Publications by authors named "Bhavna Parmar"
Article Synopsis
- - The study focused on developing a non-invasive method using through-container spatial offset Raman spectroscopy (SORS) and machine learning techniques to detect exogenous sugar adulteration in UK honeys, which is usually complex and expensive to analyze.
- - The researchers tested 17 types of natural honeys, spiked with different concentrations of rice and sugar beet syrups, and found that the Random Forest algorithm was the most accurate, misclassifying only 1% of pure samples and under 3.5% of adulterated samples.
- - Additionally, SORS successfully differentiated between pure and adulterated heather honey with high accuracy, showing potential for rapid and effective honey authentication and sugar detection using this innovative technique.
Rationale And Objectives: There are several rational and empirical methods for the measurement of dietary fibre and its components. A selection of these methods were evaluated by investigation of a range of real foods and model foods with added resistant starch (RS), non-starch polysaccharides (NSP) and resistant oligosaccharide (RO) ingredients.
Methods: A range of rational methods were applied in determining specific carbohydrate constituents: RS, NSP and RO, including fructans.
The problem of allergen analysis using ELISA kits from different commercial products giving significantly different results is widely acknowledged. The effect on proficiency testing results is that different assigned values have to be generated for the different kits used. Some experimental Food Analysis Performance Assessment Scheme (FAPAS) proficiency tests aimed to establish whether the use of a standardised calibrant could be used to normalise the complete data set without recourse to differentiation.
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