scholarly journals Detection of Adulterated Honey by Fluorescence Excitation-Emission Matrices

2018 ◽  
Vol 2018 ◽  
pp. 1-6 ◽  
Author(s):  
Tatjana Dramićanin ◽  
Lea Lenhardt Acković ◽  
Ivana Zeković ◽  
Miroslav D. Dramićanin

Honey is a frequent target of adulteration through inappropriate production practices and origin mislabelling. Current methods for the detection of adulterated honey are time and labor consuming, require highly skilled personnel, and lengthy sample preparation. Fluorescence spectroscopy overcomes such drawbacks, as it is fast and noncontact and requires minimal sample preparation. In this paper, the application of fluorescence spectroscopy coupled with statistical tools for the detection of adulterated honey is demonstrated. For this purpose, fluorescence excitation-emission matrices were measured for 99 samples of different types of natural honey and 15 adulterated honey samples (in 3 technical replicas for each sample). Statistical t-test showed that significant differences between fluorescence of natural and adulterated honey samples exist in 5 spectral regions: (1) excitation: 240–265 nm, emission: 370–495 nm; (2) excitation: 280–320 nm, emission: 390–470 nm; (3) excitation: 260–285 nm, emission: 320–370 nm; (4) excitation: 310–360 nm, emission: 370–470 nm; and (5) excitation: 375–435 nm, emission: 440–520 nm, in which majority of fluorescence comes from the aromatic amino acids, phenolic compounds, and fluorescent Maillard reaction products. Principal component analysis confirmed these findings and showed that 90% of variance in fluorescence is accumulated in the first two principal components, which can be used for the discrimination of fake honey samples. The classification of honey from fluorescence data is demonstrated with a linear discriminant analysis (LDA). When subjected to LDA, total fluorescence intensities of selected spectral regions provided classification of honey (natural or adulterated) with 100% accuracy. In addition, it is demonstrated that intensities of honey emissions in each of these spectral regions may serve as criteria for the discrimination between natural and fake honey.

Sensors ◽  
2018 ◽  
Vol 18 (12) ◽  
pp. 4479 ◽  
Author(s):  
Xavier Cetó ◽  
Núria Serrano ◽  
Miriam Aragó ◽  
Alejandro Gámez ◽  
Miquel Esteban ◽  
...  

The development of a simple HPLC-UV method towards the evaluation of Spanish paprika’s phenolic profile and their discrimination based on the former is reported herein. The approach is based on C18 reversed-phase chromatography to generate characteristic fingerprints, in combination with linear discriminant analysis (LDA) to achieve their classification. To this aim, chromatographic conditions were optimized so as to achieve the separation of major phenolic compounds already identified in paprika. Paprika samples were subjected to a sample extraction stage by sonication and centrifugation; extracting procedure and conditions were optimized to maximize the generation of enough discriminant fingerprints. Finally, chromatograms were baseline corrected, compressed employing fast Fourier transform (FFT), and then analyzed by means of principal component analysis (PCA) and LDA to carry out the classification of paprika samples. Under the developed procedure, a total of 96 paprika samples were analyzed, achieving a classification rate of 100% for the test subset (n = 25).


2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Mihaela Emanuela Crăciun ◽  
Oana Cristina Pârvulescu ◽  
Andreea Cristina Donise ◽  
Tănase Dobre ◽  
Dumitru Radu Stanciu

AbstractThree groups of Romanian acacia honey, i.e., pure, directly adulterated (by mixing the pure honey with three sugar syrups), and indirectly adulterated (by feeding the bees with the same syrups), were characterized and discriminated based on their physicochemical parameters. Moisture, ash, 5-hydroxymethylfurfural (HMF), reducing sugars (fructose and glucose), and sucrose contents, free acidity, diastase activity, ratio between stable carbon isotopes of honey and its proteins (δ13CH and δ13CP) were evaluated. Adulteration led to a significant increase in sucrose content, HMF level, and Δδ13C = δ13CH‒δ13CP as well a decrease in reducing sugar content and diastase activity. Principal component analysis (PCA) and linear discriminant analysis (LDA) were applied to experimental data in order to distinguish between pure and adulterated honey. The most relevant discriminative parameters were diastase activity, HMF, sucrose, and reducing sugar contents. Posterior classification probabilities and classification functions obtained by LDA revealed that 100% of honey samples were correctly assigned to their original group.


1999 ◽  
Vol 53 (3) ◽  
pp. 302-311 ◽  
Author(s):  
Andrés F. Zuluaga ◽  
Urs Utzinger ◽  
Anthony Durkin ◽  
Holger Fuchs ◽  
Ann Gillenwater ◽  
...  

2015 ◽  
Vol 7 (7) ◽  
pp. 9253-9268 ◽  
Author(s):  
Chun Liu ◽  
Junjun Yin ◽  
Jian Yang ◽  
Wei Gao

One key problem for the classification of multi-frequency polarimetric SAR images is to extract target features simultaneously in the aspects of frequency, polarization and spatial texture. This paper proposes a new classification method for multi-frequency polarimetric SAR data based on tensor representation and multi-linear subspace learning (MLS). Firstly, each cell of the SAR images is represented by a third-order tensor in the frequency, polarization and spatial domains, with each order of tensor corresponding to one domain. Then, two main MLS methods, i.e., multi-linear principal component analysis (MPCA) and multi-linear extension of linear discriminant analysis (MLDA), are used to learn the third-order tensors. MPCA is used to analyze the principal component of the tensors. MLDA is applied to improve the discrimination between different land covers. Finally, the lower dimension subtensor features extracted by the MPCA and MLDA algorithms are classified with a neural network (NN) classifier. The classification scheme is accessed using multi-band polarimetric SAR images (C-, L- and P-band) acquired by the Airborne Synthetic Aperture Radar (AIRSAR) sensor of the Jet Propulsion Laboratory (JPL) over the Flevoland area. Experimental results demonstrate that the proposed method has good classification performance in comparison with the classic multi-band Wishart classifier. The overall classification accuracy is close to 99%, even when the number of training samples is small.


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