Development and application of assay for determining β-glucosidase activity in human saliva
Bibliographic record
Abstract
Background β-glucosidase is an enzyme important to flavour enhancement. It hydrolyzes glucosides to release aglycones—aroma precursors that are bound to a sugar molecule—thereby making them available to contribute to the flavour of foods and beverages. While there is strong interest within the food and beverage industry to optimizing flavour through the use of exogenous and endogenous glucosidase in production, little is known regarding the possible occurrence of these enzymes within the human oral cavity. This could be an important source of flavour release and/or account for some differences between individuals in flavour perception. In the present study, we determined whether β-glucosidase is present in human saliva. First, an existing spectrophotometric assay that uses p-nitrophenyl-β-O-d-glucopyranoside as a substrate was modified and optimized for use in human saliva. The following variables were evaluated and where necessary, optimized: linearity of the assay signal, possible matrix interference, the effect of heat inactivation of the saliva, absorbance wavelength maxima, substrate saturation concentration, maximum saliva volume and the inclusion of α-cyclodextrin. The modified assay was then used to screen for β-glucosidase activity in the saliva of 20 individuals. Of the 20 samples analyzed, four were tentatively identified as containing active β-glucosidase and were further investigated. Results Significant differences (p < 0.05) in absorbance values (A400nm) between these saliva samples confirm low levels of β-glucosidase activity in approximately 20% of the population sampled. Conclusions Inter-individual variability exists in β-glucosidase activity within the oral cavity. The described method can be applied to rapidly assay a large population of individuals, and further elucidate the extent and significance of salivary β-glucosidase activity within the context of human flavour perception and enhancement.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".