Application of zNose™ for classification of enzymatically-macerated and steamed pumpkin using principal component analysis

• Main Authors: Shavakhi, Forough, Boo, Huey Chern, Osman, Azizah, Mohd Ghazali, Hasanah
• Format: Article
• Language: English; English
• Published: Faculty of Food Science and Technology, Universiti Putra Malaysia 2011
• Online Access: http://psasir.upm.edu.my/id/eprint/24037/1/24037.pdf; http://psasir.upm.edu.my/id/eprint/24037/; http://www.ifrj.upm.edu.my/volume-18-2011.html

High resolution olfactory images, called VaporPrintsTM, derived from the frequency of a surface acoustic wave (SAW) detector, are particularly useful to human because of their ability to recognize and differentiate visual images. In this study, the VaporPrintTM of fresh pumpkin (Cucurbita moschata) and different products of the pumpkin including steamed pumpkin and also pumpkin purees as affected by different enzymes (Pectinex® Ultra SP-L and Celluclast®; Novozyme, Denmark) were determined using an ultra-fast GC (zNoseTM) based on a SAW sensor. The zNose™ fingerprints served as a potential tool for qualitative and discriminative distinction of aroma between the different pumpkin products. Principal component analysis (PCA) was used to analyse the data. Based on the results, samples were categorized into three different groups. According to the score plot of PC 2 (second component) versus PC 1 (first component), aromas of enzymatically macerated pumpkin were close together. The PC 1 and PC 2 factors resulted in the model that describe the 82.9% of the total variance and seemed sufficient to define a good model.