E Restaino

Publications (3)3.45 Total impact

• Article: Discrimination of yerba mate (Ilex paraguayensis St. Hil.) samples according to their geographical origin by means of near infrared spectroscopy and multivariate analysis

  D. Cozzolino, E. Restaino, A. Fassio
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  ABSTRACT: Near infrared reflectance (NIR) spectroscopy combined with multivariate data analysis was used to discriminate between the Near infrared reflectance (NIR) spectroscopy combined with multivariate data analysis was used to discriminate between the geographical origins of yerba mate (Ilex paraguayensis St. Hil.) samples. Samples were purchased from the local market and scanned in the NIR region (1100–2500nm) in a monochromator instrument geographical origins of yerba mate (Ilex paraguayensis St. Hil.) samples. Samples were purchased from the local market and scanned in the NIR region (1100–2500nm) in a monochromator instrument in reflectance. Principal component analysis (PCA), partial least squares discriminant analysis (PLS-DA) and linear discriminant in reflectance. Principal component analysis (PCA), partial least squares discriminant analysis (PLS-DA) and linear discriminant analysis (LDA) were used to classify the samples based on their NIR spectra according to their geographical origin. Full cross analysis (LDA) were used to classify the samples based on their NIR spectra according to their geographical origin. Full cross validation was used as validation method when classification models were developed. The overall classification rates obtained validation was used as validation method when classification models were developed. The overall classification rates obtained were 76 and 100% using PLS-DA and LDA, respectively. The results demonstrated the usefulness of NIR spectra combined with were 76 and 100% using PLS-DA and LDA, respectively. The results demonstrated the usefulness of NIR spectra combined with multivariate data analysis as an objective and rapid method to classify yerba mate samples according to their geographical multivariate data analysis as an objective and rapid method to classify yerba mate samples according to their geographical origin. Nevertheless, NIR spectroscopic might provide initial screening in the food chain and enable costly methods to be origin. Nevertheless, NIR spectroscopic might provide initial screening in the food chain and enable costly methods to be used more productively on suspect specimens. used more productively on suspect specimens. KeywordsNear infrared-Spectroscopy-Principal component analysis-Linear discriminant analysis-Yerba mate- KeywordsNear infrared-Spectroscopy-Principal component analysis-Linear discriminant analysis-Yerba mate- Ilex paraguayensis Ilex paraguayensis
  Sensing and Instrumentation for Food Quality and Safety 04/2012; 4(2):67-72.
• Article: Discrimination of meat patés according to the animal species by means of near infrared spectroscopy and chemometrics Discriminación de muestras de paté de carne según tipo de especie mediante el uso de la espectroscopia en el infrarrojo cercano y la quimiometria

  E. Restaino, A. Fassio, D. Cozzolino
  CyTA - Journal of Food 11/2011; 9(3):210-213. · 0.63 Impact Factor
• Article: Verification of silage type using near-infrared spectroscopy combined with multivariate analysis.

  D Cozzolino, A Fassio, E Restaino, E Fernandez, A La Manna
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  ABSTRACT: The ability to authenticate the feed given to animals has become a major challenge in animal production, where the diet fed to the animal is one of the most important production factors affecting the composition of milk and meat from cattle, sheep, and goats. Hence, there is currently an increased consumer demand for information on herbivore production factors and particularly the animal diet. The aim of this study was to evaluate the reliability and accuracy of near-infrared (NIR) reflectance spectroscopy as a tool to verify and authenticate the type of silage used as fed for ruminants. Grain silage (GrS, n = 94), grass and legume silage (GLegS, n = 121), and sunflower silage (SunS, n = 50) samples were collected from commercial farms and analyzed in the visible and NIR regions (400-2500 nm) in a monochromator instrument in reflectance. Principal component analysis (PCA), partial least-squares discriminant analysis (PLS1-DA), and linear discriminant analysis (LDA) models were used as methods to verify the different silage types. The classification models based on the NIR data correctly classified more than 90% of the silage samples according to their type. The results from this study showed that NIR spectra combined with multivariate analysis could be used as a tool to objectively authenticate silage samples used as a feed for ruminants.
  Journal of Agricultural and Food Chemistry 02/2008; 56(1):79-83. · 2.82 Impact Factor