Page 12 - Undergraduate Research Opportunities Program Proceedings 2017-18

School of Science
Department of Chemistry

Department of Chemistry

Construction and Application of Surface Enhanced Raman Spectrometer in Biomolecules Characterization

Supervisor: HUANG Jinqing / CHEM
Student: CHAN Yin Hei Brian / SSCI Course: UROP1000, Summer

An attempt to construct a Raman spectrometer to determine the experimental Raman spectrum of different samples.
Initially, the Raman spectrum of water and ethanol would be measured experimentally. The theoretical Raman spectrum
would also be measured to compare with the experimental spectrum. Further usage of the Raman spectrometer could
not be carried out since the spectrometer was found to be inaccurate. After the Raman spectrometer is improved, it can
be used for determination of the concentration of ethanol in beer sample and monitor the chemical change of cisplatin
and transplatin with DNA. Suggestions and the future prospect will be discussed.

Supervisor: HUANG Jinqing / CHEM
Student: LEE Yung Chi / CHEM Course: UROP1100, Summer

In the study, we will attempt to construct a Surface-enhanced Raman Spectrometer to characterize biomolecules and
propose protocols to improve the application of Surface enhanced Raman Spectroscopy (SERS) in biomolecules
detection and monitoring in real life. In the first part of the study, we have constructed a Raman spectrometer and
recorded Raman spectra of simple molecules with it. The theoretical Raman spectra of the molecules were calculated
using computational chemistry software and were compared with the actual spectra obtained. The methods used for
computation of Raman spectra and the design and possible improvements of the spectrometer will be discussed in this
progress report.

Fast evaluation of Sweetness and Sourness of Local Fruits Using Near Infrared Spectroscopy and Data Modeling

Supervisor: HUANG Jinqing / CHEM
Student: LEE Yung Chi / CHEM Course: UROP1100, Spring

In this study, we attempted to construct a model for non-destructive determination of the quality of oranges using near
infrared (NIR) spectrum of orange surfaces. Total sugar content, acidity and density were selected as the quality
attributes for comparison with data from NIR spectrum and were measured from 10 sample oranges bought from local
supermarkets. Near-infrared spectrum were obtained from the oranges. A combination of Principle Component Analysis
(PCA) and Support Vector Machine (SVM) were employed to analyse the spectra. Two other oranges bought from the
same supermarket were analysed using the model built to validate and evaluate the model. The model was found to be
unreliable and inaccurate. Possible reasons and improvements of the model is discussed.

Supervisor: HUANG Jinqing / CHEM
Student: XU Anqi / SSCI Course: UROP1000, Summer

This project is aimed to construct a model for testing the quality of blueberries using a non-destructive analytical
technique, Near Infrared Spectroscopy (NIRS). The sourness, the density, the condition of the white frost covered, the
brands and the freshness were chosen to be the contributes to the quality of blueberries. To analyse the data and set up
models for these quality-related properties and the NIR spectra measured, Principle Component Analysis (PCA), Support
Vector Machine (SVM) and Python programming based data visualization methods are used in this study. In total, 119

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