Determination of Chinese honey adulterated with high fructose corn syrup by near infrared spectroscopy

Chen, Lanzhen; Xue, Xiaofeng; Ye, Zhihua; Zhou, Jinghui; Chen, Fang; Zhao, Jing

doi:10.1016/j.foodchem.2010.10.027

Cited by 112 publications

(63 citation statements)

References 26 publications

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“…The r 2 should be close to the value 1 (Chen et al 2011). Thus, r 2 values higher than 0.90 indicate excellent precision, values between 0.70 and 0.90 mean good precision and on the other hand, values lower than 0.70 indicate that the equation can be used only for screening purposes, which enable distinction between low, medium and high values for the measured parameter (UrbanoCuadrado et al 2004;Novales et al 2009).…”

Section: Samplesmentioning

confidence: 94%

“…Prior to the calibration, the spectral data were pre-processed for optimal performance (Liu et al 2011). The UV/VIS and NIR spectra were transformed using different mathematical pretreatments to remove and minimise the unwanted spectral contribution (Di Egidio et al 2010) and to reduce undesirable systematic noise, such as baseline variation, light scattering and enhance the contribution of the chemical composition (Chen et al 2011). The pre-treatments applied were: normalisation + smoothing; normalisation + 1 st derivative; normalisation + 2 nd derivative; normalisation + SNV; normalisation + detrending; normalisation + baseline; normalisation + MSC; normalisation + deresolve; normalisation + noise; detrending; baseline; baseline + smoothing; baseline + 1 st derivative; baseline + SNV; baseline + MSC; baseline + deresolve; baseline + noise; smoothing + SNV + detrending; normalisation + 1 st derivative + baseline; normalisation + 2 nd derivative + baseline; detrending + SNV + smoothing; smoothing + SNV + baseline; baseline + 1 st derivative + normalisation; smoothing + SNV + baseline; baseline + SNV + smoothing; 1 st derivative + MSC + SNV; 2 nd derivative + MSC + SNV; MSC + SNV + 1 st derivative; MSC + SNV + 2 nd derivative; SNV + detrending + 1 st derivative; SNV + detrending + 2 nd derivative; 1 st derivative + detrending + SNV; 2 nd derivative + detrending + SNV.…”

Section: Samplesmentioning

confidence: 99%

“…Cross-validation was used to validate the quality and to prevent the overfitting of the calibration model (Ribeiro et al 2010;Chen et al 2011).…”

Section: Samplesmentioning

confidence: 99%

“…Model assessment and predictive capability was evaluated by the following indices: determination coefficient (r 2 ) (Davrieux et al 2010;Chen et al 2011) and root mean square error of prediction (RMSEP) (Castritius et al 2010;Liu et al 2009). …”

Section: Samplesmentioning

confidence: 99%

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Evaluation of ultraviolet, visible, and near infrared spectroscopy for the analysis of wine compounds

Martelo-Vidal¹,

Vázquez²

2014

Czech J. Food Sci.

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Martelo-Vidal M.J., Vázquez M. (2014): Evaluation of ultraviolet, visible, and near infrared spectroscopy for the analysis of wine compounds. Czech J. Food Sci., 32: 37-47.Spectroscopy of UV-VIS-NIR combined with chemometric analyses was used as a non-destructive technique to build models for the quantitative characterisation of the main compounds of wine. The work in mixtures can give insight into how interferences affect the performance of calibrations in wines. Ethanol, glycerol, glucose, tartaric acid, malic acid, lactic acid, and acetic acid were evaluated as pure compounds and in mixtures. Different pre-treatments for the spectra and modelling strategies such as partial least squares (PLS) regression or Principal Component Regression (PCR) were evaluated. All pure compounds studied showed a good relationship between spectra and concentrations. However, interferences were observed in the mixtures and only good models for ethanol, tartaric acid, and malic acid were obtained. The best model was obtained in the NIR region for ethanol and in the UV region for tartaric acid and malic acid. The results indicate that NIR spectroscopy could be used as an alternative to conventional chemical methods for ethanol determination and UV spectroscopy for the determination of tartaric acid and malic acid.

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Section: Samplesmentioning

confidence: 94%

Section: Samplesmentioning

confidence: 99%

“…Cross-validation was used to validate the quality and to prevent the overfitting of the calibration model (Ribeiro et al 2010;Chen et al 2011).…”

Section: Samplesmentioning

confidence: 99%

Section: Samplesmentioning

confidence: 99%

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Evaluation of ultraviolet, visible, and near infrared spectroscopy for the analysis of wine compounds

Martelo-Vidal¹,

Vázquez²

2014

Czech J. Food Sci.

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“…Honey is a brown liquid or pale yellowish, translucent, thick and syrupy which is produced in the combs by Apis mellifera (Sheikh et al;1995). It is a combination of organic acids, carbohydrates, waxes, aromatic acids, enzymes, vitamins and entities with hormonal features (Bogdanov et al 1999;Chen et al;Coco et al;1996).It is investigated that honey possess about 181 substances (Richard, 2009). Laevulose and Dextrose are in elevated amount (White and Doner, 1980) while 30 organic acids are also recovered (Mato et al;2003).…”

Section: Introductionmentioning

confidence: 99%

Potential Antifungal Activity of Different Honey Brands From Pakistan: A Quest for Natural Remedy

Ahmad¹,

Khalil²,

Somayya³

et al. 2017

Afr J Tradit Complement Altern Med

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Background: Besides a wholesome food, honey is known for its therapeutic implications. We investigated the antifungal activity of five honeys of different types from Pakistan against various pathogenic fungal strains. Antifungal properties of processed and non-processed honey were determined. Materials and Methods: The antifungal assay of honey was carried out against Aspergillus niger, Aspergillus flavus, Aspergillus fumigates, Alterneria alternata, Fusarium solanai, Microsporum canis, Penicillium funiculosium and Rhizopus solanai. Different dilutions of honey samples were used (20%, 50%, 70%, 90% and 100% w/v so as to find out the minimum effective concentration of each honey type. Results: It was observed that all of the honey samples were highly active, with percent inhibition range of (3-81%) for Aspergillus niger, (2-82%) for Aspergillus flavus, (1-76%) for Aspergillus fumigates, (1-84%) for Alterneria alternata, (2-67%) for Fusarium solanai, (1-87%) Microsporum canis, (2-78%) Penicillium funiculosium and (1-86%) for Rhizopus solanai. Conclusion:The study of Pakistani honey brands reveals that they possess a substantial antifungal nature. Therefore, they may be used in curing fungal infections along with antifungal drugs as a cheaper alternative natural remedy with no side effects.

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Sensors for Non‐Destructive Quality Evaluation of Food

et al. 2021

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Determination of Chinese honey adulterated with high fructose corn syrup by near infrared spectroscopy

Cited by 112 publications

References 26 publications

Evaluation of ultraviolet, visible, and near infrared spectroscopy for the analysis of wine compounds

Evaluation of ultraviolet, visible, and near infrared spectroscopy for the analysis of wine compounds

Potential Antifungal Activity of Different Honey Brands From Pakistan: A Quest for Natural Remedy

Sensors for Non‐Destructive Quality Evaluation of Food

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