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…

VviSOC1a and VviAG1 act antagonistically in the regulation of flower formation

The SUPPRESSOR OF OVEREXPRESSION OF CONSTANS1 (SOC1) is a key floral activator that coordinates external and internal stimuli to ensure timely flowering. During early stages of flower formation, SOC1 represses floral organ identity genes such as AGAMOUS (AG) to prevent premature organ differentiation. In addition to floral organ specification, AG has been shown to regulate fleshy fruit expansion and ripening and, as such, is an important contributor to fruit quality traits. Currently, little is known about the function and gene regulatory network of the grapevine homologs VviSOC1a and VviAG1. As such, the aim of this study was to functionally characterise both genes by overexpressing them in tomato and performing phenotypic and gene expression studies.

Effect of drought on grapevine wood fungal pathogen communities using a metatranscriptomics approach

Crops are facing increasing biotic and abiotic stress pressures due to global changes. However, trade-off mechanisms between these stresses and the underlying physiological processes are still poorly understood, especially in perennial crop species. To better understand these trade-offs, we studied the effect of drought on grapevine (Vitis vinifera) physiology and esca-related wood fungal communities. Esca is a vascular disease caused by a community of wood-infecting pathogenic fungi, and characterized by trunk necrosis, leaf scorch symptoms, yield losses, and mortality.

Strategies for sample preparation and data handling in GC-MS wine applications

It is often said that wine is a complex matrix and the chemical analysis of wine with the thousands of compounds detected and often measured is proof. New technologies can assist not only in separating and identifying wine compounds, but also in providing information about the sample as a whole. Information-rich techniques can offer a fingerprint of a sample (untargeted analysis), a comprehensive view of its chemical composition. Applying statistical analysis directly to the raw data can significantly reduce the number of compounds to be identified to the ones relevant to a particular scientific question. More data can equal more information, but also more noise for the subsequent statistical handling.

Application of cyclic voltammetry to the classification of enological tannins in relationship to oxygen consumption rate and botanical origin 

Enological tannins are a diversified group of winemaking products that vary in several aspects such as chemical composition, botanical origin, and production method. In consideration of their richness in phenolic compounds, one of their main application in vinification is related to their antioxidant capacity, in particular their ability to consume oxygen during red wine maturation.

Metabolomics screening of Vitis sp. interspecific hybrids to select natural ingredients with cosmetic purposes

Introducing natural ingredients using green chemistry practices is a major challenge in cosmetics industry to follow the market trend. Among the plants of cosmetic interest, vine products show a remarkable diversity of natural substances with high potential for the cosmetic and dermatological sectors. To date, research focuses on well-known compounds like E-resveratrol and E-ε-viniferin,