Carla Armanino

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

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).

Wheat lipids to discriminate species, varieties, geographical origins and crop years

The knowledge of lipid components of wheat finds a precious information in order to differentiate between Triticum durum (TD) and Triticum aestivum (TA). The determination of the percentages of methyl esters of the differently unsaturated fatty acids with 18-carbon atoms (C18), of sterol fraction and of the other components is of particular weight. In this paper, the classification methods of linear discriminant analysis (LDA) and quadratic discriminant analysis (QDA) were applied in order to measure the classification and prediction abilities of the determined (percentages of the) components of the lipid fraction of wheat in differentiating among species, origins, varieties and crops. By univariate feature selection method (Fisher weights (FW)) and linear discriminant analysis, it was found that the only oleate is able to distinguish between the two species with a prediction rate of 100%. Inside the species Triticum durum, it was obtained a prediction rate of 83.9% while discriminating between the different origins, a prediction rate of 82.2% while discriminating among varieties and a prediction rate of 94.3% among crop years.

Principal Component Analysis Application in Polycyclic Aromatic Hydrocarbons "Mussel Watch" Analyses for Source Identification

Abstract This article aims to show how a careful pre-treatment of data can be used to demonstrate various features embedded in a given data set obtained from a “mussel watch” survey, namely site- and source-specific characteristics and weather-related changes, and to provide indications so as to allow comparison with analyses performed on another substrate matrix. Polycyclic aromatic hydrocarbons (PAHs) biomonitored in the aquatic environment by means of caged mussels are compared by site and by season. Moreover, their fingerprints were compared to marine sediments and atmospheric airborne PAHs. The characterization of the sampling stations by means of the multivariate technique called principal component analysis (PCA) allows distinguishing the prevalence of pyrogenic or petrogenic types of pollution and between two kinds of combustibles. This was confirmed by jointly analyzing the percent composition of sea (mussel) and air (filter) samples.

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.

Characterization of wheat by four analytical parameters. A chemometric study

The results of chemical analysis (ashes, humidity, fat and proteins) carried out on 195 samples of wheat of two different classes (Triticum durum and Triticum vulgare or Triticum aestivum), and from different geographical areas, variety and year of production, were evaluated by principal component analysis (PCA) and classification analysis. Patterns of samples of the same species, variety and from the same origin were displayed by principal component plots. By means of classification analysis, best results (classification and prediction abilities ≥ 90%) were achieved by discriminating between the two species and origin, also the discrimination among varieties and years of harvesting was generally successfull.

Hirsutism: A multivariate approach of feature selection and classification

Abstract Armanino, C., Lanteri, S., Forina, M., Balsamo, A., Migliardi, M. and Cenderelli, G., 1989. Hirsutism: a multivariate approach of feature selection and classification. Chemometrics and Intelligent Laboratory Systems , 5: 335–341. Supervised pattern recognition methods were applied to the results of seven hormonal tests from a population of twenty-six healthy subjects and one hundred and seven women affected by hirsutism, in order to study the discriminant information from analytical data. Eigenvector projection and raw Varimax rotation, a stepwise multivariate method of feature selection based on quadratic discriminant analysis, the classification methods of k -nearest neighbours and quadratic discriminant analysis were applied. The prediction ability of the multivariate normal models, built by five selected variables (testosterone—estradiol binding globulin, dehydroepiandrosterone sulphate, estrone, salivary testosterone, 17β-estradiol) was 87.5%. Hierarchical clustering was carried out on the analytical data from the group of hirsute patients: two principal clusters and one singleton were identified.