Next Article in Journal
Sage (Salvia officinalis L.) Essential Oil as a Potential Replacement for Sodium Nitrite in Dry Fermented Sausages
Next Article in Special Issue
Cucumis melo Enhances Enalapril Mediated Cardioprotection in Rats with Isoprenaline Induced Myocardial Injury
Previous Article in Journal
A Modified 2-DOF Control Framework and GA Based Intelligent Tuning of PID Controllers
Previous Article in Special Issue
Effect of Blanching on Enzyme Inactivation, Physicochemical Attributes and Antioxidant Capacity of Hot-Air Dried Pomegranate (Punica granatum L.) Arils (cv. Wonderful)
Article

Using Peptidomics and Machine Learning to Assess Effects of Drying Processes on the Peptide Profile within a Functional Ingredient

Nuritas Ltd., Joshua Dawson House, Dawson St. D02 RY95 Dublin, Ireland
*
Author to whom correspondence should be addressed.
Academic Editors: Jue-Liang Hsu and Olaniyi Amos Fawole
Processes 2021, 9(3), 425; https://doi.org/10.3390/pr9030425
Received: 21 January 2021 / Revised: 19 February 2021 / Accepted: 20 February 2021 / Published: 26 February 2021
(This article belongs to the Special Issue Study on Processing and Biological Activity of Functional Foods)
Bioactive peptides are known to have many health benefits beyond nutrition; yet the peptide profile of high protein ingredients has been largely overlooked when considering the effects of different processing techniques. Therefore, to investigate whether drying conditions could affect the peptide profile and bioactivity within a functional ingredient, we examined the effects of spray (SD) and freeze (FD) drying on rice natural peptide network (NPN), a characterised functional ingredient sourced from the Oryza sativa proteome, which has previously been shown to effectively modulate circulating cytokines and improve physical performance in humans. In the manufacturing process, rice NPN was either FD or SD. Employing a peptidomic approach, we investigated the physicochemical characteristics of peptides common and unique to FD and SD preparations. We observed similar peptide profiles regarding peptide count, amino acid distribution, weight, charge, and hydrophobicity in each sample. Additionally, to evaluate the effects of drying processes on functionality, using machine learning, we examined constituent peptides with predicted anti-inflammatory activity within both groups and identified that the majority of anti-inflammatory peptides were common to both. Of note, key bioactive peptides validated within rice NPN were recorded in both SD and FD samples. The present study provides an important insight into the overall stability of the peptide profile and the use of machine learning in assessing predicted retention of bioactive peptides contributing to functionality during different types of processing. View Full-Text
Keywords: functional ingredient; spray-dry; freeze-dry; bioactive peptide; hydrolysate; peptidomics; machine learning functional ingredient; spray-dry; freeze-dry; bioactive peptide; hydrolysate; peptidomics; machine learning
Show Figures

Figure 1

MDPI and ACS Style

Chauhan, S.; O’Callaghan, S.; Wall, A.; Pawlak, T.; Doyle, B.; Adelfio, A.; Trajkovic, S.; Gaffney, M.; Khaldi, N. Using Peptidomics and Machine Learning to Assess Effects of Drying Processes on the Peptide Profile within a Functional Ingredient. Processes 2021, 9, 425. https://doi.org/10.3390/pr9030425

AMA Style

Chauhan S, O’Callaghan S, Wall A, Pawlak T, Doyle B, Adelfio A, Trajkovic S, Gaffney M, Khaldi N. Using Peptidomics and Machine Learning to Assess Effects of Drying Processes on the Peptide Profile within a Functional Ingredient. Processes. 2021; 9(3):425. https://doi.org/10.3390/pr9030425

Chicago/Turabian Style

Chauhan, Sweeny, Sean O’Callaghan, Audrey Wall, Tomasz Pawlak, Ben Doyle, Alessandro Adelfio, Sanja Trajkovic, Mark Gaffney, and Nora Khaldi. 2021. "Using Peptidomics and Machine Learning to Assess Effects of Drying Processes on the Peptide Profile within a Functional Ingredient" Processes 9, no. 3: 425. https://doi.org/10.3390/pr9030425

Find Other Styles
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Back to TopTop