Literature DB >> 27719903

Qualitative and quantitative detection of honey adulterated with high-fructose corn syrup and maltose syrup by using near-infrared spectroscopy.

Shuifang Li1, Xin Zhang2, Yang Shan3, Donglin Su4, Qiang Ma1, Ruizhi Wen1, Jiaojuan Li1.   

Abstract

Near-infrared spectroscopy (NIR) was used for qualitative and quantitative detection of honey adulterated with high-fructose corn syrup (HFCS) or maltose syrup (MS). Competitive adaptive reweighted sampling (CARS) was employed to select key variables. Partial least squares linear discriminant analysis (PLS-LDA) was adopted to classify the adulterated honey samples. The CARS-PLS-LDA models showed an accuracy of 86.3% (honey vs. adulterated honey with HFCS) and 96.1% (honey vs. adulterated honey with MS), respectively. PLS regression (PLSR) was used to predict the extent of adulteration in the honeys. The results showed that NIR combined with PLSR could not be used to quantify adulteration with HFCS, but could be used to quantify adulteration with MS: coefficient (Rp2) and root mean square of prediction (RMSEP) were 0.901 and 4.041 for MS-adulterated samples from different floral origins, and 0.981 and 1.786 for MS-adulterated samples from the same floral origin (Brassica spp.), respectively.
Copyright © 2016. Published by Elsevier Ltd.

Entities:  

Keywords:  Competitive adaptive reweighted sampling (CARS); Honey adulteration; Near-infrared spectroscopy; Partial least squares linear discriminant analysis (PLS-LDA); Partial least squares regression (PLSR)

Mesh:

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Year:  2016        PMID: 27719903     DOI: 10.1016/j.foodchem.2016.08.105

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


  5 in total

1.  Rapid detection of adulteration in Anoectochilus roxburghii by near-infrared spectroscopy coupled with chemometric methods.

Authors:  Shuailing Li; Zhian Wang; Qingsong Shao; Hailing Fang; Jianjun Zhu; Xueqian Wu; Bingsong Zheng
Journal:  J Food Sci Technol       Date:  2018-07-19       Impact factor: 2.701

2.  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

3.  Rheological behavior of honey adulterated with agave, maple, corn, rice and inverted sugar syrups.

Authors:  Paula Ciursa; Mircea Oroian
Journal:  Sci Rep       Date:  2021-12-03       Impact factor: 4.379

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.  Characterization, Classification and Authentication of Spanish Blossom and Honeydew Honeys by Non-Targeted HPLC-UV and Off-Line SPE HPLC-UV Polyphenolic Fingerprinting Strategies.

Authors:  Víctor García-Seval; Clàudia Martínez-Alfaro; Javier Saurina; Oscar Núñez; Sònia Sentellas
Journal:  Foods       Date:  2022-08-05
  5 in total

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