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In situ and real-time monitoring of an ultrasonic-assisted enzymatic hydrolysis process of corn gluten meal by a miniature near infrared spectrometer

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Abstract

Ultrasound treatment during proteolysis could facilitate enzymatic hydrolysis. The degree of hydrolysis (DH) and ACE inhibitory activity of hydrolysates are the two most important factors for process control and quality assessment. This study proposed a novel method for in situ and real-time quantification of DH and ACE inhibitory activity during the ultrasonic-assisted enzymolysis process using miniature near infrared (NIR) spectroscopy with a fiber optic probe. The synergy interval partial least squares (Si-PLS) algorithms were studied via variable selection and multivariate calibrations. The results showed that the DH of CGM and ACE inhibitory activity of the CGM hydrolysates (under ultrasonic-assisted hydrolysis) were increased by 5.74% and 8.32%, respectively, compared to those of the control. The calibration model built using Si-PLS between the NIR spectra and chemical data exhibited high coefficient of prediction (Rp) of 0.9006 and root mean square error of prediction (RMSEP) of 2.98% for the DH, and Rp of 0.9516 and RMSEP of 11.20% for the ACE inhibitory activity. This study demonstrates that the miniature NIR spectroscopy technique with fiber optical probe with Si-PLS algorithms has high potential for in situ monitoring during ultrasonic-assisted enzymatic hydrolysis in the protein industry.

Graphical abstract: In situ and real-time monitoring of an ultrasonic-assisted enzymatic hydrolysis process of corn gluten meal by a miniature near infrared spectrometer

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Publication details

The article was received on 04 Apr 2017, accepted on 16 May 2017 and first published on 22 May 2017


Article type: Paper
DOI: 10.1039/C7AY00887B
Citation: Anal. Methods, 2017, Advance Article
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    In situ and real-time monitoring of an ultrasonic-assisted enzymatic hydrolysis process of corn gluten meal by a miniature near infrared spectrometer

    L. Luo, Y. Zhang, K. Wang, H. Ma and M. Dong, Anal. Methods, 2017, Advance Article , DOI: 10.1039/C7AY00887B

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