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2. Contributors

Author

Adam, Vojtěch, primary, Ahmad, Riaz, additional, Ahmed, Selena, additional, Akram, Kashif, additional, Alam, M.S., additional, Alayón Luaces, Paula, additional, Almalki, Wasayf J., additional, Anjum, Muhammad Akbar, additional, Antonopoulou, Chrysovalantou, additional, Antošovský, Jiří, additional, Arrobas, Margarida, additional, Aspiazú, Ignácio, additional, Bai, Cuihua, additional, Baldi, Elena, additional, Barker, Allen V., additional, de Bem, Betina Pereira, additional, Benavides-Mendoza, Adalberto, additional, Benkovi, Maja, additional, Botelho, Renato Vasconcelos, additional, Brighenti, Alberto Fontanella, additional, Brunetto, Gustavo, additional, Bryla, David R., additional, Burhan, Hakan, additional, Butler, Thomas O., additional, Cai, Miaomiao, additional, Canet, Rodolfo, additional, Cavani, Luciano, additional, Chapman, James, additional, Chater, John M., additional, Chatzissavvidis, Christos, additional, Chen, Jianjun, additional, Chen, Li-Song, additional, Ciotta, Marlise Nara, additional, Comin, Jucinei José, additional, De Conti, Lessandro, additional, de Medeiros Corrêa, Márcio Cleber, additional, Covarrubias-Ramírez, Juan Manuel, additional, Cozzolino, Daniel, additional, van der Zee, Sjoerd E.A.T.M., additional, de Deus, José Aridiano Lima, additional, Di Lonardo, Sara, additional, Dichio, Bartolomeo, additional, Dong, Zhihao, additional, Ducsay, Ladislav, additional, Dupont, Madeleine F., additional, Elbourne, Aaron, additional, Elmusa, Fatima, additional, Van Emon, Jeanette M., additional, Etesami, Hassan, additional, Fallas-Corrales, Róger, additional, Farooq, Umar, additional, Fuentes-Lara, Laura Olivia, additional, Gaiad, José E., additional, Gao, Bin, additional, Gąstoł, Maciej, additional, Gomez Herrera, Melanie D., additional, Gulbagca, Fulya, additional, Guo, Peng, additional, Hayat, Zafar, additional, He, Jia-Dong, additional, Hu, Chengxiao, additional, Ibacache, Antonio, additional, Intrigliolo, Diego S., additional, Jacobo-Salcedo, Maria Del Rosario, additional, Jeong, Byoung Ryong, additional, Jia, Wei, additional, Jiang, Huan-Xin, additional, Juárez-Maldonado, Antonio, additional, Jurina, Tamara, additional, Kadyampakeni, Davie, additional, Karagiannis, Evangelos, additional, Kljusurić, Jasenka Gajdoš, additional, Li, Jinxue, additional, Liu, Wenhuan, additional, Loss, Arcângelo, additional, Lourenzi, Cledimar Rogério, additional, Luo, Donglin, additional, Ma, YanYan, additional, Machado, Rui, additional, Maia, Victor Martins, additional, Martínez-Alcántara, Belén, additional, de Mello Prado, Renato, additional, de Melo, George Wellington Bastos, additional, Merhaut, Donald J., additional, Michailidis, Michail, additional, Milošević, Nebojša, additional, Milošević, Tomo, additional, Mininni, Alba N., additional, Molassiotis, Athanassios, additional, Morales, Isidro, additional, Motesharezadeh, Babak, additional, Mousavi, Seyed Majid, additional, Müller, Marcelo Marques Lopes, additional, Natale, William, additional, Nava-Reyna, Erika, additional, Nestby, Rolf, additional, Nobre, Danúbia Aparecida Costa, additional, Nyombi, Kenneth, additional, Ojeda-Barrios, Dámaris Leopoldina, additional, Oliveira, Fernanda Soares, additional, Ortaş, İbrahim, additional, Padmaperuma, Gloria, additional, Parent, Léon Etienne, additional, Parga-Torres, Víctor Manuel, additional, Parra, Margarita, additional, de Paula, Betânia Vahl, additional, Pegoraro, Rodinei Facco, additional, Pérez-Piqueres, Ana, additional, Petruccelli, Raffaella, additional, Power, Aoife, additional, Preece, John E., additional, Qiu, Fangying, additional, Quiñones, Ana, additional, Rahim, M.A., additional, Ram, R.A., additional, Restrepo-Diaz, Hermann, additional, Retamales, Jorge B., additional, Ricachenevsky, Felipe Klein, additional, Ricciuti, Patrizia, additional, Rodrigues, M. Ângelo, additional, Rodríguez-Carretero, Isabel, additional, Rozane, Danilo Eduardo, additional, Rubio-Asensio, José S., additional, Ryant, Pavel, additional, Sánchez-Reinoso, Alefsi David, additional, Sandoval-Rangel, Alberto, additional, Sapáková, Eva, additional, Schmitt, Djalma Eugênio, additional, Sen, Fatih, additional, Serralheiro, Ricardo, additional, Shafi, Afshan, additional, Shu, Bo, additional, Shuhaili, Faqih A.B. Ahmad, additional, Škarpa, Petr, additional, Sofo, Adriano, additional, Sorrenti, Giovambattista, additional, de Souza, André Luiz Kulkamp, additional, de Souza Kulmann, Matheus Severo, additional, Srivastava, A.K., additional, Stefanello, Lincon Oliveira, additional, Stewart, Alyssa L., additional, Stratton, Margie L., additional, Tan, Qiling, additional, Tang, Ning, additional, Tanou, Georgia, additional, Tassinari, Adriele, additional, Tiecher, Tadeu Luis, additional, Toselli, Moreno, additional, Truong, Vi Khanh, additional, Turinek, Matjaž, additional, Tušek, Ana Jurinjak, additional, Vaidyanathan, Seetharaman, additional, Valinger, Davor, additional, Vashisth, Tripti, additional, Verdugo-Vásquez, Nicolás, additional, Wang, Zonghua, additional, Wei, Xiangying, additional, Wu, Qiang-Sheng, additional, Xiloyannis, Cristos, additional, Yang, Lin-Tong, additional, Yao, Lixian, additional, Zalamena, Jovani, additional, Zhan, Ting, additional, Zhao, Yuanyuan, additional, Zheng, Yongqiang, additional, Ziogas, Vasileios, additional, and Zurita-Silva, Andrés, additional

