Browse
Submit Data
Databases
API
Help

Dataset Information

29 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

Indication of the geographical origin of honey using its physicochemical characteristics and multivariate analysis.

ABSTRACT: Honey from different botanical sources presents a great variability in chemical composition, physical properties, and sensorial attributes. The association between honey qualities and its geographical origin allows the recognition of honey, and beekeepers are economically benefited. The objective of this study was to evaluate the physicochemical characteristics of honey produced in Ortigueira, Brazil and to compare it with the honey produced in the neighboring localities for the request of geographical indication certification. In the 112 honey samples collected between 2010 and 2013 from Ortigueira and the neighboring localities, moisture acidity, pH, hydroxymethyl furfural (HMF), diastase activity (DA), sugars, proline (Pro), electrical conductivity (EC), color absorbance at 635 nm (C635), and Cielab color parameters were evaluated. HMF, reducing sugars (RS), total sugars (TS), proline, and color parameters (L*, a* and b*) were significantly different in Ortigueira 2010 honey seasons. Principal component analysis separated assapeixe (Vernonia sp) and capixingui (Croton floribundus) honeys from wild honeys (polyfloral). In addition, HMF, RS, L*, and lactonic acidity values promoted the separation between assapeixe and capixingui honeys. Ortigueira honey differs significantly from honeys from neighboring locality in pH, proline, HMF, DA, EC, RS, sucrose, TS, free and total acidity, and color parameters (C635, L*, a*, b*). The multivariate analysis applied to variables was efficient to discriminate honeys from different botanical sources and different locals, and is recommended for studies on the geographical indication of honey.

SUBMITTER: Scholz MBDS

PROVIDER: S-EPMC7171034 | biostudies-literature |

REPOSITORIES: biostudies-literature

ACCESS DATA

Json Xml

Similar Datasets

Origin Identification of Hungarian Honey Using Melissopalynology, Physicochemical Analysis, and Near Infrared Spectroscopy.

Project description:The objective of the study was to check the authenticity of Hungarian honey using physicochemical analysis, near infrared spectroscopy, and melissopalynology. In the study, 87 samples from different botanical origins such as acacia, bastard indigo, rape, sunflower, linden, honeydew, milkweed, and sweet chestnut were collected. The samples were analyzed by physicochemical methods (pH, electrical conductivity, and moisture), melissopalynology (300 pollen grains counted), and near infrared spectroscopy (NIRS:740-1700 nm). During the evaluation of the data PCA-LDA models were built for the classification of different botanical and geographical origins, using the methods separately, and in combination (low-level data fusion). PC number optimization and external validation were applied for all the models. Botanical origin classification models were >90% and >55% accurate in the case of the pollen and NIR methods. Improved results were obtained with the combination of the physicochemical, melissopalynology, and NIRS techniques, which provided >99% and >81% accuracy for botanical and geographical origin classification models, respectively. The combination of these methods could be a promising tool for origin identification of honey.

| S-EPMC8658813 | biostudies-literature

Determination of the Geographical Origin of Maltese Honey Using 1H NMR Fingerprinting.

Project description:The price of honey, as a highly consumed natural product, depends on its botanical source and its production environment, causing honey to be vulnerable to adulteration through mislabeling and inappropriate, fraudulent production. In this study, a fast and simple approach is proposed to tackle this issue through non-target one dimensional zg30 and noesypr1d 1H NMR fingerprint analysis, in combination with multivariate data analysis. Results suggest that composition differences in sugars, amino acids, and carboxylic acid were sufficient to discriminate between the tested honey of Maltese origin and that of non-local origin. Indeed, all chemometric models based on noesypr1d analysis of the whole fraction honey showed better prediction in geographical discrimination. The possibility of discrimination was further investigated through analysis of the honey's phenolic extract composition. The partial least squares models were deemed unsuccessful to discriminate, however, some of the linear discriminant analysis models achieved a prediction accuracy of 100%. Lastly, the best performing models of both the whole fraction and the phenolic extracts were tested on five samples of unknown geographic for market surveillance, which attained a high agreement within the models. Thus, suggesting the use of non-target 1H NMR coupled with the multivariate-data analysis and machine learning as a potential alternative to the current time-consuming analytical methods.

| S-EPMC7601949 | biostudies-literature

Physicochemical properties and biological activities of bracatinga honeydew honey from different geographical locations.

Project description:Bracatinga (Mimosa scabrella Bentham) honeydew honey is a Brazilian dark honey in increasing international appreciation. In this sense, the knowledge of its composition and potential biological properties becomes indispensable. In the present study, the physicochemical characteristics, including mineral and phenolic composition, and the scavenging, reducing, and antimicrobial proprieties of bracatinga honeydew honey (bhh) from five different geographical locations, were investigated. Bhh proved to be a potential functional food due to its high content of minerals (up to 6395 mg kg-1) and phenolic compounds (up to 2393 µg 100 g-1) and high scavenging and reducing activities. High antimicrobial activity against four bacterial strains, with minimum inhibitory concentration values ranging from 10 to 60%, were also found. Additionally, through principal component analysis, partial discrimination of bhh was observed according to the geographical location, which favored the separation of samples from Lages, and mainly due to the presence of nectar in this honey, which was proposed for the samples from Bom Retiro.Supplementary informationThe online version of this article (10.1007/s13197-020-04937-x) contains supplementary material, which is available to authorized users.

| S-EPMC8292519 | biostudies-literature

Volatile Profile and Physico-Chemical Analysis of Acacia Honey for Geographical Origin and Nutritional Value Determination.

