Detection of honey adulteration – The potential of UV-VIS and NIR spectroscopy coupled with multivariate analysis

Valinger, Davor; Longin, Lucija; Grbeš, Franjo; Benković, Maja; Jurina, Tamara; Kljusurić, Jasenka Gajdoš; Tušek, Ana Jurinjak

doi:10.1016/j.lwt.2021.111316

Cited by 36 publications

(18 citation statements)

References 42 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This can be achieved in a chromaticity diagram (or a color space) developed for the perception of the human eye, such as the CIELAB/CIE XYZ/CIELUV color space. In the case of honey analysis, the CIELAB color space is the most widely applied [ 2 , 36 , 48 , 49 ], used in this study concerning illuminant D65 and a visual angle of

[ 48 , 49 ]. The absorbance spectra of the samples were transformed to transmission spectra using the Unscrambler Software and transferred to a Chromaticity Diagram template developed in the OriginPro 2021 Software.…”

Section: Resultsmentioning

confidence: 99%

“…The template was designed to calculate the color parameters from the spectra according to the equations from the CIE Technical Committee [ 71 ]. The CIELAB color space was utilized [ 2 , 36 , 48 , 49 ], with reference to illuminant D65 and a visual angle of

(CIE 1964 Supplementary Standard Observer) [ 48 , 49 ]. The wavelength range used in the calculation was 380–780 nm [ 47 ].…”

Section: Methodsmentioning

confidence: 99%

“…Other compounds are often added to commercially sold honey for nutrition and medical purposes, and some are added to spoof the pure honey. Honey is often threatened by cheaper, commercially accessible sugar syrups with similar chemical compositions [ 2 ]. Even if these standards are followed to maintain high levels of quality, contaminations and/or adulterations might occur during manufacture, compromising the product’s quality and safety [ 3 ].…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

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

et al. 2022

View full text Add to dashboard Cite

Honey is a natural product that is considered globally one of the most widely important foods. Various studies on authenticity detection of honey have been fulfilled using visible and near-infrared (Vis-NIR) spectroscopy techniques. However, there are limited studies on stingless bee honey (SBH) despite the increase of market demand for this food product. The objective of this work was to present the potential of Vis-NIR absorbance spectroscopy for profiling, classifying, and quantifying the adulterated SBH. The SBH sample was mixed with various percentages (10–90%) of adulterants, including distilled water, apple cider vinegar, and high fructose syrup. The results showed that the region at 400–1100 nm that is related to the color and water properties of the samples was effective to discriminate and quantify the adulterated SBH. By applying the principal component analysis (PCA) on adulterants and honey samples, the PCA score plot revealed the classification of the adulterants and adulterated SBHs. A partial least squares regression (PLSR) model was developed to quantify the contamination level in the SBH samples. The general PLSR model with the highest coefficient of determination and lowest root means square error of cross-validation (RCV2=0.96 and RMSECV=5.88 %) was acquired. The aquaphotomics analysis of adulteration in SBH with the three adulterants utilizing the short-wavelength NIR region (800–1100 nm) was presented. The structural changes of SBH due to adulteration were described in terms of the changes in the water molecular matrix, and the aquagrams were used to visualize the results. It was revealed that the integration of NIR spectroscopy with aquaphotomics could be used to detect the water molecular structures in the adulterated SBH.

show abstract

Section: Resultsmentioning

confidence: 99%

(CIE 1964 Supplementary Standard Observer) [ 48 , 49 ]. The wavelength range used in the calculation was 380–780 nm [ 47 ].…”

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

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

et al. 2022

View full text Add to dashboard Cite

show abstract

“…NIR spectroscopy offers numerous advantages that include minimal or no sample preparation, speed, indestructibility and ease of process integration. There are also some challenges associated with NIR spectroscopy, such as spectral overlap and the use of multivariate models to understand and interpret the data (Valinger et al ., 2021), but as the food industry is increasingly concerned with by‐products, the importance of NIR application in the analysis of BWH could be of great interest. In this study, NIR was used to determine a possible correlation between physical, chemical and spectral properties of micronized BWH fractions.…”

