terclim by ICS banner
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.

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

Related articles…

Pro-active management of grapevine trunk diseases by means of sanitation in nurseries

Several trunk diseases cause decline and premature dieback of grapevines. In vineyards, these pathogens gain entry into plants through unprotected wounds. Wounds are also frequently infected during the propagation stages. The pathogens survive in infected plants in a latent form and cause disease in older grapevines or in plants that are

WHITE WINES OXIDATIVE STABILITY: A 2-VINTAGE STUDY OF CHARDONNAY CHAMPAGNE BASE WINES AGED ON LEES IN BARRELS

Ultra-premium champagne wines are characterized by a long stay on laths. The goal of the winemaker is to use all possible oenological techniques to keep the aromatic freshness of the future products. To that purpose, some champagne base wines can be aged on lees in oak barrels. However, if it is now acknowledged that such ageing practices contribute to the oxidative stability of dry white wines, no study has been done on Chardonnay champagne base wines designed for a long ageing on laths [1].

Effects of mechanical leafing and deficit irrigation on Cabernet Sauvignon grown in warm climate of California

San Joaquin Valley accounts for 40% of wine grape acreage and produces 70% of wine grape in California. Fruit quality is one of most important factors which impact the economical sustainability of farming wine grapes in this region. Due to the recent drought and expected labor cost increase, the wine industry is thrilled to understand how to improve fruit quality while maintaining the yield with less water and labor input. The present study aims to study the interactive effects of mechanical leafing and deficit irrigation on yield and berry compositions of Cabernet Sauvignon grown in warm climate of California.

Assessing bunch architecture for grapevine yield forecasting by image analysis

It is fundamental for wineries to know the potential yield of their vineyards as soon as possible for future planning of winery logistics. As such, non-invasive image-based methods are being investigated for early yield prediction. Many of these techniques have limitations that make it difficult to implement for practical use commercially. The aim of this study was to assess whether yield can be estimated using images taken in-field with a smartphone at different phenological stages. The accuracy of the method for predicting bunch weight at different phenological stages was assessed for seven different varieties.

Screening of different commercial wine yeast strains: the effect of sugar and copper additions on fermentation and volatile acidity production

The aims of this study were to examine the effect of high sugar concentrations of must and copper residues on different commercial wine yeasts. Copper originating from pesticides has been known to inhibit yeast, but it’s effect on fermentation performance and VA production of different yeast strains had not been investigated in detail.