising prevalence and lack of a definitive pathogeny. However, the excessive accumulation of fat droplets has been recognized as a crucial characteristic of NAFLD, accompanied with endoplasmic reticulum stress in its onset and progression as well. Therefore, real-time monitoring the dynamic of lipid droplets (LDs) and endoplasmic reticulum (ER) within cells is paramount. In this regard, four D-A-π-D structural fluorescent probes COB1-COB4 were designed and synthesized wherein coumarin connected with carbazole acted as precursors while the side chains attached to carbazole groups are different. Here, probes COB1-COB4 exhibited high sensitivity towards polarity, while COB2 was chosen for further study attributing to its excellent anti-interference property. Cell imaging demonstrated that COB2 could accurately target both LDs and ER at the same time and monitor the changes of the two organelles under different physiological conditions. Notably, probe COB2 also exhibited the ability to distinguish normal liver from fatty liver at the tissue level. The above results lay an experimental foundation for developing novel dual-targeted probes with potential for early diagnosis of non-alcoholic fatty liver.

Emulsified oil concentration is an important index for quantitative analysis of sea surface oil spill pollution, and the development of a fast and effective quantitative analysis method for emulsified oil concentration plays a crucial role in the estimation of oil spill volume and post-spill assessment. A quantitative analysis method for emulsified oil concentration based on excitation-emission matrix (EEM) fluorescence spectroscopy and chemometrics was proposed. Firstly, the EEM fluorescence spectra of two emulsified oils were measured using a FLS1000 fluorescence spectrometer. Then, the measured EEM fluorescence spectra were decomposed by parallel factor analysis (PARAFAC), and several key excitation wavelengths were filtered from the loading matrix obtained from the decomposition. Subsequently, the three-band fluorescence index (TBFI) at these excitation wavelengths was calculated and combined with the optimal band selection algorithm, from which the optimal emission band combinations were selected. Finally, the selected optimal emission bands were combined with partial least squares regression (PLSR) to establish a prediction model for emulsified oil concentration. By comparing the prediction results with those based on PARAFAC-PLSR and multivariate curve resolved-alternating least squares (MCR-ALS)-PLSR models, the TBFI-PLSR model showed the best results in the quantitative analysis of emulsified oil concentration. The coefficient of determination, mean square relative error, and ratio of performance to interquartile distance for the gasoline and diesel fuel emulsion validation sets were 0.93, 3.67%, 4.72, and 0.93, 3.72%, 4.60, respectively.

Highly responsive and optically selective (E)-1-((4-phenoxyphenyl) diazenyl)naphthalen-2-ol) sensor PDN with aggregation induced emission enhancement (AIEE) properties has been developed for the sequential detection of Cu and L- Cysteine through fluorescence On-Off-On strategy. The selectivity of sensor depends on the presence of a diazo functional group and its appropriate cavity location in sensor molecule. Azo dye-based (E)-1-((4-phenoxyphenyl) diazenyl)naphthalen-2-ol) sensor PDN has been synthesized by utilizing a simple diazotization synthetic methodology that showed extraordinary AIEE behavior with bathochromic shift owing to the formation of J-aggregates. The morphology and size of aggregates were analyzed by SEM and DLS analysis, respectively. The calculated LOD of sensor PDN for Cu, and L-cysteine is 0.113 nM, and 84 nM, respectively. Fluorescence, UV-visible, LC-MS, H and C NMR titration were carried out to understand the interaction of sensor with Cu. The sensor was practically utilized in the sequential sensing of Cu and Cys in real samples. Interestingly, sensor PDN was successfully employed for the sensing of a strong acid and base as well as the detection of Cu ions in the solid state. Moreover, these experimental results were supported through DFT calculations.

A Cr/ClO-enhanced fluorescent probe, DNS (5-(dimethylamino)-N'-(2-hydroxy-4,6-dimethoxybenzylidene)-naphthalene-1-sulfonyl hydrazide), with aggregation-induced emission (AIE) properties was synthesized using dansylhydrazide and 4,6-dimethoxysalicylaldehyde as starting materials. The probe rapidly and selectively detects Cr and ClO in a solvent system of HO/DMSO (2:8). Upon binding with Cr/ClO, the probe exhibits a significant fluorescence enhancement, with minimal interference from other ions. The detection limits (LOD) were determined to be 5.36 × 10 mol/L for Cr and 3.65 × 10 mol/L for ClO. The binding mechanisms of DNS with Cr/ClO were investigated through Job's plot, 1H NMR titration, and mass spectrometry. Furthermore, the probe's low cytotoxicity and biocompatibility suggest its potential for detecting exogenous Cr/ClO and endogenous ClO in living cells. DNS shows promise for real-time detection and bioimaging applications.

