Abstract
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.
Highlights
High protein ingredients have increasing gained commercial interest over the past number of years due to the nutritional and functional benefits associated with their consumption [1]
Using a machine learning approach, we investigate if different drying processes could affect the retention of predicted efficacy and key constituent bioactive peptides in a previously validated functional ingredient derived from the Oryza sativa proteome, rice Natural Peptide Network (NPN)
To elucidate if the drying process had an influence on amino acid distribution, the peptides exclusive to each of the Freeze drying (FD) and spray drying (SD) processes were assessed (Figure 2b). These findings were in general agreement with the global amino acid distribution analysis, with trends comparative to each process in both cases; following statistical analysis, we found that Asp (p < 0.05), His (p < 0.05) and Lys (p < 0.05) residues were significantly greater in peptides exclusive to FD samples
Summary
High protein ingredients have increasing gained commercial interest over the past number of years due to the nutritional and functional benefits associated with their consumption [1]. SD is the process of transforming a solution from a liquid to a dry state in a single operation, where a liquid suspension is atomised into a chamber with hot dry air, evaporating the droplets and resulting in fine particles with a relatively low moisture content [4] This technique is commonly used in the food industry as it is rapid, simple, and comparatively inexpensive [5]. Drying processes such as SD, studied by Chong et al (2015) and Schmitz-Schug et al (2013) [6] can lead to the loss of key actives, nutritional benefits that exist in the raw source, and proteins that are not heat resistant [7,8,9]. This process is technically challenging and requires continuous surveillance to ensure optimal product quality
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