Remo Simonetti

Review: Near infrared spectroscopy for analysing olive oils

Over the past 30 years, there has been an emphasis on the quality, safety and nutritional proprieties of food products and thus an increasing interest by food producers to reveal frauds and to assure consumers regarding the quality and provenance of their products. In particular, olive oil has become more popular as a healthy food in contrast with the rapid decrease in world demand for animal fats in all their forms. In parallel, near infrared (NIR) spectroscopy has emerged in the food sector as a rapid and very useful tool for such purposes as quality control, process monitoring and estimating shelf-life. Despite its widespread use in the food sector, NIR spectroscopy is not commonly applied online in the olive oil production process: the challenge for the future is the development of dedicated analytical systems and oil analysers that can be used directly and simply in olive mills in order to guarantee the quality and authenticity of olive oil, following its production from the implantation of the tree to the bottling of the oil. This review summarises findings over the last two decades in which NIR spectroscopy was used for analysing olive oils.

Artificial nose, NIR and UV-visible spectroscopy for the characterisation of the PDO Chianti Classico olive oil

An authentication study of the Italian PDO (Protected Designation of Origin) olive oil Chianti Classico, based on artificial nose, near-infrared and UV-visible spectroscopy, with a set of samples representative of the whole Chianti Classico production area and a considerable number of samples from other Italian PDO regions was performed. The signals provided by the three analytical techniques were used both individually and jointly, after fusion of the respective variables, in order to build a model for the Chianti Classico PDO olive oil. Different signal pre-treatments were performed in order to investigate their importance and their effects in enhancing and extracting information from experimental data, correcting backgrounds or removing baseline variations. Stepwise-Linear Discriminant Analysis (STEP-LDA) was used as a feature selection technique and, afterward, Linear Discriminant Analysis (LDA) and the class-modelling technique Quadratic Discriminant Analysis-UNEQual dispersed classes (QDA-UNEQ) were applied to sub-sets of selected variables, in order to obtain efficient models capable of characterising the extra virgin olive oils produced in the Chianti Classico PDO area.

Chemometrics for Food Authenticity Applications

Abstract Verification of food authenticity is a complex issue that generally involves the determination of many physical or chemical properties. The best way to get useful information from such data is to process them by multivariate pattern recognition methods, which allow consideration of intercorrelations among variables—a key issue for an accurate characterization of any system under study. In fact, multivariate pattern recognition approaches are usually required to obtain efficient models. Pattern recognition methods can be used for different purposes but mainly for data exploration and the development of predictive models. The output of these models can be qualitative or quantitative. A proper validation of predictive models is always required to provide reliable predictions.

A novel electroanalytical approach based on the use of a room temperature ionic liquid for the determination of olive oil acidity

In this paper, a novel voltammetric/amperometric approach for the direct determination of free acidity (FFA, expressed as mass percentage of free oleic acid) in olive oil samples is presented. The method is based on the reduction processes occurring at a platinum microdisk electrode involving the free fatty acids present in the matrices. To overcome problems related to the low conductivity of the samples investigated, olive oils were mixed with suitable amounts of the room temperature ionic liquid (RTIL), tri-hexyl(tetradecyl)phosphonium bis (trifluoromethylsulfonyl) imide ([P14,6,6,6]+[NTf2]-), which acted as a supporting electrolyte. Conditions for a reliable quantification of the acids were preliminarily investigated by performing voltammetric and chronoamperometric measurements in RTIL solutions containing oleic acid at different concentrations. Oleic acid (OA) was chosen as a model compound as it is the main component of the FFA content in olive oils. In order to establish the effect of oxygen on the electroanalytical responses, the reduction process of OA was investigated under both deoxygenated and oxygenated conditions. It was found that, in both situations, the current arising from the electrode process of OA depended linearly on the OA concentration over a wide range varying from 0.1% to 8% OA (w/w). This range includes FFA values which can be found on all categories of commercially available oil samples, including extra-virgin, virgin and lampante oils. Voltammetric and chronoamperometric experiments were also performed in oil/RTIL samples artificially acidified (extra-virgin olive oils with known addition of oleic acid) and in natural olive oils from some commercial categories. The results obtained indicated that the electrochemical procedure developed was satisfactory in terms of both sensitivity and detection limits. The reliability of the proposed approach for the detection of FFA was finally assessed by comparison of the voltammetric/chronoamperometric values with those obtained by the official method for quantification of olive oil acidity, which is an acid/base volumetric titration.

The Impact of Signal Pre-Processing on the Final Interpretation of Analytical Outcomes – A tutorial

The present tutorial paper is aimed to analyse and critically discuss the consequences of row pre-processing (conversion of measurement units, derivatives, and standard normal variate transform) on the evaluation of final outcomes of chemometric data analysis. An in-depth focus on pre-processing effects both on the signal shape and on misinterpretation of results - a crucial and disregarded issue in the analytical field - is presented. It is shown how this preliminary step of data processing may lead, in many cases, to draw incongruous conclusions, not actually based on real information embodied within data, but on artefacts arising from the mathematical transforms. This tutorial is not limited to a description of the problem, it also introduces strategies and tools for overcoming such unwanted effects, allowing a direct interpretation of the importance of original variables to be performed, explaining the chemical information that actually characterises samples. The dangerous implications of row pre-processing on instrumental signals is demonstrated on real datasets coming from different analytical techniques: transmission and attenuated total reflection infrared spectroscopy, cyclic voltammetry, X-ray fluorescence spectroscopy, Raman spectroscopy, and ultraviolet-visible spectroscopy. Hence, the impact of this widespread problem in most of the branches of analytical chemistry is illustrated.