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The need to increase food production to address the world population growth can only be fulfilled with precision agriculture strategies to increase crop yield with minimal expansion of the cultivated area. One example is site-specific fertilization based on accurate monitoring of soil nutrient levels, which can be made more cost-effective using sensors. This study developed an impedimetric multisensor array using ion-selective membranes to analyze soil samples enriched with macronutrients (N, P, and K), which is compared with another array based on layer-by-layer films. The results obtained from both devices are analyzed with multidimensional projection techniques and machine learning methods, where a decision tree model algorithm chooses the calibrations (best frequencies and sensors). The multicalibration space method indicates that both devices effectively distinguished all soil samples tested, with the ion-selective membrane setup presenting a higher sensitivity to K content. These findings pave the way for more environmentally friendly and efficient agricultural practices, facilitating the mapping of cropping areas for precise fertilizer application and optimized crop yield., Competing Interests: The authors declare no competing financial interest., (© 2024 The Authors. Published by American Chemical Society.)

Quantitative analysis of spent nuclear fuel (SNF) is a very challenging task. High radioactivity, complex chemical composition and personnel safety requirements severely limit the number of analytical tools suitable for this problem. There is an urgent need for the methods that would provide for remote on-line quantification of elements in spent nuclear fuel and its reprocessing technological solutions. Here we propose a novel approach based on the registration of X-ray fluorescence radiation from SNF samples induced by fission products radioactivity. In this case the X-ray excitation conditions will obviously vary from sample to sample; moreover the resulting spectra will be a complex superposition of numerous signals from soft gamma emitters and X-ray fluorescence of various nature. These complex spectra can be effectively treated with chemometric data processing for quantification of particular elements. We have demonstrated the validity of this approach for direct analysis of U, Zr and Mo in SNF raffinate., Competing Interests: 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., (Copyright © 2022 Elsevier B.V. All rights reserved.)

While potentiometric, plasticized membrane sensors are known as convenient, portable and inexpensive analytical instruments, their development is time- and resource-consuming, with a poorly predictable outcome. In this study, we investigated the applicability of the QSPR (quantitative structure-property relationship) method for predicting the potentiometric sensitivity of plasticized polymeric membrane sensors, using the ionophore chemical structure as model input. The QSPR model was based on the literature data on sensitivity, from previously studied, structurally similar ionophores, and it has shown reasonably good metrics in relating ionophore structures to their sensitivities towards Cu 2+ , Cd 2+ and Pb 2+ . The model predictions for four newly synthesized diphenylphosphoryl acetamide ionophores were compared with real potentiometric experimental data for these ionophores, and satisfactory agreement was observed, implying the validity of the proposed approach.

We report on the development of a very simple and inexpensive sensor device based on an inductance coil connected to a high-frequency electric field generator. The working principle of this sensor is as follows: liquid sample in a plastic tube is placed inside the inductance coil as its core and this core changes the properties of high-frequency electric current passing through the coil; these changes depend on sample conductivity, dielectric constant, magnetic properties, and capacitance. The electric signal registered after the coil represents a kind of a spectrum that can be effectively treated with chemometric tools. Our studies have shown that such a sensor can be used to distinguish between substances with different physical and chemical properties; different concentrations of ions in aqueous solutions with a linear response in a broad concentration range from 10 -3 M to 10 -1 M; can quantify the fat content in milk and cream samples with about 2% accuracy; and can distinguish between bacterial cultures and cell line cultures. The measurements are very fast and contactless, and the results show good promise for developing a number of applications of this sensor system.

Consisting of two fatty acyl groups, phospholipids are a vital part of vegetable oils and the source of essential fatty acids. Moreover, phospholipids influence oxidative and flavor stability and color evolution of vegetable oils, and their quantification has a significant role in the quality assessment of oils. In this study, we proposed a new highly efficient, affordable, environmentally friendly, and simple approach for the evaluation of phospholipid concentrations based on potentiometric multisensor systems coupled with chemometric data processing. Support vector machines, partial least squares, and multiple linear regressions were used to predict phosphatide concentrations based on potentiometric multisensor system responses. Application of multivariate regression tools yielded the following root mean square errors of prediction: 0.005 mg/100 g of oil in the range 0.0-59.4 mg/100 g for refined oils; 0.008 mg/100 g in the range 0.0-100 mg/100 g for low phosphatide oils and 0.24 mg/100 g in the range 100-2270 mg/100 g for high phosphatide oils. This approach can be considered as a rapid and straightforward method to quantify the phosphatides in sunflower oils.

