A SMART methodology for assessment of hexanal in potato crisps using electronic nose technology: sensor screening by scalar machine learning classifier method.

There is a pertinent need to develop a rapid and accurate methodology for the detection of the onset and the progression of rancidity in the most popular savory product worldwide, viz. fried potato crisps for food safety and health concerns. Rancidity in the fried crisps-one set prepared using C18:2-lean deodorized virgin coconut oil under modified deep frying conditions (140 °C, 5 min),-and another set deep fried (170 °C, 3 min) in C18:2-rich oil (simulating commercial frying conditions) was determined by 'rancidity indices' generated (using Mahalanobis distance) from the data obtained by MO-based electronic nose analysis of hexanal (in Likens-Nickerson extract of volatiles from potato crisps), the most prominent rancidity marker, using screened sensors calibrated with standard hexanal, and classified using support vector machine. This also allowed unambiguous discrimination of the two sets of potato fries. The correlation of hexanal contents with the said indices yielded robust regression models which could accurately predict rancidity status of the crisps, forgoing GC-FID analysis of rancidity marker in the same. The 'SMART' models developed would allow rapid-cum-accurate detection of the onset and progression of rancidity in fried potato crisps on an industrial scale, forgoing the need to conduct biochemical analyses. Supplementary Information: The online version contains supplementary material available at 10.1007/s13197-023-05831-y.

A study of vegetable oil modified QCM sensor to detect ß-pinene in Indian cardamom.

A quartz crystal microbalance (QCM) sensor was developed in this study with the vegetable oil from olive (OLV-QCM) to detect an important volatile organic compound, ß-pinene in Indian cardamom. Hydrophobic vegetable oil from olive, which contains oleic acid and omega-9, a monounsaturated fatty acid was found to be suitable for binding ß-pinene through non-covalent bonds. The fabricated QCM sensor coating was examined with the field emission scanning electron microscope (FESEM) and Fourier-transform infrared spectroscopy (FTIR) to determine its surface morphology and chemical compositions. The sensitivity, reproducibility, repeatability, and reusability were studied for the developed sensor. Notably, the sensor was observed to be highly selective towards ß-pinene as compared to the other volatile components present in cardamom. The limit of detection (LOD) and limit of quantitation (LOQ) parameters were determined as 5.57 mg L-1 and 18.57 mg L-1, respectively. Moreover, the adsorption isotherm models of the sensor were studied to validate the physical adsorption affinity towards ß-pinene applying Langmuir, Freundlich, and Langmuir-Freundlich isotherm models. The sensor showed a correlation factor of 0.99 with the peak area percentage of gas chromatography-mass spectrometry (GC-MS) analysis for ß-pinene in cardamom samples. The sensor was prepared with natural vegetable oil, unlike health hazard chemicals. In addition to this, the low-cost, easy fabrication process ensured the suitability of the sensor for practical deployment.

A Pencil-Drawn Electronic Tongue for Environmental Applications.

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%.

Deep Learning-Based Potential Ligand Prediction Framework for COVID-19 with Drug-Target Interaction Model.

To fight against the present pandemic scenario of COVID-19 outbreak, medication with drugs and vaccines is extremely essential other than ventilation support. In this paper, we present a list of ligands which are expected to have the highest binding affinity with the S-glycoprotein of 2019-nCoV and thus can be used to make the drug for the novel coronavirus. Here, we implemented an architecture using 1D convolutional networks to predict drug-target interaction (DTI) values. The network was trained on the KIBA (Kinase Inhibitor Bioactivity) dataset. With this network, we predicted the KIBA scores (which gives a measure of binding affinity) of a list of ligands against the S-glycoprotein of 2019-nCoV. Based on these KIBA scores, we are proposing a list of ligands (33 top ligands based on best interactions) which have a high binding affinity with the S-glycoprotein of 2019-nCoV and thus can be used for the formation of drugs.

UIISScan 1.1: A Field portable high-throughput platform tool for biomedical and agricultural applications.

The colorimetric sensing technology has evolved into an essential tool for high-throughput analysis including portability and cost-effectiveness among available biomedical and agricultural screening approach. In this endeavor, the objective of work is to focus on the development of a field-portable instrument based on an Uniform Illumination Imaging System (UIIS), which will facilitate the colorimetric biochemical sensing. The developed field-portable, wavelength independent UIIS has been exploited for (a) rotavirus detection using commercial enzymatic immunoassay based microplate kit; (b) pesticide residue detection and quantification; The proposed system exhibited a good correlation in comparison to another two conventional techniques, i.e., multi-plate reader (r = 0.9991938) and LC-MS/MS (r = 0.998877399) with a short analysis time of 5 min for 95 test samples. Moreover, the feasibility of UIIS system has also been explored as field-portable enzyme-linked immunosorbent assay (ELISA) plate reader. By incorporating the Mahalanobis distance calculation, the advanced algorithm has been investigated and developed to analyze the data. The overall dataset was transformed into a matrix format to give a good correlation with a conventional plate reader, i.e., r = 0.915389612. Internet of things (IoT) enabled decision support system can be exploited by using big data analytics. Finally, test results can be shared with concerned stakeholders and the remote users. Thus, the developed UIIS will help to identify potential public health threats expeditiosly compared to conventional time consuming process of sample submission to the laboratory for analysis.

