Detection of adulterants such as sweeteners materials in honey using near-infrared spectroscopy and chemometrics

Zhu, X.; Shui-fang, LI; Shan, Yang; Zhang, Zhuoyong; Li, Gaoyang; Su, Donglin; Liu, Feng

doi:10.1016/j.jfoodeng.2010.06.014

Cited by 132 publications

(55 citation statements)

References 33 publications

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“…About 95% accuracy was achieved by Anjos et al [21] when MLP classified honey samples according to botanical origin on the basis of the colorimetric information and the electrical conductivity. MLP was adopted by Zhu et al [22] to correctly (90.2%) classify pure and adulterated honey samples with different NIR spectral data. Four types of neural network models: MLP, probabilistic neural network, recurrent neural network, and modular neural network were developed by Oroian and Sorina [23] for the classification of honey based on their physicochemical parameters and phenolics.…”

Section: Introductionmentioning

confidence: 99%

Dielectric properties of honey: the potential usability for quality assessment

Pentoś

Łuczycka

2017

Eur Food Res Technol

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Honey, as a valuable product of high price, is subject to illegal adulteration by adding water or inexpensive sweeteners as well as overheating. Therefore, cheap, quick, and reliable methods of honey quality assessment are of interest to the food industry and consumers alike. In this study, two dielectric honey parameters: relative permittivity and dielectric loss coefficient measured at frequencies below 1 MHz and at temperatures from 20 to 40 °C were considered as potential indicators of honey quality. For the data analysis, chemometric methods (artificial neural networks and two methods of extracting the contribution of independent variables) were employed. No significant differences in results of the relative contribution of input variables were found depending on frequency. Results lead to the conclusion that relative permittivity, which is significantly affected by pH, can be potentially useful for honey microbial contamination detection and dielectric loss coefficient, influenced mostly by HMF content, can be considered as an indicator of honey freshness.

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Section: Introductionmentioning

confidence: 99%

Dielectric properties of honey: the potential usability for quality assessment

Pentoś

Łuczycka

2017

Eur Food Res Technol

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“…The FT-NIR spectroscopy in combination with suitable chemometric methods could be a useful technique to provide a better insight into the relationship between variables and samples as well as the quantification of food adulteration. Chemometrics-assisted FT-NIR spectroscopy technique has been developed for quantifying hazelnut oil adulterated with different types of oil (Ozen and Mauer 2002), sugar adulterants in apple juice (Kelly and Downey 2005), and honey with adulterants of sweetener materials (Zhu et al 2010).…”

Section: Introductionmentioning

confidence: 99%

Rapid Quantitative Analysis of Corn Starch Adulteration in Konjac Glucomannan by Chemometrics-Assisted FT-NIR Spectroscopy

Zhong

Qin

2015

Food Anal. Methods

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Konjac glucomannan (KGM) adulterated with cheaper starch cannot easily be identified by visual inspection. This study proposed a rapid and simple method to quantitatively analyze corn starch adulteration in KGM by a Fourier transform near-infrared (FT-NIR) coupled with chemometrics. The partial least-squares (PLS) regression calibration models based on the FT-NIR were designed using 90 samples. Coefficient of determination (R c 2 ) and root-mean-square error of PLS regression models in calibration set were found to be 0.982-0.990 and 3.596-2.693 % depending on the pretreatments of spectral data, respectively. The other 45 samples were used in the validation (30 samples) and external validation (15 samples) sets. Model 3 (using first derivative with 7 smoothing points) in the validation set yielded satisfactory performance with an R v 2 value and root-mean-square error of prediction of 0.989 and 4.890 %, respectively. The overall results indicate that FT-NIR spectroscopy could be a simple and efficient tool for the detection and quantification of the KGM adulterated with corn starch.

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“…The use of 13 C isotope ratio mass spectrometry (IRMS), which is an AOAC Official Method of detecting the addition of corn or cane sugars to honeys [12][13][14][15][16][17], can only detect C4 sugars, not C3 syrups such as cereal starch hydrolysates. Nevertheless, some minor sugars are reported to be good C3 sugar markers; the quantification of these sugars is a useful way to detect honey M. Wytrychowski : G. Daniele (*) : H. Casabianca [18][19][20][21][22][23][24] and RJ [11] adulteration. Herein, we report a new analytical strategy obtained by combining both of these methods (i.e., 13 C isotopic measurements and sugar content analysis), which can be utilized to unambiguously detect C3-and C4-based syrups.…”

Section: Introductionmentioning

confidence: 99%

Combination of sugar analysis and stable isotope ratio mass spectrometry to detect the use of artificial sugars in royal jelly production

2012

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The effects of feeding bees artificial sugars and/or proteins on the sugar compositions and (13)C isotopic measurements of royal jellies (RJs) were evaluated. The sugars fed to the bees were two C4 sugars (cane sugar and maize hydrolysate), two C3 sugars (sugar beet, cereal starch hydrolysate), and honey. The proteins fed to them were pollen, soybean, and yeast powder proteins. To evaluate the influence of the sugar and/or protein feeding over time, samples were collected during six consecutive harvests. (13)C isotopic ratio measurements of natural RJs gave values of around -25 ‰, which were also seen for RJs obtained when the bees were fed honey or C3 sugars. However, the RJs obtained when the bees were fed cane sugar or corn hydrolysate (regardless of whether they were also fed proteins) gave values of up to -17 ‰. Sugar content analysis revealed that the composition of maltose, maltotriose, sucrose, and erlose varied significantly over time in accordance with the composition of the syrup fed to the bees. When corn and cereal starch hydrolysates were fed to the bees, the maltose and maltotriose contents of the RJs increased up to 5.0 and 1.3 %, respectively, compared to the levels seen in authentic samples (i.e., samples obtained when the bees were fed natural food: honey and pollen) that were inferior to 0.2% and not detected, respectively. The sucrose and erlose contents of natural RJs were around 0.2 %, whereas those in RJs obtained when the bees were fed cane or beet sugar were as much as 4.0 and 1.3 %, respectively. The combination of sugar analysis and (13)C isotopic ratio measurements represents a very efficient analytical methodology for detecting (from early harvests onward) the use of C4 and C3 artificial sugars in the production of RJ.

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Detection of adulterants such as sweeteners materials in honey using near-infrared spectroscopy and chemometrics

Cited by 132 publications

References 33 publications

Dielectric properties of honey: the potential usability for quality assessment

Dielectric properties of honey: the potential usability for quality assessment

Rapid Quantitative Analysis of Corn Starch Adulteration in Konjac Glucomannan by Chemometrics-Assisted FT-NIR Spectroscopy

Combination of sugar analysis and stable isotope ratio mass spectrometry to detect the use of artificial sugars in royal jelly production

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