scholarly journals Characterization of Indonesia wild honey and its potential for authentication and origin distinction

Food Research ◽  
2020 ◽  
Vol 4 (5) ◽  
pp. 1670-1680
Author(s):  
Y. Riswahyuli ◽  
A. Rohman ◽  
F.M.C.S. Setyabudi ◽  
S. Raharjo
Keyword(s):  
Ph Value ◽  
Sugar Content ◽  
Antioxidant Properties ◽  
Principal Component ◽  
Food Product ◽  
Natural Food ◽  
Data Set ◽  
Linear Discriminant ◽  
Geographical Regions ◽  
Antioxidant Parameters

Honey is a natural food derived from flowers nectar that has many health benefits. This reason made honey become one of category food product that has a risk to be adulterated because of economically motivation. This study was conducted for characterization and authentication of Indonesia wild honey (IWH) collected from seven geographical regions (Sumatra, Bangka Belitung, Java, Kalimantan, Sulawesi, West Nusa Tenggara, and East Nusa Tenggara) and harvested during 2016–2018 based on physicochemical parameters, sugar content, minerals, and antioxidant components. The study showed that the result differs widely among the type of honey. IWH has a moisture content between 16.52- 33.41%, a pH value between 3.00 to 4.65 and color characteristic ranged from pale yellow to dark brown. All samples contain the highest amount in potassium, but several minerals found in the specific region. Evaluation of authenticity from sugar content data set by principal component analysis (PCA) and Linear Discriminant Analysis (LDA) revealed that the authentic and adulterated honey samples could be differentiated with a 95.5% accuracy. The honey samples were classified on their botanical and geographical origin using the antioxidant properties, and results of PCA and LDA demonstrated that the antioxidant parameters can provide adequate information to allow classification of the various types of IWH samples collected from different geographical regions with accuracy 80-100% for Bangka Belitung, Sulawesi, Kalimantan, West Nusa Tenggara, East Nusa Tenggara and Java island

scholarly journals Relationships Linking the Colour and Elemental Concentrations of Blossom Honeys with Their Antioxidant Activity: A Chemometric Approach

Agriculture ◽  
2021 ◽  
Vol 11 (8) ◽  
pp. 702
Author(s):  
Monika Kędzierska-Matysek ◽  
Anna Teter ◽  
Małgorzata Stryjecka ◽  
Piotr Skałecki ◽  
Piotr Domaradzki ◽  
...  
Keyword(s):  
Antioxidant Activity ◽  
Physicochemical Properties ◽  
Black Locust ◽  
Antioxidant Properties ◽  
Principal Component ◽  
Botanical Origin ◽  
Antioxidant Parameters ◽  
Colour Parameters ◽  
Hue Angle ◽  
Chemometric Approach

The antioxidant activity of honey depends on the botanical origin, which also determines their physicochemical properties. In this study, a multivariate analysis was used to confirm potential relationships between the antioxidant properties and colour parameters, as well as the content of seven elements in five types of artisanal honey (rapeseed, buckwheat, linden, black locust, and multifloral). The type of honey was found to significantly influence most of its physicochemical properties, colour parameters, and the content of potassium, manganese and copper. Antioxidant parameters were shown to be significantly positively correlated with redness and concentrations of copper and manganese, but negatively correlated with the hue angle and lightness. The principal component analysis confirmed that the darkest buckwheat honey had the highest antioxidant activity in combination with its specific colour parameters and content of antioxidant minerals (manganese, copper and zinc). The level of these parameters can be potentially used for the identification of buckwheat honey.

scholarly journals Characterization and classification of Romanian acacia honey based on its physicochemical parameters and chemometrics

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Mihaela Emanuela Crăciun ◽  
Oana Cristina Pârvulescu ◽  
Andreea Cristina Donise ◽  
Tănase Dobre ◽  
Dumitru Radu Stanciu
Keyword(s):  
Physicochemical Parameters ◽  
Stable Carbon Isotopes ◽  
Reducing Sugars ◽  
Activity Ratio ◽  
Sugar Content ◽  
Principal Component ◽  
Reducing Sugar ◽  
Linear Discriminant ◽  
Acacia Honey

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.

scholarly journals Geographical and Varietal Traceability of Chinese Jujubes Based on Physical and Nutritional Characteristics

Foods ◽  
2021 ◽  
Vol 10 (10) ◽  
pp. 2270
Author(s):  
Linxia Wu ◽  
Ling Li ◽  
Guoguang Zhang ◽  
Nan Jiang ◽  
Xihui Ouyang ◽  
...  
Keyword(s):  
Discriminant Analysis ◽  
Quality Indicators ◽  
Sugar Content ◽  
Soluble Sugar ◽  
Principal Component ◽  
Cyclic Adenosine Monophosphate ◽  
Adenosine Monophosphate ◽  
Chinese Jujube ◽  
Linear Discriminant ◽  
Different Origins

