Listar por autor "Pérez Calle, José Luis"

A Methodology Based on FT-IR Data Combined with Random Forest Model to Generate Spectralprints for the Characterization of High-Quality Vinegars

Pérez Calle, José Luis

; Ferreiro González, Marta

; Ruiz Rodríguez, Ana

; Fernández Barbero, Gerardo

; Álvarez Saura, José Ángel

; Palma Lovillo, Miguel

; Ayuso Vilacides, Jesús

(MDPI, 2021-06)

A Rapid Method for Authentication of Macroalgae Based on Vis-NIR Spectroscopy Data Combined with Chemometrics Approach

Gumansalangi, Frysye; Pérez Calle, José Luis

; Barea Sepúlveda, Marta

; Manikharda; Palma Lovillo, Miguel

; Lideman; Rafi, Mohamad; Ningrum, Andriati; Setyaningsih, Widiastuti (MDPI, 2023)

Application of FT-IR and machine learning in the characterization of organic and biodynamic wines vs conventional wines

Pérez Calle, José Luis

; Ferrari, Leonardo; Ruiz Rodríguez, Ana

; Ferreiro González, Marta

; Palma Lovillo, Miguel

(Elsevier, 2026-03)

Characterization of Biodegraded Ignitable Liquids by Headspace-Ion Mobility Spectrometry

Pérez Calle, José Luis

; Ferreiro González, Marta

; Aliaño González, María José

; Fernández Barbero, Gerardo

; Palma Lovillo, Miguel

(MDPI, 2020-11)

Comparison of different processing approaches by SVM and RF on HS-MS eNose and NIR Spectrometry data for the discrimination of gasoline samples

Barea Sepúlveda, Marta

; Ferreiro González, Marta

; Pérez Calle, José Luis

; Fernández Barbero, Gerardo

; Ayuso Vilacides, Jesús

; Palma Lovillo, Miguel

(ELSEVIER, 2022-01)

Detecting Honey Adulteration: Advanced Approach Using UF-GC Coupled with Machine Learning

Punta Sánchez, Irene

; Dymerski, Tomasz; Pérez Calle, José Luis

; Ruiz Rodríguez, Ana

; Ferreiro González, Marta

; Palma Lovillo, Miguel

(Multidisciplinary Digital Publishing Institute (MDPI), 2024)

Detection of Adulterations in Fruit Juices Using Machine Learning Methods over FT-IR Spectroscopic Data

Pérez Calle, José Luis

; Ferreiro González, Marta

; Ruiz Rodríguez, Ana

; Fernández, Daniel; Palma Lovillo, Miguel

(MDPI, 2022)

Development of a Novel HS-GC/MS Method Using the Total Ion Spectra Combined with Machine Learning for the Intelligent and Automatic Evaluation of Food-Grade Paraffin Wax Odor Level

Barea Sepúlveda, Marta

; Pérez Calle, José Luis

; Ferreiro González, Marta

; Palma Lovillo, Miguel

(Multidisciplinary Digital Publishing Institute (MDPI), 2024)

Discrimination of Ignitable Liquid Residues in Burned Petroleum-Derived Substrates by Using HS-MS eNose and Chemometrics

Falatová, Barbara; Ferreiro González, Marta

; Pérez Calle, José Luis

; Álvarez Saura, José Ángel

; Palma Lovillo, Miguel

(MDPI, 2021-02)

Enhanced Differentiation of Wild and Feeding Civet Coffee Using Near-Infrared Spectroscopy with Various Sample Pretreatments and Chemometric Approaches

Prajna, Deyla; Alvarez, Maria; Barea Sepúlveda, Marta

; Pérez Calle, José Luis

; Suhandy, Diding; Setyaningsih, Widiastuti; Palma Lovillo, Miguel

(MDPI, 2023)

Enhancing Competency-Based Education in Instrumental Analysis: A Novel Approach Using High-Performance Liquid Chromatography for Real-World Problem Solving

