Immobilized Poly(aryleneethynylene) pH Strips Discriminate Different Brands of Cola

A poly(arylene ethynylene)-based microfluidic fluorescence sensor array for discrimination of polycyclic aromatic hydrocarbons

Polycyclic aromatic hydrocarbons (PAHs) were discriminated using a microfluidic paper-based sensor array device.

Combination of two analytical techniques improves wine classification by Vineyard, Region, and vintage

Three important wine parameters: vineyard, region, and vintage year, were evaluated using fifteen Vitis vinifera L. 'Pinot noir' wines derived from the same scion clone (Pinot noir 667). These wines were produced from two vintage years (2015 and 2016) and eight different regions along the Pacific Coast of the United States. We successfully improved the classification of the selected Pinot noir wines by combining an untargeted 1D ¹H NMR analysis with a targeted peptide based differential sensing array. NMR spectroscopy was used to evaluate the chemical fingerprint of the wines, whereas the peptide-based sensing array is known to mimic the senses of taste, smell, and palate texture by characterizing the phenolic profile. Multivariate and univariate statistical analyses of the combined NMR and differential sensing array dataset classified the genetically identical Pinot noir wines on the basis of distinctive metabolic signatures associated with the region of growth, vineyard, and vintage year.

Identification of milk adulteration by a sensor array based on cationic polymer induced aggregation of a perylene probe

A novel fluorescence sensor array based on cationic polymer induced self-assembly of a perylene probe is developed. Cationic polymer induced aggregation of the carboxyl modified negatively charged perylene probe, and resulted in large quenching of monomer emission and generation of excimer emission. Upon the addition of negatively charged protein, monomer fluorescence restored with a decrease in excimer fluorescence. Based on these observations, we developed a six-channel sensor array to discriminate five main proteins in milk. In addition, we successfully identified pure milk out of different drinks using the developed sensor array since different drinks contained distinct species and contents of proteins. Furthermore, the sensor array exhibited excellent performance to discriminate milk adulterated by different concentrations of adulterants with 100% accuracy of cross validation. The analysis results also presented excellent linear correlation of adulterants contents and thus the developed sensor array shows great potential for quantitative detection of milk adulteration.

A colorimetric sensor array based on natural pigments for the discrimination of saccharides

A colorimetric sensor array based on natural pigments was developed to discriminate between various saccharides. Anthocyanins, pH-sensitive natural pigments, were extracted from fruits and flowers and used as components of the sensor array. Variation in pH, due to the reaction between saccharides and boronic acids, caused obvious colour changes in the natural pigments. Only by observing the difference map with the naked eye could 11 common saccharides be divided into independent individuals. In conjunction with pattern recognition, the sensor array clearly differentiated between sugar and sugar alcohol with highly accuracy and allowed rapid quantification of different concentrations of maltitol and fructose. This sensor array for saccharides is expected to become a promising alternative tool for food monitoring. The link between anthocyanin and saccharide detection opened a new guiding direction for the application of anthocyanins in foods.

Fluorescent carbon nanodots-based artificial tongue for determining and discriminating cigarettes

Smoking can cause cigarette-related diseases and pose serious threat to human health. Its dangers can be effectively controlled by discriminating cigarettes and monitoring cigarette quality. Herein, a kind of artificial tongue technique based on the indicator displacement assay (IDA) was developed and applied to determine and discriminate cigarettes and their main ingredients (saccharides, organic acids and nicotine). This method was constructed using carbon nanodots (CDs) as a fluorescent indicator and various concentrations of silver ion (Ag⁺) as a fluorescent regulator. A cigarette extracting solution was prepared to interact with an artificial tongue and produce fluorescence fingerprints. Twenty-nine kinds of cigarettes can be well discriminated in terms of category (flue-cured cigarette, blended cigarette and cigar), brand, origin (domestic or foreign cigarettes) after processing and visualizing the response fingerprints. The artificial tongue fluorescent sensor array can sensitively detect nine kinds of tobacco-based chemical ingredients and discriminate them between different concentrations. The as-prepared fluorescent artificial tongue is a promising platform for monitoring cigarette quality and controlling the harmful effects of smoking because of its cheap material requirements, simple operation, and good performance.

Surfactant and alcohol induced disaggregation of perylene probes and a novel sensing strategy for distinguishing the brand and authenticity of makeup removers

A novel sensing strategy based on four perylene probes to distinguish the brand and authenticity of makeup removers.

Investigating the Time Response of an Optical pH Sensor Based on a Polysiloxane-Polyethylene Glycol Composite Material Impregnated with a pH-Responsive Triangulenium Dye

Determining the time it takes a sensor to report a change in the concentration of its target analyte may appear to be an easy task, but it is not. The dynamic characteristic of a sensor is determined by all components in the sensor system and the hydrodynamics of the sample. Here, the dynamic properties of an optical pH sensor were determined using the IUPAC-recommended activity step method in experimental setups that can determine sensor-limited response times longer than 5 s. In order to do so, experimental setups for the injection and for the dipping method of determining the sensor time response were developed, tested, and shown to be able to determine time-response curves with 1 s time resolution. This time resolution is shown to be sufficient for determining dynamic characterization of this optical pH sensor. The sensor chemistry-limited time-response curves were analyzed using curve fitting. It was found that the optode response time is limited by diffusion of protons within the sensor material when the proton concentration is reduced and limited by diffusion from the bulk to the boundary layer at the optode surface when proton concentration is increased. The latter is dependent on the magnitude of the change in analyte concentration and cannot be reported as a single response time. The investigation of the time response of the optical pH sensor reveals detailed information of the sensor chemistry, but does not yield a single response time of the sensor capable of describing the dynamic sensor characteristics of the optical pH sensor system.

Machine-Learning Identification of the Sensing Descriptors Relevant in Molecular Interactions with Metal Nanoparticle-Decorated Nanotube Field-Effect Transistors

Carbon nanotube-based field-effect transistors (NTFETs) are ideal sensor devices as they provide rich information regarding carbon nanotube interactions with target analytes and have potential for miniaturization in diverse applications in medical, safety, environmental, and energy sectors. Herein, we investigate chemical detection with cross-sensitive NTFETs sensor arrays comprised of metal nanoparticle-decorated single-walled carbon nanotubes (SWCNTs). By combining analysis of NTFET device characteristics with supervised machine-learning algorithms, we have successfully discriminated among five selected purine compounds, adenine, guanine, xanthine, uric acid, and caffeine. Interactions of purine compounds with metal nanoparticle-decorated SWCNTs were corroborated by density functional theory calculations. Furthermore, by testing a variety of prepared as well as commercial solutions with and without caffeine, our approach accurately discerns the presence of caffeine in 95% of the samples with 48 features using a linear discriminant analysis and in 93.4% of the samples with only 11 features when using a support vector machine analysis. We also performed recursive feature elimination and identified three NTFET parameters, transconductance, threshold voltage, and minimum conductance, as the most crucial features to analyte prediction accuracy.