Search Results (14)

This article describes a new software architecture for the non-invasive detection of canine leishmaniasis disease. The proposed platform combines gas-sensing technologies, artificial intelligence (AI), and modular cloud-based software components to identify disease-specific volatile organic compounds (VOCs) found in dog breath and hair samples. The system, which has a multi-tier architecture that includes data collection, pre-processing, machine learning-based analysis, diagnosis-request processing, and user interfaces for veterinarians, faculty researchers, and dog owners, has been integrated into a Li-ion Power website plug-in. The primary goal of implementing the proposed platform is to detect parasites at any point they are infectious to a host. This includes detecting parasites at all stages of their life cycle, where they can infect a new host. In addition, this is crucial for accurate diagnosis, effective treatment, and preventing further transmission. Full article

This study evaluates the potential of an electronic tongue (E-tongue) as an innovative and alternative method for detecting and classifying lead concentrations in wastewater generated by coal mining activities in North Santander, Colombia. The E-tongue aims to complement traditional environmental monitoring techniques with a more efficient and accurate solution. A total of 110 wastewater samples were collected from two locations at a coal mine in the municipality of Toledo: one inside the mine (Point 2) and another outside the mine (Point 1). This research involved the physicochemical analysis of parameters such as pH, biochemical oxygen demand (BOD), chemical oxygen demand (COD), total suspended solids (TSS), hardness, and alkalinity, conducted at the University of Pamplona’s laboratories. The integration of PCA with machine learning algorithms highlighted the E-tongue’s capability for the real-time, on-site detection and discrimination of lead concentrations in coal mining wastewater. Achieving a precision and accuracy above 90%, the SVM classifier outperformed alternative models such as the k-NN, Random Forest, Naïve Bayes, and Quadratic Discriminant Analysis. This demonstrates the system’s robustness and reliability in environmental monitoring, enabling the accurate classification of lead concentrations within the critical range of 0.05 to 1 ppm, essential for assessing contamination levels and ensuring water safety. These findings highlight the E-tongue system’s capability as a rapid, cost-effective tool for monitoring lead contamination in mining wastewater, presenting a viable alternative to conventional methods such as atomic absorption spectroscopy. Full article

The combination of an electronic nose and an electronic tongue represents a significant advance in the pursuit of effective detection methods for prostate cancer, a widespread form of cancer affecting men across the globe. These cutting-edge devices, collectively called “E-Senses”, use data fusion to identify distinct chemical compounds in exhaled breath and urine samples, potentially improving existing diagnostic techniques. This study combined the information from two sensory perception devices to detect prostate cancer in biological samples (breath and urine). To achieve this, data from patients diagnosed with the disease and from control individuals were collected using a gas sensor array and chemical electrodes. The signals were subjected to data preprocessing algorithms to prepare them for analysis. Following this, the datasets for each device were individually analyzed and subsequently merged to enhance the classification results. The data fusion was assessed and it successfully improved the accuracy of detecting prostate-related conditions and distinguishing healthy patients, achieving the highest success rate possible (100%) in classification through machine learning methods, outperforming the results obtained from individual electronic devices. Full article

In this study, an electronic tongue (E-tongue) and electronic nose (E-nose) systems were applied to detect pesticide residues, specifically Preza, Daconil, Curzate, Bricol, Accros, Amistar, and Funlate, in fruits such as cape gooseberries, apples, plums, and strawberries. These advanced systems present several advantages over conventional methods (e.g., GC-MS and others), including faster analysis, lower costs, ease of use, and portability. Additionally, they enable non-destructive testing and real-time monitoring, making them ideal for routine screenings and on-site analyses where effective detection is crucial. The collected data underwent rigorous analysis through multivariate techniques, specifically principal component analysis (PCA) and linear discriminant analysis (LDA). The application of machine learning (ML) algorithms resulted in a good outcome, achieving high accuracies in identifying fruits contaminated with pesticides and accurately determining the concentrations of those pesticides. This level of precision underscores the robustness and reliability of the methodologies employed, highlighting their potential as alternative tools for pesticide residue detection in agricultural products. Full article

This work consists of a study to detect prostate cancer using E-senses devices based on electronic tongue and electronic nose systems. Therefore, two groups of confirmed prostate cancer and control patients were invited to participate through urine and exhaled breath samples, where the control patients group was categorized as Benign Prostatic Hyperplasia, Prostatitis, and Healthy patients. Afterward, the samples were subsequently classified using Pattern Recognition and machine learning methods, where the results were compared through clinical history, obtaining a 92.9% success rate in the PCa and control samples’ classification accuracy by using eTongue and a 100% success rate of classification using eNose. Full article

