NIR detection of honey adulteration reveals differences in water spectral pattern

Bázár, George; Romvári, Róbert; Szabó, A.; Somogyi, T.; Éles, Viktória; Tsenkova, Roumiana

doi:10.1016/j.foodchem.2015.08.092

Cited by 138 publications

(64 citation statements)

References 30 publications

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“…In aquaphotomics applications, the second derivative is a very popular and efficient approach for discovering activated water absorbance bands that are not visible in the original spectrum (see for example Jinendra et al, 2010; Jinendra, 2011; Kinoshita et al, 2012; Bázár et al, 2016; Kovacs et al, 2016). …”

Section: Spectral Preprocessing—improving and Enhancing Spectral Infomentioning

confidence: 99%

Essentials of Aquaphotomics and Its Chemometrics Approaches

et al. 2018

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Aquaphotomics is a novel scientific discipline involving the study of water and aqueous systems. Using light-water interaction, it aims to extract information about the structure of water, composed of many different water molecular conformations using their absorbance bands. In aquaphotomics analysis, specific water structures (presented as water absorbance patterns) are related to their resulting functions in the aqueous systems studied, thereby building an aquaphotome—a database of water absorbance bands and patterns correlating specific water structures to their specific functions. Light-water interaction spectroscopic methods produce complex multidimensional spectral data, which require data processing and analysis to extract hidden information about the structure of water presented by its absorbance bands. The process of extracting information from water spectra in aquaphotomics requires a field–specific approach. It starts with an appropriate experimental design and execution to ensure high-quality spectral signals, followed by a multitude of spectral analysis, preprocessing and chemometrics methods to remove unwanted influences and extract water absorbance spectral pattern related to the perturbation of interest through the identification of activated water absorbance bands found among the common, consistently repeating and highly influential variables in all analytical models. The objective of this paper is to introduce the field of aquaphotomics and describe aquaphotomics multivariate analysis methodology developed during the last decade. Through a worked-out example of analysis of potassium chloride solutions supported by similar approaches from the existing aquaphotomics literature, the provided instruction should give enough information about aquaphotomics analysis i.e. to design and perform the experiment and data analysis as well as to represent water absorbance spectral pattern using various forms of aquagrams—specifically designed aquaphotomics graphs. The explained methodology is derived from analysis of near infrared spectral data of aqueous systems and will offer a useful and new tool for extracting data from informationally rich water spectra in any region. It is the hope of the authors that with this new tool at the disposal of scientists and chemometricians, pharmaceutical and biomedical spectroscopy will substantially progress beyond its state-of-the-art applications.

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Section: Spectral Preprocessing—improving and Enhancing Spectral Infomentioning

confidence: 99%

Essentials of Aquaphotomics and Its Chemometrics Approaches

et al. 2018

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“…(iii) Detecting insecticides, pesticides, veterinary drug or multi-class antibiotic residues in honey [54][55][56][57]. (iv) Detecting honey adulterations [24,30,[58][59][60][61][62][63][64].…”

Section: Introductionmentioning

confidence: 99%

Honey Evaluation Using Electronic Tongues: An Overview

et al. 2018

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Honey-rich composition in biologically active compounds makes honey a food products highly appreciated due to the nutritional and healthy properties. Food-manufacturing is very prone to different types of adulterations and fraudulent labelling making it urgent to establish accurate, fast and cost-effective analytical techniques for honey assessment. In addition to the classical techniques (e.g., physicochemical analysis, microscopy, chromatography, immunoassay, DNA metabarcoding, spectroscopy), electrochemical based-sensor devices have arisen as reliable and green techniques for food analysis including honey evaluation, allowing in-situ and on-line assessment, being a user-friendly procedure not requiring high technical expertise. In this work, the use of electronic tongues, also known as taste sensor devices, for honey authenticity and assessment is reviewed. Also, the versatility of electronic tongues to qualitative (e.g., botanical and/or geographical origin assessment as well as detection of adulteration) and quantitative (e.g., assessment of adulterants levels, determination of flavonoids levels or antibiotics and insecticides residues, flavonoids) honey analysis is shown. The review is mainly focused on the research outputs reported during the last decade aiming to demonstrate the potentialities of potentiometric and voltammetric multi-sensor devices, pointing out their main advantages and present and future challenges for becoming a practical quality analytical tool at industrial and commercial levels.

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“…Combining chemometrics, intelligent algorithms, and so forth, the hyperspectral technology can visualize the inner and outer information of the samples [13] and can nondestructively and accurately determine the component, content, state, and spatial distribution of the samples [14]. In view of these merits, this technology has been widely applied in the field of nondestructive quality detection of food such as vegetables [15], fruits [16], seafood [17], milk [18], honey [19], etc., [20]. In particular, the technique has received a considerable attention in meat quality, safety, and authenticity.…”

Section: Introductionmentioning

confidence: 99%

Nondestructive Identification of Salmon Adulteration with Water Based on Hyperspectral Data

Zhang

Wang

Yan

et al. 2018

Journal of Food Quality

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For the identification of salmon adulteration with water injection, a nondestructive identification method based on hyperspectral images was proposed. The hyperspectral images of salmon fillets in visible and near-infrared ranges (390–1050 nm) were obtained with a system. The original hyperspectral data were processed through the principal-component analysis (PCA). According to the image quality and PCA parameters, a second principal-component (PC2) image was selected as the feature image, and the wavelengths corresponding to the local extremum values of feature image weighting coefficients were extracted as feature wavelengths, which were 454.9, 512.3, and 569.1 nm. On this basis, the color combined with spectra at feature wavelengths, texture combined with spectra at feature wavelengths, and color-texture combined with spectra at feature wavelengths were independently set as the input, for the modeling of salmon adulteration identification based on the self-organizing feature map (SOM) network. The distances between neighboring neurons and feature weights of the models were analyzed to realize the visualization of identification results. The results showed that the SOM-based model, with texture-color combined with fusion features of spectra at feature wavelengths as the input, was evaluated to possess the best performance and identification accuracy is as high as 96.7%.

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NIR detection of honey adulteration reveals differences in water spectral pattern

Cited by 138 publications

References 30 publications

Essentials of Aquaphotomics and Its Chemometrics Approaches

Essentials of Aquaphotomics and Its Chemometrics Approaches

Honey Evaluation Using Electronic Tongues: An Overview

Nondestructive Identification of Salmon Adulteration with Water Based on Hyperspectral Data

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