Citations (22)
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Adilson Roberto Locali-Pereira, Nathalie Almeida Lopes & Vânia Regina Nicoletti. (2022) Pink Pepper (Schinus terebinthifolius Raddi) from Extracts to application: Truths about a Fake Pepper. Food Reviews International 0:0, pages 1-30.
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Daniele M. de Oliveira, Diego B. Menezes, Lucas R. Andrade, Felipe da C. Lima, Luciana Hollanda, Aleksandra Zielinska, Elena Sanchez-Lopez, Eliana B. Souto & Patrícia Severino. (2021) Silver nanoparticles obtained from Brazilian pepper extracts with synergistic anti-microbial effect: production, characterization, hydrogel formulation, cell viability, and in vitro efficacy. Pharmaceutical Development and Technology 26:5, pages 539-548.
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Iman Golpour, Mohammad Kaveh, Reza Amiri Chayjan & Raquel P. F. Guiné. (2020) Optimization of Infrared-convective Drying of White Mulberry Fruit Using Response Surface Methodology and Development of a Predictive Model through Artificial Neural Network. International Journal of Fruit Science 20:sup2, pages S1015-S1035.
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Yago Matheus da Silva Veloso, Marcello Maia de Almeida, Odelsia Leonor Sanchez de Alsina & Manuela Souza Leite. (2020) Artificial neural network model for the flow regime recognition in the drying of guava pieces in the spouted bed. Chemical Engineering Communications 207:4, pages 549-558.
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Qing Sun, Min Zhang & Arun S. Mujumdar. (2019) Recent developments of artificial intelligence in drying of fresh food: A review. Critical Reviews in Food Science and Nutrition 59:14, pages 2258-2275.
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Articles from other publishers (17)
Mohammad Kaveh, Necati Çetin, Esmail Khalife, Yousef Abbaspour‐Gilandeh, Maryam Sabouri & Faroogh Sharifian. (2023) Machine learning approaches for estimating apricot drying characteristics in various advanced and conventional dryers. Journal of Food Process Engineering 46:12.
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Mohamad A. Bannoud, Beatriz P. Gomes, Marcela C. de S. P. Abdalla, Mariana V. Freire, Kaciane Andreola, Tiago D. Martins, Carlos A. M. da Silva, Luciane F. G. de Souza & Matheus B. Braga. (2023) Mathematical modeling of drying kinetics of ground Açaí (Euterpe oleracea) kernel using artificial neural networks. Chemical Papers.
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Bruno Guzzo da Silva, Juliana Martin do Prado, Ana Maria Frattini Fileti, Mary Ann Foglio & Paulo de Tarso Vieira e Rosa. (2023) Kinetic models for extraction with supercritical carbon dioxide from pink pepper: Theoretical, empirical, and semi-empirical models and artificial neural network approach. Chemical Engineering Journal Advances 15, pages 100514.
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Mona Mary Varghese, Teja Reddy Vakamalla, Ravi Gujjula & Narasimha Mangadoddy. (2022) Prediction of solid circulation rate in an internal circulating fluidized bed: An empirical and ANN approach. Flow Measurement and Instrumentation 88, pages 102274.
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Juan F. Saldarriaga, Yuby Cruz, Idoia Estiati, Mikel Tellabide & Martin Olazar. (2022) Assessment of pressure drop in conical spouted beds of biomass by artificial neural networks and comparison with empirical correlations. Particuology 70, pages 1-9.
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Xiaoxia Nie. (2022) Construction and Application of Comprehensive Nursing Information Service Platform Based on Internet of Things Technology. Journal of Healthcare Engineering 2022, pages 1-8.
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Pengfei Jiang, Kaiyue Zhu, Shan Shang, Wengang Jin, Wanying Yu, Shuang Li, Shen Wang & Xiuping Dong. (2022) Application of Artificial Neural Network in the Baking Process of Salmon. Journal of Food Quality 2022, pages 1-12.
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Mónica Leon García, María Hernández Carrión, Sebastián Escobar, Aureliano Rodríguez & Jader Rodríguez Cortina. (2020)
Optimization of the antioxidant capacity of mangosteen peels (
Garcinia mangostana
L.) extracts: Management of the drying extraction processes
. Food Science and Technology International 27:5, pages 404-412.
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Julia L. Governici, Naiara C. Z. Sperotto, Evandro de C. Melo, Diego A. Gonzaga & Antônio P. S. Carneiro. (2020) Drying and essential oil extraction of Brazilian peppertree (Schinus terebinthifolius Raddi) fruits. Revista Brasileira de Engenharia Agrícola e Ambiental 24:9, pages 637-643.
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Yago Matheus da Silva Veloso, Marcello Maia de Almeida, Odelsia Leonor Sanchez de Alsina, Maria Laura Passos, Arun S. Mujumdar & Manuela Souza Leite. (2020) Hybrid phenomenological/ANN-PSO modelling of a deformable material in spouted bed drying process. Powder Technology 366, pages 185-196.
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I. Estiati, M. Tellabide, J.F. Saldarriaga, H. Altzibar, F.B. Freire, J.T. Freire & M. Olazar. (2019) Comparison of artificial neural networks with empirical correlations for estimating the average cycle time in conical spouted beds. Particuology 42, pages 48-57.
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Shahpour Jahedi Rad, Mohammad Kaveh, Vali Rasooli Sharabiani & Ebrahim Taghinezhad. (2018) Fuzzy logic, artificial neural network and mathematical model for prediction of white mulberry drying kinetics. Heat and Mass Transfer 54:11, pages 3361-3374.
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Mohammad Kaveh, Reza Amiri Chayjan & Behrooz Khezri. (2018) Modeling Drying Properties of Pistachio Nuts, Squash and Cantaloupe Seeds under Fixed and Fluidized Bed Using Data-Driven Models and Artificial Neural Networks. International Journal of Food Engineering 14:1.
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Bruno Guzzo da Silva, Ana Maria Frattini Fileti, Mary Ann Foglio, Ana Lúcia Tasca Gois Ruiz & Paulo de Tarso Vieira e Rosa. (2017) Supercritical carbon dioxide extraction of compounds from Schinus terebinthifolius Raddi fruits: Effects of operating conditions on global yield, volatile compounds, and antiproliferative activity against human tumor cell lines. The Journal of Supercritical Fluids 130, pages 10-16.
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Mohammad Kaveh, Reza Amiri Chayjan & Ali Mohammad Nikbakht. (2016) Mass transfer characteristics of eggplant slices during length of continuous band dryer. Heat and Mass Transfer 53:6, pages 2045-2059.
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Bruno Guzzo Silva, Ana Maria Frattini Fileti, Mary Ann Foglio, Paulo de Tarso Vieira Rosa & Osvaldir Pereira Taranto. (2017)
Effects of Different Drying Conditions on Key Quality Parameters of Pink Peppercorns (
Schinus terebinthifolius
Raddi)
. Journal of Food Quality 2017, pages 1-12.
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Idoia Estiati, Fábio B. Freire, José T. Freire, Roberto Aguado & Martin Olazar. (2016) Fitting performance of artificial neural networks and empirical correlations to estimate higher heating values of biomass. Fuel 180, pages 377-383.
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