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The current work proposes a novel determination method for enantiomeric excess (ee) in (mono- and di-) amines using molecular self-assembly. A pyridine-attached binaphthyl derivative ((R)-1) exhibits fluorescence responses based on imine formation between the aldehyde group of (R)-1 and target chiral amines (i.e. cyclohexane diamine (CHDA), 2-amino-1,2-diphenylethanol (ADPE), 1,2-diphenylethylenediamine (DPDA), 1-amino-2-indanol (AID), and leucinol) in the presence of zinc(II) ions (Zn2+). Because of the multi-optical responses which are derived from the variation of chiral complexes, pattern recognition-based discrimination (i.e. linear discriminant analysis (LDA)) has been achieved for five types of enantiomeric pairs of amines. Possessing such a discrimination capability in combination with data processing (LDA and an artificial neural network) allows accurate determination (prediction error < 1.8%) of the % ee of individual targets such as CHDA which is one of the main components of pharmaceutical drugs. The simple molecular self-assembled system enabled simultaneous multi-chiral discrimination and % ee determination of unknown samples.

We believe that "the simpler we are, the more complete we become" is a key concept of chemical sensing systems. In this work, a "turn-on" fluorescence chemosensor array relying on only two self-assembled molecular chemosensors with ability of both qualitative and quantitative detection of phosphorylated saccharides has been developed. The easy-to-prepare chemosensor array was fabricated by in situ mixing of off-the-shelf reagents (esculetin, 4-methylesculetin, and 3-nitrophenylboronic acid). The fluorescence-based saccharide sensing system was carried out using indicator displacement assay accompanied by photoinduced electron transfer (PeT) under various pH conditions. The simultaneous recognition of 14 types of saccharides including glucose-6-phosphate (G6P) and fructose-6-phosphate (F6P) was achieved with a successful classification rate of 100%. We also succeeded in the quantitative analysis of a mixture of glucose (Glc), as an original substrate, G6P and F6P, as enzymatic products in pseudoglycolysis pathway. Finally, levels of Glc and F6P in human induced pluripotent stem (hiPS) cells were indirectly monitored by using our proposed chemosensor array. Glc and F6P in supernatants of hiPS cells were classified by linear discriminant analysis as a pattern recognition model and the observed clusters represent the activity of hiPS cells. The results show the high accuracy of the proposed chemosensor array in detection of phosphorylated and similarly modified saccharides.

Although the determination of oxyanions due to correlation with metabolic processes and diseases is in high demand, most of the developed methods are suffering from a shortage of a capability of on-site analysis, sensitivity, and user-friendliness. This paper introduces the first colorimetric chemosensor array targeting various anions including glyphosate. The proposed sensor benefits from some notable features such as utilizing only commercially available reagents, recognizing similarly structured compounds by biomaterial-free sensors, and providing a fingerprint-like response originating from pattern recognition. The detection mechanism is based on an anion sensing strategy named coordination binding-based sensor array (CBSA). In CBSA, competitive coordinative bonding of a metal ion (Zn

Simple, rapid, and accurate detection methods for saccharides are potentially applicable to various fields such as clinical and food chemistry. However, the practical applications of on-site analytical methods are still limited. To this end, herein, we propose a 96-well microtiter plate made of paper as a paper-based chemosensor array device (PCSAD) for the simultaneous classification of 12 saccharides and the quantification of fructose and glucose among 12 saccharides. The mechanism of the saccharide detection relied on an indicator displacement assay (IDA) on the PCSAD using four types of catechol dyes, 3-nitrophenylboronic acid, and the saccharides. The design of the PCSAD and the experimental conditions for the IDA were optimized using a central composite design. The chemosensors exhibited clear color changes upon the addition of saccharides on the paper because of the competitive boronate esterification. The color changes were employed for the subsequent qualitative, semiquantitative, and quantitative analyses using an automated algorithm combined with pattern recognition for digital images. A qualitative linear discrimination analysis offered discrimination of 12 saccharides with a 100% classification rate. The semiquantitative analysis of fructose in the presence of glucose was carried out from the viewpoint of food analysis utilizing a support vector machine, resulting in clear discrimination of the various concentrations of fructose. Most importantly, the quantitative detection of fructose in two types of commercial soft drinks was also successfully carried out without sample pretreatments. Thus, the proposed PCSAD can be a powerful method for on-site food analyses that can meet the increasing demand from consumers for sensors of saccharides.

