Quantification of extra virgin olive oil adulteration using smartphone videos

Weiran Song, Zhiyuan Song, Jordan Vincent, Hui Wang, Zhe Wang

Research output: Contribution to journalArticle

1 Citation (Scopus)

Abstract

Edible oil adulteration is a main concern for consumers. This paper presents a study on the use of smartphone, coupled with image processing and chemometrics, to quantify adulterant levels in extra virgin olive oil. A sequence of light with varying colours is generated on the phone screen, which is used to illuminate oil samples. Videos are recorded to capture the colour changes on sample surface and are subsequently converted into spectral data for analysis. To evaluate the performance of this video approach, partial least squares regression models constructed from such video data as well as near-infrared, ultraviolet-visible and digital imaging data are compared in the task of quantifying the level of vegetable oil in extra virgin olive oil in the range 5%−50% (v/v). The results show that the video approach (R2 = 0.98 and RMSE = 0.02) yields comparable performance to baseline spectroscopy techniques and outperforms computer vision system approach. Since the smartphone-based sensor system is low-cost and easy to operate, it has high potential to become a consumer-oriented solution for detecting edible oil adulteration.
Original languageEnglish
Article number120920
Number of pages14
JournalTalanta
Volume216
Early online date13 Mar 2020
DOIs
Publication statusPublished - 15 Aug 2020

Keywords

  • Chemometrics
  • Computer vision
  • NIR
  • Olive oil adulteration
  • Smartphone video
  • UV–Vis

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