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

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.

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Hyperspectral monitoring of fructose content in marzipan

Progress in Agricultural Engineering Sciences ◽

10.1556/446.14.2018.s1.8 ◽

2018 ◽

Vol 14 (s1) ◽

pp. 79-88

Author(s):

Katalin Badak-Kerti ◽

Szabina Németh ◽

Andreas Zitek ◽

Ferenc Firtha

Keyword(s):

Principal Component Analysis ◽

Spatial Distribution ◽

Spectral Properties ◽

Oil Content ◽

Sugar Content ◽

Principal Component ◽

Reducing Sugar ◽

Hyperspectral Data ◽

Hyperspectral Images ◽

Analysis Technique

In our research marzipan samples of different sugar to almond paste ratios (1:1, 2:1, 3:1) were stored at 17 °C. Reducing sugar content was measured by analytical method, texture analysis was done by penetrometry, electric characteristics were measured by conductometry and hyperspectral images were taken 6–8 times during the 16 days of storage. For statistical analyses (discriminant analysis, principal component analysis) SPSS program was used. According to our findings with the hyperspectral analysis technique, it is possible to identify how long the samples were stored (after production), and to which class (ratio of sugar to almond) the sample belonged. The main wavelengths which gave the best discrimination results among the days of storage were between 960 and 1100 nm. The type of the marzipan was easy to distinguish with the hyperspectral data; the biggest differences were observed at 1200 and 1400 nm, which are connected to the first overtone of C-H bound, therefore correlate with the oil content. The spatial distribution of penetrometric, electric and spectral properties were also characteristic to fructose content. The fructose content of marzipan is difficult to measure by usual optical ways (polarimetry, spectroscopy), but since fructose is hygroscopic, the spatial distribution of spectral properties can be characteristic.

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Determination of HPLC-UV Fingerprints of Spanish Paprika (Capsicum annuum L.) for Its Classification by Linear Discriminant Analysis

Sensors ◽

10.3390/s18124479 ◽

2018 ◽

Vol 18 (12) ◽

pp. 4479 ◽

Cited By ~ 6

Author(s):

Xavier Cetó ◽

Núria Serrano ◽

Miriam Aragó ◽

Alejandro Gámez ◽

Miquel Esteban ◽

...

Keyword(s):

Discriminant Analysis ◽

Linear Discriminant Analysis ◽

Principal Component ◽

Reversed Phase ◽

Classification Rate ◽

Phenolic Profile ◽

Linear Discriminant ◽

Sample Extraction

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).

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Screening of potato genotypes based on glucose and Asparagines content to minimize Acrylamide formation in potato chips and French fries

Research in Agriculture Livestock and Fisheries ◽

10.3329/ralf.v3i2.29355 ◽

2016 ◽

Vol 3 (2) ◽

pp. 295-304

Author(s):

Fatema Zahan ◽

Md Masudul Karim ◽

Tahmina Akter ◽

Md Alamgir Hossain

Keyword(s):

Reducing Sugars ◽

Sugar Content ◽

Reducing Sugar ◽

Potato Chips ◽

French Fries ◽

Potato Tubers ◽

Botany Department ◽

Factors Affecting ◽

Acrylamide Formation ◽

Significant Difference

Seven potato genotypes that are available in Bangladesh, were grown at the field laboratory under the Crop Botany Department, Bangladesh Agricultural University in 2014. Reducing sugars and free asparagine were determined at freshly harvested potato tubers and those after storing at 80C for 8 months. There was no significant variation of asparagine content in all genotypes of freshly harvested tubers. But a significant difference was found in reducing sugar content. The lowest was in the samples of the genotypes Cardinal and Rumanapakri, and the highest in Hagrai. The variety Diamant appeared to contain the lowest amount of reducing sugars after 8 months storage. The results showed that freshly harvested Cardinal, Rumanapakri and Diamant after storage produced less amount of acrylamide after frying as potato chips or French fries. It may be concluded that screening potato genotypes primarily on their reducing sugar contents could be useful tool to minimize acrylamide formation in potato chips and French fries. Further investigation is needed to find out the factors affecting reducing sugar and asparagine content in potato tubers.Res. Agric., Livest. Fish.3(2): 295-304, August 2016

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Some effects of gamma radiation on several varieties of Tasmanian potatoes. 2. Biochemical changes

Australian Journal of Experimental Agriculture ◽

10.1071/ea9650289 ◽

1965 ◽

Vol 5 (18) ◽

pp. 289 ◽

Cited By ~ 2

Author(s):

PA Wills

Keyword(s):