Heavy metal pollution has severe threats to the ecological environment and human health. Thus, it is urgent to achieve the rapid, selective, sensitive and portable detection of heavy metal ions. To overcome the defects of traditional methods such as time-consuming, low sensitivity, high cost and complicated operation, QDs (Quantum dots)-based nanomaterials have been used in sensors to significantly improve the sensing performance. Due to their excellent physicochemical properties, high specific surface area, high adsorption and reactive capacity, nanomaterials could act as potential probes or offer enhanced sensitivity and create a promising nanosensors platform. In this review, the rapidly advancing types of QDs for heavy metal ions detection are first summarized. Modified with ligands, nanomaterials, or biomaterials, QDs are assembled on sensors by the interaction of electrostatic adsorption, chemical bonding, steric hindrance, and base-pairing. The stability of QDs-based nanosensors is improved by doping the elements to QDs, providing the reference substance, optimizing the assemble strategies and so on. Then, according to transducer principles, the two most typical sensor categories based on QDs: optical and electrochemical sensors are highlighted to be discussed. In the meanwhile, portable devices combining with QDs to adapt the practical detection in complex situations are summarized. The deficiencies and future challenges of QDs in toxicity, specificity, portability, multi-metal co-detection and degradation during the detection are also pointed out. In the end, the development trends of QDs-based nanosensors for heavy metal ions detection are discussed. This review presents an overall understanding, recent advances, current challenges and future outlook of QDs-based nanosensors for heavy metal detection., (Copyright © 2021. Published by Elsevier B.V.)

In recent years, convolutional neural networks and deep neural networks have been used extensively in various fields of analytical chemistry. The use of these models for calibration tasks has been highly effective; however, few reports have been published on their properties and characteristics of analytical figures of merit. Currently, most performance measures for these types of networks only incorporate some function of prediction error. While useful, these measures are incomplete and cannot be used as an objective comparison among different models. In this report, a new method for calculating the sensitivity of any type of neural network is proposed and studied on both simulated and real datasets. Generalized analytical sensitivity is defined and calculated for neural networks as an additional figure of merit. Moreover, the dependence of convolutional neural networks on regularization dataset size is studied and compared with other conventional calibration methods., Competing Interests: 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., (Copyright © 2021 Elsevier B.V. All rights reserved.)

We report on the feasibility study exploring the potential of a simple electrochemical multisensor system as a tool for distinguishing between urine samples from patients with confirmed bladder cancer (36 samples) and healthy volunteers (51 samples). The potentiometric sensor responses obtained in urine samples were employed as the input data for various machine learning classification algorithms (logistic regression, random forest, extreme gradient boosting classifier, support vector machine, and voting classifier). The performance metrics of the classifiers were evaluated via Monte-Carlo cross-validation. The best model combining all the acquired data from the people aged 19-88 with different tumor grades and malignancies, including patients with recurrent bladder cancer, yielded 72% accuracy, 71% sensitivity, and 58% specificity. It was found that these metrics can be improved to 76% accuracy, 80% sensitivity, and 75% specificity when only a limited age group (50-88 years of age) is considered. Taking into account the simplicity of the proposed screening method, this technique appears to be a promising tool for further research., (Copyright © 2021 Elsevier B.V. All rights reserved.)

Okadaic acid (OA) is one of the marine toxins that are widely distributed and harmful to humans. However, the current detection methods for OA involve complex procedures, need long detection time, and rely on large-scale laboratory equipment. In this work, a multi-channel handheld automatic spectrometer (MHAS) based on a spectral sensor was developed with the advantages of small size, simple operation and low cost. It could achieve rapid detection within 30 s and a wide spectral detection range of 470-780 nm with a broadband LED as the light source and a microplate containing 8 wells as a sample cell. Moreover, through the combination of gold nanoparticles (AuNPs) and aptamer-OA34, a highly sensitive and rapid system for OA detection was established with a LOD of 1.80 μg L -1 and a wide detection range of 20-10 000 μg L -1 , which is comparable to a microplate reader. Compared with other studies, the proposed MHAS realized rapid on-site detection of OA with a wider detection range, shorter detection time and higher portability. Therefore, the MHAS promises to be a stable and efficient optical detection instrument for on-site detection in the fields of food safety, disease diagnosis and environmental monitoring.

Light-addressable potentiometric sensor (LAPS) has been widely used in biomedical applications since its advent. As a member of the potentiometric sensors, ion-sensitive LAPS (ISLAPS) can be obtained by modifying ion selective sensing membrane on the sensor surface. Compared with the conventional ion-selective electrodes (ISEs) with liquid contact, the all-solid-state ISEs have more advantages such as easy maintenance, more convenient for miniaturization and practical applications. However, the commonly used ion-sensitive membrane (ISM) matrix like PVC has many limitations such as poor adhesion to silicone-based sensor and easy overflow of the plasticizer from the membrane. In this work, LAPS was combined with a variety of ionophore-doped all-solid-state silicone-rubber ISMs for the first time, to establish a program-controlled multiplexed ISLAPS system for physiological ions (Na + , K + , Ca 2+ and H + ) detection. The silicone-rubber ISMs have better adhesion to silicon-based sensors without containing plasticizers, which can avoid the plasticizer pollution and improve the long-term stability. A layer of poly(3-octylthiophene-2,5-diyl) (P3OT) was pre-modified on the sensor surface to inhibit the formation of an aqueous layer and improve the sensor lifetime. With the aid of a translation stage, the light spot automatically illuminated the detection sites in sequence, and the response of the four ions could be obtained in one measurement within 1 min. The proposed multiplexed ISLAPS has good sensitivity with micromolar limit of detection (LOD), good selectivity and long-term stability (more than 3 months). The results of the real Dulbecco's Modified Eagle Medium (DMEM) sample detection proved that the ISLAPS system can be used for the physiological ions detection, and is promising to realize a multi-parameter microphysiometer., Competing Interests: 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., (Copyright © 2021 Elsevier B.V. All rights reserved.)