This paper describes different E-Senses systems, such as Electronic Nose, Electronic Tongue, and Electronic Eyes, which were used to build several machine learning models and assess their performance in classifying a variety of Colombian herbal tea brands such as Albahaca, Frutos Verdes, Jaibel, Toronjil, and Toute. To do this, a set of Colombian herbal tea samples were previously acquired from the instruments and processed through multivariate data analysis techniques (principal component analysis and linear discriminant analysis) to feed the support vector machine, K-nearest neighbors, decision trees, naive Bayes, and random forests algorithms. The results of the E-Senses were validated using HS-SPME-GC-MS analysis. The best machine learning models from the different classification methods reached a 100% success rate in classifying the samples. The proposal of this study was to enhance the classification of Colombian herbal teas using three sensory perception systems. This was achieved by consolidating the data obtained from the collected samples. Full article

This study highlights the implementation of an electronic tongue composed of carbon screen-printed electrodes, which were used to discriminate and classify pesticides, such as Curathane, Numetrin, and Nativo in water. Therefore, to verify the capacity and performance of the sensory system, solutions of each of the pesticides at a concentration of 10 ppm were prepared in the laboratory and compared with distilled water. Furthermore, to evaluate the minimum detection limit of the electronic tongue, solutions were prepared at different concentrations: 0.02, 0.04, 0.06, 0.08, 0.1, 0.15, 0.2, and 0.25 ppm, respectively. The analysis and classification of the different categories and concentrations were obtained from the use of pattern recognition and automatic learning methods, such as principal component analysis (PCA), linear discriminant analysis (LDA), support vector machine (SVM), k-nearest neighbors (kNN), and naïve Bayes, during this process; the techniques accomplished more than 90% accuracy in pesticide concentrations. Finally, a 100% success rate in classifying the compound types was completely achieved. Full article

This article studies the development and implementation of different electronic devices for measuring signals during stress situations, specifically in academic contexts in a student group of the Engineering Department at the University of Pamplona (Colombia). For the research’s development, devices for measuring physiological signals were used through a Galvanic Skin Response (GSR), the electrical response of the heart by using an electrocardiogram (ECG), the electrical activity produced by the upper trapezius muscle (EMG), and the development of an electronic nose system (E-nose) as a pilot study for the detection and identification of the Volatile Organic Compounds profiles emitted by the skin. The data gathering was taken during an online test (during the COVID-19 Pandemic), in which the aim was to measure the student’s stress state and then during the relaxation state after the exam period. Two algorithms were used for the data process, such as Linear Discriminant Analysis and Support Vector Machine through the Python software for the classification and differentiation of the assessment, achieving 100% of classification through GSR, 90% with the E-nose system proposed, 90% with the EMG system, and 88% success by using ECG, respectively. Full article

More effective methods to detect bovine tuberculosis, caused by Mycobacterium bovis, in wildlife, is of paramount importance for preventing disease spread to other wild animals, livestock, and human beings. In this study, we analyzed the volatile organic compounds emitted by fecal samples collected from free-ranging wild boar captured in Doñana National Park, Spain, with an electronic nose system based on organically-functionalized gold nanoparticles. The animals were separated by the age group for performing the analysis. Adult (>24 months) and sub-adult (12–24 months) animals were anesthetized before sample collection, whereas the juvenile (<12 months) animals were manually restrained while collecting the sample. Good accuracy was obtained for the adult and sub-adult classification models: 100% during the training phase and 88.9% during the testing phase for the adult animals, and 100% during both the training and testing phase for the sub-adult animals, respectively. The results obtained could be important for the further development of a non-invasive and less expensive detection method of bovine tuberculosis in wildlife populations. Full article

