Juan Martín Cáceres Tarazona
@unipamplona.edu.co
Facultad de ingenierías y arquitectura
Universidad de Pamplona
RESEARCH INTERESTS
• Sistemas multisensoriales y reconocimiento de patrones.
• Sistemas de control
• Automatización industrial.
• Adquisición de datos.
• Inteligencia Artificial
• Robótica.
Scopus Publications
Scholar Citations
Scholar h-index
Scholar i10-index
Scopus Publications
Raluca Suschinel, Aylen Lisset Jaimes-Mogollón, Siong Fong Sim, Woei Ting, Juan Martín Cáceres-Tarazona, Eliana Alvarez-Valdez, Milton Rosero-Moreano, Mohamed Fethi Diouani, Emira Chouihi, Mihai Brebu,et al.
Springer Science and Business Media LLC
Jeniffer Katerine Carrillo, Cristhian Manuel Durán, Juan Martin Cáceres, Carlos Alberto Cuastumal, Jordana Ferreira, José Ramos, Brian Bahder, Martin Oates, and Antonio Ruiz
MDPI AG
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.
Cristhian Manuel Durán-Acevedo and Juan Martín Cáceres-Tarazona
Elsevier BV
Kelvin de Jesús Beleño-Sáenz, Juan Martín Cáceres-Tarazona, Pauline Nol, Aylen Lisset Jaimes-Mogollón, Oscar Eduardo Gualdrón-Guerrero, Cristhian Manuel Durán-Acevedo, Jose Angel Barasona, Joaquin Vicente, María José Torres, Tesfalem Geremariam Welearegay,et al.
MDPI AG
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.
Cristhian Manuel Durán-Acevedo, Aylen Lisset Jaimes-Mogollón, Oscar Eduardo Gualdrón-Guerrero, Tesfalem Geremariam Welearegay, Julián Davíd Martinez-Marín, Juan Martín Caceres-Tarazona, Zayda Constanza Sánchez-Acevedo, Kelvin de Jesus Beleño-Saenz, Umut Cindemir, Lars Österlund,et al.
Impact Journals, LLC
We present here the first study that directly correlates gastric cancer (GC) with specific biomarkers in the exhaled breath composition on a South American population, which registers one of the highest global incidence rates of gastric affections. Moreover, we demonstrate a novel solid state sensor that predicts correct GC diagnosis with 97% accuracy. Alveolar breath samples of 30 volunteers (patients diagnosed with gastric cancer and a controls group formed of patients diagnosed with other gastric diseases) were collected and analyzed by gas-chromatography/mass-spectrometry (GC-MS) and with an innovative chemical gas sensor based on gold nanoparticles (AuNP) functionalized with octadecylamine ligands. Our GC-MS analyses identified 6 volatile organic compounds that showed statistically significant differences between the cancer patients and the controls group. These compounds were different from those identified in previous studied performed on other populations with high incidence rates of this malady, such as China (representative for Eastern Asia region) and Latvia (representative for Baltic States), attributable to lifestyle, alimentation and genetics differences. A classification model based on principal component analysis of our sensor data responses to the breath samples yielded 97% accuracy, 100% sensitivity and 93% specificity. Our results suggest a new and non-intrusive methodology for early diagnosis of gastric cancer that may be deployed in regions lacking well-developed health care systems as a prediagnosis test for selecting the patients that should undergo deeper investigations (e.g., endoscopy and biopsy).
J. Caceres, Cristhian Manuel Durán Acevedo and Eduardo Gualdron Guerrero Oscar
This work describes the development of a low cost thermal desorption system to analyze a set of 31 exhaled breath samples previously acquired from CA and control patients (i.e. with gastritis and ulcer), which were concentrated using Tenax tubes in order to remove the moisture and trap the volatile compounds. The samples were stored at a temperature of 4°C for further analysis. The proposed system allowed that the volatile compounds were trapped inside the tubes to be extracted and sent to a measuring chamber with a gas sensor array sensitive to these compounds. The overall detection system composed of the measuring chamber, a high-precision power supply, advanced high-resolution data acquisition equipment and a computer that acquired and supervised the sensor responses. Once the information was acquired, different pre-processing (normalization) and data processing techniques such as: Principal Component Analysis (PCA), Probabilistic Neural Network (PNN) and Support Vector Machine (SVM), were applied for the analysis and data classification of the exhaled breath. The thermal desorption system was able to extract the volatile compounds emitted from the breath, reducing the humidity of the samples to increase the selectivity, sensitivity and the performance of the system. A 99,44 % of the total variance by using PCA analysis was achieved and a 93.54 % of classification success rate using SVM was obtained.
O. E. Gualdrón, T. G. Welearegay, A. L. Jaimes, J. M. Cáceres, C. M. Durán, R. Ionescu, M. Maestre, and G. Pugliese
Springer Singapore
T.G. Welearegay, O.E. Gualdrón, A.L. Jaimes, J.M. Cáceres, G. Pugliese, U. Cindemir, C.M. Durán, L. Österlund, and R. Ionescu
Elsevier BV