Silvia Lanteri

Chemometric analysis of Tuscan olive oils

Abstract Armanino, C., Leardi, R., Lanteri, S. and Modi, G., 1989. Chemometric analysis of Tuscan olive oils. Chemometrics and Intelligent Laboratory Systems, 5: 343–354. The chemical information (fatty acids, sterols, triterpenic alcohols) on 120 olive oil samples from Tuscany, Italy, collected in 88 different areas of production, was evaluated by display methods and cluster analysis. Inside this small region of varied orography, four groups of similar samples were detected and some relationships with the geographic profile were revealed by using a CAD package to produce effective geographical representations of variables, eigenvectors and clusters.

Transfer of calibration function in near-infrared spectroscopy

Abstract A procedure for the transfer of the regression equation in near-infrared spectroscopy (NIRS), from a first instrument to a second instrument, is presented. The procedure uses partial least squares (PLS) regression twice: in the first step to compute the relationship between the spectra of transfer samples of the two instruments, and in the second step to compute the regression equation (relationship between chemical variables and spectral variables) of the first instrument. These two PLS steps are combined to predict the regression equation of the second instrument. Sometimes the PLS relationship between the two instruments is obtained from the principal components of the spectra of the two instruments. The procedure is applied to a set of 60 samples of soy flour, representative of the Italian soy production. 40 samples were used both as transfer samples and to compute the regression equation. 20 samples were used as evaluation set. Spectra were recorded with four different instruments, in four different laboratories. The result of the transfer procedure were evaluated by means of the standard error of prediction ( SEP ) with the predicted regression equation. Owing also to the great number of samples in the transfer set, and to the noise filtering effect of the twin PLS procedure, SEP with the predicted regression equation is not greater than that with the regression equation computed directly from the second instrument. The effect of some parameters, such as the number of PLS latent variables in the two steps, is also studied.

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.

Three-mode principal component analysis of monitoring data from Venice lagoon

A data set obtained by 44 monthly determinations of 11 variables from 13 sampling sites in the Venice lagoon has been treated by three‐mode principal component analysis. The results show that the sampling sites are grouped according to their geographical location, following an inner–outer lagoon direction. In terms of sampling periods, a very strong seasonal effect has been detected, together with an almost linear decrease in nutrients (P and NO   3− ) and increase in eutrophication. Copyright

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.

Detection of addition of barley to coffee using near infrared spectroscopy and chemometric techniques.

The current study presents an application of near infrared spectroscopy for identification and quantification of the fraudulent addition of barley in roasted and ground coffee samples. Nine different types of coffee including pure Arabica, Robusta and mixtures of them at different roasting degrees were blended with four types of barley. The blending degrees were between 2 and 20 wt% of barley. D-optimal design was applied to select 100 and 30 experiments to be used as calibration and test set, respectively. Partial least squares regression (PLS) was employed to build the models aimed at predicting the amounts of barley in coffee samples. In order to obtain simplified models, taking into account only informative regions of the spectral profiles, a genetic algorithm (GA) was applied. A completely independent external set was also used to test the model performances. The models showed excellent predictive ability with root mean square errors (RMSE) for the test and external set equal to 1.4% w/w and 0.8% w/w, respectively.

Study of oils from Calabrian olive cultivars by chemometric methods

Abstract A study of characterisation of a typical Italian food is performed by chemometrics. The olive oils from some cultivars of Calabria have been characterised according to their origin and olive genotype using the chemical information mainly provided by 14 chemical parameters of virgin olive oils. In particular, the models of three cultivars of Calabria (Carolea, Cassanese, Dolce di Rossano) were deeply studied. The microclimate of this region has a lower influence on fatty acid composition than the genotype. Using simple and relatively inexpensive analytical parameters as fatty acids and chemometric techniques it has been possible to characterise and classify the olive oils (60–94% prediction rate).

Classification models: Discriminant analysis, SIMCA, CART

Abstract Frank, I.E. and Lanteri, S., 1989. Classification models: discriminant analysis, SIMCA, CART. Chemometrics and Intelligent Laboratory Systems , 5: 247–256. Four classification methods: linear and quadratic discriminant analyses, SIMCA and CART, are discussed and compared from prediction and model complexity point of views. The former three are well known techniques in chemometrics, while CART is a newly introduced model that could find many chemical applications in the future. The theoretical conclusions are demonstrated on four selected data sets from various fields of chemistry.

Full validation procedures for feature selection in classification and regression problems

Abstract Lanteri, S., 1992. Full validation procedures for feature selection in classification and regression problems. Chemometrics and Intelligent Laboratory Systems . 15:159–169. Those validation procedures are discussed which are commonly applied in regression and classification methods. When a chemometric technique works through several steps (scaling plus classification, feature selection plus regression, parameter optimization plus classification, etc.), validation can give an overestimate of the performance of methods if it is applied only to the final step. The idea of full validation, applied to the whole technique, is explained with reference to examples of synthetic and real data sets.

Differentiation of green tea samples by chiral CD-MEKC analysis of catechins content.

A chiral CD‐MEKC method, enantioselective for catechin and gallocatechin, was developed, validated and applied to the analysis of tea samples. The method was addressed to the fast and simultaneous quantitation of the most represented and biologically important green tea catechins and methylxanthines. The CD‐MEKC was based on SDS as surfactant (90 mM) and hydroxypropyl‐β‐CD (25 mM) as chiral selector, under acidic conditions (25 mM borate–phosphate buffer, pH 2.5). The method was first applied to study the thermal epimerisation of epi‐structured catechins, (−)‐epicatechin and (−)‐epigallocatechin, to non‐epi‐structured (−)‐catechin and (−)‐gallocatechin. The latter compounds, being non‐native molecules, were for the first time regarded as useful phytomarkers of tea samples subjected to thermal treatment. The proposed method was applied to the analysis of more than twenty tea samples of different geographical origins (China, Japan, Ceylon), having undergone different storage conditions and manufacturing processes. Finally, factor analysis was used to visualise the useful information contained in the data set, showing that it was possible to distinguish tea samples on the basis of their different contents of native and non‐native catechins.