Project description:Honey composition and color depend greatly on the botanical and geographical origin. Water content, water activity and color of 50 declared acacia samples, collected from three different geographical zones of Romania, together with chromatographic determination of sugar spectrum were analyzed. A number of 79 volatile compounds from the classes of: Alcohols, aldehydes, esters, ketones, sulphur compounds, aliphatic hydrocarbons, nitrogen compounds, carboxylic acids, aromatic acids and ethers were identified by solid-phase micro-extraction and gas-chromatography mass spectrometry. The overall volatile profile and sugar spectrum of the investigated honey samples allow the differentiation of geographical origin for the acacia honey samples subjected to analysis. The statistical models of the chromatic determination, physicochemical parameters and volatile profile was optimal to characterize the honey samples and group them into three geographical origins, even they belong to the same botanical origin.

| S-EPMC6836064 | biostudies-literature

Rapid geographical indication of peppercorn seeds using corona discharge mass spectrometry.

Project description:With increasing demands for more rapid and practical analyses, various techniques of ambient ionization mass spectrometry have gained significant interest due to the speed of analysis and abundance of information provided. Herein, an ambient ionization technique that utilizes corona discharge was applied, for the first time, to analyze and categorize whole seeds of black and white peppers from different origins. This setup requires no solvent application nor gas flow, thus resulting in a very simple and rapid analysis that can be applied directly to the sample without any prior workup or preparation. Combined with robust data pre-processing and subsequent chemometric analyses, this analytical method was capable of indicating the geographical origin of each pepper source with up to 98% accuracies in all sub-studies. The simplicity and speed of this approach open up the exciting opportunity for onsite analysis without the need for a highly trained operator. Furthermore, this methodology can be applied to a variety of spices and herbs, whose geographical indication or similar intellectual properties are economically important, hence it is capable of creating tremendous impact in the food and agricultural industries.

| S-EPMC8352875 | biostudies-literature

Characterization of Lavandula spp. Honey Using Multivariate Techniques.

Project description:Traditionally, melissopalynological and physicochemical analyses have been the most used to determine the botanical origin of honey. However, when performed individually, these analyses may provide less unambiguous results, making it difficult to discriminate between mono and multifloral honeys. In this context, with the aim of better characterizing this beehive product, a selection of 112 Lavandula spp. monofloral honey samples from several regions were evaluated by association of multivariate statistical techniques with physicochemical, melissopalynological and phenolic compounds analysis. All honey samples fulfilled the quality standards recommended by international legislation, except regarding sucrose content and diastase activity. The content of sucrose and the percentage of Lavandula spp. pollen have a strong positive association. In fact, it was found that higher amounts of sucrose in honey are related with highest percentage of pollen of Lavandula spp.. The samples were very similar for most of the physicochemical parameters, except for proline, flavonoids and phenols (bioactive factors). Concerning the pollen spectrum, the variation of Lavandula spp. pollen percentage in honey had little contribution to the formation of samples groups. The formation of two groups regarding the physicochemical parameters suggests that the presence of other pollen types in small percentages influences the factor termed as "bioactive", which has been linked to diverse beneficial health effects.

| S-EPMC5010229 | biostudies-literature

Analysis and Discrimination of Canadian Honey Using Quantitative NMR and Multivariate Statistical Methods.

Project description:To address the growing concern of honey adulteration in Canada and globally, a quantitative NMR method was developed to analyze 424 honey samples collected across Canada as part of two surveys in 2018 and 2019 led by the Canadian Food Inspection Agency. Based on a robust and reproducible methodology, NMR data were recorded in triplicate on a 700 MHz NMR spectrometer equipped with a cryoprobe, and the data analysis led to the identification and quantification of 33 compounds characteristic of the chemical composition of honey. The high proportion of Canadian honey in the library provided a unique opportunity to apply multivariate statistical methods including PCA, PLS-DA, and SIMCA in order to differentiate Canadian samples from the rest of the world. Through satisfactory model validation, both PLS-DA as a discriminant modeling technique and SIMCA as a class modeling method proved to be reliable at differentiating Canadian honey from a diverse set of honeys with various countries of origins and floral types. The replacement method of optimization was successfully applied for variable selection, and trigonelline, proline, and ethanol at a lower extent were identified as potential chemical markers for the discrimination of Canadian and non-Canadian honeys.

| S-EPMC9959790 | biostudies-literature

Botanical Origin, Pollen Profile, and Physicochemical Properties of Algerian Honey from Different Bioclimatic Areas.

