Supervised pattern recognition applied to the discrimination of the floral origin of six types of Italian honey samples

Development of a LC-QTOF-MS based dilute-and-shoot approach for the botanical discrimination of honeys

Honeys of particular botanical origins can be associated with premium market prices, a trait which also makes them susceptible to fraud. Currently available authenticity testing methods for botanical classification of honeys are either time-consuming or only target a few "known" types of markers. Simple and effective methods are therefore needed to monitor and guarantee the authenticity of honey. In this study, a 'dilute-and-shoot' approach using liquid chromatography (LC) coupled to quadrupole time-of-flight-mass spectrometry (QTOF-MS) was applied to the non-targeted fingerprinting of honeys of different floral origin (buckwheat, clover and blueberry). This work investigated for the first time the impact of different instrumental conditions such as the column type, the mobile phase composition, the chromatographic gradient, and the MS fragmentor voltage (in-source collision-induced dissociation) on the botanical classification of honeys as well as the data quality. Results indicated that the data sets obtained for the various LC-QTOF-MS conditions tested were all suitable to discriminate the three honeys of different floral origin regardless of the mathematical model applied (random forest, partial least squares-discriminant analysis, soft independent modelling by class analogy and linear discriminant analysis). The present study investigated different LC-QTOF-MS conditions in a "dilute and shoot" method for honey analysis, in order to establish a relatively fast, simple and reliable analytical method to record the chemical fingerprints of honey. This approach is suitable for marker discovery and will be used for the future development of advanced predictive models for honey botanical origin.

Botanical origin discrimination of Greek honeys: physicochemical parameters versus Raman spectroscopy

BACKGROUND

The authenticity of honey is of high importance since it affects its commercial value. The discrimination of the origin of honey is of prime importance to reinforce consumer trust. In this study, four chemometric models were developed based on the physicochemical parameters according to European and Greek legislation and one using Raman spectroscopy to discriminate Greek honey samples from three commercial monofloral botanical sources.

RESULTS

The results of physicochemical (glucose, fructose, electrical activity) parameters chemometric models showed that the percentage of correct recognition fluctuated from 92.2% to 93.8% with cross-validation 90.6-92.2%, and the placement of test set was 79.0-84.3% successful. The addition of maltose content in the previous discrimination models did not significantly improve the discrimination. The corresponding percentages of the Raman chemometric model were 95.3%, 90.6%, and 84.3%.

CONCLUSION

The five chemometric models developed presented similar and very satisfactory results. Given that the recording of Raman spectra is simple, fast, a minimal amount of sample is needed for the analysis, no solvent (environmentally friendly) is used, and no specialized personnel are required, we conclude that the chemometric model based on Raman spectroscopy is an efficient tool to discriminate the botanical origin of fir, pine, and thyme honey varieties. © 2020 Society of Chemical Industry.

SPME-GC-MS and FTIR-ATR Spectroscopic Study as a Tool for Unifloral Common Greek Honeys’ Botanical Origin Identification

Among the variants of Greek honey, the most commonly available are pine, fir, thyme, and citrus honey. Samples of the above kinds of honey, identified according to European and Greek legislation, were studied using gas chromatography coupled with mass spectrometry (GC-MS) and the attenuated total reflection Fourier transform infrared (ATR-FTIR) spectroscopic techniques. Two chemometric models were developed based on statistically significant volatile compounds (octane; 2-phenylacetaldehyde; 1-nonanol; methyl 2-hydroxybenzoate; 2-(4-methylcyclohex-3-en-1-yl); nonanoic acid) and the 1390–945 and 847–803 cm−1 spectral regions, mainly vibrations of fructose and glucose, combined with the stepwise linear discriminant analysis (stepwise LDA) statistical technique. In total, 85.5% of standard samples, and 82.3% through internal validation and 88.5% through external validation, were identified correctly using the GC-MS-stepwise-LDA chemometric model. The corresponding results for the ATR-FTIR-stepwise-LDA chemometric model were 93.5%, 82.5%, and 84.6%. The double validation (internal, external) enhances the robustness of the proposed chemometric models. The developed models are considered statistically equivalent, but FTIR spectroscopy is simple, rapid, and more economical.

