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ITERATIVE PREDICTOR WEIGHTING (IPW) PLS: A TECHNIQUE FOR THE ELIMINATION OF USELESS PREDICTORS IN REGRESSION PROBLEMS

A new method for the elimination of useless predictors in multivariate regression problems is proposed. The method is based on the cyclic repetition of PLS regression. In each cycle the predictor importance (product of the absolute value of the regression coefficient and the standard deviation of the predictor) is computed, and in the next cycle the predictors are multiplied by their importance. The algorithm converges after 10–20 cycles. A reduced number of relevant predictors is retained in the final model, whose predictive ability is acceptable, frequently better than that of the model built with all the predictors. Results obtained on many real and simulated data are presented, and compared with those obtained from other techniques. Copyright

Electronic nose based on metal oxide semiconductor sensors as a fast alternative for the detection of adulteration of virgin olive oils

Abstract An “electronic nose” has been used for the detection of adulterations of virgin olive oil. The system, comprising 12 metal oxide semiconductor sensors, was used to generate a pattern of the volatile compounds present in the samples. Prior to different supervised pattern recognition treatments, feature selection techniques were employed to choose a set of optimally discriminant variables. Linear discriminant analysis (LDA), quadratic discriminant analysis (QDA) and artificial neural networks (ANN) were applied. Excellent results were obtained in the differentiation of adulterated and non-adulterated olive oils and it was even possible to identify the type of oil used in the adulteration. Promising results were also obtained as regards quantification of the percentages of adulteration.

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.

Development of a voltammetric electronic tongue for discrimination of edible oils

AbstractIn this paper, we propose a novel strategy to perform cyclic voltammetric measurements with a platinum microelectrode directly in edible oil samples. The microelectrode was employed as an electronic tongue that, along with the application of chemometrics to the current–potential responses, proved useful for discriminating oils on the basis of their quality and geographical origin. The method proposed here is based on the use of suitable room temperature ionic liquids, added to oils as supporting electrolytes to provide conductivity to the low-polarity samples. The entire voltammograms, recorded directly on the oil/RTIL mixtures, were processed via principal component analysis and a classification technique (K nearest neighbors), to extract information on samples characteristics. Data processing showed that oils having different nature (i.e. maize and olive) or geographical origin (i.e. olive oils coming from different regions) can be distinguished. FigureA novel strategy to perform voltammetric measurements with a platinum microelectrode directly in edible oil samples is presented. The microelectrode is employed as an electronic tongue that, along with the application of chemometrics to the voltammetric responses, allows oil discrimination according to their quality and geographical origin.

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.

Extraction of representative subsets by potential functions method and genetic algorithms

Abstract Two procedures are suggested to select a representative subset from a large data set. The first is based on the use of the estimate of the multivariate probability density distribution by means of the potential functions technique. The first object selected for the subset is that for which the probability density is larger. Then, the distribution is corrected, by subtraction of the contribution of the selected object multiplied by a selection factor. The second procedure uses genetic algorithms to individuate the subset that reproduces the variance–covariance matrix with the minimum error. Both methods meet the requirement to obtain a representative subset, but the results obtained with the method based on potential functions are generally more satisfactory in the case when the original set is not a random sample from an infinite population, but is the finite population itself. Several examples show how the extraction of a representative subset from a large data set can give some advantages in the use of representation techniques (i.e., eigenvector projection, non-linear maps, Kohonen maps) and in class modelling techniques.

Chemometric brains for artificial tongues.

The last years showed a significant trend toward the exploitation of rapid and economic analytical devices able to provide multiple information about samples. Among these, the so-called artificial tongues represent effective tools which allow a global sample characterization comparable to a fingerprint. Born as taste sensors for food evaluation, such devices proved to be useful for a wider number of purposes. In this review, a critical overview of artificial tongue applications over the last decade is outlined. In particular, the focus is centered on the chemometric techniques, which allow the extraction of valuable information from nonspecific data. The basic steps of signal processing and pattern recognition are discussed and the principal chemometric techniques are described in detail, highlighting benefits and drawbacks of each one. Furthermore, some novel methods recently introduced and particularly suitable for artificial tongue data are presented.

Data Analysis in Food Chemistry

Every day, numerous analyses are carried out on food products to answer a host of problems, in modern food science. These analyses are ultimately aimed at the improvement of food quality, reduction in price, increase in production yield, the elimination of undesirable effects during preservation, and determining the correlation between the food and the frequency of some diseases. In addition, food scientists are requested to assess some kind of guarantee of food quality, with regard to its content, geographic origin and age.

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.