CURRENT‐STATUS SURVIVAL ANALYSIS METHODOLOGY APPLIED TO ESTIMATING SENSORY SHELF LIFE OF READY‐TO‐EAT LETTUCE (LACTUCA SATIVA)

Abstract

The objective of the present work was to develop a method for predicting sensory shelf life for situations in which each consumer evaluates only one sample corresponding to one storage time. This type of data is known as current‐status data in survival analysis statistics. The methodology was applied to estimate the sensory shelf life of ready‐to‐eat lettuce (Lactuca sativa var. capitata cv.“Alpha”). For each of six storage times, 50–52 consumers answered yes or no to whether they would normally consume the presented sample. The results were satisfactory, showing that the methodology can be applied when necessary. The Weibull model was found adequate to model the data. Estimated shelf lives ± 95% confidence intervals were 11.3 ± 1.2 days and 15.5 ± 0.9 days for a 25% and a 50% consumer rejection probability, respectively. PRACTICAL APPLICATIONS When considering shelf‐life evaluations by consumers, the first idea is to have each consumer evaluate six or seven samples with different storage times in a single session. To do this, a reverse storage design is necessary, and in the case of a product such as lettuce, it would lead to different batches being confused with storage times. The methodology proposed in this article avoids this problem by having each consumer evaluate a single sample. Another issue with consumers tasting several samples in a single session is how representative this situation is of real consumption. The present methodology allows for a consumer to take home, e.g., a bottle of beer with an established storage time, and later collecting the information as to whether they found the beer acceptable or not. This is a situation much closer to real consumption.

Estimating Residential Appliance Lifetime for Energy Efficient Policy Analysis

“…Many common distributions such as normal, exponential, or Rayleigh are exact or approximate matches of special forms of the Weibull distribution (Abernathy 2006). In addition, the Weibull distribution has been used to successfully model survival probabilities of appliances (Lutz 2011;Welch 2010;Young 2008;Fernandez 2001) and has been widely used in analyses of lifetime and reliability of mechanical and electronic equipment (Araneda 2008;Mahzar 2007).…”

Exploring the potential of an Andean fruit : an interdisciplinary study on the cape gooseberry (Physalis peruviana L.) value chain

“…At the end of the day, this is what companies are looking for, the satisfaction of consumer. Thanks to the methodology developed by previous research [163] the use of survival analysis was possible, which to some extent gave more conclusive results than the other measurements, because in the end it was the consumer who judged the end of the shelf-life. The other measurements such as physicochemical and microbiological are very important to get insights about how to tackle the issues with the food under study, but the consumer indeed has a big impact in deciding when the food is not suitable for consumption.…”

“…Weibull distribution model (equation 1) was selected because it had the lowest Akaike criterium [207]. To estimate shelf life, authors usually choose rejection probability of 50 % ( = 0.5) [163,208,209]. From this, shelf lives of cape gooseberry thus calculated based on the value and the estimations are shown in table 3.3.…”

“…Because general appearance can be evaluated visually without sample destruction and during preliminary pilot experiment it was difficult to reach the same people every week for the study, the chosen methodology was current-status survival analysis based on the evaluation of a single sample [163,165].…”

“…Between the available techniques for consumer acceptance, survival analysis has emerged as a relative simple methodology to determine shelf life of foods [161,162]. Survival analysis has been used before in fresh foods like lettuce and broccoli, using the current data methodology, where a consumer evaluates a single sample [163][164][165]. The combination of both perspectives (product and consumer) avoids the bias caused by the 'arbitrary' choices when deciding about shelf life based only on the product point of view;…”