Authentication of P.G.I. Gragnano pasta by near infrared (NIR) spectroscopy and chemometrics

[1]  A. Savitzky,et al.  Smoothing and Differentiation of Data by Simplified Least Squares Procedures. , 1964 .

[2]  Svante Wold,et al.  Pattern recognition by means of disjoint principal components models , 1976, Pattern Recognit..

[3]  Ronald D. Snee,et al.  Validation of Regression Models: Methods and Examples , 1977 .

[4]  S. Wold,et al.  SIMCA: A Method for Analyzing Chemical Data in Terms of Similarity and Analogy , 1977 .

[5]  S. Wold,et al.  The multivariate calibration problem in chemistry solved by the PLS method , 1983 .

[6]  Svante Wold,et al.  PLS DISCRIMINANT PLOTS , 1986 .

[7]  S. Wold,et al.  Partial least squares analysis with cross‐validation for the two‐class problem: A Monte Carlo study , 1987 .

[8]  R. Barnes,et al.  Standard Normal Variate Transformation and De-Trending of Near-Infrared Diffuse Reflectance Spectra , 1989 .

[9]  Bruce R. Kowalski,et al.  Qualitative Information from Multivariate Calibration Models , 1990 .

[10]  S. Wold,et al.  PLS: Partial Least Squares Projections to Latent Structures , 1993 .

[11]  Anthony Fardet,et al.  Textural Images Analysis of Pasta Protein Networks to Determine Influence of Technological Processes , 1998 .

[12]  S. Joe Qin,et al.  Reconstruction-Based Fault Identification Using a Combined Index , 2001 .

[13]  M. Barker,et al.  Partial least squares for discrimination , 2003 .

[14]  T. Næs,et al.  From dummy regression to prior probabilities in PLS‐DA , 2007 .

[15]  Paolo Menesatti,et al.  Quality classification of Italian wheat durum spaghetti by means of different spectrophometric techniques , 2007, SPIE Optics East.

[16]  Li Wang,et al.  Near-infrared spectroscopy for classification of licorice (Glycyrrhizia uralensis Fisch) and prediction of the glycyrrhizic acid (GA) content , 2007 .

[17]  M. Forina,et al.  Class-modeling techniques, classic and new, for old and new problems , 2008 .

[18]  R. Boqué,et al.  Calculation of the reliability of classification in discriminant partial least-squares binary classification , 2009 .

[19]  C. Berthomieu,et al.  Fourier transform infrared (FTIR) spectroscopy , 2009, Photosynthesis Research.

[20]  Paolo Oliveri,et al.  Multivariate class modeling for the verification of food-authenticity claims , 2012 .

[21]  Roberto Massini,et al.  Evaluation of Pasta Thermal Treatment By Determination of Carbohydrates, Furosine, and Color Indices , 2013, Food and Bioprocess Technology.

[22]  E. Testani,et al.  HS-SPME/GC-MS Method to Characterise the Flavour of Italian Pasta: Potential Application to Assess the Quality of the Products , 2013, Food Analytical Methods.

[23]  C. Costa,et al.  Spectrophotometric Qualification of Italian Pasta Produced by Traditional or Industrial Production Parameters , 2014, Food and Bioprocess Technology.

[24]  Knut Kvaal,et al.  Surface texture characterization of an Italian pasta by means of univariate and multivariate feature extraction from their texture images , 2013 .

[25]  Characterization of the Authenticity of Pasta di Gragnano Protected Geographical Indication Through Flavor Component Analysis by Gas Chromatography-Mass Spectrometry and Chemometric Tools. , 2016, Journal of AOAC International.

[26]  P. Oliveri Class-modelling in food analytical chemistry: Development, sampling, optimisation and validation issues - A tutorial. , 2017, Analytica chimica acta.

[27]  Alessandra Biancolillo,et al.  Authentication of an Italian PDO hazelnut (“Nocciola Romana”) by NIR spectroscopy , 2018, Environmental Science and Pollution Research.

[28]  Determining moisture content in pasta by vibrational spectroscopy. , 2018, Talanta.

[29]  Marina Cocchi,et al.  Chemometric methods for classification and feature selection , 2018 .

[30]  J. Amigo,et al.  NIR spectroscopy and chemometrics for the typification of Spanish wine vinegars with a protected designation of origin , 2018, Food Control.

[31]  Alessandra Biancolillo,et al.  Chemometrics Applied to Plant Spectral Analysis , 2018 .

[32]  F. Marini,et al.  Simultaneous quantification of caffeine and chlorogenic acid in coffee green beans and varietal classification of the samples by HPLC-DAD coupled with chemometrics , 2018, Environmental Science and Pollution Research.

[33]  J. A. Fernández Pierna,et al.  Discrimination between durum and common wheat kernels using near infrared hyperspectral imaging , 2018, Journal of Cereal Science.

[34]  Alessandra Biancolillo,et al.  Near infrared (NIR) spectroscopy-based classification for the authentication of Darjeeling black tea , 2019, Food Control.

[35]  Y. Kurata,et al.  Nondestructive Classification Analysis of Green Coffee Beans by Using Near-Infrared Spectroscopy , 2019, Foods.

[36]  Marina Cocchi,et al.  Data Fusion Strategies in Food Analysis , 2019, Data Handling in Science and Technology.

[37]  Alessandra Biancolillo,et al.  Authentication of “Avola almonds” by near infrared (NIR) spectroscopy and chemometrics , 2019, Journal of Food Composition and Analysis.

[38]  G. Buttafuoco,et al.  Assessing the Feasibility of a Miniaturized Near-Infrared Spectrometer in Determining Quality Attributes of San Marzano Tomato , 2019, Food Analytical Methods.

[39]  F. Marini,et al.  Determination of insect infestation on stored rice by near infrared (NIR) spectroscopy , 2019, Microchemical Journal.