Collecting Samples from the Shelf: Does This Contribute to Shelf‐Life Knowledge?

Abstract

AbstractSamples with different storage times were collected from supermarket shelves for three products: a strawberry fruit drink (SFD), digestive biscuits and shortbread. Sensory descriptive analysis and chemical measurements were performed on the samples. Consumers measured sensory acceptability and answered an accept/reject question for each sample. These last data were used to estimate the sensory shelf life of the SFD applying survival analysis methodology. For the digestive biscuits and shortbread, this estimation was not possible because of the low rejection probability and that acceptability differences between samples did not follow the expected trend in relation to dates. Partial least squares regressions showed the relationships between acceptability (Y‐matrix) and trained panel descriptive analysis + chemical measurements (X‐matrix). Collecting samples from the shelf could be a means of obtaining cut‐off points for shelf‐life estimations. However, in two of the three examples presented, confidence intervals were wide. This was due to the relatively low number of experimental points in the regression and/or the batch variability inherent in the sample collection method. Overall, collecting samples from the shelf contributed limited information to shelf‐life knowledge.Practical ApplicationsIf samples with different storage times could be collected from the company's deposits and/or from supermarket shelves, this would mean an important saving in resources, especially as this would mean not having to wait for the different storage times to elapse. The title of this article proposed answering the question of whether collecting samples from the shelf contributes to shelf‐life knowledge. The answer is that it does, although with limitations. Depending on the product, it can provide sensory shelf‐life estimations or establish if current “best‐before” dates are adequate. Correlations between acceptability and objective sensory or instrumental data can help in understanding modes of deterioration.