Analyte Discrimination Research Articles

19F-Labeled NMR Probes for the Detection and Discrimination of Nitrogen-Containing Analytes

AbstractThe development of 19F-labeled NMR probes has become a pivotal tool in analytical chemistry. Recent advancements in probe design enable precise identification of nitrogen-containing analytes, significantly enhancing the analysis of these biologically important analytes in complex mixtures. This short review highlights recent progress with probes based on covalent derivatization and dynamic exchange strategies, which yield distinct 19F NMR signals for each nitrogen-containing analyte. These strategies facilitate separation-free multicomponent analysis and chiral discrimination. Discussions will cover design principles, scope, limitations, and strategies to enhance the sensitivity and resolving ability. By addressing current challenges, 19F-labeled NMR probes hold the potential to revolutionize the detection of biologically relevant molecules, catalyzing new discoveries in chemical and biochemical research.1 Introduction2 19F-Labeled Probes Based on Covalent Derivatization2.1 Non-Chiral 19F-Labeled Probes Based on Covalent Derivatization2.2 Chiral 19F-Labeled Probes Based on Covalent Derivatization3 19F-Labeled Probes Based on Dynamic Ligand Exchange3.1 Non-Chiral 19F-Labeled Probes Based on Dynamic Ligand Exchange3.2 Chiral 19F-Labeled Probes Based on Dynamic Ligand Exchange4 Conclusion and Outlook

A Glycopolymer Sensor Array That Differentiates Lectins and Bacteria.

Identification of bacterial lectins offers an attractive route to the development of new diagnostics, but the design of specific sensors is complicated by the low selectivity of carbohydrate-lectin interactions. Here we describe a glycopolymer-based sensor array which can identify a selection of lectins with similar carbohydrate recognition preferences through a pattern-based approach. Receptors were generated using a polymer scaffold functionalized with an environmentally sensitive fluorophore, along with simple carbohydrate motifs. Exposure to lectins induced changes in the emission profiles of the receptors, enabling the discrimination of analytes using linear discriminant analysis. The resultant algorithm was used for lectin identification across a range of concentrations and within complex mixtures of proteins. The sensor array was shown to discriminate different strains of pathogenic bacteria, demonstrating its potential application as a rapid diagnostic tool to characterize bacterial infections and identify bacterial virulence factors such as production of adhesins and antibiotic resistance.

Multistep Array-Based Sensing of Bioanalytes Using Modified MoS2, Fluorescence Proteins, and Cucurbituril.

One pot sensor by multiplexing in the array is an attractive system for rapid discrimination of multiple analytes. Multiplexing can be achieved in two ways, i.e., using multiple signal transducers or adding sequential agents to the sensor media. Herein, we have used a combination of both multichannel and sequential ON-OFF strategies for the discrimination of different bioanalytes. The sensor array was constructed by implementing positively charged MoS2 as a receptor and different fluorescent proteins possessing distinguishable emission profiles as signal transducers. The sensing setup was constructed with the interaction between oppositely charged MoS2 and the host-guest combination between a cationic headgroup of MoS2 and Cucurbit [7] uril (CB7) to alter the fluorescence of signal transducers in situ noncovalently. Electrodynamic analysis and optical assays suggest that the electrostatic interaction played a major role in the modulation of the fluorescence outcomes in the array. Both cationic and anionic proteins were discriminated at a 50 nM concentration. The detection limit of the sensor array by using β-gal protein was found to be 1 nM. The sensor array was further implemented for the discrimination of normal and diseased cell lines and lysates, which indicates the versatile detection ability of this reported sensor array.

Enantioselective Discrimination via Wireless Chemiresistive Devices.