Differentiation of oak honeydew and chestnut honeys from the same geographical origin using chemometric methods

Oak honeydew and chestnut honeys often share the same production area in Atlantic landscapes. Consequently these honeys have common physicochemical properties and pollen composition, making their differentiation by routine methods, a difficult task. The increase in the demands of consumers for clear honey labelling, identifying floral make-ups and the substantial health properties of both honey types, make it necessary to improve methods to differentiate the honeys. Statistical multivariate techniques were used to study the differences in the physicochemical composition and pollen spectra between chestnut honey and oak honeydew honey. Palynological analysis, moisture, pH, electrical conductivity, hydroxymethylfurfural, diastase number, colour, phenolic content, minerals and sugars were used for this purpose. The variables that had more weight in the differentiation by principal component analysis were Castanea, Cytisus type, CIELab coordinates (a^* and L), RSA, Mg and trehalose; 97.6% of the honey samples were correctly classified by linear discriminant analysis.

Validation of botanical origins and geographical sources of some Saudi honeys using ultraviolet spectroscopy and chemometric analysis

This study aims at distinguishing honey based on botanical and geographical sources. Different floral honey samples were collected from diverse geographical locations of Saudi Arabia. UV spectroscopy in combination with chemometric analysis including Hierarchical Cluster Analysis (HCA), Principal Component Analysis (PCA), and Soft Independent Modeling of Class Analogy (SIMCA) were used to classify honey samples. HCA and PCA presented the initial clustering pattern to differentiate between botanical as well as geographical sources. The SIMCA model clearly separated the Ziziphus sp. and other monofloral honey samples based on different locations and botanical sources. The results successfully discriminated the honey samples of different botanical and geographical sources validating the segregation observed using few physicochemical parameters that are regularly used for discrimination.

Discriminatory value of carotid artery elasticity changes for the evaluation of thyroid dysfunction in patients with hashimoto's thyroiditis

PURPOSE

To evaluate the discriminatory value of carotid artery wall thickness and elasticity for thyroid dysfunction in Hashimoto's thyroiditis (HT) patients.

METHODS

A total of 180 female HT patients were assigned to three groups on the basis of laboratory testing: HT hyperthyroidism group (group A), HT hypothyroidism group (group B), and HT euthyroid group (group C). We used radiofrequency sonographic signal analysis for the measurement of intima-media thickness and arterial stiffness.

RESULTS

Intima-media thickness was significantly higher in group A than in other groups. After stepwise variable selection, distensibility coefficient (DC), compliance coefficient, stiffness index (β), and pulse wave velocity (PWV) were selected to discriminate different thyroid hormone levels, resulting in the following discriminant function: Z1 = -0.398DC + 0.803PWV. The receiver-operator characteristic curve analysis showed that the critical points were DC = 0.022 kPa(-1) and PWV = 5.36 m/s for HT hyperthyroidism, and DC = 0.016 kPa(-1) and PWV = 6.875 m/s for HT hypothyroidism.

CONCLUSIONS

DC and PWV may be useful for the evaluation of thyroid function in HT patients. © 2016 Wiley Periodicals, Inc. J Clin Ultrasound 44:298-304, 2016.

