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Measuring water residue in olive oil by means of a smartphone-connected pocket spectrometer and artificial intelligence
Author(s): L. Ciaccheri; B. Adinolfi; A. A. Mencaglia; C. Pellegrini Strozzi; A. G. Mignani
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Paper Abstract

SCiO is a smartphone-connected pocket spectrometer operating in the 700-1100 nm band. Together with a learning machine algorithm, it already demonstrated the effectiveness for distinguishing extra virgin from non extra virgin olive oils and for the multi-analysis of nutraceutical indicators. This paper shows a new experiment for the assessment of water residue at the end of the olive oil production process. Principal Component Analysis and Linear Discriminant Analysis were used to demonstrate a qualitative screening with a threshold of 0.5% v/v of water content and an accuracy of 93%. Also, a model for predicting the water concentration was created by means of the Partial Least Square regression, providing a regression coefficient R2 =0.92, and an error of 0.26%.

Paper Details

Date Published: 9 September 2019
PDF: 4 pages
Proc. SPIE 11199, Seventh European Workshop on Optical Fibre Sensors, 111993H (9 September 2019); doi: 10.1117/12.2540041
Show Author Affiliations
L. Ciaccheri, Istituto di Fisica Applicata "Nello Carrara" (Italy)
B. Adinolfi, Istituto di Fisica Applicata "Nello Carrara" (Italy)
A. A. Mencaglia, Istituto di Fisica Applicata "Nello Carrara" (Italy)
C. Pellegrini Strozzi, Fattoria Castel Ruggero Pellegrini (Italy)
A. G. Mignani, Istituto di Fisica Applicata "Nello Carrara" (Italy)


Published in SPIE Proceedings Vol. 11199:
Seventh European Workshop on Optical Fibre Sensors
Kyriacos Kalli; Sinead O. O'Keeffe; Gilberto Brambilla, Editor(s)

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