Various surface-enhanced Raman scattering (SERS) biosensors offer powerful tools for the ultrasensitive detection of circulating tumor cells (CTCs) and tumor diagnosis. Despite their efficacy, the swift and precise preparation of SERS plasmonic nanostructures poses an ongoing challenge. In this study, we introduce DNA-assisted plasmonic nanostructures capable of producing dual signals and facilitating DNA Walker signal amplification, resulting in the development of a SERS/Fluorescent (FL) dual-mode cytosensor for CTCs detection. Firstly, Au@Ag nanoparticle multimers (Au@AgNMs) featuring interparticle nano-gaps were synthesized through DNA self-assembly and in-situ deposition, which provided plasmonic nanostructures. Hence, the nano-gap distance among Au@AgNMs was meticulously regulated after optimization to achieve both SERS enhancement and fluorescence quenching. Subsequently, the aptamer (Apt) of MUC1 recognized CTCs specifically for strand displacement reaction (SDR) and further triggered the DNA Walker reaction for signal amplification. The limit of detection (LOD) of proposed cytosensor can be obtained as low as 5 cells/mL in SERS mode and 21 cells/mL in FL mode. Hence, SERS mode confers highly precise information, while FL mode allow for rapid quantitative analysis. This dual-mode cytosensor based on plasmonic nanostructures facilitates the early detection and precise treatment of cancer or infectious diseases.

Glutathione (GSH) levels have been well validated to correlate with a variety of physiological and pathological conditions, such as malignancy, cardiovascular disease and aging, making the development of accurate, robust and sensitive GSH detection methods highly desirable. In this study, a novel metal-organic framework (MOF-Fe(DTNB))-based colorimetric method with a favorable dual-triggering function was proposed. MOF-Fe(DTNB) exhibits high peroxidase activity, which can catalyze the oxidation of colorless 3,3',5,5'-tetramethylbenzidine (TMB) to blue ox-TMB by hydrogen peroxide (HO). This oxidation process of TMB can be inhibited not only by the reducing action of GSH, but also by the thiol exchange reaction between DTNB and GSH, in which the disulfide bond of DTNB in MOF-Fe(DTNB) is cleaved. Thus, with this dual triggering mechanism, the GSH concentration can be robustly measured in the MOF-Fe(DTNB)-derived colorimetric strategy. Significantly, this method is accurate (RSD < 6 %), selective and sensitive in biological plasma samples, with satisfactory recovery rates (96.7-103.3 %). It requires less instrumentation and has less interference from other substances. The linear range of the method is 0-80 µM, and the detection limit is as low as 0.28 µM. This dual-triggering MOF-Fe(DTNB)-derived colorimetric strategy has greatly simplified the GSH detection processes with improved accuracy, in both acidic and basic environments, which has potent applications in biochemical analysis and point-of-care testing.

Aquatic systems with low zinc levels can experience a significant decrease in carbon dioxide uptake and limited growth of phytoplankton species. In this study, we describe the use of a new fluorescent sensor based on NH-MIL-53(Al), and modified with glutaraldehyde and sulfadoxine, for selectively detecting zinc ions in water and blood serum samples. Characterization of the synthesized material was performed using X-ray diffraction (XRD), Fourier-transform infrared spectroscopy (FTIR), Brunauer-Emmett-Teller (BET) surface area analysis, X-ray photoelectron spectroscopy (XPS), and scanning electron microscopy (SEM), confirming successful functionalization and preservation of the MOF structure. The sensor's performance for Zn detection was evaluated by spectrofluorometry, demonstrating a significant fluorescence enhancement upon Zn binding due to the interaction between Zn ions and the sulfonamide groups. With a detection limit as low as 3.14 × 10 ppm, the sensor demonstrates high selectivity for Zn over other common metal ions. The sensor's response is rapid, stable, and reproducible, making it suitable for practical applications. Real sample analysis was conducted in tap water and blood serum samples, with the results compared to those obtained using ICP-OES and a colorimetric test with 5-bromo-PAPS. The comparison confirmed the high accuracy and reliability of the fluorescent sensor in detecting Zn ions in complex matrices. NH-MIL-53(Al) modified with glutaraldehyde and sulfadoxine shows potential as a selective fluorescent sensor for Zn detection, making it a valuable tool for monitoring the environment and biology.