We report on the development of a simple and cost-effective potentiometric sensor array that is based on manual "drawing" on the polymeric support with the pencils composed of graphite and different types of zeolites. The sensor array demonstrates distinct sensitivity towards a variety of inorganic ions in aqueous media. This multisensor system has been successfully applied to quantitative analysis of 100 real-life surface waters sampled in Mahananda and Hooghly rivers in the West Bengal state (India). Partial least squares regression has been utilized to relate responses of the sensors to the values of different water quality parameters. It has been found that the developed sensor array, or electronic tongue, is capable of quantifying total hardness, total alkalinity, and calcium content in the samples, with the mean relative errors below 18%.

The development of screening methods for various types of cancer is of utmost importance as the early diagnostics of these diseases significantly increases the chances for patient's successful medical treatment and recovery. In this study we have developed the procedure for chromatographic profiling of urine samples based on solid-phase microextraction and GC-MS. 50 urine samples (20 from the patients with biopsy conformed prostate cancer and 30 from control group) were studied in the optimized experimental conditions. Application of chemometric classification algorithms such as k-nearest neighbors and partial least squares-discriminant analysis allowed construction of predictive models yielding very high sensitivity, specificity and accuracy values all close to 100%. This gives a good promise for further validation of this approach with a broader sample sets., Competing Interests: 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. 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., (Copyright © 2020 Elsevier B.V. All rights reserved.)

Multivariate calibration transfer is widely used to expand the applicability of the existing regression model to new analytical devices of the same or similar type. The present research proves the feasibility of calibration model transfer between a full-scale laboratory spectrometer and an optical multisensor system based on only four light-emitting diodes with different wavelengths. The model transfer between two multisensor systems of this kind has also been studied. Both possibilities were successfully performed without any significant loss of precision using a designed set of training and transfer samples. Direct standardization and slope and bias correction protocols for model transfer were tested and compared. The best model transfer between two optical multisensor systems was obtained using direct standardization.

The field of biosensing is rapidly developing, and the number of novel sensor architectures and different sensing elements is growing fast. One of the most important features of all biosensors is their very high selectivity stemming from the use of bioreceptor recognition elements. The typical calibration of a biosensor requires simple univariate regression to relate a response value with an analyte concentration. Nevertheless, dealing with complex real-world sample matrices may sometimes lead to undesired interference effects from various components. This is where chemometric tools can do a good job in extracting relevant information, improving selectivity, circumventing a non-linearity in a response. This brief review aims to discuss the motivation for the application of chemometric tools in biosensing and provide some examples of such applications from the recent literature.

Water quality is one of the most critical indicators of environmental pollution and it affects all of us. Water contamination can be accidental or intentional and the consequences are drastic unless the appropriate measures are adopted on the spot. This review provides a critical assessment of the applicability of various technologies for real-time water quality monitoring, focusing on those that have been reportedly tested in real-life scenarios. Specifically, the performance of sensors based on molecularly imprinted polymers is evaluated in detail, also giving insights into their principle of operation, stability in real on-site applications and mass production options. Such characteristics as sensing range and limit of detection are given for the most promising systems, that were verified outside of laboratory conditions. Then, novel trends of using microwave spectroscopy and chemical materials integration for achieving a higher sensitivity to and selectivity of pollutants in water are described.

The quantification of plutonium in technological streams during spent nuclear fuel (SNF) reprocessing is an important practical task that has to be solved to ensure the safety of the process. Currently applied methods are tedious, time-consuming and can hardly be implemented in on-line mode. A fast and simple quantitative plutonium (IV) analysis using a potentiometric sensor array based on extracting agents is suggested in this study. The response of the set of specially designed PVC-plasticized membrane sensors can be related to plutonium content in solutions simulating real SNF-reprocessing media through multivariate regression modeling, providing 30% higher precision of plutonium quantification than optical spectroscopy.

Multisensor arrays employing various sensing principles are a rapidly developing field of research as they allow simple and inexpensive quantification of various parameters in complex samples. Quantitative analysis with such systems is based on multivariate regression techniques, and deriving of traditional analytical figures of merit (e.g., sensitivity, selectivity, limit of detection, and limit of quantitation) for such systems is not obvious and straightforward. Nevertheless, it is absolutely needed for further development of the multisensor research field and for introducing these instruments into the general context of analytical chemistry. Here, we report on the protocol for calculation of sensitivity, selectivity, and detection limits for multisensor arrays. The results are provided and discussed in detail for several real-world data sets.

