Literature DB >> 31202332

A screening method based on Visible-NIR spectroscopy for the identification and quantification of different adulterants in high-quality honey.

Ma José Aliaño-González1, Marta Ferreiro-González2, Estrella Espada-Bellido3, Miguel Palma4, Gerardo F Barbero5.   

Abstract

According to European Union regulations, honey is a pure product and adding to or removing from it any kind of substance is illegal. Nevertheless, its adulteration by adding inexpensive and artificial adulterants is a common practice. This paper deals with the use of visible and near-infrared spectroscopy (Vis-NIRS) combined with chemometric tools as a screening technique for the identification and quantification of different types of adulterants (inverted sugar, rice syrup, brown cane sugar and fructose syrup) added to high-quality honey (Granada Protected Designation of Origin, Spain) at different levels (5%-50%). A complete discrimination between non-adulterated and adulterated samples was achieved. A general regression model to quantify the adulteration levels was developed as well as specific models for each adulterant. The coefficients of determination were higher than 0.96 for all the models. These results demonstrate the capacity of Vis-NIRS combined with chemometric tools for honey quality control.
Copyright © 2019 Elsevier B.V. All rights reserved.

Entities:  

Keywords:  Adulteration; Authentication; Chemometrics; Honey; Quantification; Visible-near infrared spectroscopy

Mesh:

Substances:

Year:  2019        PMID: 31202332     DOI: 10.1016/j.talanta.2019.05.067

Source DB:  PubMed          Journal:  Talanta        ISSN: 0039-9140            Impact factor:   6.057


  7 in total

1.  Rapid Detection and Quantification of Adulterants in Fruit Juices Using Machine Learning Tools and Spectroscopy Data.

Authors:  José Luis P Calle; Marta Barea-Sepúlveda; Ana Ruiz-Rodríguez; José Ángel Álvarez; Marta Ferreiro-González; Miguel Palma
Journal:  Sensors (Basel)       Date:  2022-05-19       Impact factor: 3.847

2.  Raman Spectroscopy and Chemometric Modeling to Predict Physical-Chemical Honey Properties from Campeche, Mexico.

Authors:  F Anguebes-Franseschi; M Abatal; Lucio Pat; A Flores; A V Córdova Quiroz; M A Ramírez-Elias; L San Pedro; O May Tzuc; A Bassam
Journal:  Molecules       Date:  2019-11-13       Impact factor: 4.411

Review 3.  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

4.  Identification of Stingless Bee Honey Adulteration Using Visible-Near Infrared Spectroscopy Combined with Aquaphotomics.

Authors:  Muna E Raypah; Ahmad Fairuz Omar; Jelena Muncan; Musfirah Zulkurnain; Abdul Rahman Abdul Najib
Journal:  Molecules       Date:  2022-04-03       Impact factor: 4.411

5.  A Rheological Analysis of Biomaterial Behaviour as a Tool to Detect the Dilution of Heather Honey.

Authors:  Antonín Přidal; Petr Trávníček; Jan Kudělka; Šárka Nedomová; Sylvie Ondrušíková; Daniel Trost; Vojtěch Kumbár
Journal:  Materials (Basel)       Date:  2021-05-11       Impact factor: 3.623

6.  Fast Quantification of Honey Adulteration with Laser-Induced Breakdown Spectroscopy and Chemometric Methods.

Authors:  Jiyu Peng; Weiyue Xie; Jiandong Jiang; Zhangfeng Zhao; Fei Zhou; Fei Liu
Journal:  Foods       Date:  2020-03-14

Review 7.  Historical Evolution and Food Control Achievements of Near Infrared Spectroscopy, Electronic Nose, and Electronic Tongue-Critical Overview.

Authors:  Balkis Aouadi; John-Lewis Zinia Zaukuu; Flora Vitális; Zsanett Bodor; Orsolya Fehér; Zoltan Gillay; George Bazar; Zoltan Kovacs
Journal:  Sensors (Basel)       Date:  2020-09-24       Impact factor: 3.576

  7 in total

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