2024
DOI: 10.1016/j.phrs.2024.107211
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A Pharmacological perspective on the temporal properties of sweeteners

Guy Servant,
Terry Kenakin
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Cited by 1 publication
(3 citation statements)
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“…Both the onset and lingering metrics originate from a complex process of taste perception in which the molecular basis that governs these taste attributes is not fully explained yet. 21,38 We propose here to match the signaling behavior of sweet receptor expressing cells in vitro to this complex system of taste perception based on the analogies in metrics that we observed. Machine learning algorithms may be developed to more closely match the results from taste panels with the tongue-on-a-chip, but whether it would or could hold in all cases needs to be explored further.…”
Section: ■ Resultsmentioning
confidence: 99%
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“…Both the onset and lingering metrics originate from a complex process of taste perception in which the molecular basis that governs these taste attributes is not fully explained yet. 21,38 We propose here to match the signaling behavior of sweet receptor expressing cells in vitro to this complex system of taste perception based on the analogies in metrics that we observed. Machine learning algorithms may be developed to more closely match the results from taste panels with the tongue-on-a-chip, but whether it would or could hold in all cases needs to be explored further.…”
Section: ■ Resultsmentioning
confidence: 99%
“…The combination of a microfluidic assay with an array of sweet and bitter taste receptors was shown to emulate the functionality of a tongue-on-a-chip, generating sensory parameters analogous to taste panels including those for off-taste, taste threshold, onset, lingering, and blocking interactions of pure and mixed tastant samples. However, while there is not an exact similarity with panel ratings, the tongue-on-a-chip kinetic parameters could explain, at least in part, what panels rate based on conventional methods . Larger data sets are needed to assess the benefits and limitations of the platform and to evaluate the power of machine learning for more accurate predictions.…”
Section: Discussionmentioning
confidence: 99%
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