As a powerful tool for medical diagnosis and bioanalysis, conventional optical spectrometers are generally expensive, bulky and always require an accompanying data processing device. In this work, we developed a novel smartphone-based CD-spectrometer (SCDS) for high sensitive and ultra-portable colorimetric analysis, with the advantage of cost-effective and simplicity. The distance between the light source and slit, the structure of SCDS and the parameters of camera in the smartphone were all optimized to ensure the best analytical performance. Besides, the SCDS employed HSV color model and utilized the overall intensity calculated by summing V-value of adjacent position for the absorbance measurement. In this way the errors caused by the low resolution of CD-grating can effectively be eliminated to promote the sensitivity of the SCDS. The performance of the SCDS was first validated for colorimetric detection of BSA with a detection limit of 0.0073 mg/mL, which is superior compared to that of the microtiter plate reader (MTPR). Moreover, by combining with 3,3',5,5'-tetramethylbenzidine-manganese dioxide (TMB-MnO 2 ) nanosheets reaction, a high sensitive and specific system for ascorbic acid detection was established. The SCDS gives a detection range from 0.6250 μM to 40 μM with a detection limit of 0.4946 μM for AA detection. Compared to other studies, the SCDS features wide detection range and very low detection limit with low cost instrument. Therefore, the SCDS will be an ideal and promising colorimetric system for point-of-care (POC) application in food security, disease diagnosis and environmental monitoring., (Copyright © 2019 Elsevier B.V. All rights reserved.)

"Electronic tongues", "taste sensors", and similar devices (further named as "multisensor systems", or MSS) have been studied and applied mostly for the analysis of edible analytes. This is not surprising, since the MSS development was sometimes inspired by the mainstream idea that they could substitute human gustatory tests. However, the basic principle behind multisensor systems-a combination of an array of cross-sensitive chemical sensors for liquid analysis and a machine learning engine for multivariate data processing-does not imply any limitations on the application of such systems for the analysis of inedible media. This review deals with the numerous MSS applications for the analysis of inedible analytes, among other things, for agricultural and medical purposes.

The paper describes a wide-range practical application of the potentiometric multisensor system (MS) (1) for integral safety evaluation of a variety of natural waters at multiple locations, under various climatic conditions and anthropogenic stress and (2) for close to real consistency evaluation of waste water purification processes at urban water treatment plants. In total, 25 natural surface water samples were collected around St. Petersburg (Russia), analyzed as is, and after ultrasonic treatment. Toxicity of the samples was evaluated using bioassay and MS. Relative errors of toxicity assessment with MS in these samples were below 20%. The system was also applied for fast determination of integral water quality using chemical oxygen demand (COD) values in 20 samples of water from river and ponds in Kolkata (India) and performed with an acceptable precision of 20% to 22% in this task. Furthermore, the MS was applied for fast simultaneous evaluation of COD, biochemical oxygen demand, inorganic phosphorous, ammonia, and nitrate nitrogen at two waste water treatment plants (over 320 samples). Reasonable precision (within 25%) of such analysis is acceptable for rapid water safety evaluation and enables fast control of the purification process. MS proved to be a practicable analytical instrument for various real-world tasks related to water safety monitoring., Competing Interests: The authors declare no conflict of interest.

This report describes a full-scale experiment on intradermal Ehrlich carcinoma (EC) differentiation in mouse model using NIR spectroscopy in diffuse reflectance mode and chemometric data processing. EC is widely used as an experimental tumor model due to its resemblance with human undifferentiated epithelial tumors and can be applied as a preclinical testing in order to verify the capability of NIR spectroscopy to distinguish cancer from healthy tissues before a clinical research with an aim of creating a new analytical tool for on-line intraoperative tumor margins assessment. The study consists of five steps of NIR spectra measurements: in vivo on the early stage of carcinoma growth; in vivo on the advanced stage of carcinoma growth; in vivo during the surgery; in vitro study of the post-operative materials stored in formalin; in vitro study of the post-operative materials stored in paraffin. It was shown that reliable tumor differentiation with a compact optic fiber probe was possible in all these cases. The classification models were built on two data sets, obtained during in vivo and in vitro measurements; both of them demonstrated 100% specificity and sensitivity., (Copyright © 2019 Elsevier B.V. All rights reserved.)