Chinese jujube fruits are known for their high nutritional and functional values. To protect advantageous regional jujube fruits, it is important to monitor quality indicators and trace the origin and variety. In this study, 31 quality indicators of Chinese jujubes collected from 6 main producing areas were determined. According to different origins and varieties, Chinese jujube fruits were divided into five and six categories, respectively. To simplify the parameters, eight of the main characteristics, namely, soluble sugar content, fresh mass, edible rate, Na, Mg, K, Zn, and cyclic adenosine monophosphate (cAMP), were screened based on multiple comparison, correlation analysis, and principal component analysis (PCA). According to the eight main parameters, it was found that that both the categorical and cross-validated classification accuracy of linear discriminant analysis (LDA) were 100%. The discrimination accuracy of the testing set samples based on the orthogonal partial least squares-discriminant analysis (OPLS-DA) model were 90 and 93% for geographical and varietal classification, respectively. This indicated that the eight main parameters could be used as the characteristic parameters for the origin and variety traceability of Chinese jujubes.

SHORT PROKARYOTIC DNA FRAGMENT BINNING USING A HIERARCHICAL CLASSIFIER BASED ON LINEAR DISCRIMINANT ANALYSIS AND PRINCIPAL COMPONENT ANALYSIS

2010 ◽  
Vol 08 (06) ◽  
pp. 995-1011 ◽  
Author(s):  
HAO ZHENG ◽  
HONGWEI WU
Keyword(s):  
Principal Component Analysis ◽  
Discriminant Analysis ◽  
Linear Discriminant Analysis ◽  
Simulated Data ◽  
Principal Component ◽  
Component Analysis ◽  
Dna Fragments ◽  
Data Set ◽  
Linear Discriminant ◽  
Hierarchical Classifier

Metagenomics is an emerging field in which the power of genomic analysis is applied to an entire microbial community, bypassing the need to isolate and culture individual microbial species. Assembling of metagenomic DNA fragments is very much like the overlap-layout-consensus procedure for assembling isolated genomes, but is augmented by an additional binning step to differentiate scaffolds, contigs and unassembled reads into various taxonomic groups. In this paper, we employed n-mer oligonucleotide frequencies as the features and developed a hierarchical classifier (PCAHIER) for binning short (≤ 1,000 bps) metagenomic fragments. The principal component analysis was used to reduce the high dimensionality of the feature space. The hierarchical classifier consists of four layers of local classifiers that are implemented based on the linear discriminant analysis. These local classifiers are responsible for binning prokaryotic DNA fragments into superkingdoms, of the same superkingdom into phyla, of the same phylum into genera, and of the same genus into species, respectively. We evaluated the performance of the PCAHIER by using our own simulated data sets as well as the widely used simHC synthetic metagenome data set from the IMG/M system. The effectiveness of the PCAHIER was demonstrated through comparisons against a non-hierarchical classifier, and two existing binning algorithms (TETRA and Phylopythia).

scholarly journals Mushroom’s Evaluation Based on FT-IR Fingerprint and Chemometrics

2021 ◽  
Author(s):  
Ioana Feher ◽  
Cornelia Veronica Floare-Avram ◽  
Florina-Dorina Covaciu ◽  
Olivian Marincas ◽  
Romulus Puscas ◽  
...  
Keyword(s):  
Principal Component ◽  
Therapeutic Effects ◽  
Data Set ◽  
Linear Discriminant ◽  
Long Time ◽  
Fuzzy C Means Clustering ◽  
Ft Ir ◽  
Ir Analysis ◽  
Fcm Analysis ◽  
Sample Set

Edible mushrooms have been recognized as highly nutritional food for a long time, due to their specific flavor, texture and also for therapeutic effects. This study proposes a new simple approach, based on FT-IR analysis, followed by statistical methods, in order to differentiate three wild mushrooms species from Romanian spontaneous flora, namely Armillaria mellea, Boletus edulis and Cantharellus cibarius. The preliminary data treatment consisted of data set reduction with principal component analysis (PCA), which provided scores for the next methods. Linear discriminant analysis (LDA) manage to 100% classify the three species and the cross validation step of the method returned 97.4% of correctly classified samples. Only one A. mellea sample overlapped on B. edulis group. When kNN was used in the same manner as LDA, the overall percent of correctly classified samples from the training step was 86.21%, while for holdout set the percent raised at 94.74%. The lowered values obtained for the training set was due to one C. cibarius sample, two B. edulis and five A. mellea, which were placed to other species. Anyway, for holdout sample set, only one sample from B. edulis was misclassified. The fuzzy c-means clustering (FCM) analysis successfully classified investigated mushroom samples according to their species, meaning that in every partition the predominant specie had the biggest DOMs, while samples belonging to other specie had lower DOMs.

