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Potential of NIR spectroscopy coupled with Artificial Neural Network modeling for prediction of oil-in-mint aqueous extract emulsion droplets diameter produced in continuously operated microfluidic device (CROSBI ID 708606)

Prilog sa skupa u zborniku | sažetak izlaganja sa skupa | međunarodna recenzija

Grgić, Filip ; Benković, Maja ; Jurina, Tamara ; Gajdoš Kljusurić, Jasenka ; Valinger, Davor ; Jurinjak Tušek, Ana Potential of NIR spectroscopy coupled with Artificial Neural Network modeling for prediction of oil-in-mint aqueous extract emulsion droplets diameter produced in continuously operated microfluidic device // Book of Abstracts of 1st international conference Food and Climate Change / Šamec, Dunja ; Šarkanj, Bojan ; Sviličić Petrić, Ines (ur.). Koprivnica: Univesrity North, 2021. str. 73-73

Podaci o odgovornosti

Grgić, Filip ; Benković, Maja ; Jurina, Tamara ; Gajdoš Kljusurić, Jasenka ; Valinger, Davor ; Jurinjak Tušek, Ana

engleski

Potential of NIR spectroscopy coupled with Artificial Neural Network modeling for prediction of oil-in-mint aqueous extract emulsion droplets diameter produced in continuously operated microfluidic device

Emulsions are mixtures of two immiscible phases and are traditionally prepared by techniques based on reducing droplet size, such as stirring and homogenization. The disadvantage of these classical techniques is high energy consumption and production of emulsions with polydisperse droplet sizes. A very efficient alternative to the classical techniques are microfluidic devices. Microfluidic devices are characterised by a high surface-to-volume ratio, high mixing rates as well as precise process control and can be used for the preparation of emulsions with monodisperse droplets. In this work, oil-in-mint aqueous extract emulsions were prepared in microfluidic device with microchannels equipped with teardrop micromixers. The influence of emulsifier concentration (PEG 6000 2%, 4% and 6%), oil phase ratio (25%, 30% and 35%) and the total flow rate (200, 300 and 400 µL/min) on emulsion droplet size were analysed. Near infrared spectra (NIRs) were recorded for all the prepared emulsions and artificial neural network models (ANN) were developed to predict the size of the emulsion droplets based on NIR spectra. The obtained results show that NIR spectroscopy coupled with ANN modeling, has a high potential for predicting the emulsion droplet size, with the R-squared value for validation above 0.9.

microfluidics ; emulsion droplets ; NIR spectroscopy ; ANN modeling

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Podaci o prilogu

73-73.

2021.

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objavljeno

978-953-7986-1-5

Podaci o matičnoj publikaciji

Book of Abstracts of 1st international conference Food and Climate Change

Šamec, Dunja ; Šarkanj, Bojan ; Sviličić Petrić, Ines

Koprivnica: Univesrity North

Podaci o skupu

1st international conference Food and Climate Change

poster

15.10.2021-16.10.2021

Koprivnica, Hrvatska

Povezanost rada

Biotehnologija