Literature DB >> 31685372

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

Patrizia Firmani1, Alessandro Nardecchia1, Francesca Nocente2, Laura Gazza2, Federico Marini1, Alessandra Biancolillo3.   

Abstract

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.
Copyright © 2019 Elsevier Ltd. All rights reserved.

Entities:  

Keywords:  Alveographic indices; Classification; Durum wheat; LDA; Multi-block; Near Infrared Spectroscopy (NIR); SO-PLS; Triticum durum semolina

Mesh:

Year:  2019        PMID: 31685372     DOI: 10.1016/j.foodchem.2019.125677

Source DB:  PubMed          Journal:  Food Chem        ISSN: 0308-8146            Impact factor:   7.514


  2 in total

1.  Geographical Classification of Italian Saffron (Crocus sativus L.) by Multi-Block Treatments of UV-Vis and IR Spectroscopic Data.

Authors:  Alessandra Biancolillo; Martina Foschi; Angelo Antonio D'Archivio
Journal:  Molecules       Date:  2020-05-16       Impact factor: 4.411

Review 2.  QCM Sensor Arrays, Electroanalytical Techniques and NIR Spectroscopy Coupled to Multivariate Analysis for Quality Assessment of Food Products, Raw Materials, Ingredients and Foodborne Pathogen Detection: Challenges and Breakthroughs.

Authors:  David K Bwambok; Noureen Siraj; Samantha Macchi; Nathaniel E Larm; Gary A Baker; Rocío L Pérez; Caitlan E Ayala; Charuksha Walgama; David Pollard; Jason D Rodriguez; Souvik Banerjee; Brianda Elzey; Isiah M Warner; Sayo O Fakayode
Journal:  Sensors (Basel)       Date:  2020-12-07       Impact factor: 3.576

  2 in total

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