Differentiation of Organically and Conventionally Grown Tomatoes by Chemometric Analysis of Combined Data from Proton Nuclear Magnetic Resonance and Mid-infrared Spectroscopy and Stable Isotope Analysis

Because the basic suitability of proton nuclear magnetic resonance spectroscopy ((1)H NMR) to differentiate organic versus conventional tomatoes was recently proven, the approach to optimize (1)H NMR classification models (comprising overall 205 authentic tomato samples) by including additional data of isotope ratio mass spectrometry (IRMS, δ(13)C, δ(15)N, and δ(18)O) and mid-infrared (MIR) spectroscopy was assessed. Both individual and combined analytical methods ((1)H NMR + MIR, (1)H NMR + IRMS, MIR + IRMS, and (1)H NMR + MIR + IRMS) were examined using principal component analysis (PCA), partial least squares discriminant analysis (PLS-DA), linear discriminant analysis (LDA), and common components and specific weight analysis (ComDim). With regard to classification abilities, fused data of (1)H NMR + MIR + IRMS yielded better validation results (ranging between 95.0 and 100.0%) than individual methods ((1)H NMR, 91.3-100%; MIR, 75.6-91.7%), suggesting that the combined examination of analytical profiles enhances authentication of organically produced tomatoes.

Prospects of apicultural entrepreneurship in coastal districts of eastern India: a melissopalynological evaluation

A melissopalynological analysis of fifty-one natural honey samples (twenty four spring, fifteen summer and twelve winter) collected during 2010–2011 from two east-coastal districts (20⁰20^/ to 22⁰11^/ N, 82⁰39^/ to 87⁰01^/ E) of Orissa, India was performed. Out of 37 unifloral samples found 25 were contributed by Apis cerana indica, seven by A. dorsata and the remaining five by A. florea. Out of 14 multifloral samples five were contributed by A. cerana indica, five by A. dorsata and the remaining four by A. florea. Principal component analysis confirmed the palynological classification of the unifloral honey samples. Eighty-two bee-plant taxa belonging to forty four families were recovered. The predominant nectariferous taxa of the spring season were Acanthus ilicifolius, Avicennia marina, Bruguiera gymnorrhiza, Cocos nucifera, Eucalyptus globulus, Phoenix paludosa, Pongamia pinnata, Prosopis juliflora, Sonneratia apetala and Syzygium cumini. In the summer the predominant nectariferous taxa were Borassus flabellifer, C. nucifera, E. globulus, Syzygium cumini, Terminalia arjuna, Aegiceras corniculatum, P. paludosa and Sonneratia apetala while those of the winter were Brassica nigra, Coriandrum sativum, Zizyphus jujuba, Alstonia scholaris, E. globulus and Bruguiera gymnorrhiza. Very low (<0.09) HDE/P for 98% of the samples and absence of toxic palynotaxa assure that these honeys are suitable for human consumption. Quite extended honey flow period with spring and summer as best forage seasons for the honeybees and occurrence of 82% of these honeys with APC Group II, III and IV justify the sustainability of the present study area for establishing moderate to large-scale apicultural entrepreneurship. This should improve the socio-economic status of the people of this region.

Authentication of monofloral Yemeni Sidr honey using ultraviolet spectroscopy and chemometric analysis

This work describes a simple model developed for the authentication of monofloral Yemeni Sidr honey using UV spectroscopy together with chemometric techniques of hierarchical cluster analysis (HCA), principal component analysis (PCA), and soft independent modeling of class analogy (SIMCA). The model was constructed using 13 genuine Sidr honey samples and challenged with 25 honey samples of different botanical origins. HCA and PCA were successfully able to present a preliminary clustering pattern to segregate the genuine Sidr samples from the lower priced local polyfloral and non-Sidr samples. The SIMCA model presented a clear demarcation of the samples and was used to identify genuine Sidr honey samples as well as detect admixture with lower priced polyfloral honey by detection limits >10%. The constructed model presents a simple and efficient method of analysis and may serve as a basis for the authentication of other honey types worldwide.

Identification of milk mixtures by 1H NMR profiling

Nuclear magnetic resonance profiling, combined with a single-layer artificial neural network, is used for the evaluation of the content of mixtures of different kinds of milk. In particular, aqueous fractions of cow and sheep milk mixtures are analyzed by (1) H NMR. The spectral differences are highlighted by an analysis of the variance and a principal component analysis. The species classification problem is solved by a linear discriminant analysis. The quantification of the relative amount of the milk of two different species is then achieved by solving the appropriate multilinear problem.