scholarly journals Fatty Acids-Based Quality Index to Differentiate Worldwide Commercial Pistachio Cultivars

2018 ◽  
Author(s):  
Mahnaz Esteki ◽  
Parvin Ahmadi ◽  
Yvan Vander Heyden ◽  
Jesus Simal-Gandara
Keyword(s):  
Fatty Acid ◽  
Quality Index ◽  
Predictive Power ◽  
Principal Component ◽  
Chromatographic Fingerprint ◽  
Data Set ◽  
Linear Discriminant ◽  
Fatty Acid Data ◽  
Percent Accuracy ◽  
Almost All

The fatty-acid profiles of five main commercial pistachio cultivars, including Ahmad-Aghaei, Akbari, Chrok, Kalle-Ghouchi and Ohadi, were determined by gas chromatography: palmitic (C16:0), palmitoleic (C16:1), stearic (C18:0), oleic (C18:1), linoleic (C18:2), linolenic (C18:3) arachidic (C20:0) and gondoic (C20:1) acid. Based on the oleic to linoleic acid (O/L) ratio, a quality index was determined for these five cultivars: Ohadi (2.40) < Ahmad-Aghaei (2.60) < Kale-Ghouchi (2.94) < Chrok (3.05) < Akbari (3.66). Principal component analysis (PCA) of the fatty-acid data yielded three significant PCs, which together account for 80.0% of the total variance in the data set. A linear discriminant analysis (LDA) model evaluated with cross validation correctly classified almost all samples: the average percent accuracy for the prediction set was 98.0%. The high predictive power for the prediction set shows the ability to indicate the cultivar of an unknown sample based on its fatty-acid chromatographic fingerprint.

scholarly journals Cancer Influences the Elemental Composition of the Myocardium More Strongly than Conjugated Linoleic Acids-Chemometric Approach to Cardio-Oncological Studies

Molecules ◽  
2021 ◽  
Vol 26 (23) ◽  
pp. 7127
Author(s):  
Agnieszka Białek ◽  
Małgorzata Białek ◽  
Tomasz Lepionka ◽  
Anna Ruszczyńska ◽  
Ewa Bulska ◽  
...  
Keyword(s):  
Inductively Coupled Plasma ◽  
Antioxidant Properties ◽  
Principal Component ◽  
Mammary Tumors ◽  
Pathological Process ◽  
Elemental Content ◽  
Sprague Dawley ◽  
Conjugated Linoleic Acids ◽  
Linear Discriminant ◽  
Diet Supplementation

The aim of the study was to verify in a cardio-oncological model experiment if conjugated linoleic acids (CLA) fed to rats with mammary tumors affect the content of selected macro- and microelements in their myocardium. The diet of Sprague–Dawley females was supplemented either with CLA isomers or with safflower oil. In hearts of rats suffering from breast cancer, selected elements were analyzed with a quadrupole mass spectrometer with inductively coupled plasma ionization (ICP-MS). In order to better understand the data trends, cluster analysis, principal component analysis and linear discriminant analysis were applied. Mammary tumors influenced macro- and microelements content in the myocardium to a greater extent than applied diet supplementation. Significant influences of diet (p = 0.0192), mammary tumors (p = 0.0200) and interactions of both factors (p = 0.0151) were documented in terms of Fe content. CLA significantly decreased the contents of Cu and Mn (p = 0.0158 and p = 0.0265, respectively). The level of Ni was significantly higher (p = 0.0073), which was more pronounced in groups supplemented with CLA. The obtained results confirmed antioxidant properties of CLA and the relationship with Se deposition. Chemometric techniques distinctly showed that the coexisting pathological process induced differences to the greater extent than diet supplementation in the elemental content in the myocardium, which may impinge on cardiac tissue’s susceptibility to injuries.

scholarly journals Chemometric approach to quality characterization of milk-based kombucha beverages

Mljekarstvo ◽  
2021 ◽  
Vol 71 (2) ◽  
pp. 83-94
Author(s):  
Jasmina Vitas ◽  
Keyword(s):  
Fatty Acids ◽  
Milk Fat ◽  
Unsaturated Fatty Acids ◽  
Principal Component ◽  
Unknown Origin ◽  
Hierarchical Cluster ◽  
Monounsaturated Fatty Acids ◽  
Data Set ◽  
Linear Discriminant

