An approach of anomaly detection and neural network classifiers to measure cellulolytic activity
Abstract: Aim and Objective: A common method used for massive detection of cellulolytic microorganisms is based on the formation of halos on solid medium. However, this is a subjective method and real-time monitoring is not possible. The objective of this work was to develop a method of computational analysis of the visual patterns created by cellulolytic activity through artificial neural networks description. Materials and Methods: Our method learns by an adaptive prediction model and automatically determines when enzymatic activity on a chromogenic indicator such as the hydrolysis halo occurs. To achieve this goal, we generated a data library with absorbance readings and RGB values of enzymatic hydrolysis, obtained by spectrophotometry and a prototype camera-based equipment (Enzyme Vision), respectively. We used the first part of the library to generate a linear regression model, which was able to predict theoretical absorbances using the RGB color patterns, which agreed with values obtained by spectrophotometry. The second part was used to train, validate, and test the neural network model in order to predict cellulolytic activity based on color patterns. Results: As a result of our model, we were able to establish six new descriptors useful for the prediction of the temporal changes in the enzymatic activity. Finally, our model was evaluated on one halo from cellulolytic microorganisms, achieving the regional classification of the generated halo in three of the six classes learned by our model. Conclusion: We assume that our approach can be a viable alternative for high throughput screening of enzymatic activity in real time.
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KOHA-OAI-ECOSUR:597372024-03-11T15:19:21ZAn approach of anomaly detection and neural network classifiers to measure cellulolytic activity Barbosa Santillán, Luis Fracisco Calixto Romo, María de los Ángeles Doctora autor/a 12568 Sánchez Escobar, Juan Jaime autor/a Barbosa Santillán, Liliana Ibeth autor/a textengAbstract: Aim and Objective: A common method used for massive detection of cellulolytic microorganisms is based on the formation of halos on solid medium. However, this is a subjective method and real-time monitoring is not possible. The objective of this work was to develop a method of computational analysis of the visual patterns created by cellulolytic activity through artificial neural networks description. Materials and Methods: Our method learns by an adaptive prediction model and automatically determines when enzymatic activity on a chromogenic indicator such as the hydrolysis halo occurs. To achieve this goal, we generated a data library with absorbance readings and RGB values of enzymatic hydrolysis, obtained by spectrophotometry and a prototype camera-based equipment (Enzyme Vision), respectively. We used the first part of the library to generate a linear regression model, which was able to predict theoretical absorbances using the RGB color patterns, which agreed with values obtained by spectrophotometry. The second part was used to train, validate, and test the neural network model in order to predict cellulolytic activity based on color patterns. Results: As a result of our model, we were able to establish six new descriptors useful for the prediction of the temporal changes in the enzymatic activity. Finally, our model was evaluated on one halo from cellulolytic microorganisms, achieving the regional classification of the generated halo in three of the six classes learned by our model. Conclusion: We assume that our approach can be a viable alternative for high throughput screening of enzymatic activity in real time.Abstract: Aim and Objective: A common method used for massive detection of cellulolytic microorganisms is based on the formation of halos on solid medium. However, this is a subjective method and real-time monitoring is not possible. The objective of this work was to develop a method of computational analysis of the visual patterns created by cellulolytic activity through artificial neural networks description. Materials and Methods: Our method learns by an adaptive prediction model and automatically determines when enzymatic activity on a chromogenic indicator such as the hydrolysis halo occurs. To achieve this goal, we generated a data library with absorbance readings and RGB values of enzymatic hydrolysis, obtained by spectrophotometry and a prototype camera-based equipment (Enzyme Vision), respectively. We used the first part of the library to generate a linear regression model, which was able to predict theoretical absorbances using the RGB color patterns, which agreed with values obtained by spectrophotometry. The second part was used to train, validate, and test