Nuclear magnetic resonance profiling of wine blends

Nuclear magnetic resonance (NMR) profiling is used for characterization of monocultivar binary wine mixtures. Classification and quantification of the relative amount of wine in the mixture are made in two steps. First, each sample is classified as a mixture of a determined type by solving the appropriate classification problem using NMR profiles. The relative amount of the two corresponding monovarietal wines is then evaluated by multilinear regression of a selected set of NMR variables. Linear discriminant analysis (LDA), used in the classification step, gives a very good separation among the different mixture classes. On the other hand, a single layer artificial neural network, used to solve the multilinear problem, gives the relative amount of wine type in the mixture with a precision of about 10%.

Biomonitoring of chemical elements in an urban environment using arboreal and bush plant species

PURPOSE

The aim of this work was to investigate the possibility of using several bush and arboreal plant species, usually present as ornamental plants in street and parks, as environmental indicators of pollution. This is a research paper that evaluates the real possibility of using a fast and low-cost procedure to evaluate the pollution degree through data obtained from plant species growing within an urban environment.

METHODS

Leaves of six different bush and arboreal species were collected from different parts of Madrid (Spain), ranging from highly polluted considered areas to medium and low contaminated ones. A total of 66 chemical elements, from major to minor and trace, were determined for every leaf sample by inductively coupled plasma-mass spectrometry. Statistical analyses were carried out using mainly box and whisker plots, linear discriminant analysis and cluster analysis.

RESULTS

The pollution by different elements of the studied areas of Madrid cannot be considered generally dangerous for human health. The level detected for the contaminants, in general, is similar or lower than other urban cities. Pb and V concentrations in plant samples tend to increase as traffic density increases. The different studied plant species showed a different capability of accumulation of certain elements. Cedrus deodara accumulates specially Ag, Hg, Mo and V; Cupressus sempervirens, Zr; Pinus pinea, As and Sb; Nerium oleander Ni, Pb, Mo and Se; Ligustrum ovalifolium, Sc and V; and Pittosporum tobira, Ag, Cd, Rb and Sc.

CONCLUSIONS

The leaves and needles collected from bush and arboreal plants common in this city have demonstrated to be useful to evaluate the level of pollution not only through the chemical analysis but also through the recognition of the visual injury symptoms. The application of multivariate statistical techniques combined with determining of element concentration and correlation analysis has been proved to be an effective tool for reach the objectives of the present work. This allows visualising quickly the damages and leading the sampling through the points of high-level pollution, saving analysis, time and money.

Geographical origin of durum wheat studied by 1H-NMR profiling

(1)H-Nuclear magnetic resonance (NMR) spectroscopy was used to detect metabolic profiles of wheat flour samples of different geographical and botanical origin. The NMR profiles were analyzed by multivariate statistical techniques in order to establish the origin of the samples. A linear model, able to discriminate among three different locations, was built achieving a prediction level of about 80% of correctly assigned samples. The principal classes of compounds responsible for the geographic origin discrimination were individuated in aromatic compounds and amino acids. The statistical modeling also indicated that botanical origin information is very poor in the NMR profiles of the analyzed wheat samples.

Class modeling analysis of heparin 1H NMR spectral data using the soft independent modeling of class analogy and unequal class modeling techniques