Milk-based kombucha beverages were obtained conducting kombucha lead fermentation of milk. In order to discriminate the analysed samples and to detect similarities or dissimilarities among them in the space of experimentally determined variables, hierarchical cluster analysis (HCA) and principal component analysis (PCA) were applied. Linear discriminant analysis (LDA) was conducted on the raw data set in order to find a rule for allocating a new sample of unknown origin to the correct group of samples. In the space of the variables analysed by HCA, the dominant discriminating factor for the studied samples of kombucha beverages is the milk fat (MF) content, followed by total unsaturated fatty acids content (TUFA), monounsaturated fatty acids content (MUFA) and polyunsaturated fatty acids content (PUFA). The samples with 0.8 and 1.6% milk fat belong to the same cluster in the space of the analysed variables due to similarities in their AADPPH. It was determined by LDA that there was the biggest difference in quality between the groups of products with winter savoury and stinging nettle, while the highest similarity is between groups of products with wild thyme and peppermint regarding their pH values and antioxidant activity expressed as AADPPH.

Bursztynowe źródło zdrowia – zastosowanie miodu we współczesnej medycynie

2018 ◽  
Vol 21 (1) ◽  
Author(s):  
Jacek Jagiełło ◽  
Elżbieta Kołeczek ◽  
Michalina Horochowska ◽  
Zygmunt Zdrojewicz ◽  
Amelia Głowaczewska
Keyword(s):  
Antioxidant Properties ◽  
Food Product ◽  
Sweet Taste ◽  
Eye Diseases ◽  
Natural Food ◽  
Health It ◽  
Manuka Honey ◽  
The World ◽  
Infant Botulism ◽  
Ancient Times

Honey is a natural food product with sweet taste and precious nutrients, produced by Apis mellifera, Vespa sp. or Meliponini sp. from nectar of various types of flowers. It’s taste, color and smell depends on the flower, from which nectar was collected. From ancient times it was used as food product and it’s considered to be the oldest sweetening substance in human’s cuisine. In recent times scientists got interested in medical properties of honey and it’s influence on people’s health. It was proved that honey has antioxidant, anti-inflammatory, anti-proliferative and antineoplastic properties. Honey has an influence on our immune system and helps to kill bacteria. It is confirmed that honey have an influence on diabetes, cardiovascular system, oral cavity, respiratory tract or eye diseases. What more, honey can improve fertility, because of it’s antioxidant properties. The most famous honey is Manuka honey, which is known all over the world due to containment of methyloglyoxal (substance that is capable of killing bacteria). Unfortunately, in some cases honey can contain endospores and botuline toxin, that can cause infant botulism.

scholarly journals An Improved Comparison of Chemometric Analyses for the Identification of Acids and Bases With Colorimetric Sensor Arrays

2018 ◽  
Vol 10 (2) ◽  
pp. 36 ◽  
Author(s):  
Michael James Kangas ◽  
Christina L Wilson ◽  
Raychelle M Burks ◽  
Jordyn Atwater ◽  
Rachel M Lukowicz ◽  
...  
Keyword(s):  
Image Analysis ◽  
Discriminant Analysis ◽  
Linear Discriminant Analysis ◽  
Sensor Arrays ◽  
Principal Component ◽  
Colorimetric Sensor ◽  
Support Vector ◽  
Chemometric Methods ◽  
Data Set ◽  
Linear Discriminant

Colorimetric sensor arrays incorporating red, green, and blue (RGB) image analysis use value changes from multiple sensors for the identification and quantification of various analytes. RGB data can be easily obtained using image analysis software such as ImageJ. Subsequent chemometric analysis is becoming a key component of colorimetric array RGB data analysis, though literature contains mainly principal component analysis (PCA) and hierarchical cluster analysis (HCA). Seeking to expand the chemometric methods toolkit for array analysis, we explored the performance of nine chemometric methods were compared for the task of classifying 631 solutions (0.1 to 3 M) of acetic acid, malonic acid, lysine, and ammonia using an eight sensor colorimetric array. PCA and LDA (linear discriminant analysis) were effective for visualizing the dataset. For classification, linear discriminant analysis (LDA), (k nearest neighbors) KNN, (soft independent modelling by class analogy) SIMCA, recursive partitioning and regression trees (RPART), and hit quality index (HQI) were very effective with each method classifying compounds with over 90% correct assignments. Support vector machines (SVM) and partial least squares – discriminant analysis (PLS-DA) struggled with ~85 and 39% correct assignments, respectively. Additional mathematical treatments of the data set, such as incrementally increasing the exponents, did not improve the performance of LDA and KNN. The literature precedence indicates that the most common methods for analyzing colorimetric arrays are PCA, LDA, HCA, and KNN. To our knowledge, this is the first report of comparing and contrasting several more diverse chemometric methods to analyze the same colorimetric array data.

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