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Evaluation of Portable Visible/Near-Infrared Spectroscopic Technique Combined with Multivariate Classification and Regression Methods for Honey Authentication

Fallah Kalamsari, Zeinab | 2023

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  1. Type of Document: M.Sc. Thesis
  2. Language: Farsi
  3. Document No: 56806 (03)
  4. University: Sharif University of Technology
  5. Department: Chemistry
  6. Advisor(s): Parastar Shahri, Hadi
  7. Abstract:
  8. Our lives are redefined by the constant emergence of new products that are easier, smarter and faster to use than their predecessors. Enabling this evolution are advances in miniaturization that have produced smaller mechanical, optical, and electronic products and devices. In the field of analytical chemistry, portable and handheld devices are now commercially available for a wide range of spectroscopic techniques. One of the most important applications of portable spectroscopic devices is in determining the authenticity of food. Nowadays, the development of a low-cost, fast and non-destructive method that can be implemented in the entire food supply chain has become a research field of interest to researchers. Among different food items, honey is a vital product in the food market and is known as a natural sweetener that can be used alone or as a component in a wide range of food products. Therefore, the authenticity of honey is an important global problem for consumers as well as producers. The aim of this project is to investigate the effectiveness of visible and infrared spectrometers in the wavelength range of 400-1000 nm to determine the authenticity of honey. In this regard, 19 genuine honey samples belonging to different geographical locations were collected and then the ability of the developed method to identify three common adulterants including: glucose syrup, sugar syrup and the combination of these two syrups was evaluated. The performance of the model developed with the help of data driven soft independent modelling of Class Analogies (DD-SIMCA) in terms of sensitivity in glucose syrup, sugar syrup and mixture of glucose syrup and sugar syrup is equal to was equals to 95.65%, 97.10% and 97.10% respectively. Also, the specificity values in all said frauds were reported as 100%. In the next step, partial least squares-discriminant analysis (PLS-DA) modeling was done to determine the percentage of adulteration in two and four categories and acceptable results were obtained. For example, the values of accuracy in binary classification for frauds of glucose syrup, sugar syrup and their combination were calculated as 94.25%, 100% and 95.5% respectively
  9. Keywords:
  10. Chemometrics Method ; Food Authenticity ; Honey ; Machine Learning ; Portable Spectrometry ; Multivariate Methods

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