To differentiate heparin samples with varying amounts of dermatan sulfate (DS) impurities and oversulfated chondroitin sulfate (OSCS) contaminants, proton NMR spectral data for heparin sodium active pharmaceutical ingredient samples from different manufacturers were analyzed using multivariate chemometric techniques. A total of 168 samples were divided into three groups: (a) Heparin, [DS] ≤ 1.0% and [OSCS] = 0%; (b) DS, [DS] > 1.0% and [OSCS] = 0%; (c) OSCS, [OSCS] > 0% with any content of DS. The chemometric models were constructed and validated using two well-established methods: soft independent modeling of class analogy (SIMCA) and unequal class modeling (UNEQ). While SIMCA modeling was conducted using the entire set of variables extracted from the NMR spectral data, UNEQ modeling was combined with variable reduction using stepwise linear discriminant analysis to comply with the requirement that the number of samples per class exceed the number of variables in the model by at least 3-fold. Comparison of the results from these two modeling approaches revealed that UNEQ had greater sensitivity (fewer false positives) while SIMCA had greater specificity (fewer false negatives). For Heparin, DS, and OSCS, respectively, the sensitivity was 78% (56/72), 74% (37/50), and 85% (39/46) from SIMCA modeling and 88% (63/72), 90% (45/50), and 91% (42/46) from UNEQ modeling. Importantly, the specificity of both the SIMCA and UNEQ models was 100% (46/46) for Heparin with respect to OSCS; no OSCS-containing sample was misclassified as Heparin. The specificity of the SIMCA model (45/50, or 90%) was superior to that of the UNEQ model (27/50, or 54%) for Heparin with respect to DS samples. However, the overall prediction ability of the UNEQ model (85%) was notably better than that of the SIMCA model (76%) for the Heparin vs DS vs OSCS classes. The models were challenged with blends of heparin spiked with nonsulfated, partially sulfated, or fully oversulfated chondroitin sulfate A, dermatan sulfate, or heparan sulfate at the 1.0, 5.0, and 10.0 wt % levels. The results from the present study indicate that the combination of (1)H NMR spectral data and class modeling techniques (viz., SIMCA and UNEQ) represents a promising strategy for assessing the quality of commercial heparin samples with respect to impurities and contaminants. The methodologies show utility for applications beyond heparin to other complex products.

Composition of sulla (Hedysarum coronarium L.) honey solvent extractives determined by GC/MS: norisoprenoids and other volatile organic compounds

Samples of unifloral sulla (Hedysarum coronarum L.) honey from Sardinia (Italy) were analysed. To investigate the chemical composition of the honey volatiles two solvent systems were used for ultrasonic solvent extraction (USE): 1) a 1:2 (v/v) pentane and diethyl ether mixture and 2) dichloromethane. All the extracts were analysed by GC and GC/MS. These procedures have permitted the identification of 56 compounds that include norisoprenoids, benzene derivatives, aliphatic compounds and Maillard reaction products. Norisoprenoids were the major compounds in both extracts, dominated by vomifoliol (5.3-11.2%; 9.6-14.0%) followed by minor percentages of other norisoprenoids such as α-isophorone, 4-ketoisophorone, 3-oxo-α-ionol or 3-oxo-α-ionone. Other abundant single compounds in the extracts were 3-hydroxy-4-phenylbutan-2-one (0.8-5.4%; 0.6-5.7%) and methyl syringate (3.0-5.7%; 2.2-4.1%). The composition of the volatiles and semi-volatiles in the obtained extracts suggests that sulla honey is quite distinctive relative to the other honeys that have been chemically studied by GC/MS, but no specific markers of the honey botanical origin were found.

Application of total reflection X-ray spectrometry in combination with chemometric methods for determination of the botanical origin of Slovenian honey

This work on the botanical origin of various types of honey produced in Slovenia and based on the mineral content analyses by the total reflection X-ray spectrometry (TXRF) is a continuation of this group's preliminary work (Golob, T.; Doberšek, U.; Kump, P.; Nečemer, M. Food Chem. 2005, 91, 593-600), which introduced the analytical methodology and employed only a simple statistical evaluation and which examined the possibility to determine the botanical origin of honey samples via elemental content. A much more comprehensive study on a total of 264 major types of honey samples harvested in 2004, 2005, and 2006 and interpreting the results with up to date chemometric methods was performed in this work. Slovenia is a small country by surface area, but it is pedologically and climatically diverse, therefore offering interesting possibilities for studying the influence of these diversities on the elemental content of natural products. By employing principal component analysis (PCA) and regularized discriminant analysis (RDA) it was established that from all of the measured elements only the four characteristic key elements Cl, K, Mn, and Rb could be used to best discriminate the types of honey. It was established that the employed combination of a simple, fast, and inexpensive multielement TXRF analytical approach and the evaluation of data by chemometric methods has the potential to discriminate the botanical origins of various types of honey.