Ascorbic Acid ◽

Gamma Radiation ◽

Dry Matter ◽

Reducing Sugars ◽

Starch Content ◽

Sugar Content ◽

Dehydroascorbic Acid ◽

Ascorbic Acid Content ◽

Reducing Sugar ◽

Total Sugars

Tasmanian Up-to-date, Kennebec, Sebago, and Sequoia tubers were treated with low doses of gamma radiation to inhibit sprouting and were subsequently stored at 68�F or 45�F. Tubers were examined at bi-monthly intervals for some, or all, of the following biochemical constituents : dry matter, thiamin, reduced ascorbic acid, dehydroascorbic acid, total ascorbic acid, soluble reducing sugars, sucrose, total sugars, and starch. Radiation, independent of dose level, led to lower dry matter contents in the Up-to-date, Kennebec, and Sequoia tubers after four months storage, and in the Sebago tubers after six months storage at 68�F, but not at 45�F, when compared to the controls. The thiamin content of Up-to-date tubers w-as not influenced by radiation (16,000 rads). Storage w-as a more important factor than radiation in altering the ascorbic acid content of tubers. Radiation maintained, or even increased the normal level. Radiation increased the soluble reducing sugar content of Sequoia tubers, the total sugar of Sequoia and Sebago tubers, and the sucrose content of the Sebago tubers, but had little effect on the starch content of these varieties. Storage influenced some of these constituents, particularly in the Sebago variety.

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Using Chemometric Analyses for Tracing the Regional Origin of Multifloral Honeys of Montenegro

Foods ◽

10.3390/foods9020210 ◽

2020 ◽

Vol 9 (2) ◽

pp. 210 ◽

Cited By ~ 1

Author(s):

Vesna Vukašinović-Pešić ◽

Nada Blagojević ◽

Snežana Brašanac-Vukanović ◽

Ana Savić ◽

Vladimir Pešić

Keyword(s):

Industrial Pollution ◽

Physicochemical Parameters ◽

Mineral Content ◽

Cross Validation ◽

Classification Rate ◽

Linear Discriminant ◽

Allowable Level ◽

Regional Origin ◽

Maximum Allowable Level

This is the first study of mineral content and basic physicochemical parameters of honeys of Montenegro. We examined honey samples from eight different micro-regions of Montenegro, and the results confirm that, with the exception of cadmium in samples from two regions exposed to industrial pollution, none of the 12 elements analyzed exceeded the maximum allowable level. The samples from areas exposed to industrial pollution were clearly distinguished from samples from other regions of Montenegro in the detectable contents of Pb, Cd, and Sr. This study showed that chemometric techniques might enhance the classification of Montenegrin honeys according to their micro-regional origin using the mineral content. Linear discriminant analysis revealed that the classification rate was 79.2% using the cross-validation method.

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Discrimination of Natural Mature Acacia Honey Based on Multi-Physicochemical Parameters Combined with Chemometric Analysis

Molecules ◽

10.3390/molecules24142674 ◽

2019 ◽

Vol 24 (14) ◽

pp. 2674 ◽

Cited By ~ 3

Author(s):

Tianchen Ma ◽

Haoan Zhao ◽

Caiyun Liu ◽

Min Zhu ◽

Hui Gao ◽

...

Keyword(s):

Principal Component Analysis ◽

Physicochemical Parameters ◽

Phenolic Content ◽

Principal Component ◽

Component Analysis ◽

Chemometric Analysis ◽

Total Phenolic ◽

Total Protein Content ◽

Acacia Honey

Honey maturity is an important factor in evaluating the quality of honey. We established a method for the identification of natural mature acacia honey with eighteen physicochemical parameters combined with chemometric analysis. The analysis of variance showed significant differences between mature and immature acacia honey in physicochemical parameters. The principal component analysis explained 82.64% of the variance among samples, and indicated that total phenolic content, total protein content, and total sugar (glucose, fructose, sucrose) were the major variables. The cluster analysis and orthogonal partial least squares-discriminant analysis demonstrated that samples were grouped in relation to the maturity coinciding with the results of the principal component analysis. Meanwhile, the 35 test samples were classified with 100% accuracy with the method of multi-physicochemical parameters combined with chemometric analysis. All the results presented above proved the possibility of identifying mature acacia honey and immature acacia honey according to the chemometric analysis based on the multi-physicochemical parameters.

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Classification of Multi-Frequency Polarimetric SAR Images Based on Multi-Linear Subspace Learning of Tensor Objects

Remote Sensing ◽

10.3390/rs70709253 ◽

2015 ◽

Vol 7 (7) ◽

pp. 9253-9268 ◽

Cited By ~ 9

Author(s):

Chun Liu ◽

Junjun Yin ◽

Jian Yang ◽

Wei Gao

Keyword(s):

Linear Subspace ◽

Principal Component ◽

Subspace Learning ◽

Classification Performance ◽

Polarimetric Sar ◽

Third Order ◽

Sar Images ◽

Linear Discriminant ◽

Multi Band

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

Foods ◽

10.3390/foods10102270 ◽

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.