Developing chemiresistive devices for the wireless detection of complex analytes has gained considerable interest. In particular, the enantioselective recognition of chiral molecules is still a challenge. Here, we design a hybrid chemiresistive device for the wireless enantioselective discrimination of chiral analytes by combining the enantiorecognition capabilities of an inherently chiral oligomer, that is, oligo-(3,3'-dibenzothiophene) (BT2T4) and the insulating/conducting transition of polypyrrole (Ppy). The device is obtained by modifying each extremity of an interdigitated electrode (IDE) with Ppy on the interdigitated area and oligo-BT2T4 on the connection pads. Due to the asymmetric electroactivity triggered by bipolar electrochemistry, the wireless enantioselective discrimination of both enantiomers of tryptophan and DOPA was achieved. A difference in the onset resistance values was obtained for both enantiomers due to a favorable or unfavorable diastereomeric interaction between the inherently chiral oligomer and the antipode of the chiral molecule. Interestingly, such a device showed a wide quantification range, from μM to mM levels. This work opens up new alternatives to designing advanced wireless devices in enantiorecognition.

Smartphone-based detection and discrimination of amine vapors by a single dye-adsorbed material

Smartphone-assisted analysis has become widely utilized for detecting various species in recent years. In such studies, multiple dyes should be employed to ensure selectivity and analyte discrimination. In our research, we have demonstrated the capability of a specially synthesized dye to selectively detect and discriminate liquid amine vapors. The developed material employs meso-toluene-α,β,α’,β’-tetrabromoBODIPY immobilized on a thin-layer chromatography plate, exhibiting structure-specific color changes in response to amine vapors. The hue values of these colors, observed under both ambient and UV light, enable discrimination even among closely related amine structures. A mobile application has also been developed for the rapid interpretation of test results.

Femtosecond Laser Ionization Mass Spectrometry for Online Analysis of Human Exhaled Breath.

A variety of organic compounds in human exhaled breath were measured online by mass spectrometry using the fifth (206 nm) and fourth (257 nm) harmonic emissions of a femtosecond ytterbium (Yb) laser as the ionization source. Molecular ions were enhanced significantly by means of resonance-enhanced, two-color, two-photon ionization, which was useful for discrimination of analytes against the background. The limit of detection was 0.15 ppm for acetone in air. The concentration of acetone in exhaled breath was determined for three subjects to average 0.31 ppm, which lies within the range of normal healthy subjects and is appreciably lower than the range for patients with diabetes mellitus. Many other constituents, which could be assigned to acetaldehyde, ethanol, isoprene, phenol, octane, ethyl butanoate, indole, octanol, etc., were observed in the exhaled air. Therefore, the present approach shows potential for use in the online analysis of diabetes mellitus and also for the diagnosis of various diseases, such as COVID-19 and cancers.

A portable and versatile fluorescent platform for high-throughput screening of toxic phosgene, diethyl chlorophosphate and volatile acyl chlorides

Highly toxic phosgene, diethyl chlorophosphate (DCP) and volatile acyl chlorides endanger our life and public security. To achieve facile sensing and discrimination of multiple target analytes, herein, we presented a single fluorescent probe (BDP-CHD) for high-throughput screening of phosgene, DCP and volatile acyl chlorides. The probe underwent a covalent cascade reaction with phosgene to form boron dipyrromethene (BODIPY) with bright green fluorescence. By contrast, DCP, diphosgene and acyl chlorides can covalently assembled with the probe, giving rise to strong blue fluorescence. The probe has demonstrated high-throughput detection capability, high sensitivity, fast response (within 3 s) and parts per trillion (ppt) level detection limit. Furthermore, a portable platform based on BDP-CHD was constructed, which has achieved high-throughput discrimination of 16 analytes through linear discriminant analysis (LDA). Moreover, a smartphone adaptable RGB recognition pattern was established for the quantitative detection of multi-analytes. Therefore, this portable fluorescence sensing platform can serve as a versatile tool for rapid and high-throughput detection of toxic phosgene, DCP and volatile acyl chlorides. The proposed “one for more” strategy simplifies multi-target discrimination procedures and holds great promise for various sensing applications.