In this study, synchronous fluorescence spectroscopic methods coupled with chemometric techniques were developed and evaluated for the simultaneous quantification of ezetimibe and propranolol, two commonly prescribed cardiovascular drugs. Both drugs exhibit overlapping native fluorescence, posing a challenge for their selective determination. To address this, chemometric models including partial least squares (PLS) and genetic algorithm-based variable selection (GA) were constructed using a calibration dataset based on a 5 factorial design resulting in 25 synthetic mixtures. The developed method has been optimized to account for factors such as solvent composition, micellar systems, and excitation/emission wavelengths that affect the fluorescence signals. The PLS and GA-PLS models were validated using an independent test set of 13 samples based on central composite design revealing the GA-PLS model provided improved quantitative performance with relative root mean square error of prediction (RRMSEP) values of 1.3939 and 1.0005 % for ezetimibe and propranolol, respectively, compared to 2.2502 and 2.3526 % for the PLS models. Hence, the GA-PLS models were successfully applied for the determination of ezetimibe and propranolol in pharmaceutical formulations and spiked plasma samples. Furthermore, the greenness and blueness of the proposed methods were compared against reported HPLC procedures using the AGREE and BAGI tools, revealing a greener analytical footprint for the developed method and higher analytical practicability posing as an environmental-friendly alternative to the standard HPLC technique.

In this study, simultaneous determination of levodopa (LEV) and carbidopa (CBD) in binary mixtures, pharmaceutical formulation, and biological sample was conducted using the application of simple, fast, sensitive, and accurate UV-spectrophotometry in combination with chemometrics methods. The first method is net analyte signal (NAS) based on the multivariate calibration methods. The limit of detection (LOD) and limit of quantification (LOQ) were 0.9758, 0.7633 µg/mL and 2.956, 2.313 µg/mL over the linear range of 5-40 and 0.5-20 µg/mL for LEV and CBD, respectively. In the NAS approach, the mean recovery values of mixtures were 100.12 % for LEV and 99.65 % for CBD, where root mean square error (RMSE) values were 0.0106 and 0.0141 for LEV and CBD, respectively. The second method is absorbance subtraction (AS) based on the absorption factor technique for analyzing the isosbestic point. This model was constructed at an isosbestic point of 261 nm in the range of 5-40 and 0.5-20 µg/mL with coefficient determination (R) of 0.9985 and 0.9996 for LEV and CBD, respectively. AS method could estimate LEV and CBD with LOD values of 1.924 and 0.5657 μg/mL and LOQ values of 5.833 and 1.714 μg/mL, respectively. The recovery percentage was between 91.50 % to 104.60 % with RMSE of 0.1455 for LEV and 92.00 % to 106.66 % with RMSE of 0.2508 for CBD. The introduced approaches have the benefit of concurrent analysis of the mentioned components without any pretreatment. Statistical comparison of the results of real sample analysis with high-performance liquid chromatography (HPLC) did not show a significant difference. These methods can replace HPLC in quality control laboratories when fast, precise, and low-cost analysis is needed.

A colorimetric fluorescent probe, 4-(1H-imidazolo[4,5-f][1,10]phenanthroline-2-yl)-N, N-diphenylaniline (PIN), was designed, synthesized and characterized for the sensitive and selective detection of Zn and Cd. The color of the solution changed from blue to yellow visible to the naked eye with the addition of Zn and Cd. The probe PIN showed good anti-interference to Zn and Cd in the presence of a variety of metal ions, and the fluorescence intensity showed a good linear relationship with the concentrations of Zn and Cd, with detection limits of 34.84 nM and 35.76 nM, respectively. The probe PIN complexed 2:1 with Zn and Cd, and the complexation constants were 1.03 × 10 M (PIN - Zn, R = 0.9971) and 1.50 × 10 M (PIN - Cd, R = 0.9981), respectively. In addition, the PIN could be recovered by EDTA and could be effectively monitored for Zn and Cd at pH 4-11, with good results in actual water samples. The HepG-2 cells maintained over 95 % of viability after 24 h exposure to PIN, which identified the extremely low toxic of PIN and could be used for in vivo cell imaging.