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.

Sensory evaluationCookies enriched with 0, 5, 10, 15, 20, and 25% full-fat sweet lupine flour (FFSL) were evaluated by a sensory panel using the rank of preference and paired comparison tests. Cookies with 0, 5, and 10% FFSL were preferred while those containing 20 and 25% FFSL were rejected (~50.01). Studies conducted with school children showed similar acceptability for 0 and 10% FFSL-containing cookies which was different (p=O.O5) from those containing 20% FFSL. Fortification of the basic formula with 10% FFSL was recommended on the basis of acceptability.

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Different goat cheeses from Chile were studied by Free‐Choice Profile (FCP) analysis. Generalized Procrustes Analysis (GPA) applied to FCP data permitted differentiation between samples and informed on the attributes responsible for the observed differences. Appearance was a dominant factor in discriminating samples and to a lesser degree textural variables were also correlated with GPA dimensions. In acceptability the fresh cheeses were significantly preferred over the ripened ones.

The flavor of eight samples of commercial strawberry yogurt was studied by Free‐Choice Profile analysis (FCP). Generalized Procrustes Analysis (GPA) applied to FCP allowed differentiation between samples and highlighted flavor attributes responsible for the observed differences. The relation between sensory and physicochemical datasets was studied by means of GPA. Those samples with higher carbohydrate content were perceived as sweeter, having stronger strawberry flavor, and with more dairy and yogurt flavors. Samples with higher titratable acidity, ash and protein content were perceived as more acidic and higher in intensity of “faulty” or “defective” flavors. Higher moisture content was associated with lower intensity of “dairy” flavors (creamy, dairy, and yogurt) and greater intensity of rancid flavor. It is concluded that, though not often used to this end, GPA is a suitable method to study the relationship of sensory and instrumental measurements.

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