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Characterizing Protein Shape by a Volume Distribution Asymmetry Index

The Open Bioinformatics Journal ◽

10.2174/1875036201206010020 ◽

2012 ◽

Vol 6 (1) ◽

pp. 20-27

Author(s):

Nicola Arrigo ◽

Paola Paci ◽

Luisa Di Paola ◽

Daniele Santoni ◽

Micol De Ruvo ◽

...

Keyword(s):

Principal Component ◽

Shape Index ◽

Asymmetry Index ◽

Volume Distribution ◽

Linear Discriminant ◽

Contact Maps ◽

Topological Features ◽

Three Space ◽

Protein Shape

A fully quantitative shape index relying upon the asymmetry of mass distribution of protein molecules along the three space dimensions is proposed. Multidimensional statistical analysis, based on principal component extraction and subsequent linear discriminant analysis, showed the presence of three major ‘attractor forms’ roughly correspondent to rod-like, discoidal and spherical shapes. This classification of protein shapes was in turn demonstrated to be strictly connected with topological features of proteins, as emerging from complex network invariants of their contact maps.

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Multivariate characterisation of Italian monovarietal red wines using MIR spectroscopy

OENO One ◽

10.20870/oeno-one.2019.53.4.2558 ◽

2019 ◽

Vol 53 (4) ◽

Cited By ~ 5

Author(s):

Giuseppina P. Parpinello ◽

Arianna Ricci ◽

Panagiotis Arapitsas ◽

Andrea Curioni ◽

Luigi Moio ◽

...

Keyword(s):

Principal Component ◽

Rapid Screening ◽

Support Vector ◽

Red Wines ◽

Grape Variety ◽

Absorption Bands ◽

Linear Discriminant ◽

Mir Spectroscopy ◽

Grape Varieties

Aim: The aim of this study was to investigate the application of mid-infrared (MIR) spectroscopy combined with multivariate analysis, to provide a rapid screening tool for discriminating among different Italian monovarietal red wines based on the relationship between grape variety and wine composition in particular phenolic compounds.Methods and results: The MIR spectra (from 4000 to 700 cm‒1) of 110 monovarietal Italian red wines, vintage 2016, were collected and evaluated by selected multivariate data analyses, including principal component analysis (PCA), linear discriminant analysis (DA), support vector machine (SVM), and soft intelligent modelling of class analogy (SIMCA). Samples were collected directly from companies across different regions of Italy and included 11 grape varieties: Sangiovese, Nebbiolo, Aglianico, Nerello Mascalese, Primitivo, Raboso, Cannonau, Teroldego, Sagrantino, Montepulciano and Corvina. PCA showed five wavelengths that mainly contributed to the PC1, including a much-closed peak at 1043 cm‒1, which correspond to the C–O stretch absorption bands that are important regions for glycerol, whereas the ethanol peaks at around 1085 cm‒1. The band at 877 cm‒1 are related to the C–C stretching vibration of organic molecules, whereas the asymmetric stretching for C–O in the aromatic –OH group of polyphenols is within spectral regions from 1050 to 1165 cm‒1. In particular, the (1175)–1100–1060 cm‒1 vibrational bands are combination bands, involving C–O stretching and O–H deformation of phenolic rings. The 1166–1168 cm‒1 peak is attributable to in-plane bending deformations of C–H and C–O groups of polyphenols, respectively, for which polymerisation may cause a slight peak shift due to the formation of H-bridges.The best result was obtained with the SVM, which achieved an overall correct classification for up to 72.2% of the training set, and 44.4% for the validation set of wines, respectively. The Sangiovese wines (n=19) were split into two sub-groups (Sang-Romagna, n=12 and Sang-Tuscany, n=7) considering the indeterminacy of its origins, which is disputed between Romagna and Tuscany. Although the classification of three grape varieties was problematic (Nerello Mascalese, Raboso and Primitivo), the remaining wines were almost correctly assigned to their actual classes.Conclusions: MIR spectroscopy coupled with chemometrics represents an interesting approach for the classification of monovarietal Italian red wines, which is important in quality control and authenticity monitoring.Significance and impact of the study: Authenticity is a main issue in winemaking in terms of quality evaluation and adulteration, in particular for origin certified/protected wines, for which the added marketing value is related to the link of grape variety with the area of origin. This study is part of the D-wine project “The diversity of tannins in Italian red wines”.

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