Determination of Optimum Concentrations of a Food Ingredient Using Survival Analysis Statistics

Garitta, Lorena; Serrat, Carles; Hough, Guillermo; Curia, Ana

doi:10.1111/j.1750-3841.2006.00107.x

Cited by 25 publications

(56 citation statements)

References 14 publications

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“…Based on an adopted rejection probability (usually 50%, Hough (2010)) the sensory shelf life of the product can thus be estimated. The methodology was then extended to estimating concentration limits of sensory defects ) and optimum concentrations of a food ingredient (Garitta et al 2006). Survival analysis has the advantage that experimental sensory work is relatively simple: a group of consumers answer if they accept or reject samples with different storage times or different levels of a sensory defect.…”

Section: Introductionmentioning

confidence: 99%

Sensory cut-off point obtained from survival analysis statistics

Garitta¹,

Langohr

Gómez

et al. 2015

Food Quality and Preference

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In the present work we applied interval-censored survival analysis techniques to estimate sensory cut-off points based on consumer's decision to accept or reject food products taking into account the inherent variability in sensory measurements. We compared the values obtained using this survival analysis methodology with those obtained by applying a previous regression based method. Cut-off point (COP) estimations were made for acid flavor in yogurt, strawberry flavor in a strawberry flavored drink and appearance quality index in broccoli. For two of these products the regression based cut-off points were unrealistic, and would lead to much too conservative COP's, leading to unnecessary rejection of samples in quality control inspections or very short shelf-lives. For one of the products (strawberry flavored drink), the survival and regression-based COP's were comparable. The survival analysis methodology is recommended for estimating sensory cut-off points in food products.

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Section: Introductionmentioning

confidence: 99%

Sensory cut-off point obtained from survival analysis statistics

Garitta¹,

Langohr

Gómez

et al. 2015

Food Quality and Preference

View full text Add to dashboard Cite

show abstract

“…18 consumers presented this type of data (1 from Argentina, 12 from Spain and 5 from USA), most probably because they did not concentrate on the task or did not understand it to start with. In other studies (Garitta et al, 2006;Hough, Langohr, Gó mez, & Curia, 2003) this type of inconsistent behavior was also observed. Although, as stated above, subjects who indicated they had vision problems were excluded from the test, there could have been some consumers who did not report their problem and thus produced inconsistent results.…”

Section: Resultsmentioning

confidence: 51%

“…The model is basically the same as that developed by Garitta et al (2006) for determining the optimum color of yogurt. In that research, the explanatory variable was the color of the yogurt expressed by the Hunter Lab a * parameter.…”

Section: Survival Analysis Conceptsmentioning

confidence: 99%

“…Survival analysis mathematical procedures have proved to be an adequate tool to estimate optimum concentrations of a food ingredient (Garitta, Serrat, Hough, & Curia, 2006). These authors applied their model to red color in a strawberry yogurt, where the color can be too light, ok or too dark; leading to two events of interest: the transition of too light to ok and the transition of ok to too dark.…”

Section: Introductionmentioning

confidence: 99%

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Beef’s optimum internal cooking temperature as seen by consumers from different countries using survival analysis statistics

Osornio

Hough

Salvador

et al. 2008

Food Quality and Preference

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“…Based on an adopted rejection probability (usually 50%), the sensory shelf life of the product can thus be estimated. The methodology was then extended to estimating concentration limits of sensory defects (Hough et al 2004) and optimum concentrations of a food ingredient (Garitta et al 2006). In these studies, there was no "correct" result as consumers simply stated their acceptance or rejection for samples with different levels of the substance of interest.…”

Section: Introductionmentioning

confidence: 99%

Survival Analysis Statistics Applied to Threshold Data Obtained from the Ascending Forced‐Choice Method of Limits

Hough

Methven

Lawless

2013

Journal of Sensory Studies

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Sensory thresholds are often collected through ascending forced-choice methods. Group thresholds are important for comparing stimuli or populations; yet, the method has two problems. An individual may correctly guess the correct answer at any concentration step and might detect correctly at low concentrations but become adapted or fatigued at higher concentrations. The survival analysis method deals with both issues. Individual sequences of incorrect and correct answers are adjusted, taking into account the group performance at each concentration. The technique reduces the chance probability where there are consecutive correct answers. Adjusted sequences are submitted to survival analysis to determine group thresholds. The technique was applied to an aroma threshold and a taste threshold study. It resulted in group thresholds similar to ASTM or logarithmic regression procedures. Significant differences in taste thresholds between younger and older adults were determined. The approach provides a more robust technique over previous estimation methods. PRACTICAL APPLICATIONSDetection thresholds can indicate the level at which the average person is likely to notice a taste or smell. Therefore, they serve as an informative benchmark of when a tastant or aroma material will have an effect on consumer perception of that taste or flavor. They also serve as an indicator of the biological (sensory) potency of various taste and flavor materials. The survival analysis method outlined in this paper is recommended for the analysis of threshold data collected through the forced-choice ascending concentration series method of limits. The new method takes into consideration an individual's sequence of incorrect and correct answers and the group's overall performance for a particular concentration, adjusting the individual's answers in a uniform manner. The adjusted answers can then be submitted to survival analysis statistical estimations to obtain more accurate group thresholds.

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Determination of Optimum Concentrations of a Food Ingredient Using Survival Analysis Statistics

Cited by 25 publications

References 14 publications

Sensory cut-off point obtained from survival analysis statistics

Sensory cut-off point obtained from survival analysis statistics

Beef’s optimum internal cooking temperature as seen by consumers from different countries using survival analysis statistics

Survival Analysis Statistics Applied to Threshold Data Obtained from the Ascending Forced‐Choice Method of Limits

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