Multi-block classification of Italian semolina based on Near Infrared Spectroscopy (NIR) analysis and alveographic indices.

Durum wheat (Triticum turgidum ssp. durum) is widely grown in the Mediterranean area. The semolina obtained by this grain is used to prepare pasta, couscous, and baked products all over the world. The growing area affects the characteristics of Durum wheat; consequently, it is relevant to trace this product. The present study aims at developing an analytical methodology which would allow tracing durum semolina harvested in 7 different Italian macro-areas. In order to achieve this goal, 597 samples of semolina have been analysed by Near Infrared Spectroscopy, and by measuring alveographic parameters. Eventually, the information collected have been handled by a multi-block classifier (SO-PLS-LDA) in order to predict the origin of samples. The proposed approach provided extremely satisfactory results (in external validation, on a test set of 140 objects), correctly classifying all samples according to their growing area, confirming it represents a suitable solution for tracing durum wheat semolina.

[1]  A. A. D’Archivio,et al.  Geographical Classification of Italian Saffron (Crocus sativus L.) by Multi-Block Treatments of UV-Vis and IR Spectroscopic Data , 2020, Molecules.

[2]  J. Hernández-Hierro,et al.  Chilean flour and wheat grain: tracing their origin using near infrared spectroscopy and chemometrics. , 2014, Food chemistry.

[3]  Andrea Marchetti,et al.  A mid level data fusion strategy for the Varietal Classification of Lambrusco PDO wines , 2014 .

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

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

[6]  T. Næs,et al.  Path modelling by sequential PLS regression , 2011 .

[7]  Near-infrared prediction of milling and baking parameters of wheat varieties , 2008 .

[8]  Rasmus Bro,et al.  Extension of SO-PLS to multi-way arrays: SO-N-PLS , 2017 .

[9]  R. Fisher.  THE USE OF MULTIPLE MEASUREMENTS IN TAXONOMIC PROBLEMS , 1936 .

[10]  M. A. Pagani,et al.  Characterization of Durum Wheat Semolina by Means of a Rapid Shear-Based Method , 2014 .

[11]  Bernard Cuq,et al.  In-line monitoring of durum wheat semolina wet agglomeration by near infrared spectroscopy for different water supply conditions and water addition levels , 2013 .

[12]  Yimin Wei,et al.  Multi-element composition of wheat grain and provenance soil and their potentialities as fingerprints of geographical origin , 2013 .

[13]  N. E. Bari,et al.  E-Nose and e-Tongue combination for improved recognition of fruit juice samples. , 2014, Food chemistry.

[14]  M. Suman,et al.  Erratum to: Development of a combined SEM and ICP-MS approach for the qualitative and quantitative analyses of metal microparticles and sub-microparticles in food products , 2011 .

[15]  Bo Zhang,et al.  Near infrared reflectance spectroscopy for determination of the geographical origin of wheat. , 2013, Food chemistry.

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

[17]  Angelo Zanella,et al.  The potential of near infrared spectroscopy (NIRS) to trace apple origin: Study on different cultivars and orchard elevations , 2019, Postharvest Biology and Technology.

[18]  Rasmus Bro,et al.  Variable selection in multi-block regression , 2016 .

[19]  M. Hohmann,et al.  Differentiation of Organically and Conventionally Grown Tomatoes by Chemometric Analysis of Combined Data from Proton Nuclear Magnetic Resonance and Mid-infrared Spectroscopy and Stable Isotope Analysis. , 2015, Journal of agricultural and food chemistry.

[20]  Dolores Pérez-Marín,et al.  Pre-harvest screening on-vine of spinach quality and safety using NIRS technology. , 2019, Spectrochimica acta. Part A, Molecular and biomolecular spectroscopy.

[21]  F. Reniero,et al.  Differentiation of the geographical origin of durum wheat semolina samples on the basis of isotopic composition. , 2002, Rapid communications in mass spectrometry : RCM.

[22]  Andrea D. Magrì,et al.  Data-fusion for multiplatform characterization of an Italian craft beer aimed at its authentication. , 2014, Analytica chimica acta.

[23]  Tormod Næs,et al.  Combining SO-PLS and linear discriminant analysis for multi-block classification , 2015 .

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

[25]  J. Bietz,et al.  Electrophoresis and chromatography of wheat proteins: available methods, and procedures for statistical evaluation of the data. , 1992, Journal of chromatography.

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

[27]  R. Consonni,et al.  Nuclear magnetic resonance and chemometrics to assess geographical origin and quality of traditional food products. , 2010, Advances in food and nutrition research.

[28]  A. Sacco,et al.  Characterization of the Geographical and Varietal Origin of Wheat and Bread by Means of Nuclear Magnetic Resonance (NMR), Isotope Ratio Mass Spectrometry (IRMS) Methods and Chemometrics: A Review , 2015 .

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

[30]  M. Monteleone,et al.  Metabolic profiling and analysis of volatile composition of durum wheat semolina and pasta. , 2009 .

[31]  E. Testani,et al.  Furosine and flavour compounds in durum wheat pasta produced under different manufacturing conditions: Multivariate chemometric characterization , 2014 .

[32]  M. Suman,et al.  Development of a combined SEM and ICP-MS approach for the qualitative and quantitative analyses of metal microparticles and sub-microparticles in food products , 2011, Analytical and bioanalytical chemistry.

[33]  I. Lumley,et al.  Detection and quantification of adulteration of durum wheat flour by flour from common wheat using reverse phase HPLC , 1990 .

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

[35]  M. A. Pagani,et al.  Classification of bread wheat flours in different quality categories by a wavelet-based feature selection/classification algorithm on NIR spectra , 2005 .

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

[37]  Heng Tao Shen,et al.  Principal Component Analysis , 2009, Encyclopedia of Biometrics.

[38]  T. Burnouf,et al.  Reversed-phase high-performance liquid chromatography of durum wheat gliadins: Relationships to durum wheat quality , 1984 .

[39]  Rita Acquistucci,et al.  Estimation of color of durum wheat. Comparison of WSB, HPLC, and reflectance colorimeter measurements. , 2005, Journal of agricultural and food chemistry.