• Title of article

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

  • Author/Authors

    Shavakhi, F. Universiti Putra Malaysia - Faculty of Food Science and Technology - Department of Food Science, Malaysia , Boo, H.C. Universiti Putra Malaysia - Faculty of Food Science and Technology - Department of Food Service and Management, Malaysia , Osman, A. Universiti Putra Malaysia - Faculty of Food Science and Technology - Department of Food Science, Malaysia , Ghazali, H.M. Universiti Putra Malaysia - Faculty of Food Science and Technology - Department of Food Science, Malaysia

  • From page
    311
  • To page
    318
  • Abstract
    High resolution olfactory images, called VaporPrints™, 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 VaporPrint™ 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 (zNose™) 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.
  • Keywords
    zNose™ , vapor print™ , pumpkin , principal component analysis , enzyme
  • Journal title
    International Food Research Journal
  • Journal title
    International Food Research Journal
  • Record number

    2559849