Urolithiasis commonly occurs in kidney and ureteral, and may cause local organ/tissue damage, even kidney failure. The incidence of this disease is increasing worldwide, in which calcium oxalate is the major composition forming the urinary calculus. Therefore, to monitor this disease for the prevention and treatment, measuring the oxalate in the urine is of great significance. Here, a rapid and sensitive colorimetric method was developed based on 3,3',5,5'-tetramethylbenzidine-manganese dioxide (TMB-MnO 2 ) nanosheets for oxalate detection. MnO 2 nanosheets acted as an efficient biomimetic oxidase to catalyze the reaction with TMB and oxalate. Pale yellow TMB can be oxidized to blue oxide TMB catalyzed by BSA-stabilized MnO 2 nanosheets, and oxalate can selectively inhibit this reaction by consuming and reacting with MnO 2 nanosheets, thus achieving the quantitative detection of oxalate. Moreover, a home-made bionic electronic-eye (E-eye) system was developed as a portable in-situ detection platform to efficiently measure the oxalate concentrations in 10 s by direct photographing. By optimizing experimental conditions, this method shows a wide linear range (7.8 μM to 250 μM) and a low detection limit (0.91 μM) for oxalate detection. Besides, this method exhibits high selectivity even with 80-fold interfering chemicals. Furthermore, the performance of the method was validated by testing the artificial urine samples, indicating its great potential for monitoring and diagnosis of urolithiasis in point-of-care applications., (Copyright © 2019 Elsevier B.V. All rights reserved.)

Currently, there are no established procedures for limit of detection (LOD) evaluation in multisensor system studies, which complicates their correct comparison with other analytical techniques and hinders further development of the method. In this study we propose a simple and visually comprehensible approach for LOD estimation in multisensor analysis. The suggested approach is based on the assessment of evolution of mean relative error values in calibration series with growing analyte concentration. The LOD value is estimated as the concentration starting from which MRE values become stable from sample to sample. This intuitive procedure was successfully tested with a variety of real data from potentiometric multisensor systems.

The procedures for response standardization in "electronic tongue" (ET) studies are described. The construction of reliable multivariate calibration for "electronic tongue" requires the analysis of a large number of representative samples both with ET and reference techniques. This is a laborious and expensive process. Long-term sensor array operation leads to the changes in sensor response characteristics and thus invalidates the multivariate predictive models. Moreover, due to the individual parameters of each sensor in different sensor arrays, it is not possible to use the calibration model for one system together with the data acquired by another system, even if they have the same sensors. Both of these issues lead to the necessity of frequent sensor array calibration which would be ideal to avoid. Instead of recalibration, these two problems can be handled using mathematical methods intended for sensor response standardization. This chapter describes two popular methods of standardization which can be used for both drift correction and calibration transfer. Thus, significant efforts on measuring representative sample sets for sensor array recalibration can be avoided.

X-ray fluorescence (XRF) spectrometry is a modern method of element analysis, widely applied in forensic science, archeology, geochemistry and other fields. Chemometric data processing tools and approaches may help XRF to become even more informative. These tools allow for convenient data visualization, exploration of hidden relations in the data, classification of samples and quantitative treatment of noisy and overlapped spectra. This review describes the most popular chemometric techniques employed in XRF studies and provides typical examples of such applications., (Copyright © 2018 Elsevier B.V. All rights reserved.)

The potentiometric electronic tongue system has been tested as a potential analytical tool for brand uniformity control of monoculture Apulian red wines (Primitivo and Negroamaro). The sensor array was composed of eight porphyrin coatings obtained by electrochemical polymerization process and was employed for both wines discrimination and quantitative detection of wine defect compounds: "off-odour" 3-(methylthio)-propanol; isoamyl alcohol fusel oil; benzaldehyde (marker of the yeast activity) and acetic acid (marker of vinegar formation). PLS-DA applied to Electronic tongue output data has permitted a correct discrimination of more than 70% of analysed wines in respect to the original brand affiliation. Satisfactory PLS1 predictions were obtained in real wine samples; with R² = 0.989 for isoamyl alcohol and R² = 0.732 for acetic acid. Moreover; the possibility to distinguish wine samples on the base of permitted levels of fault compounds content was shown.

Smoothing of instrumental signals is an important prerequisite in data processing. Various smoothing methods were suggested through the last decades each having their own benefits and drawbacks. Most of the filtering methods are based on averaging in a certain window (e.g., Savitzky-Golay) or on frequency-domain representation (e.g., Fourier filtering). The present study introduces novel approach to signal filtering based on signal variance through PLS (projections on latent structures) regression. The influence of filtering parameters on the smoothed spectrum is explained and real world examples are shown.

The development of efficient analytical procedures for the selective detection of magnesium is an important analytical task, since this element is one of the most abundant metals in cells and plays an essential role in a plenty of cellular processes. Magnesium misbalance has been related to several pathologies and diseases both in plants and animals, as far as in humans, but the number of suitable methods for magnesium detection especially in life sample and biological environments is scarce. Chemical sensors, due to their high reliability, simplicity of handling and instrumentation, fast and real-time in situ and on site analysis are promising candidates for magnesium analysis and represent an attractive alternative to the standard instrumental methods. Here the recent achievements in the development of chemical sensors for magnesium ions detection over the last decade are reviewed. The working principles and the main types of sensors applied are described. Focus is placed on the optical sensors and multisensory systems applications for magnesium assessment in different media. Further, a critical outlook on the employment of multisensory approach in comparison to single selective sensors application in biological samples is presented., (Copyright © 2017 Elsevier B.V. All rights reserved.)