Geometric Transformation of Modified Multiwalled Carbon Nanotubes-Based Heterometallic Nanostructured Material: A Model for the Electrochemical Discrimination of Insecticides.

Multifunctional carbon-based materials exhibit a large number of unprecedented active sites via an electron transfer process and act as a desired platform for exploring high-performance electroactive material. Herein, we exemplify the holistic design of a heterometallic nanostructured material (MWCNTs@KR-6/Mn/Sn/Pb) formed by the integration of metals (Mn2+, Sn2+, and Pb2+) and a dipodal ligand (KR-6) at the surface of multiwalled carbon nanotubes (MWCNTs). First, MWCNTs@KR-6 was readily synthesized via a noncovalent approach, which was further sequentially doped by Mn2+, Sn2+, and Pb2+ to give MWCNTs@KR-6/Mn/Sn/Pb. The designed material showed excellent electrochemical activity for the discrimination of insecticides belonging to structurally different classes. In contrast to that of the individual building components, both the stability and electrochemical activity of heterometallic nanostructured material were remarkably enhanced, resulting in a magnificent electrochemical performance of the developed material. Hence, the current work reports a comprehensive synthetic approach for MWCNTs@KR-6/Mn/Sn/Pb synthesis by synergizing unique properties of the heterometallic complex with MWCNTs. This work also offers a new insight into the design of multifunctional carbon-based materials for discrimination of different analytes on the basis of their redox potential.

Colorimetric sensor array for versatile detection and discrimination of model analytes with environmental relevance

In the current work, a rapid, simple, low-cost, and sensitive smartphone-based colorimetric sensor array coupled with pattern-recognition methods was proposed for the determination and differentiation of some organic and inorganic bases (i.e., OH−, CO32−, PO43−, NH3, ClO−, diethanolamine, triethanolamine) as model compounds. The sensing system has been designed based on color-sensitive dyes (Fuchsine, Giemsa, Thionine, and CoCl2) which were used as sensor elements. The color changes of a sensor array were observed by the naked eye. The color patterns were recorded using digital imaging in a three-dimensional (red, green, and blue) space and quantitatively analyzed with color calibration techniques. Distinctive colorimetric patterns for target bases via linear discriminant analysis (LDA) and hierarchical clustering analysis (HCA) were observed. The results indicated that the analytes related to each class (at the different concentration levels in the range of 0.001–1.0 mol L−1) were clustered together in the canonical discriminant plot and HCA dendrogram with high sensitivity and an overall precision of 85%. Furthermore, the first function factor of LDA correlated with the concentration of each target analyte in a correlation coefficient (R2) range of 0.864–0.996. These described procedures based on the colorimetric sensor array technique could be a promising candidate for practical applications in package technology and facile detection of pollutants.

Machine learning assisted discrimination and detection of antibiotics by using multicolor microfluidic chemiluminescence detection chip

The fabrication of multicolor chemiluminescence (CL) sensing chip for the discrimination and detection of multianalytes remains a great challenge. Herein, machine learning assisted multicolor microfluidic CL detection chip for the identification and concentration prediction of antibiotics was presented. Firstly, a three-channel microfluidic CL detection chip was fabricated. The three detection zones of the microfluidic detection chip were modified with CL catalyst Co(II) and different CL reagents including luminol, luminol mixed with fluorescein, and luminol mixed with phloxine B, respectively. Strong blue, green and pink-purple colored light emissions can be generated from the three detection zones in the presence of H2O2 solution. The three multicolor CL emissions show different degrees of reduce in intensity and change in color in the presence of different antibiotics, including diethylstilbestro (DES), metronidazole (MNZ), kanamycin (KAN), isoniazide (INH), and ceftiofur sodium (CS), resulting in distinct fingerprint-like response patterns. The red (R), green (G), blue (B) and gray scale values of the three multicolor light emissions were extracted and ten characteristic sensing parameters were chosen to obtain multicolor CL response database. Then, machine learning assisted data analysis were carried out. The five antibiotics can be facilely classified by using principal component analysis (PCA) and hierarchical clustering analysis (HCA), and further quantified by using deep neural networks (DNN) algorithm. Good results were obtained for identification of binary antibiotic mixtures, spiked antibiotics in water samples, and unknown antibiotic samples. Satisfied results were obtained for concentration prediction of antibiotics. This work provides a simple machine learning assisted and multicolor microfluidic CL detection chip based CL sensing strategy for discrimination and quantitative detection of multiple analytes.

