Journal of Food Processing & Technology

Shelf-life prediction for Non-accelerated studies (SheNon) applied to eggplant

6^th Global Summit and Expo on Food & Beverages

August 03-05, 2015 Orlando-FL, USA

Natalia S Martins1, Eric B Ferreira1, Flavia Della Lucia1 and Sonia M S Piedade2

Posters-Accepted Abstracts: J Food Process Technol

Abstract:

In this work we propose a statistical method here called SheNon (Shelf-life prediction for Non-accelerated studies). Such method is based on principal components and linear regression analysis. Here we present shelf-life estimation for minimally processed eggplants. Samples were stored without chemical preservatives and evaluated by appearance, color, aroma, overall acceptance (OA), purchase intention (PI), L, a *, b *, Hue angle (h), soluble solids (SS), acidity, pH, browning index (BI) and chromaticity. PC1 and PC5 presented the highest correlations with time, respectively and then moved on to regression fitting step. Eggplants showed to age mainly along PC1. Some attributes like aroma, appearance, color, OA, PI, L and h were related to fresh samples and a*, b*, SS, BI and chromaticity were associated to aged products. To predict the shelf-life, a linear regression model was selected considering these principal components. A borderline sample vector was provided by an expert in food technology, which, applied to in the adjusted regression model, yielded the shelf-life estimative of 9.6 days (�4 days, confidence level of 95%). By the results it was found that the proposed method is promising for estimating shelf-life.