Vázquez Espinosa, Mercedes

; Sancho Galán, Pau

; Velasco González de Peredo, Ana

; Pérez Calle, José Luis

; Ruiz Rodríguez, Ana

; Fernández Barbero, Gerardo

; Ferreiro González, Marta

(Maria José Sá and Sandro Serpa, 2024-04-25)

Headspace gas chromatography with various sample preparation and chemometric approaches to improve discrimination of wild and feeding civet coffee

Prajna, D.; Barea Sepúlveda, Marta

; Pérez Calle, José Luis

; Suhandy, D.; Setyaningsih, W.; Palma Lovillo, Miguel

(Purpose-Led Publishing, 2024)

Herramientas analíticas basadas en la aplicación de machine learning sobre datos espectroscópicos

Pérez Calle, José Luis

(2024-12-11)

Machine learning approaches over ion mobility spectra for the discrimination of ignitable liquids residues from interfering substrates

Pérez Calle, José Luis

; Falatová, Barbara; Aliaño González, María José

; Ferreiro González, Marta

; Palma Lovillo, Miguel

(ELSEVIER, 2022)

Machine learning-based approaches to Vis-NIR data for the automated characterization of petroleum wax blends

Barea Sepúlveda, Marta

; Pérez Calle, José Luis

; Ferreiro González, Marta

; Palma Lovillo, Miguel

(2024)

Novel Method Based on Ion Mobility Spectrometry Combined with Machine Learning for the Discrimination of Fruit Juices

Pérez Calle, José Luis

; Vázquez Espinosa, María de las Mercedes; Barea Sepúlveda, Marta

; Ruiz Rodríguez, Ana

; Ferreiro González, Marta

; Palma Lovillo, Miguel

(MDPI, 2023)

Optimized HSSE-GC-MS and machine learning for forensic gender classification of human scent from clothing

Mateo, John Marty C.; Pérez Calle, José Luis

; Durán Guerrero, Enrique

; Palma Lovillo, Miguel

; Ferreiro González, Marta

(Elsevier, 2026-03)

Rapid and Automated Method for Detecting and Quantifying Adulterations in High-Quality Honey Using Vis-NIRs in Combination with Machine Learning

Pérez Calle, José Luis

; Punta Sánchez, Irene; Velasco González de Peredo, Ana

; Ruíz Rodríguez, Ana

; Ferreiro González, Marta

; Palma Lovillo, Miguel

(MDPI, 2023)

Rapid Classification of Petroleum Waxes: A Vis-NIR Spectroscopy and Machine Learning Approach

Barea Sepúlveda, Marta

; Pérez Calle, José Luis

; Ferreiro González, Marta

; Palma Lovillo, Miguel

(Multidisciplinary Digital Publishing Institute (MDPI), 2023)

Rapid Detection and Quantification of Adulterants in Fruit Juices Using Machine Learning Tools and Spectroscopy Data

Pérez Calle, José Luis

; Barea Sepúlveda, Marta

; Ruiz Rodríguez, Ana

; Álvarez Saura, José Ángel

; Palma Lovillo, Miguel

(MDPI, 2022-05)

Listar por autor "Pérez Calle, José Luis"

A Methodology Based on FT-IR Data Combined with Random Forest Model to Generate Spectralprints for the Characterization of High-Quality Vinegars ﻿

A Rapid Method for Authentication of Macroalgae Based on Vis-NIR Spectroscopy Data Combined with Chemometrics Approach ﻿

Application of FT-IR and machine learning in the characterization of organic and biodynamic wines vs conventional wines ﻿

Characterization of Biodegraded Ignitable Liquids by Headspace-Ion Mobility Spectrometry ﻿

Comparison of different processing approaches by SVM and RF on HS-MS eNose and NIR Spectrometry data for the discrimination of gasoline samples ﻿

Detecting Honey Adulteration: Advanced Approach Using UF-GC Coupled with Machine Learning ﻿

Detection of Adulterations in Fruit Juices Using Machine Learning Methods over FT-IR Spectroscopic Data ﻿

Development of a Novel HS-GC/MS Method Using the Total Ion Spectra Combined with Machine Learning for the Intelligent and Automatic Evaluation of Food-Grade Paraffin Wax Odor Level ﻿

