Analysis of Macronutrients in Soil Using Impedimetric Multisensor Arrays.

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.

Multidimensional calibration spaces in Staphylococcus Aureus detection using chitosan-based genosensors and electronic tongue.

Mastitis diagnosis can be made by detecting Staphylococcus aureus (S. aureus), which requires high sensitivity and selectivity. Here, we report on microfluidic genosensors and electronic tongues to detect S.

Nanoarchitectonic E-Tongue of Electrospun Zein/Curcumin Carbon Dots for Detecting in Milk.

We report a nanoarchitectonic electronic tongue made with flexible electrodes coated with curcumin carbon dots and zein electrospun nanofibers, which could detect () in milk using electrical impedance spectroscopy. Electronic tongues are based on the global selectivity concept in which the electrical responses of distinct sensing units are combined to provide a unique pattern, which in this case allowed the detection of through non-specific interactions. The electronic tongue used here comprised 3 sensors with electrodes coated with zein nanofibers, carbon dots, and carbon dots with zein nanofibers.

Multivariate Data Explanation by Jumping Emerging Patterns Visualization.

Multivariate or multidimensional visualization plays an essential role in exploratory data analysis by allowing users to derive insights and formulate hypotheses. Despite their popularity, it is usually users' responsibility to (visually) discover the data patterns, which can be cumbersome and time-consuming. Visual Analytics (VA) and machine learning techniques can be instrumental in mitigating this problem by automatically discovering and representing such patterns.

Using machine learning and an electronic tongue for discriminating saliva samples from oral cavity cancer patients and healthy individuals.

The diagnosis of cancer and other diseases using data from non-specific sensors - such as the electronic tongues (e-tongues) - is challenging owing to the lack of selectivity, in addition to the variability of biological samples. In this study, we demonstrate that impedance data obtained with an e-tongue in saliva samples can be used to diagnose cancer in the mouth. Data taken with a single-response microfluidic e-tongue applied to the saliva of 27 individuals were treated with multidimensional projection techniques and non-supervised and supervised machine learning algorithms.

Explainable Matrix - Visualization for Global and Local Interpretability of Random Forest Classification Ensembles.

Over the past decades, classification models have proven to be essential machine learning tools given their potential and applicability in various domains. In these years, the north of the majority of the researchers had been to improve quantitative metrics, notwithstanding the lack of information about models' decisions such metrics convey. This paradigm has recently shifted, and strategies beyond tables and numbers to assist in interpreting models' decisions are increasing in importance.