Premium
Empirical Models to Predict Shelf Life of Sunflower Oil Stabilized with Oleoresin Sage ( Salvia officinalis L.) and Ascorbyl Palmitate
Author(s) -
Upadhyay Rohit,
Sehwag Sneha,
Niwas Mishra Hari
Publication year - 2017
Publication title -
journal of food processing and preservation
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.511
H-Index - 48
eISSN - 1745-4549
pISSN - 0145-8892
DOI - 10.1111/jfpp.13121
Subject(s) - sunflower oil , shelf life , food science , peroxide value , oleoresin , preservative , sage , chemistry , officinalis , partial least squares regression , mathematics , botany , biology , statistics , physics , nuclear physics
The oxidative stability measures (OSM) of sunflower oil (SO) stabilized with oleoresin sage ( Salvia officinalis L.) and ascorbyl palmitate was estimated in terms of induction period (IP) for the formation of conjugated dienes (IP CDV ) and Rancimat at 60C and 100–130C, respectively. Partial least squares (PLS) regression was used to derive the relationship between OSM and compositional parameters (peroxide value, acid value, total polar matter, antioxidant capacity and total added antioxidants). The shelf life prediction at 60C (SL 60 ) using PLS and Rancimat models resulted in the over‐prediction by 0.22 and 30.14%, respectively. The shortcomings of Rancimat model were corrected by developing a unified model using IP CDV values as a function of IP at 100–130C, which over‐predicted the SL 60 by 0.24%. The SL 25 was estimated with an error of ±7.37% using unified model that was significantly similar to PLS (±7.29%) while lesser than Rancimat (±13.07%) models. Practical Applications From a practical point of view, the unified model can be utilized as an initial step for quick and reliable estimation of the oxidative stability and shelf life of oil samples. It can also be utilized to assess the preservative effects of food additives in stabilizing the oil blends. The approach may be useful to fats and oils researchers, quality control laboratories and other organizations to develop in‐house shelf life prediction models under different temperature conditions.