Quantification of proteins is a key biochemical assay in molecular biology, biotechnology, medicine and pharmacology. Protein quantification protocols can be based on spectrophotometry, enzyme-linked immunosorbent assay, mass spectrometry or quantitative immunoblotting depending on analyte. In case of immobilized protein these methods require suitable sample preparation. Thus, sophisticated analysis becomes even more complex, expensive and time-consuming. Such drawbacks are highly undesirable in industry. In this study we propose a new approach for evaluation of immobilized protein concentration based on application of bio-assisted potentiometric multisensor system. Surface-immobilized recombinant protein A from Staphylococcus aureus (SpA, expressed in Escherichia coli), which is commonly used as affinity ligand immobilized to stationary phase (сhromatography media) for monoclonal antibody purification was employed as the model object. Chromatography media samples containing different amounts of immobilized SpA were analyzed. Proteinase K from Tritirachium album was employed as a bio-transducer. We demonstrated that the suggested approach provides information about immobilized SpA concentration with 0.8mg/ml accuracy in the range 1-6.7mg/ml and within just 16min. Moreover, the proposed procedure requires no expensive materials and equipment and no bio-transducer immobilization. This method has potential of application for fast monitoring of other immobilized proteins in different tasks., (Copyright © 2017 Elsevier B.V. All rights reserved.)

Optical spectroscopy is increasingly used for cancer diagnostics. Tumor detection feasibility in human kidney samples using mid- and near-infrared (NIR) spectroscopy, fluorescence spectroscopy, and Raman spectroscopy has been reported (Artyushenko et al., Spectral fiber sensors for cancer diagnostics in vitro. In Proceedings of the European Conference on Biomedical Optics, Munich, Germany, 21-25 June 2015). In the present work, a simplification of the NIR spectroscopic analysis for cancer diagnostics was studied. The conventional high-resolution NIR spectroscopic method of kidney tumor diagnostics was replaced by a compact optical sensing device constructively represented by a set of four light-emitting diodes (LEDs) at selected wavelengths and one detecting photodiode. Two sensor prototypes were tested using 14 in vitro clinical samples of 7 different patients. Statistical data evaluation using principal component analysis (PCA) and partial least-squares discriminant analysis (PLS-DA) confirmed the general applicability of the LED-based sensing approach to kidney tumor detection. An additional validation of the results was performed by means of sample permutation., Competing Interests: The authors declare no conflict of interest.

The use of oak products as a cheaper alternative to expensive wood barrels was recently permitted in Europe, which led to a continuous increase in the use of oak chips and staves in winemaking. The feasibility of the potentiometric electronic tongue as a tool for monitoring the effects of wine maceration with oak chips was evaluated. Four types of commercially available oak chips subjected to different thermal treatments and washing procedures and their mixture were studied. Ethanolic extracts of the chips were analysed using electrospray mass spectrometry and 28 phenolic and furanic compounds were identified. The electronic tongue comprising 22 potentiometric chemical sensors could distinguish artificial wine solutions and Cabernet Sauvignon wine macerated with different types of oak chips, quantify total and non-flavonoid phenolic content, as well as the concentrations of added oak chips. Using measurements at two pH levels, 3.2 and 6.5, improved the accuracy of quantification., (Copyright © 2017 Elsevier Ltd. All rights reserved.)

The procedure for transfer of calibration models between different analytical methods is suggested. It is based on the direct standardization (DS) algorithm earlier suggested for data conversion between physically different instruments of the same type. As a result, multivariate regression models obtained e.g. for NIR spectroscopic measurements in one wavelength range can be successfully applied for predictions from the data obtained with another NIR spectrometer in another wavelength range. The performance of the suggested method was tested with two simulated and two real datasets. In the latter case calibration models constructed for energy-dispersive X-ray fluorescence, UV-Vis spectrometry and NIR spectrometry were addressed. The observed performance of the method implies that it can have a broad range of possible applications in analytical chemistry; some of them are suggested in the paper., (Copyright © 2017 Elsevier B.V. All rights reserved.)

Orally disintegrating tablets (ODTs) are challenged by the need for simple technology to ensure good mechanical strength coupled with rapid disintegration. The objective of this work was to evaluate microwave-assisted development of ODTs based on simple direct compression tableting technology. Placebo ODTs comprising directly compressible mannitol and lactose as diluents, super disintegrants, and lubricants were prepared by direct compression followed by exposure to >97% relative humidity and then microwave irradiation for 5 min at 490 W. Placebo ODTs with hardness (>5 kg/cm 2 ) and disintegration time (<60 s) were optimized. Palatable ODTs of Lamotrigine (LMG), which exhibited rapid dissolution of LMG, were then developed. The stability of LMG to microwave irradiation (MWI) was confirmed. Solubilization was achieved by complexation with beta-cyclodextrin (β-CD). LMG ODTs with optimal hardness and disintegration time (DT) were optimized by a 2 3 factorial design using Design Expert software. Taste masking using sweeteners and flavors was confirmed using a potentiometric multisensor-based electronic tongue, coupled with principal component analysis. Placebo ODTs with crospovidone as a superdisintegrant revealed a significant increase in hardness from ∼3 to ∼5 kg/cm 2 and a decrease in disintegration time (<60 s) following microwave irradiation. LMG ODTs had hardness >5 kg/cm 2 , DT < 30s, and rapid dissolution of LMG, and good stability was optimized by DOE and the design space derived. While β-CD complexation enabled rapid dissolution and moderate taste masking, palatability, which was achieved including flavors, was confirmed using an electronic tongue. A simple step of humidification enabled MWI-facilitated development of ODTs by direct compression presenting a practical and scalable advancement in ODT technology.