Intensity modulation of UV light in gas sensor array to discriminate several analytes at room temperature

AbstractIn this study, a gas sensor array along with intensity modulation of UV light was utilized to discriminate several gases at room temperature. The sensor array was consisted of two interdigitated microelectrodes and TiO2 nanofibres were electrospun on them and calcined at 540°C for 90 min. One of these sensors was coated by 2‐nm Pt using the DC sputtering method and the other one remained uncoated. In each experiment, the sensor array was located at a distance of 30 mm from a 365‐nm UV LED. For intensity modulation of UV light, a staircase waveform voltage was applied to the UV LED. The voltage was included of three voltage steps and the measured powers at a distance of 30 mm from the UV‐LED were about 450, 560, and 680 µW/cm2, respectively. Analytes including acetone, ethanol, methanol, 2‐propanol, and carbon monoxide (CO) at various concentrations ranging from 50 to 500 ppm were examined. Three‐dimensional Principal Component Analysis mapping was successfully used for the segregation of all examined gases. The examinations revealed that using sensor array along with intensity modulation of UV light is an effective method for discrimination of several analytes at room temperature.

Adaptive Supramolecular Networks: Emergent Sensing from Complex Systems.

Molecular differentiation by supramolecular sensors is typically achieved through sensor arrays, relying on the pattern recognition responses of large panels of isolated sensing elements. Here we report a new one-pot systems chemistry approach to differential sensing in biological solutions. We constructed an adaptive network of three cross-assembling sensor elements with diverse analyte-binding and photophysical properties. This robust sensing approach exploits complex interconnected sensor-sensor and sensor-analyte equilibria, producing emergent supramolecular and photophysical responses unique to each analyte. We characterize the basic mechanisms by which an adaptive network responds to analytes. The inherently data-rich responses of an adaptive network discriminate among very closely related proteins and protein mixtures without relying on designed protein recognition elements. We show that a single adaptive sensing solution provides better analyte discrimination using fewer response observations than a sensor array built from the same components. We also show the network's ability to adapt and respond to changing biological solutions over time.

Adaptive Supramolecular Networks: Emergent Sensing from Complex Systems

AbstractMolecular differentiation by supramolecular sensors is typically achieved through sensor arrays, relying on the pattern recognition responses of large panels of isolated sensing elements. Here we report a new one‐pot systems chemistry approach to differential sensing in biological solutions. We constructed an adaptive network of three cross‐assembling sensor elements with diverse analyte‐binding and photophysical properties. This robust sensing approach exploits complex interconnected sensor‐sensor and sensor‐analyte equilibria, producing emergent supramolecular and photophysical responses unique to each analyte. We characterize the basic mechanisms by which an adaptive network responds to analytes. The inherently data‐rich responses of an adaptive network discriminate among very closely related proteins and protein mixtures without relying on designed protein recognition elements. We show that a single adaptive sensing solution provides better analyte discrimination using fewer response observations than a sensor array built from the same components. We also show the network's ability to adapt and respond to changing biological solutions over time.

Portable Sensor Array for On-Site Detection and Discrimination of Pesticides and Herbicides Using Multivariate Analysis.