Section: Resultsmentioning

confidence: 99%

The effect of cryo‐grinding and size separation on bioactive profile of buckwheat hulls

Radoš

Mustač

Drakula

et al. 2021

Int J of Food Sci Tech

Self Cite

View full text Add to dashboard Cite

Summary Buckwheat hulls (BWH) are a lignocellulosic material with valuable nutrient contents, especially rutin and dietary fibre, which are worth exploring from a food industry perspective. BWH are also a voluminous material that is difficult to grind to very fine particle sizes, which can be achieved under cryogenic conditions. Therefore, we investigated the effect of fractionation by cryo‐grinding and size separation (<56, 56–180 and >180 µm) on the dietary fibre content and bioactive profile of BWH. While we hypothesised that micronisation would increase fibre solubility and release of bioactive compounds, we found that BWH must be fractionated to a size <56 µm to show a significant increase in soluble fibre content (106%), rutin (66%), total free phenolic compounds (94%) and antioxidant capacity (45–63%). Moreover, we have shown that near‐infrared spectroscopy combined with partial least squares regression can be easily used for rapid scanning of BWH to select the most suitable pre‐processing conditions for maximum nutritional value of this food industry by‐product.

show abstract

“…The RPD p was higher than two for both individual predictions of wet and dry samples and combined samples. According to Chang et al [ 43 ] and Valinger et al [ 44 ] models with RPD > 2.0 are excellent descriptions and predictions of experimental data. In terms of RER, a global PLSR with RER in the range of 3 to 10, for both individual and combined prediction samples, is classified as a good practical utility model according to Jia et al [ 45 ].…”

Section: Resultsmentioning

confidence: 98%

Quantification of Corn Adulteration in Wet and Dry-Processed Peaberry Ground Roasted Coffees by UV–Vis Spectroscopy and Chemometrics

Yulia

Suhandy

2021

Molecules

View full text Add to dashboard Cite

In this present research, a spectroscopic method based on UV–Vis spectroscopy is utilized to quantify the level of corn adulteration in peaberry ground roasted coffee by chemometrics. Peaberry coffee with two types of bean processing of wet and dry-processed methods was used and intentionally adulterated by corn with a 10–50% level of adulteration. UV–Vis spectral data are obtained for aqueous samples in the range between 250 and 400 nm with a 1 nm interval. Three multivariate regression methods, including partial least squares regression (PLSR), multiple linear regression (MLR), and principal component regression (PCR), are used to predict the level of corn adulteration. The result shows that all individual regression models using individual wet and dry samples are better than that of global regression models using combined wet and dry samples. The best calibration model for individual wet and dry and combined samples is obtained for the PLSR model with a coefficient of determination in the range of 0.83–0.93 and RMSE below 6% (w/w) for calibration and validation. However, the error prediction in terms of RMSEP and bias were highly increased when the individual regression model was used to predict the level of corn adulteration with differences in the bean processing method. The obtained results demonstrate that the use of the global PLSR model is better in predicting the level of corn adulteration. The error prediction for this global model is acceptable with low RMSEP and bias for both individual and combined prediction samples. The obtained RPDp and RERp in prediction for the global PLSR model are more than two and five for individual and combined samples, respectively. The proposed method using UV–Vis spectroscopy with a global PLSR model can be applied to quantify the level of corn adulteration in peaberry ground roasted coffee with different bean processing methods.

show abstract

Detection of honey adulteration – The potential of UV-VIS and NIR spectroscopy coupled with multivariate analysis

Cited by 36 publications

References 42 publications

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

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

The effect of cryo‐grinding and size separation on bioactive profile of buckwheat hulls

Quantification of Corn Adulteration in Wet and Dry-Processed Peaberry Ground Roasted Coffees by UV–Vis Spectroscopy and Chemometrics

Contact Info

Product

Resources

About