Monica Casale

Characterisation of PDO olive oil Chianti Classico by non-selective (UV–visible, NIR and MIR spectroscopy) and selective (fatty acid composition) analytical techniques

An authentication study of the Italian PDO (protected designation of origin) extra virgin olive oil Chianti Classico was performed; UV-visible (UV-vis), Near-Infrared (NIR) and Mid-Infrared (MIR) spectroscopies were applied to a set of samples representative of the whole Chianti Classico production area. The non-selective signals (fingerprints) provided by the three spectroscopic techniques were utilised both individually and jointly, after fusion of the respective profile vectors, in order to build a model for the Chianti Classico PDO olive oil. Moreover, these results were compared with those obtained by the gas chromatographic determination of the fatty acids composition. In order to characterise the olive oils produced in the Chianti Classico PDO area, UNEQ (unequal class models) and SIMCA (soft independent modelling of class analogy) were employed both on the MIR, NIR and UV-vis spectra, individually and jointly, and on the fatty acid composition. Finally, PLS (partial least square) regression was applied on the UV-vis, NIR and MIR spectra, in order to predict the content of oleic and linoleic acids in the extra virgin olive oils. UNEQ, SIMCA and PLS were performed after selection of the relevant predictors, in order to increase the efficiency of both classification and regression models. The non-selective information obtained from UV-vis, NIR and MIR spectroscopy allowed to build reliable models for checking the authenticity of the Italian PDO extra virgin olive oil Chianti Classico.

Chemometrical strategies for feature selection and data compression applied to NIR and MIR spectra of extra virgin olive oils for cultivar identification.

The possibility provided by Chemometrics to extract and combine (fusion) information contained in NIR and MIR spectra in order to discriminate monovarietal extra virgin olive oils according to olive cultivar (Casaliva, Leccino, Frantoio) has been investigated. Linear discriminant analysis (LDA) was applied as a classification technique on these multivariate and non-specific spectral data both separately and jointly (NIR and MIR data together). In order to ensure a more appropriate ratio between the number of objects (samples) and number of variables (absorbance at different wavenumbers), LDA was preceded either by feature selection or variable compression. For feature selection, the SELECT algorithm was used while a wavelet transform was applied for data compression. Correct classification rates obtained by cross-validation varied between 60% and 90% depending on the followed procedure. Most accurate results were obtained using the fused NIR and MIR data, with either feature selection or data compression. Chemometrical strategies applied to fused NIR and MIR spectra represent an effective method for classification of extra virgin olive oils on the basis of the olive cultivar.

Near infrared spectroscopy and class modelling techniques for the geographical authentication of Ligurian extra virgin olive oil

An authentic food is one which is what it purports to be. Food processors and consumers need to be assured that when they pay for a specific product, they are receiving exactly what they pay for. In this paper, a particular food authenticity study is considered: the classification of extra virgin olive oils from Liguria, a region in northern Italy, according to their geographical origin. One hundred and ninety five olive oil samples were analysed using a near infrared (NIR) instrument and the recorded spectra were used to build a class model for Ligurian olive oil. Different class modelling techniques were used, i.e. potential functions techniques (POTFUN), soft independent modelling of class analogy (SIMCA), unequal-quadratic discriminant analysis (UNEQ-QDA) and multivariate range modelling (MRM). In order to remove systematic variation in experimental data such as base-line and multiplicative scatter effects, an evaluation of different data pre-processing methods was performed. Ligurian olive oil was clearly differentiated from the other oils and the multivariate analysis allowed the construction of Liguria class models with good predictive ability, high sensitivity and sufficient specificity. The results obtained suggest that NIR and chemometrics are useful tools in the geographic traceability of olive oil.

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.

Comparison between classical and innovative class-modelling techniques for the characterisation of a PDO olive oil

An authentication study of the Italian PDO (protected designation of origin) olive oil Chianti Classico, based on near-infrared and UV–Visible spectroscopy, an artificial nose and an artificial tongue, with a set of samples representative of the whole Chianti Classico production and a considerable number of samples from a close production area (Maremma) was performed. The non-specific signals provided by the four fingerprinting analytical techniques, after a proper pre-processing, were used for building class models for Chianti Classico oils. The outcomes of classical class-modelling techniques like soft independent modelling of class analogy and quadratic discriminant analysis—unequal dispersed classes were compared with those of two techniques recently introduced into Chemometrics: multivariate range modelling and CAIMAN analogues modelling methods.

Modelling aroma of three Italian red wines by headspace-mass spectrometry and potential functions

The aromas of 41 samples of wine from two Italian regions, Piedmont and Tuscany, were analysed by headspace-mass spectrometry. Samples were from three Italian wines (Barbera, Dolcetto and Chianti) produced in the same vintage, from different grape varieties and producing zones. The headspace generating conditions were optimised by full factorial experimental design then chemometric techniques were applied to verify the discriminating power of headspace-mass spectrometry among the three wine aromas. The modelling method based on potential function, applied on the first nine significant components of the 201 measured m/z, revealed best discrimination among the three wine aromas: cross-validated mean prediction rate of 96.7% and mean prediction rate of 83.3% on external test sets were obtained.

Application of Chemometrics to Food Chemistry

The applications of chemometrics in food chemistry are evaluated especially in the case of research work performed on proprietary data by food chemists, with an overview on the food studied, on the chemometric techniques applied, the type and the number of variables, and the number of samples. Some frequent misunderstandings and errors are indicated. Improvements in the application of the fundamental chemometric techniques are suggested – the design to collect representative samples, the careful use of clustering techniques, the evaluation of the uncertainty in classification parameters, and the evaluation of the effect of noisy information.

A spectral transfer procedure for application of a single class-model to spectra recorded by different near-infrared spectrometers for authentication of olives in brine

Analytical methods for confirmation of food authenticity claims should be rapid, economic, non-destructive and should not require highly skilled personnel for their deployment. All such conditions are satisfied by spectroscopic techniques. In order to be extensively implemented in routine controls, an ideal method should also give a response independent of the particular equipment used. In the present study, near-infrared (NIR) spectroscopy was used for verifying authenticity of commercial olives in brine of cultivar Taggiasca. Samples were analysed in two laboratories with different NIR spectrometers and a mathematical spectral transfer correction - the boxcar signal transfer (BST) - was developed, allowing to minimise the systematic differences existing between signals recorded with the two instruments. Class models for the verification of olive authenticity were built by the unequal dispersed classes (UNEQ) method, after data compression by disjoint principal component analysis (PCA). Models were validated on an external test set.

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.

Classification of pernambuco (Caesalpinia echinata Lam.) wood quality by near infrared spectroscopy and linear discriminant analysis

Near infrared (NIR) spectroscopy, coupled with multivariate data analysis, is proposed as a rapid and effective analytical method for evaluating the quality of pernambuco (Caesalpina echinata Lam.) wood for making bows for stringed instruments. For this purpose, a set of 30 pernambuco sticks were ranked based on their suitability for making high-quality bows and they were assigned to one of the following categories: 0=very poor to poor, 1 = good to very good and 2 = excellent. Considering the low number of samples in the poor category, the classification study focused on the discrimination between samples of the two higher quality groups. Linear discriminant analysis (LDA) was applied to the NIR data as a classification technique and in order to ensure a more appropriate ratio between the number of objects (samples) and number of variables (absorbance at different wavenumbers), LDA was preceded by feature selection. Based on LDA, 100% of the samples were correctly classified and 92.6% of the samples were correctly predicted by the cross-validation procedure.