Quantitative Determination of Spring Water Quality Parameters via Electronic Tongue

Improvement strategies of food supply chain through novel food processing technologies during COVID-19 pandemic

Coronavirus disease-19 (COVID-19) is a contagious disease caused by a novel corona virus (SARS-CoV-2). No medical intervention has yet succeeded, though vaccine success is expected soon. However, it may take months or years to reach the vaccine to the whole population of the world. Therefore, the technological preparedness is worth to discuss for the smooth running of food processing activities. We have explained the impact of the COVID-19 pandemic on the food supply chain (FSC) and then discussed the technological interventions to overcome these impacts. The novel and smart technologies during food processing to minimize human-to-human and human-to-food contact were compiled. The potential virus-decontamination technologies were also discussed. Finally, we concluded that these technologies would make food processing activities smarter, which would ultimately help to run the FSC smoothly during COVID-19 pandemic.

High Altitude Is Beneficial for Antioxidant Components and Sweetness Accumulation of Rabbiteye Blueberry

To better understand the effect of growing location on the phytochemical compounds and sensory properties of blueberry (Vaccinium spp.), here we investigated rabbiteye blueberry 'Brightwell' (Vaccinium ashei cv. 'Brightwell') grown in 10 locations of China. Significant differences in terms of total soluble solids, titratable acidity, flavonoids, phenols, as well as proanthocyanidins and anthocyanins, were found in the fruits (berries) of blueberry plants among the different sampled locations. Furthermore, their sensory properties, which evaluated by the electronic tongue method, also significantly differed among the 10 locations. The content of flavonoids, phenols, proanthocyanidins, and anthocyanins all had significant correlations with sensory properties, except that of aftertaste-astringency. A key finding to emerge was that blueberry plants grown at high altitude locations harbored a high content of total soluble solids, flavonoids, phenols, proanthocyanidins, and anthocyanins along with high scores for the sweetness. These results suggested cultivating blueberry at high altitude can produce fruit that not only possess pronounced beneficial health effects but also good taste.

3D-Printed Graphene Electrodes Applied in an Impedimetric Electronic Tongue for Soil Analysis

The increasing world population leads to the growing demand for food production without expanding cultivation areas. In this sense, precision agriculture optimizes the production and input usage by employing sensors to locally monitor plant nutrient within agricultural fields. Here, we have used an electronic tongue sensing device based on impedance spectroscopy to recognize distinct soil samples (sandy and clayey) enriched with macronutrients. The e-tongue setup consisted of an array of four sensing units formed by layer-by-layer (LbL) films deposited onto 3D-printed graphene-based interdigitated electrodes (IDEs). The IDEs were fabricated in 20 min using the fused deposition modeling process and commercial polylactic acid-based graphene filaments. The e-tongue comprised one bare and three IDEs functionalized with poly(diallyldimethylammonium chloride) solution/copper phthalocyanine-3,4′,4′′,4′′′-tetrasulfonic acid tetrasodium salt (PDDA/CuTsPc), PDDA/montmorillonite clay (MMt-K), and PDDA/poly(3,4-ethylenedioxythiophene)-poly(styrenesulfonate) (PEDOT:PSS) LbL films. Control samples of sandy and clayey soils were enriched with different concentrations of nitrogen (N), phosphorus (P), and potassium (K) macronutrients. Sixteen soil samples were simply diluted in water and measured using electrical impedance spectroscopy, with data analyzed by principal component analysis. All soil samples were easily distinguished without pre-treatment, indicating the suitability of 3D-printed electrodes in e-tongue analysis to distinguish the chemical fertility of soil samples. Our results encourage further investigations into the development of new tools to support precision agriculture.

Voltammetric Electronic Tongues in Food Analysis

A critical revision is made on recent applications of voltammetric electronic tongues in the field of food analysis. Relevant works are discussed dealing with the discrimination of food samples of different type, origin, age and quality and with the prediction of the concentration of key substances and significant indexes related to food quality.

Multisensory System Used for the Analysis of the Water in the Lower Area of River Danube

The present paper describes the development of a multisensory system for the analysis of the natural water in the Danube, water collected in the neighboring area of Galati City. The multisensory system consists of a sensor array made up of six screen-printed sensors based on electroactive compounds (Cobalt phthalocyanine, Meldola’s Blue, Prussian Blue) and nanomaterials (Multi-Walled Carbon Nanotubes, Multi-Walled Graphene, Gold Nanoparticles). The measurements with the sensors array were performed by using cyclic voltammetry. The cyclic voltammograms recorded in the Danube natural water show redox processes related to the electrochemical activity of the compounds in the water samples or of the electro-active compounds in the sensors detector element. These processes are strongly influenced by the composition and physico-chemical properties of the water samples, such as the ionic strength or the pH. The multivariate data analysis was performed by using the principal component analysis (PCA) and the discriminant factor analysis (DFA), the water samples being discriminated according to the collection point. In order to confirm the observed classes, the partial least squares discriminant analysis (PLS-DA) method was used. The classification of the samples according to the collection point could be made accurately and with very few errors. The correlations established between the voltammetric data and the results of the physico-chemical analyses by using the PLS1 method were very good, the correlation coefficients exceeding 0.9. Moreover, the predictive capacity of the multisensory system is very good, the differences between the measured and the predicted values being less than 3%. The multisensory system based on voltammetric sensors and on multivariate data analysis methods is a viable and useful tool for natural water analysis.

Application of Least-Squares Support Vector Machines for Quantitative Evaluation of Known Contaminant in Water Distribution System Using Online Water Quality Parameters

In water-quality, early warning systems and qualitative detection of contaminants are always challenging. There are a number of parameters that need to be measured which are not entirely linearly related to pollutant concentrations. Besides the complex correlations between variable water parameters that need to be analyzed also impairs the accuracy of quantitative detection. In aspects of these problems, the application of least-squares support vector machines (LS-SVM) is used to evaluate the water contamination and various conventional water quality sensors quantitatively. The various contaminations may cause different correlative responses of sensors, and also the degree of response is related to the concentration of the injected contaminant. Therefore to enhance the reliability and accuracy of water contamination detection a new method is proposed. In this method, a new relative response parameter is introduced to calculate the differences between water quality parameters and their baselines. A variety of regression models has been examined, as result of its high performance, the regression model based on genetic algorithm (GA) is combined with LS-SVM. In this paper, the practical application of the proposed method is considered, controlled experiments are designed, and data is collected from the experimental setup. The measured data is applied to analyze the water contamination concentration. The evaluation of results validated that the LS-SVM model can adapt to the local nonlinear variations between water quality parameters and contamination concentration with the excellent generalization ability and accuracy. The validity of the proposed approach in concentration evaluation for potassium ferricyanide is proven to be more than 0.5 mg/L in water distribution systems.