Evaluation of olive oil quality using a miniature spectrometer: a machine learning approach

Fayas Asharindavida; Omar Nibouche; James Uhomoibhi; Hui Wang; Jordan Vincent

doi:10.1117/12.2587878

12 April 2021 Evaluation of olive oil quality using a miniature spectrometer: a machine learning approach

Fayas Asharindavida, Omar Nibouche, James Uhomoibhi, Hui Wang, Jordan Vincent

Proceedings Volume 11754, Sensing for Agriculture and Food Quality and Safety XIII; 1175405 (2021) https://doi.org/10.1117/12.2587878
Event: SPIE Defense + Commercial Sensing, 2021, Online Only

Abstract

Food authentication and quality checks can be carried out by applying machine learning algorithms on spectral data acquired from miniature spectrometers. This is a very appealing solution as the cost-effectiveness of miniature spectrometers extends the range of consumer electronics available for ordinary citizens in the fight against food fraud, widens the range of their applications and shortens the processing time for any in-situ scenario. In this paper, a study of olive oil purity and quality check feasibility carried out on spectral data acquired from a miniature spectrometer is presented. The aim is to gauge the ability of such a device to differentiate between pure olive oil from ones adulterated with vegetable oils on a relatively large dataset. The paper presents a pipeline encompassing various steps including data pre-processing, dimension reduction, classification, and regression analysis. That is, data collected from miniature spectrometers can be of low quality and exhibit distortions, high dimensionality, and collinearity. Hence, various filtering techniques including wavelets analysis, and normalisation algorithms including multiplicative scatter correction are used for pre-processing. Once the dimensionality is reduced using PCA, classical machine learning classification and regression analysis algorithms are deployed as part of a quality evaluation pipeline. This includes SVM, LDA, KNN, Random Forest and PLS. The obtained results show that very high rates of up to 98% can be achieved. An important consequence is that cost-effective miniature spectrometers augmented with a suitable machine learning component can attain comparable results obtained using non-portable and more expensive spectrometers.

Conference Presentation

Citation Download Citation

Fayas Asharindavida, Omar Nibouche, James Uhomoibhi, Hui Wang, and Jordan Vincent "Evaluation of olive oil quality using a miniature spectrometer: a machine learning approach", Proc. SPIE 11754, Sensing for Agriculture and Food Quality and Safety XIII, 1175405 (12 April 2021); https://doi.org/10.1117/12.2587878

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available