DSC Melting Profile of Cold-Pressed Hemp Seed Oil as an Authenticity Fingerprint Influenced by Scanning Rate

Author:

Islam Mahbuba1ORCID,Kaczmarek Anna1,Rudzińska Magdalena2ORCID,Tomaszewska-Gras Jolanta1ORCID

Affiliation:

1. Department of Food Quality and Safety Management, Poznań University of Life Sciences, ul. Wojska Polskiego 31/33, 60-637 Poznań, Poland

2. Department of Food Technology of Plant Origin, Poznań University of Life Sciences, ul. Wojska Polskiego 31/33, 60-624 Poznań, Poland

Abstract

Among the variety of edible cold-pressed oils on the market, hemp seed oil is becoming increasingly popular among scientists and consumers due to its plethora of nutritional compounds. In this study, the goal was to examine the thermal characteristics of cold-pressed hemp seed oil pressed from seeds of the Henola cultivar procured by five different suppliers in two different seasons. This aim of this research was to establish how various scanning rates can affect the unique thermal profile of cold-pressed hemp seed oil in terms of an authenticity assessment. The melting transition was manifested by curves with four peaks for all hemp seed oils; however, they differed for each scanning rate in terms of the shape and peak intensity. Comparing the curves obtained at heating rates of 1 and 2 °C/min, noticeable differences were observed in the melting transition parameters between hemp seed oils, showing that small differences in fatty acid composition can cause changes in DSC profiles. In contrast, at a scanning rate 5 °C/min, the melting curves were similar for all hemp seed oils. It was also observed that for all the scanning rates, there was a strong negative correlation between the total content of polyunsaturated fatty acids (ƩPUFAs) and the peak temperature of the three peaks (Tm2, Tm3, and Tm4). The most abundant fatty acids were PUFAs, i.e., linoleic acid (C18:2), with contents ranging from 47 to 55%; and α-linolenic acid (C 18:3 n–3), with contents ranging from 17 to 25%. The application of linear discriminant analysis (LDA) enabled a discriminant model to be built based on the DSC data obtained for differentiation of oils pressed from fresh and stored seeds.

Funder

National Science Centre

Publisher

MDPI AG

Subject

Fluid Flow and Transfer Processes,Computer Science Applications,Process Chemistry and Technology,General Engineering,Instrumentation,General Materials Science

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