Salivary proteomics in ingestive behaviour research: advances, potentialities and limitations

Lamy, Elsa

doi:10.5584/jiomics.v8i1.231

Cited by 2 publications

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“…Saliva study gained interest due to the potential of this fluid as a non-invasive and stress-free source of biomarkers for different pathologies and physiological conditions [8,9]. In the nutrition field of study, the relationship between saliva and diet starts now to be studied (e.g., [10]) but many aspects remain to be understood.…”

Section: Introductionmentioning

confidence: 99%

Salivary Protein Profile and Food Intake: A Dietary Pattern Analysis

Louro

Simões

Lima

et al. 2021

Journal of Nutrition and Metabolism

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Saliva research has gained interest due to its potential as a source of biomarkers. One of the factors inducing changes in saliva, in the short term, is food intake, and evidence exist about changes in salivary proteome induced by some food components. Since this topic of research is in its early stages, it was hypothesized that saliva protein composition could be associated with different levels of adherence to dietary patterns that contain higher amounts of plant products. The aim of the present study was to test this hypothesis, in adults, by comparing salivary protein electrophoretic profiles of individuals with different diet characteristics, particularly dietary patterns (DP) that exhibit different proportions of animal and plant-based products. Dietary habits were assessed in 122 adults (61 from each sex, with ages ranging from 20 to 59 years) using Food Frequency Questionnaires. To identify the dietary patterns, a principal component analysis was used. Individual’s non-stimulated saliva was evaluated for flow rate, pH, protein concentration, α-amylase activity, and electrophoretic protein profiles. Seven dietary patterns (DP) were identified. Salivary amylase enzymatic activity was positively associated with animal-based and starchy foods DP, and with plant-based fatty foods without wine DP. At the same time, protein bands containing amylase and type S cystatins were positively associated with the cheese/yoghurt and wine DP. Our results support the association of salivary proteomics and different dietary patterns and highlight the need of considering food consumption habits in studies using saliva, since this is a factor associated with variations in the composition of this fluid.

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Section: Introductionmentioning

confidence: 99%

Salivary Protein Profile and Food Intake: A Dietary Pattern Analysis

Louro

Simões

Lima

et al. 2021

Journal of Nutrition and Metabolism

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“…There is a lack of consensus in the scientific literature when defining the perceptual phenomenon and mechanisms driving astringency, however, the interactions between phenolics and proteins seem to be the most important driver (García-Estévez et al, 2018). It is important to note that there is great interindividual variability in astringency perception, which has been attributed to differences in saliva composition and buccal microbiota among consumers (Lamy, 2018). This variability in salivary proteome is related to differences in the sensory sensitivity for astringency (Lamy et al, 2017) and has shown to influence the acceptability of phenolrich foods (Dinnella et al, 2011;Masi et al, 2015).…”

Section: Introductionmentioning

confidence: 99%

Modelling wine astringency from its chemical composition using machine learning algorithms

Ferrero-del-Teso

Romero²,

Pascual³

et al. 2019

OENO One

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Aims: The present work aims to predict sensory astringency from wine chemical composition using machine learning algorithms.Material and results: Moristel grapes from different vineblocks and at different stages of ripening were collected. Eleven different wines were produced in 75 L tanks in triplicate, and further sensory factors were described by the rate-all-that-apply method with a trained panel of participants. The polyphenolic composition was characterised in wines by measuring the concentration and activity of tannins using UHPLC-UV/VIS, the mean degree of polymerisation (mDP. and the composition of tannins using thiolysis followed by UHPLC-MS. Conventional oenological parameters were analysed using FTIR and UV-Vis. Machine learning was applied to build models for predicting a wines astringency from its chemical composition. The best model was obtained using the support vector regressor (radial kernel) algorithm presenting a root-mean-square error (RMSE) value of 0.190.Conclusions: The main variables of the astringency model were the % of procyanidins constituting tannins and ethanol content, followed by other eight variables related to tannin structure and acidity.Significance of the study: These results increase the knowledge of chemical variables related to the perception of wine astringency and provide tools to control and optimise grape and wine production stages to modulate astringency and maximise quality and the consumer appeal of wines.

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Salivary proteomics in ingestive behaviour research: advances, potentialities and limitations

Cited by 2 publications

References 0 publications

Salivary Protein Profile and Food Intake: A Dietary Pattern Analysis

Salivary Protein Profile and Food Intake: A Dietary Pattern Analysis

Modelling wine astringency from its chemical composition using machine learning algorithms

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