Although microfluidic paper-based analytical devices (μPADs) get a lot of attention in the scientific literature, they rarely reach the level of commercialization. One possible reason for this is a lack of application of machine learning techniques supporting the design, optimization and fabrication of such devices. This work demonstrates the potential of two chemometric techniques including design of experiments (DoE) and digital image processing to support the production of μPADs. On the example of a simple colorimetric assay for isoniazid relying on the protonation equilibrium of methyl orange, the experimental conditions were optimized using a D-optimal design (DO) and the impact of multiple factors on the μPAD response was investigated. In addition, this work demonstrates the impact of automatic image processing on accelerating color value analysis and on minimizing errors caused by manual detection area selection. The employed algorithm is based on morphological recognition and allows the analysis of RGB (red, green, and blue) values in a repeatable way. In our belief, DoE and digital image processing methodologies are keys to overcome some of the remaining weaknesses in μPAD development to facilitate their future market entry.

During the past decades, colorimetric detection of sugars in a low-cost and user-friendly way has been a challenge. The current study introduces the first effort to develop a simplified colorimetri...

In this work, an application of a design of experiments approach for the optimization of an isoniazid assay on a single-area inkjet-printed paper-based analytical device (PAD) is described. For this purpose, a central composite design was used for evaluation of the effect of device geometry and amount of assay reagents on the efficiency of the proposed device. The factors of interest were printed length, width, and sampling volume as factors related to device geometry, and amounts of the assay reagents polyvinyl alcohol (PVA), NH4OH, and AgNO3. Deposition of the assay reagents was performed by a thermal inkjet printer. The colorimetric assay mechanism of this device is based on the chemical interaction of isoniazid, ammonium hydroxide, and PVA with silver ions to induce the formation of yellow silver nanoparticles (AgNPs). The in situ-formed AgNPs can be easily detected by the naked eye or with a simple flat-bed scanner. Under optimal conditions, the calibration curve was linear in the isoniazid concentration range 0.03-10 mmol L-1 with a relative standard deviation of 3.4% (n = 5 for determination of 1.0 mmol L-1). Finally, the application of the proposed device for isoniazid determination in pharmaceutical preparations produced satisfactory results.

In this work, a simple, rapid and sensitive method using in situ surfactant-based solid phase extraction (ISS-SPE) combined with UV–vis spectrophotometry has been developed for the preconcentration and determination of trace amounts of quinoline yellow in food and water samples. The Box–Behnken design was employed to optimize the extraction efficiency. The variables of interest were pH, surfactant volume, extraction time and NaI volume. In the optimal conditions, the calibration graph was linear in the range of 10.0–750 μg L−1 with a correlation coefficient of 0.9982. The limit of detection (LOD) was 2.1 μg L−1, and the preconcentration factor was calculated to be 51.8.

A simple and rapid method based on ultrasound-assisted temperature-controlled ionic liquid microextraction combined with flame atomic absorption spectrometry (FAAS) has been developed for determination of tin. In this method, Sn(IV)-ammonium pyrrolidinedithiocarbamate (APDC) complex was extracted into a small volume of 1-hexyl-3-methylimidazolium hexafluorophosphate as ionic liquid and after phase separation, the enriched analyte in the final solution is determined by FAAS. Experimental design approaches were used to obtain the best conditions. The variables of interest were temperature, pH, buffer volume, extraction time, centrifugation time and volumes of ionic liquid, methanol, and APDC. The Plackett–Burman design was employed for screening to determine the variables significantly affecting the extraction efficiency. Then, the significant factors were optimized by using a central composite design. In the optimal conditions the calibration graph was linear in the range of 0.10–6.0 mg L−1 with a correlation coefficient of 0.997. The limit of detection and relative standard deviation (n = 5 for determination of 1.0 mg L−1) were 42 μg L−1 and 1.6 %, respectively. The preconcentration factor was calculated to be 52.7. Finally, the method was successfully applied to the determination of tin in various canned products including peach, pineapple and aloe vera juice, canned pea, and canned cheese.