Project description:The palynological and physicochemical analysis of 62 honey samples produced in different biogeographical areas of Algeria was conducted. Results showed high variety in the botanical origin of samples and their physicochemical profile. Twenty-six samples were polyfloral honey, 30 were unifloral honey from different botanical sources such as Eucalyptus, Citrus, Apiaceae, Punica, Erica, Rosmarinus, Eriobotrya, or Hedysarum, and 6 were characterized as honeydew honey. Pollen analysis allowed the identification of 104 pollen types belonging to 51 botanical families, whereas the physicochemical profile showed important variations between samples. Multivariate techniques were used to compare the characteristics of samples from different biogeographical areas, showing significant differences between humid-area samples, located in the northeast of the country, and samples taken in semiarid, subhumid, and arid zones. Principal-component analysis (PCA) extracted nine components explaining 72% of data variance, being 30%, the sum of Component 1 and Component 2. The plot of both components showed samples grouped upon botanical and geographical origin. The results of this paper highlighted the great variability in honey production of Algeria, evidencing the importance of honey characterization to guarantee authenticity and to valorize local production.

| S-EPMC7404483 | biostudies-literature

Detection of syrup adulterants in manuka and jarrah honey using HPTLC-multivariate data analysis.

Project description:High-Performance Thin-Layer Chromatography (HPTLC) was used in a chemometric investigation of the derived sugar and organic extract profiles of two different honeys (Manuka and Jarrah) with adulterants. Each honey was adulterated with one of six different sugar syrups (rice, corn, golden, treacle, glucose and maple syrups) in five different concentrations (10%, 20%, 30%, 40%, and 50% w/w). The chemometric analysis was based on the combined sugar and organic extract profiles' datasets. To obtain the respective sugar profiles, the amount of fructose, glucose, maltose, and sucrose present in the honey was quantified and for the organic extract profile, the honey's dichloromethane extract was investigated at 254 and 366 nm, as well as at T (Transmittance) white light and at 366 nm after derivatisation. The presence of sugar syrups, even at a concentration of only 10%, significantly influenced the honeys' sugar and organic extract profiles and multivariate data analysis of these profiles, in particular cluster analysis (CA), principal component analysis (PCA), principal component regression (PCR), partial least-squares regression (PLSR) and Machine Learning using an artificial neural network (ANN), were able to detect post-harvest syrup adulterations and to discriminate between neat and adulterated honey samples. Cluster analysis and principal component analysis, for instance, could easily differentiate between neat and adulterated honeys through the use of CA or PCA plots. In particular the presence of excess amounts of maltose and sucrose allowed for the detection of sugar adulterants and adulterated honeys by HPTLC-multivariate data analysis. Partial least-squares regression and artificial neural networking were employed, with augmented datasets, to develop optimal calibration for the adulterated honeys and to predict those accurately, which suggests a good predictive capacity of the developed model.

| S-EPMC8464195 | biostudies-literature

Chemical Analysis Combined with Multivariate Statistical Methods to Determine the Geographical Origin of Milk from Four Regions in China.

Project description:Traceability of milk origin in China is conducive to the implementation of the protection of regional products. In order to distinguish milk from different geographical distances in China, we traced the milk of eight farms in four neighboring provinces of China (Inner Mongolia autonomous region, Hebei, Ningxia Hui autonomous and Shaanxi), and multivariate data analysis was applied to the data including elemental analysis, stable isotope analysis and fatty acid analysis. In addition, orthogonal partial least squares discriminant analysis (OPLS-DA) is used to determine the optimal classification model, and it is explored whether the combination of different technologies is better than a single technical analysis. It was confirmed that in the inter-provincial samples, the combination of the two techniques was better than the analysis using a single technique (fatty acids: R2 = 0.716, Q2 = 0.614; fatty acid-binding isotopes: R2 = 0.760, Q2 = 0.635). At the same time, milk produced by farms with different distances of less than 11 km in each province was discriminated, and the discriminant distance was successfully reduced to 0.7 km (Ningxia Hui Autonomous Region: the distance between the two farms was 0.7 km, R2 = 0.771, Q2 = 0.631). For short-distance samples, the combination multiple technologies are not completely superior to a single technique, and sometimes, it is easy to cause model over-fitting.

| S-EPMC8158098 | biostudies-literature

OmicsDI is part of the ELIXIR infrastructure

OmicsDI is an Elixir interoperability service. Learn more ›

Tweets

OmicsDI Databases

PRIDE
PeptideAtlas
MassIVE
JPOST Repository
Physiome Model Repository

EGA
EVA
ENA
LINCS
PAXDB
Cell Collective

MetaboLights
Metabolomics Workbench
MetabolomeExpress
GNPS
BioModels
FAIRDOMHub

ArrayExpress
dbGaP
ExpressionAtlas
GEO
NODE

Information

Databases
Help
API
Contact us
Code on GitHub
Terms of use
Submit Data