Published
2020
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3. Application of NIRs coupled with PLS and ANN modelling to predict average droplet size in oil-in-water emulsions prepared with different microfluidic devices.

Author

Jurinjak Tušek A, Jurina T, Čulo I, Valinger D, Gajdoš Kljusurić J, and Benković M

Subjects
Emulsions, Least-Squares Analysis, Water, Lab-On-A-Chip Devices, Neural Networks, Computer
Abstract

In this study, the potential of microfluidic systems with different microchannel geometries (microchannel with teardrop micromixers and microchannel with swirl micromixers) for the preparation of oil-in-water (O/W) emulsions using two different emulsifiers (2 % and 4 % Tween 20 and 2% and 4 % PEG 2000) at total flow rates of 20-280 μL/min was investigated. The results showed that droplets with a smaller average Feret diameter were obtained when a microfluidic device with tear drop micromixers was used. To predict the average Feret diameter of O/W emulsion droplets, near-infrared (NIR) spectra of all prepared emulsions were collected and coupled with partial least squares (PLS) regression and artificial neural network modelling (ANN). The results showed that PLS models based on NIR spectra can ensure acceptable qualitative prediction, while highly non-linear ANN models are more suitable for predicting the average Feret diameter of O/W droplets. High R 2 values (R 2 validation  greater than 0.8) confirm that ANNs can be used to monitor the emulsification process., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2022 Elsevier B.V. All rights reserved.)

Published
2022
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4. Rapid quantification of dissolved solids and bioactives in dried root vegetable extracts using near infrared spectroscopy.

Author

Jurinjak Tušek A, Benković M, Malešić E, Marić L, Jurina T, Gajdoš Kljusurić J, and Valinger D

Subjects
Plant Extracts, Polyphenols, Principal Component Analysis, Spectroscopy, Near-Infrared, Vegetables
Abstract

Artificial neural networks (ANN) were developed for prediction of total dissolved solids, polyphenol content and antioxidant capacity of root vegetables (celery, fennel, carrot, yellow carrot, purple carrot and parsley) extracts prepared from the (i) fresh vegetables, (ii) vegetables dried conventionally at 50 °C and 70 °C, and (iii) the lyophilised vegetables. Two types of solvents were used: organic solvents (acetone mixtures and methanol mixtures) and water. Near-infrared (NIR) spectra were recorded for all samples. Principal Component Analysis (PCA) of the pre-treated spectra using Savitzky-Golay smoothing showed specific grouping of samples in two clusters (1st: extracts prepared using methanol mixtures and water as the solvents; 2nd: extracts prepared using acetone mixtures as the solvents) for all four types of extracts. Furthermore, obtained results showed that the developed ANN models can reliably be used for prediction of total dissolved solids, polyphenol content and antioxidant capacity of dried root vegetable extracts in relation to the recorded NIR spectra., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2021 Elsevier B.V. All rights reserved.)

Published
2021
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