the neural network model in order to predict cellulolytic activity based on color patterns. Results: As a result of our model, we were able to establish six new descriptors useful for the prediction of the temporal changes in the enzymatic activity. Finally, our model was evaluated on one halo from cellulolytic microorganisms, achieving the regional classification of the generated halo in three of the six classes learned by our model. Conclusion: We assume that our approach can be a viable alternative for high throughput screening of enzymatic activity in real time.Adobe Acrobat profesional 6.0 o superiorActividad celulíticaMicroorganismos celulolíticosRedes neurales (Computación)Cribado farmacológicoDisponible en líneaCombinatorial Chemistry & High Throughput Screeninghttp://sii.ecosur.mx/Content/ProductosActividades/archivos/30854/textocompleto.pdfDisponible para usuarios de ECOSUR con su clave de acceso |
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Actividad celulítica Microorganismos celulolíticos Redes neurales (Computación) Cribado farmacológico Actividad celulítica Microorganismos celulolíticos Redes neurales (Computación) Cribado farmacológico |
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Actividad celulítica Microorganismos celulolíticos Redes neurales (Computación) Cribado farmacológico Actividad celulítica Microorganismos celulolíticos Redes neurales (Computación) Cribado farmacológico Barbosa Santillán, Luis Fracisco Calixto Romo, María de los Ángeles Doctora autor/a 12568 Sánchez Escobar, Juan Jaime autor/a Barbosa Santillán, Liliana Ibeth autor/a An approach of anomaly detection and neural network classifiers to measure cellulolytic activity |
| description |
Abstract: Aim and Objective: A common method used for massive detection of cellulolytic microorganisms is based on the formation of halos on solid medium. However, this is a subjective method and real-time monitoring is not possible. The objective of this work was to develop a method of computational analysis of the visual patterns created by cellulolytic activity through artificial neural networks description. Materials and Methods: Our method learns by an adaptive prediction model and automatically determines when enzymatic activity on a chromogenic indicator such as the hydrolysis halo occurs. To achieve this goal, we generated a data library with absorbance readings and RGB values of enzymatic hydrolysis, obtained by spectrophotometry and a prototype camera-based equipment (Enzyme Vision), respectively. We used the first part of the library to generate a linear regression model, which was able to predict theoretical absorbances using the RGB color patterns, which agreed with values obtained by spectrophotometry. The second part was used to train, validate, and test the neural network model in order to predict cellulolytic activity based on color patterns. Results: As a result of our model, we were able to establish six new descriptors useful for the prediction of the temporal changes in the enzymatic activity. Finally, our model was evaluated on one halo from cellulolytic microorganisms, achieving the regional classification of the generated halo in three of the six classes learned by our model. Conclusion: We assume that our approach can be a viable alternative for high throughput screening of enzymatic activity in real time. |
| format |
Texto |
| topic_facet |
Actividad celulítica Microorganismos celulolíticos Redes neurales (Computación) Cribado farmacológico |
| author |
Barbosa Santillán, Luis Fracisco Calixto Romo, María de los Ángeles Doctora autor/a 12568 Sánchez Escobar, Juan Jaime autor/a Barbosa Santillán, Liliana Ibeth autor/a |
| author_facet |
Barbosa Santillán, Luis Fracisco Calixto Romo, María de los Ángeles Doctora autor/a 12568 Sánchez Escobar, Juan Jaime autor/a Barbosa Santillán, Liliana Ibeth autor/a |
| author_sort |
Barbosa Santillán, Luis Fracisco |
| title |
An approach of anomaly detection and neural network classifiers to measure cellulolytic activity |
| title_short |
An approach of anomaly detection and neural network classifiers to measure cellulolytic activity |
| title_full |
An approach of anomaly detection and neural network classifiers to measure cellulolytic activity |
| title_fullStr |
An approach of anomaly detection and neural network classifiers to measure cellulolytic activity |
| title_full_unstemmed |
An approach of anomaly detection and neural network classifiers to measure cellulolytic activity |
| title_sort |
approach of anomaly detection and neural network classifiers to measure cellulolytic activity |
| url |
http://sii.ecosur.mx/Content/ProductosActividades/archivos/30854/textocompleto.pdf |
| work_keys_str_mv |
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| _version_ |
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