Water quality control remains an important topic of public health since some diseases, such as diarrhea, hepatitis, and cholera, are caused by its consumption. The microbiological quality of drinking water relies mainly on monitoring of Escherichia coli, a bacteria indicator which serves as an early sentinel of potential health hazards for the population. In this study, an electronic nose coupled to a volatile extraction system (was evaluated for the detection of the emitted compounds by E. coli in water samples where its capacity for the quantification of the bacteria was demonstrated). To achieve this purpose, the multisensory system was subjected to control samples for training. Later, it was tested with samples from drinking water treatment plants in two locations of Colombia. For the discrimination and classification of the water samples, the principal component analysis method was implemented obtaining a discrimination variance of 98.03% of the measurements to different concentrations. For the validation of the methodology, the membrane filtration technique was used. In addition, two classification methods were applied to the dataset where a success rate of 90% of classification was obtained using the discriminant function analysis and having a probabilistic neural network coupled to the cross-validation technique (leave-one-out) where a classification rate of 80% was obtained. The application of this methodology achieved an excellent classification of the samples, discriminating the free samples of E. coli from those that contained the bacteria. In the same way, it was observed that the system could correctly estimate the concentration of this bacteria in the samples. The proposed method in this study has a high potential to be applied in the determination of E. coli in drinking water since, in addition for estimating concentration ranges and having the necessary sensitivity, it significantly reduces the time of analysis compared to traditional methods. Full article

The aim of this study was to develop and implement a methodology composed by a Static Head-Space-Sampler (SHS) coupled to a Sensory Perception System (SPS) for the extraction of Volatile Organic Compounds (VOC’s) emitted by bacterial species in the water. The SPS was performed by means of a chamber of 16 Metal-Oxide-Semiconductor (MOS) gas sensors and a software with pattern recognition methods for the detection and identification of bacteria. At first, the tests were conducted from the sterile and polluted water with the Escherichia coli bacteria and modifying the incubation temperatures (50 °C, 70 °C and 90 °C), with the objective to obtain an optimal temperature for the distinguishing of species. Furthermore, the capacity of the methodology to distinguish the important compounds was assessed, in this case, E. coli and other bacteria like Pseudomonas aeruginosa and Klebsiella oxytoca, which formed similar analytes. The validation of the proposed methodology was done by acquiring water samples from different unitary operations of an aqueduct of the municipality of Toledo (North of Santander, Colombia), which were analyzed by the membrane filter technique in the laboratories of the University of Pamplona, along with the SHS-SPS system. The results showed that it was possible to distinguish polluted water samples in a fast way through the sensory measurement equipment using pattern recognition techniques such as Principal Component Analysis (PCA), Discriminant Function Analysis (DFA) and a probabilistic neural network (PNN), where a 95% of differentiation was obtained through PCA and 100% of the classification with DFA. The PNN network achieved the 86.6% of success rate with the cross-validation technique “leave one out”. Full article

This paper consists of the design and implementation of a simple conditioning circuit to optimize the electronic nose performance, where a temperature modulation method was applied to the heating resistor to study the sensor’s response and confirm whether they are able to make the discrimination when exposed to different volatile organic compounds (VOC’s). This study was based on determining the efficiency of the gas sensors with the aim to perform an electronic nose, improving the sensitivity, selectivity and repeatability of the measuring system, selecting the type of modulation (e.g., pulse width modulation) for the analytes detection (i.e., Moscatel wine samples (2% of alcohol) and ethyl alcohol (70%)). The results demonstrated that by using temperature modulation technique to the heating resistors, it is possible to realize the discrimination of VOC’s in fast and easy way through a chemical sensors array. Therefore, a discrimination model based on principal component analysis (PCA) was implemented to each sensor, with data responses obtaining a variance of 94.5% and accuracy of 100%. Full article

Electronic noses (ENs), are used for many applications, but we must emphasize the importance of their application to foodstuffs like coffee. This paper presents a research study about the analysis of Colombian coffee samples for the detection and classification of defects (i.e., using “Cup Tests”), which was conducted at the Almacafé quality control laboratory in Cúcuta, Colombia. The results obtained show that the application of an electronic nose called “A-NOSE”, may be used in the coffee industry for the cupping tests. The results show that e-nose technology can be a useful tool for quality control to evaluate the excellence of the Colombian coffee produced by National Federation of Coffee Growers. Full article

In this paper, the use of a new technique to obtain transient sensor information is introduced and its usefulness to improve the selectivity of metal oxide gas sensors is discussed. The method is based on modulating the flow of the carrier gas that brings the species to be measured into the sensor chamber. In such a way, the analytes’ concentration at the surface of the sensors is altered. As a result, reproducible patterns in the sensor response develop, which carry important information for helping the sensor system, not only to discriminate among the volatiles considered but also to semi-quantify them. This has been proved by extracting features from sensor dynamics using the discrete wavelet transform (DWT) and by building and validating support vector machine (SVM) classification models. The good results obtained (100% correct identification among 5 volatile compounds and nearly a 89% correct simultaneous identification and quantification of these volatiles), which clearly outperform those obtained when the steady-state response is used, prove the concept behind flow modulation. Full article