Modern agricultural practice relies heavily on pesticides and herbicides to increase crop productivity, and consequently, their residues have a negative impact on the environment and public health. Thus, keeping these issues in account, herein we developed an azodye-based chromogenic sensor array for the detection and discrimination of pesticides and herbicides in food and soil samples, utilizing machine learning approaches such as hierarchical clustering analysis, principal component analysis, linear discriminant analysis (LDA), and partial least square regression (PLSR). The azodye-based sensor array was developed in combination with various metal ions owing to their different photophysical properties, which led to distinct patterns toward various pesticides and herbicides. The obtained distinct patterns were recognized and processed through automated multivariate analysis, which enables the selective and sensitive identification and discrimination of various target analytes. Further, the qualitative and quantitative determination of target analytes were performed using LDA and PLSR; the results obtained show a linear correlation with varied concentrations of target analytes with R2 values from 0.89 to 0.96, the limit of detection from 5.3 to 11.8 ppm with a linear working range from 1 to 30 μM toward analytes under investigation. Further, the developed sensor array was successfully utilized for the discrimination of a binary mixture of pesticide (chlorpyrifos) and herbicide (glyphosate).

Temperature Modulation of MOS Sensors for Enhanced Detection of Volatile Organic Compounds

Disease diagnosis through biological fluids, particularly exhaled breath analysis, has gained increasing importance. Volatile organic compounds (VOCs) present in exhaled breath offer diagnostic potential as they reflect altered and disease-specific metabolic pathways. While gas chromatography–mass spectrometry (GC–MS) has been traditionally used for VOCs detection, electronic noses have emerged as a promising alternative for disease screening. Metal oxide semiconductor (MOS) sensors play an essential role in these devices due to their simplicity and cost-effectiveness. However, their limited specificity and sensitivity pose challenges for accurate diagnosis at lower VOCs concentrations, typical of exhaled breath. To address specificity and sensitivity issues, temperature modulation (TM) has been proposed in this paper, introducing a custom-developed electronic nose based on multiple and heterogeneous gas sensors located within an analysis chamber. Four different TM patterns (i.e., square, sine, triangular, and a combination of square and triangular) were applied to the gas sensors to test their response to three different analytes at three distinct concentrations. Data were analyzed by extracting meaningful features from the sensor raw data, and dimensionality reduction using principal component analysis (PCA) was performed. The results demonstrated distinct clusters for each experimental condition, indicating successful discrimination of analytes and concentrations. In addition, an analysis of which set of sensors and modulation pattern yielded the best results was performed. In particular, the most promising TM pattern proved to be the square and triangular combination, with optimal discrimination accuracy between both concentrations and analytes. One specific sensor, namely, TGS2600 from Figaro USA, Inc., provided the best performance. While preliminary results highlighted the potential of TM to improve the sensitivity of gas sensors in electronic nose devices, paving the way for further advancements in the field of exhaled breath analysis.

New modes of converting chemical information with colloidal photonic crystal sensing units

Photonic crystal is a kind of device which can convert a chemical signal into an optical signal and is commonly used in sensing and detection. The maximum reflection wavelength representing the photonic band gap has been the most common converting mode in analytical usage which however discard too much valuable chemical information. In this work, we established two additional modes for mining chemical information more deeply in time and space as the sensing information to distinguish analytes. They are respectively based on dynamic analysis of the spectrum shift and the distinction of the RGB partition block value information of optical image. The molecular imprinting sensing mechanism worked well on three organophosphorus compounds to the detection limit of 10−4 M. The principle component analysis of above data did present a good discrimination of organophosphorus analytes from interfering counter anions to a low detection limit of 10−6 M. To make the detection more convenient and to achieve real-time on-site detection, we have designed the portable photonic crystal signal acquisition kit. Together with the mobile terminal, the kit connects the optical image collected on site, the algorithm working on the cloud and the input/output interactive interface of users in detection. The methods were constructed on an example made of a three-dimensional molecularly imprinted photonic crystal hydrogels sensing unit targeting on organo-phosphides.

