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IVES 9 IVES Conference Series 9 NIR based sensometric approach for consumer preference evaluation

NIR based sensometric approach for consumer preference evaluation

Abstract

Climate change has had a global impact on grape production, and as a result, developing table grape varieties that can withstand climate-related threats has become a significant goal. However, it is equally important to ensure that these new grape varieties meet the preferences of consumers. To achieve this goal, a procedure has been developed that combines sensory analysis with spectroscopic data collected in the NIR region. Each sample was analyzed using both traditional analytical techniques and non-destructive NIR spectroscopy. The FT-NIR spectrophotometer used for this purpose is a TANGO (Bruker, Germany). The chemometric analyses were performed using the statistical software R version 4.1.2. The hedonic testing was performed using a 9-point hedonic scale which is the most widely used scale for measuring food acceptability. The NIR data sets were combined with the chemical, textural, and sensorial data to create multivariate models using interval partial least squares (iPLS) regressions or artificial neural networks (ANNs). The models produced in this way are applied to the spectra of samples that have undergone sensory analysis to predict their composition. This procedure enables non-destructive sensory analysis of new samples, as a single NIR spectrum is sufficient to quantify consumer appreciation and determine the chemical and physical characteristics of each berry. This information can then be used to identify the most suitable combinations for each target panel. Consumers could access this information via a QR code on the grape box, allowing them to select the perfect grape for their preferences.

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DOI:

Publication date: June 14, 2024

Issue: Open GPB 2024

Type: Poster

Authors

Teodora Basile1*, Lucia Rosaria Forleo1, Rocco Perniola1, Flavia Angela Maria Maggiolini1, Margherita D’Amico1, Carlo Bergamini1, Maria Francesca Cardone1

1 Research Centre for Viticulture and Enology, Council for Agricultural Research and Economics (CREA-VE), via Casamassima 148, 70010 Turi (BA), Italy

Contact the author*

Keywords

Vitis vinifera, NIR machine learning; prediction model, sensory analysis

Tags

IVES Conference Series | Open GPB | Open GPB 2024

Citation

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