Application of Laser-Induced Breakdown Spectroscopy (LIBS) and Neural Networks to Olive Oils Analysis

Abstract

The adulteration and traceability of olive oils are serious problems in the olive oil industry. In this work, a method based on laser-induced breakdown spectroscopy (LIBS) and neural networks (NNs) has been developed and applied to the identification, quality control, traceability, and adulteration detection of extra virgin olive oils. Instant identification of the samples is achieved using a spectral library, which was obtained by analysis of representative samples using a single laser pulse and treatment by NNs. The samples used in this study belong to four countries. The study also included different regions of each country. The results obtained allow the identification of the oils tested with a certainty of more than 95%. Single-shot measurements were enough for clear identification of the samples. The method can be developed for automatic real-time, fast, reliable, and robust measurements, and the system can be packed into portable form for non-specialist users.

Multivariate calibration in Laser-Induced Breakdown Spectroscopy quantitative analysis: The dangers of a ‘black box’ approach and how to avoid them

“…Another multivariate calibration approach that has become popular in LIBS quantitative analysis is based on the use of Artificial Neural Networks (ANN) [19][20][21][22][23]. The ANN approach is an extremely powerful algorithm; however, as for many other statistical approaches, the structure of the network has to be carefully considered for avoiding issues that might compromise the predictive capabilities of the method.…”

Research and Application of Quality Detection Method for Corn Oil Based on Neural Network Algorithm

“…Prediction model in near-infrared analysis mainly solve the problems in two aspects: the first one is that the relationship between near infrared spectral absorption of the sample and its composition doesn't always conform to "Lambert-Beer" law [6]; the second one is that the non-linear relationship is presented between measured material composition itself and component concentration, when the qualitative change range of a sample is wider, the nonlinear response is more obvious. Spectroscopic data and chemical values of the sample shall be related including MLR, PCR and PLS etc.…”

A likelihood ratio model for the determination of the geographical origin of olive oil

“…Among the many analytical techniques used over past years, the following are mentioned as techniques used to detect olive oil adulteration: high-performance liquid chromatography [3,4], Fourier transform infrared spectroscopy [5], micro-ultraviolet spectroscopy [6], total synchronous fluorescence spectroscopy [7], diffuse-light absorption spectroscopy [8] laser-induced breakdown spectroscopy [9] and electronic nose [10]. These techniques have been supported by partial least squares regression [3,5,7], principal component regression [5], discriminant analysis [5,6,8], principal component analysis [3], [4,6,8], cluster analysis [4,6], soft independent modeling of class analogy [10], neural networks [9] and other chemometric methods in order to extract more information about the geographical designation of olive oils.…”