Tartaric acid-functionalized tetraphenylethylenes as fluorescent sensors for enantioselective discrimination of chiral analytes

Tartaric acid-functionalized tetraphenylethylenes as fluorescent sensors for enantioselective discrimination of chiral analytes

Biosynthesis of Salbutamol-4′-O-sulfate as Reference for Identification of Intake Routes and Enantiopure Salbutamol Administration by Achiral UHPLC-MS/MS

The aim of the study was a comprehensive and quantitative determination of salbutamol and its sulfoconjugated major metabolite in urine samples using achiral ultrahigh performance liquid chromatography-tandem mass spectrometry (UHPLC-MS/MS). Therefore, salbutamol-4′-O-sulfate was biosynthesized as a reference using genetically modified fission yeast cells, and the product was subsequently characterized by NMR and HRMS. In competitive sports, salbutamol is classified as a prohibited drug; however, inhalation at therapeutic doses is permitted with a maximum allowance of 600 µg/8 h. In contrast, the enantiopure levosalbutamol is prohibited under any condition. For analytical discrimination, the amount of salbutamol and its main metabolite excreted in the urine was studied. As proof of concept, a longitudinal study in one healthy volunteer was performed in order to investigate excreted amounts and to study potential discrimination using achiral chromatography. Discrimination of administration of racemic salbutamol or the enantiopure levosalbutamol was not achieved by solely analyzing salbutamol as the parent compound. However, a distinction was possible by evaluation of the proportion of salbutamol-4′-O-sulfate in relation to salbutamol. Therefore, reference material of metabolites is of great importance in doping control, especially for threshold substances.

Development of an object-based image analysis tool for mass spectrometry imaging ion classification.

Mass spectrometry imaging (MSI) is an analytical technique that can detect and visualize thousands of m/z values resolved in two- and three-dimensional space. These m/z values lead to hundreds of molecular annotations, including on-tissue and background ions. Discrimination of sample-related analytes from ambient ions conventionally involves manual investigation of each ion heatmap, which requires significant researcher time and effort (for a single tissue image, it can take an hour to determine on-tissue and off-tissue species). Moreover, manual investigation lends itself to subjectivity. Herein, we present the utility of an ion classification tool (ICT) developed using object-based image analysis in MATLAB. The ICT functions by segmenting ion heatmap images into on-tissue and off-tissue objects through binary conversion. The binary images are analyzed and within seconds used to classify the ions as on-tissue or background using a binning approach based on the number of detected objects. In a representative dataset with 50 randomly selected annotations, the ICT was able to accurately classify 45/50 ions as on-tissue or background.

Chrono-colorimetric sensor array for detection and discrimination of halide ions using an all-in-one plasmonic sensor element

Most nanoparticle based colorimetric sensor array utilize several sensor elements and static response for discrimination of target analytes. This approach can be complicated and costly to synthesize or functionalize different nanoparticles for providing wide color variation. Herein, triangular silver nanoparticles (TSNPs) were used to develop a colorimetric sensor array by time-dimension responses. The principle of this sensor array is based on the diverse etching process of TSNPs in the presence of three halide ions, including bromide (Br−), iodide (I−) and chloride (Cl−). Various etchings of TSNPs induced color changes at different reaction time intervals, which produced a colorimetric pattern for each ion. Therefore, using time dependent etching responses of TSNPs as a single sensing component can produce a wide color variation which can be distinguished by naked eyes. The colorimetric responses of TSNPs upon the addition of different concentrations of halide ions have been analyzed by PLS regression (PLS-R) and PLS discriminant analysis (PLS-DA). The analytical figures of merit confirmed that the developed chrono-colorimetric TSNPs -based sensor array is successful in both the discrimination and quantitative detection of halide ions. At the final step, the three halide ions were accurately determined in a real water sample, which verified the potential of the developed sensor in a real sample.