Proper monitoring of drug's dissolution is a prerequisite for assessing of taste masking efficacy of pharmaceuticals. Corresponding dissolution procedure is likely to be performed with water. Since the objective of these tests is to examine fate of a pharmaceutical formulation in oral cavity, this choice of solvent seems unsuitable because physical and chemical properties of human saliva are quite far from those of water. Obviously, dissolution profiles registered in water may differ significantly from what really happens with a drug in a mouth cavity. In order to address this discrepancy we examined three different compositions of artificial saliva in dissolution test context in present study. It was found that certain compositions preclude the employment of traditional UV-vis spectroscopy as a detection tool due to strong light scattering in the media caused by viscosity and protein composition modifiers. This issue was circumvented by the use of in-line applied potentiometric multisensor system (e-tongue) and the potential of this new approach for more biorelevant dissolution tests was demonstrated with two model formulations of quinine and ibuprofen., (Copyright © 2016 Elsevier B.V. All rights reserved.)

When it comes to address quantitative analysis in complex mixtures, Partial Least Squares (PLS) is often referred to as a standard first-order multivariate calibration method. The set of samples used to build the PLS regression model should ideally be large and representative to produce reliable predictions. In practice, however, the large number of calibration samples is not always affordable and the choice of these samples should be handled with care as it can significantly affect the accuracy of the predictive model. Correlation constrained multivariate curve resolution (CC-MCR) is an alternative regression method for first-order datasets where, unlike PLS, calibration and prediction stages are performed iteratively and optimized under constraints until the decomposition meets the convergence criterion. Both calibration and test samples are fitted into a unique bilinear model so that the number of calibration samples is no longer a critical issue. In this paper we demonstrate that under certain conditions CC-MCR models can provide for reasonable predictions in quantitative analysis of complex mixtures even when only three calibration samples are employed. The latter are defined as samples having the minimum, the maximum and the average concentration, providing for a simple and rapid strategy to build reliable calibration model. The feasibility of three-point multivariate calibration approach was assessed with several case studies featuring mixtures of different analytes in presence of interfering species. Satisfactory predictions with relative errors in the range 3-15% were achieved and good agreement with classical PLS models built from a larger set of calibration samples was observed., (Copyright © 2016 Elsevier B.V. All rights reserved.)

Recombinant proteins became essential components of drug manufacturing. Quality control of such proteins is routine task, which usually requires a lot of time, expensive reagents, specialized equipment and highly educated personnel. In this study we propose a new concept for protein purity evaluation that is based on application of bio-assisted potentiometric multisensor system. The model object for analysis was recombinant protein A from Staphylococcus aureus (SpA), which is commonly used for monoclonal antibody purification. SpA solutions with different amount of host cell related impurities (Escherichia coli, bacterial lysate) were analyzed. Two different bio-transducers were employed: proteinase K from Tritirachium album and baker's yeast Saccharomyces cerevisiae. It was shown that both bio-transducers are able to induce changes in pure and lysate-contaminated SpA samples. Different products of yeast digestion and proteolysis with proteinase of pure SpA and lysate were detected with size exclusion high-performance liquid chromatography (SE-HPLC). The induced changes of chemical composition are detectible with potentiometric multisensor system and can be related to SpA purity through projection on latent structures (PLS) regression technique. The proposed method allows for estimation of the impurity content with 12% accuracy using proteinase K and 16% accuracy using baker's yeast. The suggested approach could be useful for early contamination warning at initial protein purification steps. The analysis requires no expensive materials and equipment, no bio-material immobilization, and its duration time is comparable with other commonly used methods like chromatography or electrophoresis though the main part of this time is related to the sample preparation., (Copyright © 2016 Elsevier B.V. All rights reserved.)

Instrumental bitterness assessment of traditional Chinese herbal medicine (TCM) preparations was addressed in this study. Three different approaches were evaluated, high-performance liquid chromatography coupled to UV detector (HPLC), capillary electrophoresis coupled to UV detector (CE) and a potentiometric multisensor system - electronic tongue (ET). Most studies involving HPLC and CE separations use these as selective instruments for quantification of individual substances. However we employed these techniques to provide chromatographic or electrophoretic sample profiles. These profiles are somewhat analogous to the profiles produced by the ET. Profiles from all instruments were then related to professional sensory panel evaluations using projections on latent structures (PLS) regression. It was found that all three methods allow for bitterness assessment in TCM samples in terms of human sensory panel with root mean squared errors of prediction ca. 0.9 within bitterness scale from 0 (no bitterness) to 6 (maximal bitterness)., (Copyright © 2016 Elsevier B.V. All rights reserved.)

