![Table 3. Descriptive statistics for the variables of sucrose, glucose, fructose, and total sugars (g kg! FW) of the 84 cultivars studied. For each compound, minimum, maximum, and mean values and standard deviation (SD) are reported. In our study, sucrose was present with highest quantities (mean 67.22 g kg~!), followed by lower levels of fructose and glucose (8.06 and 8.79 g kg~!, respectively), confirming the results obtained by other authors [45-48]. Sugar alcohol sorbitol, a very important translocated sugar, showed a concentration between 0.70 and 4.44 g kg! with an average concentration of 1.90 gkg~!. The total amount of sugars ranged from 60.11 to 115.29 kg! FW, with an average concentration of 85.96 g ke~! FW.](https://figures.academia-assets.com/107044653/table_004.jpg)
![Figure 4. Box plots for total carotenoid contents (mg gt) of yellow- and white-flesh fruits. The central line displays the median, the bottom and top of the box are the first and third quartiles, respectively In white-fleshed nectarines, the average concentration of total carotenoids was 3.43 mg g~!; the Maria Anna cultivar exhibited the highest value (6.71 mg g~!). The total carotenoid amount found in the extracts of yellow-flesh peaches was in a large range of 3.03-16.65 mg et The cultivars with the highest content were ‘Maria Silvia’, ‘Rich Lady’, and ‘Vistarich’. Among the yellow-fleshed nectarines, Licinia and Vega cvs showed the highest contents, 12.38 and 16.65 mg a, respectively; in contrast, Guglielmina, Lizbeth, and Symphonie cvs recorded the lowest carotenoid contents (Table 6). In this study, the mean concentration of carotenoids, found in yellow-flesh types (peaches and nectarines), almost doubled that observed in the white-fleshed fruits (Figure 4), confirming results previously reported by other authors [24,40].](https://figures.academia-assets.com/107044653/figure_004.jpg)
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Key research themes
1. How do chemical compositions and molecular interactions shape flavor perception and consumer liking in natural food products?
This research area investigates the complex relationships between specific chemical compounds (e.g., sugars, acids, aroma volatiles, metabolites) and the sensory experience of flavor in natural foods. Understanding these interactions is critical for predicting consumer liking and optimizing natural product formulations, as chemical presence alone is insufficient to determine flavor preferences. This theme employs integrative metabolomics, sensory evaluation, and statistical modeling approaches to dissect how molecular constituents contribute directly and through interactions (e.g., taste-olfaction interplay) to flavor intensity and hedonic response.
Key finding: Using targeted metabolomics on 278 tomato samples across 152 heirloom varieties, this study revealed immense chemical diversity, up to 3,000-fold in volatile concentrations, and demonstrated that abundant volatiles alone do... Read more
Key finding: By integrating LC-MS and GC-MS metabolomic profiles with trained sensory panel data across 27 tomato soup formulations, this paper identified key metabolites—such as dipeptides and sulfur-containing amino acid... Read more
Key finding: Combining sensory-guided fractionation and multivariate flavoromics on 18 Baijiu liquors of varying sweetness intensities identified 43 volatile esters contributing to sweetness perception. Multivariate analysis prioritized... Read more
Key finding: Through sensory characterization and multivariate regression, this study linked molecular features—such as the number of ketones, aromatic rings, double bonds, and calculated logP—to temporal sensory profiles (onset,... Read more
2. Can odour-taste interactions be leveraged to enhance specific taste perceptions, such as sweetness or saltiness, in food formulations?
This research theme focuses on the bidirectional modulation between olfactory stimuli (odours/aromas) and gustatory perception (taste qualities like sweetness and saltiness). It explores how congruent or associated aromas can alter the intensity and quality of tastes without changing chemical concentrations of taste compounds. Understanding these cross-modal sensory interactions can guide formulation of foods with reduced sugar or salt content while maintaining or enhancing perceived taste, aiding products aligned with health guidelines.
Key finding: Using a two-stage consumer study, this investigation demonstrated that odours semantically associated with salty foods can induce and enhance saltiness perceptions in aqueous solutions containing low sodium chloride. In... Read more
Key finding: This controlled study showed that inhalation of fruity (pineapple, banana) and confection-associated aromas (vanilla, caramel/malty) alongside aqueous mixtures of propylene glycol and vegetable glycerine significantly... Read more
3. How do multisensory interactions among aroma, taste, and texture influence flavor perception and food acceptability?
This research area examines complex bidirectional interactions between aroma, taste, and texture within food matrices, and their combined effects on perceived flavor and consumer acceptance. Investigations include psychophysical studies, sensory descriptive analyses, and cognitive neuroscience approaches to elucidate both physico-chemical mechanisms (e.g., flavor release modulation, partitioning) and learned cognitive associations. Insight into such interactions enables improved food product design that optimizes sensory integration for enhanced palatability.
Key finding: This comprehensive review highlights that flavor perception arises from complex integration of olfactory, gustatory, and tactile cues, with multisensory interactions mediated by physico-chemical mechanisms such as flavor... Read more
4. How can computational and network science approaches facilitate flavor compound discovery and food recipe design?
This theme explores the application of data-driven and computational methods including machine learning, flavor network analysis, and reinforcement learning to accelerate identification of novel flavor-active molecules and to predict ingredient compatibility for innovative food recipes. These approaches harness big data and molecular descriptors to model flavor perception and ingredient pairing, facilitating rapid, rational design in flavor science and culinary innovation while reducing reliance on traditional trial-and-error experimentation.
Key finding: This study developed a novel integrated scientific machine learning framework combining generative models and reinforcement learning to design new flavor molecules with high synthetic accessibility, natural product likeness,... Read more
Key finding: Applying flavor network analysis based on shared chemical flavor compounds, this study designed a novel dessert recipe comprising rice, milk, bean, and figs, ingredients with high shared flavor compound counts. Eight flavor... Read more