Rapid Evaluation of Integral Quality and Safety of Surface and Waste Waters by a Multisensor System (Electronic Tongue).

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.

Microorganism and Agricultural Based Biosorbents Towards Removal of Cadmium from Waste-Water: An Overview.

BACKGROUND: Non-degradable and persistent nature of Cadmium (Cd) poses high toxicity to human, plants and animals. Several industrial processes generated wastes are the main anthropogenic pathway through which Cd enters into the environment. Although, World Health Organization (WHO) has set the limit of Cd in drinking water is 0.005 mg L-1, the industrial activities release much higher concentrations of metal ions to the water stream than the prescribed limits, which leads to the increasing health hazards and environmental pollution. OBJECTIVE: To address this issue, one of the major applicable solutions is the treatment or purification of contaminated water and effluents. The implantation of wastewater treatment systems aims to minimize environmental impacts, but ultimately generates waste materials, such as sewage sludge, which must be properly recycled. METHOD: In this review, we focus on the research efforts being made towards the removal of Cd (II) from waste waters using biological means with a special emphasis on the microorganism and agricultural based biosorbents. RESULTS: Mechanistic pathway towards removal of Cd (II) ions from the wastewater, efficiency of the adsorbents and the factors affecting the process have been studied with specific examples. Also the recent patents related to this area have been taken into considerations to understand applicability of microorganism and agricultural based biosorbents. CONCLUSION: This overview presents various scientific reports towards low-cost microorganism and agricultural based biosorbents for efficient removal of Cd (II) by producing less toxic waste and the future perspective of the process.

Application of electronic nose for industrial odors and gaseous emissions measurement and monitoring--An overview.

The present review evaluates the key modules of the electronic nose, a biomimetic system, with specific examples of applications to industrial emissions monitoring and measurement. Regulations concerning the odor control are becoming very strict, due to ever mounting environmental pollution and its subsequent consequences and it is advantageous to employ real time measurement system. In this perspective, systems like the electronic nose are an improved substitute for assessing the complex industrial emissions over other analytical techniques (odorant concentration measurement) and olfactometry (odor concentration measurement). Compared to tools like gas chromatography, electronic nose systems are easy to develop, are non-destructive and useful for both laboratory and on field purposes. Although there has been immense development of more sensitive and selective sensor arrays and advanced data mining techniques, there have been limited reports on the application of electronic nose for the measurement of industrial emissions. The current study sheds light on the practical applicability of electronic nose for the effective industrial odor and gaseous emissions measurement. The applications categorization is based on gaseous pollutants released from the industries. Calibration and calibration transfer methodologies have been discussed to enhance the applicability of electronic nose system. Further, industrial gas grab sampling technique is reviewed. Lastly, the electronic mucosa system, which has the ability to overcome the flaws of electronic nose system, has been examined. The review ends with the concluding remarks describing the pros and cons of artificial olfaction technique for the industrial applications.

Calibration transfer between electronic nose systems for rapid in situ measurement of pulp and paper industry emissions.

Electronic nose systems when deployed in network mesh can effectively provide a low budget and onsite solution for the industrial obnoxious gaseous measurement. For accurate and identical prediction capability by all the electronic nose systems, a reliable calibration transfer model needs to be implemented in order to overcome the inherent sensor array variability. In this work, robust regression (RR) is used for calibration transfer between two electronic nose systems using a Box-Behnken (BB) design. Out of the two electronic nose systems, one was trained using industrial gas samples by four artificial neural network models, for the measurement of obnoxious odours emitted from pulp and paper industries. The emissions constitute mainly of hydrogen sulphide (H2S), methyl mercaptan (MM), dimethyl sulphide (DMS) and dimethyl disulphide (DMDS) in different proportions. A Box-Behnken design consisting of 27 experiment sets based on synthetic gas combinations of H2S, MM, DMS and DMDS, were conducted for calibration transfer between two identical electronic nose systems. Identical sensors on both the systems were mapped and the prediction models developed using ANN were then transferred to the second system using BB-RR methodology. The results showed successful transmission of prediction models developed for one system to other system, with the mean absolute error between the actual and predicted concentration of analytes in mg L(-1) after calibration transfer (on second system) being 0.076, 0.1801, 0.0329, 0.427 for DMS, DMDS, MM, H2S respectively.

Comparison of multivariate preprocessing techniques as applied to electronic tongue based pattern classification for black tea.

In an electronic tongue, preprocessing on raw data precedes pattern analysis and choice of the appropriate preprocessing technique is crucial for the performance of the pattern classifier. While attempting to classify different grades of black tea using a voltammetric electronic tongue, different preprocessing techniques have been explored and a comparison of their performances is presented in this paper. The preprocessing techniques are compared first by a quantitative measurement of separability followed by principle component analysis; and then two different supervised pattern recognition models based on neural networks are used to evaluate the performance of the preprocessing techniques.