Discrimination of Ignitable Liquid Residues in Burned Petroleum-Derived Substrates by Using HS-MS eNose and Chemometrics ﻿

Enhanced Differentiation of Wild and Feeding Civet Coffee Using Near-Infrared Spectroscopy with Various Sample Pretreatments and Chemometric Approaches ﻿

Enhancing Competency-Based Education in Instrumental Analysis: A Novel Approach Using High-Performance Liquid Chromatography for Real-World Problem Solving ﻿

Headspace gas chromatography with various sample preparation and chemometric approaches to improve discrimination of wild and feeding civet coffee ﻿

Herramientas analíticas basadas en la aplicación de machine learning sobre datos espectroscópicos ﻿

Machine learning approaches over ion mobility spectra for the discrimination of ignitable liquids residues from interfering substrates ﻿

Machine learning-based approaches to Vis-NIR data for the automated characterization of petroleum wax blends ﻿

Novel Method Based on Ion Mobility Spectrometry Combined with Machine Learning for the Discrimination of Fruit Juices ﻿

Optimized HSSE-GC-MS and machine learning for forensic gender classification of human scent from clothing ﻿

Rapid and Automated Method for Detecting and Quantifying Adulterations in High-Quality Honey Using Vis-NIRs in Combination with Machine Learning ﻿

Rapid Classification of Petroleum Waxes: A Vis-NIR Spectroscopy and Machine Learning Approach ﻿

Rapid Detection and Quantification of Adulterants in Fruit Juices Using Machine Learning Tools and Spectroscopy Data ﻿

A Methodology Based on FT-IR Data Combined with Random Forest Model to Generate Spectralprints for the Characterization of High-Quality Vinegars

A Rapid Method for Authentication of Macroalgae Based on Vis-NIR Spectroscopy Data Combined with Chemometrics Approach

Application of FT-IR and machine learning in the characterization of organic and biodynamic wines vs conventional wines

Characterization of Biodegraded Ignitable Liquids by Headspace-Ion Mobility Spectrometry

Comparison of different processing approaches by SVM and RF on HS-MS eNose and NIR Spectrometry data for the discrimination of gasoline samples

Detecting Honey Adulteration: Advanced Approach Using UF-GC Coupled with Machine Learning

Detection of Adulterations in Fruit Juices Using Machine Learning Methods over FT-IR Spectroscopic Data

Development of a Novel HS-GC/MS Method Using the Total Ion Spectra Combined with Machine Learning for the Intelligent and Automatic Evaluation of Food-Grade Paraffin Wax Odor Level

Discrimination of Ignitable Liquid Residues in Burned Petroleum-Derived Substrates by Using HS-MS eNose and Chemometrics

Enhanced Differentiation of Wild and Feeding Civet Coffee Using Near-Infrared Spectroscopy with Various Sample Pretreatments and Chemometric Approaches

Enhancing Competency-Based Education in Instrumental Analysis: A Novel Approach Using High-Performance Liquid Chromatography for Real-World Problem Solving

Headspace gas chromatography with various sample preparation and chemometric approaches to improve discrimination of wild and feeding civet coffee

Herramientas analíticas basadas en la aplicación de machine learning sobre datos espectroscópicos

Machine learning approaches over ion mobility spectra for the discrimination of ignitable liquids residues from interfering substrates

Machine learning-based approaches to Vis-NIR data for the automated characterization of petroleum wax blends

Novel Method Based on Ion Mobility Spectrometry Combined with Machine Learning for the Discrimination of Fruit Juices

Optimized HSSE-GC-MS and machine learning for forensic gender classification of human scent from clothing

Rapid and Automated Method for Detecting and Quantifying Adulterations in High-Quality Honey Using Vis-NIRs in Combination with Machine Learning

Rapid Classification of Petroleum Waxes: A Vis-NIR Spectroscopy and Machine Learning Approach

Rapid Detection and Quantification of Adulterants in Fruit Juices Using Machine Learning Tools and Spectroscopy Data