Influence of storage conditions on chemical composition and sensory properties of citrus honey

Fresh citrus honey was stored at 10, 20, and 40 degrees C for 12 months. The effect of storage on the quality of honey was evaluated using physicochemical parameters, volatile compounds, mono-, di-, and trisaccharides, and sensory analysis. Diastase activity and HMF were out of the legal limit in honey stored 12 months at 40 degrees C. Volatile compounds (especially terpenes and terpene derivatives), monosaccharides, and disaccharides presented important losses during honey storage at any temperature. Honey storage at 10 or 20 degrees C maintained their floral, fresh, citric, and fresh fruit aroma, while the intensities of these attributes were diminished. Storage at 40 degrees C during 12 months resulted in the appearance of attributes such as "medicinal, smoked, toasted, cooked vegetable, and ripened fruit", associated with compounds formed during the Maillard reaction or through degradation of sugars such as volatile pyrroles, furanones, pyranones, and pyrazines, which appeared or increased in concentration during honey storage mainly at high temperature.

Supervised pattern recognition in food analysis

Data analysis has become a fundamental task in analytical chemistry due to the great quantity of analytical information provided by modern analytical instruments. Supervised pattern recognition aims to establish a classification model based on experimental data in order to assign unknown samples to a previously defined sample class based on its pattern of measured features. The basis of the supervised pattern recognition techniques mostly used in food analysis are reviewed, making special emphasis on the practical requirements of the measured data and discussing common misconceptions and errors that might arise. Applications of supervised pattern recognition in the field of food chemistry appearing in bibliography in the last two years are also reviewed.

Investigation, by single photon ionisation (SPI)-time-of-flight mass spectrometry (TOFMS), of the effect of different cigarette-lighting devices on the chemical composition of the first cigarette puff

Soft single-photon ionisation (SPI)-time-of-flight mass spectrometry (TOFMS) has been used to investigate the effect of different cigarette-lighting devices on the chemical composition of the mainstream smoke from the first cigarette puff. Lighting devices examined were a Borgwaldt electric lighter, a propane/butane gas lighter, a match, a candle, and the burning zone of another cigarette. To eliminate the effects of the different masses of tobacco burnt by use of the different lighting methods a normalisation procedure was performed which enabled investigation of changes in the chemical patterns of the resulting smoke. When another cigarette was used as the lighting device, elevated levels of ammonia and other nitrogen-containing substances were observed. These are high in the sidestream smoke of the cigarette used for lighting and would be drawn into the mainstream smoke of the cigarette being lit. In contrast, smoke from the cigarette lit by the electric lighter contained slightly higher normalised amounts of isoprene. Lighting the cigarette by use of a candle resulted in larger amounts of substances, e.g. benzene, which most probably originated from thermal decomposition of wax. The composition of the first puff of smoke obtained by use of the three lighting methods with open flames (gas lighter, match, and candle) was usually similar whereas the composition of the smoke produced by use of the electric lighter and the cigarette as the lighter were more unique. The chemical patterns generated by the different lighting devices could, however, be separated by principal-component analyses. Two additional test series were also studied. In the first the cigarette was lit with an electric lighter, then extinguished, the ash was cut off, and the cigarette was re-lit. In the second the cigarette was heated in an oven to 80 degrees C for 5 min before being lit. These treatments did not result in changes in the chemical composition compared with cigarettes lit in the ordinary way.