QCM-based sensor for the determination of mango ripeness

• Main Author: Fathi, Nashrullah
• Format: Thesis
• Language: English
• Published: Universiti Malaysia Perlis (UniMAP) 2014
• Subjects: Quartz Crystal Microbalance (QCM); Ripeness sensor; Fruit ripeness; Ripeness detectors; QCM sensor device
• Online Access: http://dspace.unimap.edu.my:80/dspace/handle/123456789/31297

This thesis presents a new method for fruit maturity stage determination using electronic nose based on Quartz Crystal Microbalance (QCM) which is well known for gas sensing. To allow the QCM selectively detect volatiles, a thin sensing layer has to be coated on its surface with several polymers using spin coating technique. These polymers have overlapping selectivity that interacts with analytes (samples). By combining different kinds of sensing layers for array QCM, each QCM has some response to the samples to give samples profile instead of selectively detect each component from the samples. This QCM characteristic is used to determine fruit maturity level. A climacteric fruit release an elevated amount of volatiles during ripening. These volatiles are contained and accumulated in a container before being exposed to the array of QCMs. The volatiles will be adsorbed at the sensing layer of the QCMs causing a slight mass change and thus a shift in its resonant frequency. From the value of this shift, the mass of the adsorbed materials could be determined. The amount of adsorbed volatiles depend on its molecular interaction with the sensing layer and are directly correlated to the fruit maturity level. The study is divided into three parts. The first one is the development of the QCM sensor device. This development covers the QCM sensor preparation; that is selecting suitable sensing materials, and design and development of electronic circuitry. Secondly is the data collection. Raja and the Namdokmai cultivars of mango were chosen. The fruits ripening process are observed for 6 days. Finally is the data analysis. In this part, the data from the observation are analyzed by using simple plot and multivariate method. However, simple plot cannot distinguish daily basis datasets completely. By applying multivariate analyses namely PCA and LDA, the datasets from different day of monitoring are discriminated properly. However, the QCM sensor alone is somewhat inadequate for a proper discrimination of the maturity levels. By implementing data fusion with acoustic firmness sensor, better results have been obtained.