The recent upgrade of nucleic acid-protein interaction database (NPIDB, http://npidb.belozersky.msu.ru/) includes a newly elaborated classification of complexes of protein domains with double-stranded DNA and a classification of families of related complexes. Our classifications are based on contacting structural elements of both DNA: the major groove, the minor groove and the backbone; and protein: helices, beta-strands and unstructured segments. We took into account both hydrogen bonds and hydrophobic interaction. The analyzed material contains 1942 structures of protein domains from 748 PDB entries. We have identified 97 interaction modes of individual protein domain-DNA complexes and 17 DNA-protein interaction classes of protein domain families. We analyzed the sources of diversity of DNA-protein interaction modes in different complexes of one protein domain family. The observed interaction mode is sometimes influenced by artifacts of crystallization or diversity in secondary structure assignment. The interaction classes of domain families are more stable and thus possess more biological sense than a classification of single complexes. Integration of the classification into NPIDB allows the user to browse the database according to the interacting structural elements of DNA and protein molecules. For each family, we present average DNA shape parameters in contact zones with domains of the family., (© The Author(s) 2015. Published by Oxford University Press on behalf of Nucleic Acids Research.)

Electronic tongue technology based on arrays of cross-sensitive chemical sensors and chemometric data processing has attracted a lot of researchers' attention through the last years. Several so far reported applications dealing with pharmaceutical related tasks employed different e-tongue systems to address different objectives. In this situation, it is hard to judge on the benefits and drawbacks of particular e-tongue implementations for R&D in pharmaceutics. The objective of this study was to compare the performance of six different e-tongues applied to the same set of pharmaceutical samples. For this purpose, two commercially available systems (from Insent and AlphaMOS) and four laboratory prototype systems (two potentiometric systems from Warsaw operating in flow and static modes, one potentiometric system from St. Petersburg, one voltammetric system from Barcelona) were employed. The sample set addressed in the study comprised nine different formulations based on caffeine citrate, lactose monohydrate, maltodextrine, saccharin sodium and citric acid in various combinations. To provide for the fair and unbiased comparison, samples were evaluated under blind conditions and data processing from all the systems was performed in a uniform way. Different mathematical methods were applied to judge on similarity of the e-tongues response from the samples. These were principal component analysis (PCA), RV' matrix correlation coefficients and Tuckeŕs congruency coefficients., (Copyright © 2015 Elsevier B.V. All rights reserved.)

In clinical analysis creatinine is a routine biomarker for the assessment of renal and muscular dysfunctions. Although several techniques have been proposed for a fast and accurate quantification of creatinine in human serum or urine, most of them require expensive or complex apparatus, advanced sample preparation or skilled operators. To circumvent these issues, we propose two home-made platforms based on a CD Spectroscope (CDS) and Computer Screen Photo-assisted Technique (CSPT) for the rapid assessment of creatinine level in human urine. Both systems display a linear range (r(2) = 0.9967 and 0.9972, respectively) from 160 μmol L(-1) to 1.6 mmol L(-1) for standard creatinine solutions (n = 15) with respective detection limits of 89 μmol L(-1) and 111 μmol L(-1). Good repeatability was observed for intra-day (1.7-2.9%) and inter-day (3.6-6.5%) measurements evaluated on three consecutive days. The performance of CDS and CSPT was also validated in real human urine samples (n = 26) using capillary electrophoresis data as reference. Corresponding Partial Least-Squares (PLS) regression models provided for mean relative errors below 10% in creatinine quantification., (Copyright © 2015 Elsevier B.V. All rights reserved.)

The ionic composition of urine is a good indicator of patient's general condition and allows for diagnostics of certain medical problems such as e.g., urolithiasis. Due to environmental factors and malnutrition the number of registered urinary tract cases continuously increases. Most of the methods currently used for urine analysis are expensive, quite laborious and require skilled personnel. The present work deals with feasibility study of potentiometric multisensor system of 18 ion-selective and cross-sensitive sensors as an analytical tool for determination of urine ionic composition. In total 136 samples from patients of Urolithiasis Laboratory and healthy people were analyzed by the multisensor system as well as by capillary electrophoresis as a reference method. Various chemometric approaches were implemented to relate the data from electrochemical measurements with the reference data. Logistic regression (LR) was applied for classification of samples into healthy and unhealthy producing reasonable misclassification rates. Projection on Latent Structures (PLS) regression was applied for quantitative analysis of ionic composition from potentiometric data. Mean relative errors of simultaneous prediction of sodium, potassium, ammonium, calcium, magnesium, chloride, sulfate, phosphate, urate and creatinine from multisensor system response were in the range 3-13% for independent test sets. This shows a good promise for development of a fast and inexpensive alternative method for urine analysis., (Copyright © 2014 Elsevier B.V. All rights reserved.)