Determination of Shelf Life of Concentrated Yogurt (Labneh) Produced by In-Bag Straining of Set Yogurt using Hazard Analysis

Abstract

Strained yogurt, labneh, produced by straining cow's milk set yogurt in cloth bags, was stored at 5, 15, and 25 degrees C, and changes in microbial counts, pH, titratable acidity, percentage of free whey, and sensory attributes were monitored during storage. Counts of total aerobes, psychrotrophic yeasts, yeasts and molds, and lactic acid bacteria, except in samples stored at 25 degrees C, increased irrespective of storage temperature. The pH of samples decreased, titratable acidity and percentage of free whey increased, and texture defects were detected at a later stage than flavor changes during storage. Shelf-life data of labneh was adequately described by the Weibull distribution. The nominal shelf life determined using sensory changes and yeast counts as failure criteria ranged from 8.5 to 10.5, 4.7 to 5.8 and 2.3 to 2.7 d at 5, 15 and 25 degrees C, respectively. Q10 (shelf life at T degrees C/shelf life at T+10 degrees C) for flavor quality loss was 1.98 at 5 degrees C, and the corresponding activation energy was 11.3 kcal/mol.

Survival analysis methodology to predict the shelf-life of probiotic flavored yogurt

“…The Weibull distribution showed the most adequate to the data. In fact, it has been using to preview the shelf-life of dairy foods such as cottage cheese (Schmidt & Bouma, 1992), concentrated yogurt (Al-Kadamany et al, 2002), ricotta cheese (Hough, Puglieso, Sanchez, & Silva, 1999) and yayik butter (Arslan, Sert, Ayar, & Ozcan, 2009). Fig.…”

Application of Weibull analysis and artificial neural networks to predict the useful life of the vacuum packed soft cheese

“…The shelf life and activation energy of soft cheese show that soft cheese ripened during 2 days presents more time of acceptability compared with the soft cheese ripened during 4 days, see Table 2 The selected panel, using the criterion of [6], has a tendency acceptable in the decisions, because the values of parameter β (Table 2) are between 2 and 4 [11].…”

“…To evaluate the process capability to preserve quality in the food, we commonly use survival analysis; Weilbull distribution is applied to estimate the shelf life on the rejection of the product by the consumers [5][6][7]. In this method, it is necessary to know the probability of failure F(x), defined as the probability that a consumer rejects a product stored prematurely (x); in this case, the risk is focused on the rejection by the consumer.…”

Estimation of the shelf-life of halloumi cheese using survival analysis

“…The determination of the shelf-life of foods entails fitting the failure times based on sensory, chemical, and microbiological cut-off points to different statistical distributions and calculating the expected timeto-failure at different probability levels (Gacula et al 2009). This approach has been utilised in the determination of the shelf-life of foods using either trained panelists to determine the limits of sensory acceptability (Hough et al 1999;Al Kadamany et al 2002;Guerra et al 2008) or ordinary consumers who are asked to indicate their willingness/refusal to buy/consume the product (Ares et al 2008;Cruz et al 2010). The latter technique, employing regular consumers of the products and survival analysis statistics, is considered as being more reflective of the interaction between the consumers and food products and is increasingly used in the determination of the shelf-life of foods (Hough 2010).…”

“…The determination of the shelf-life at different temperatures is very often used to derive the values for Q 10 , which defines the change in shelflife of foods at storage temperatures differing by 10°C, and the activation energy (E a ), which gives a measure of the temperature dependence of the sensory quality change upon storage (Hough et al 1999;Al Kadamany et al 2002). These values are of practical significance in predicting the changes in the shelf-life with variations in temperatures during the distribution and storage of foods (Taoukis et al 1997).…”