scholarly journals RIGHT-ANGLE FLUORESCENCE SPECTROSCOPY FOR DIFFERENTIATION OF DISTILLED ALCOHOLIC BEVERAGES

2013 ◽  
Vol 12 (2) ◽  
pp. 83-92 ◽  
Author(s):  
Veronika Uríčková ◽  
Jana Sádecká ◽  
Pavel Májek
Keyword(s):  
Fluorescence Spectroscopy ◽  
Emission Spectra ◽  
Principal Component ◽  
Hierarchical Cluster ◽  
Alcoholic Beverages ◽  
Front Face ◽  
Excitation Wavelength ◽  
Spectroscopic Techniques ◽  
Excitation Spectra ◽  
Linear Discriminant

Abstract Total luminescence and synchronous scanning fluorescence spectroscopic techniques were investigated for differentiating brandies from mixed wine spirits. The studies were performed on 16 brandies from 3 different producers and 30 mixed wine spirits from 5 different producers. Differentiation between samples was accomplished by multivariate data analysis methods (principal component analysis, hierarchical cluster analysis, and linear discriminant analysis). Correct classification was obtained using emission spectra (400-650 nm) recorded at excitation wavelength 390 nm, excitation spectra (225-460 nm) obtained at emission wavelength 470 nm and synchronous fluorescence spectra (200-700 nm) collected at wavelength interval 80 nm. These results indicate that right-angle fluorescence spectroscopy offers a promising approach for the authentication of brandies as neither sample preparation nor special qualification of the personnel are required, and data acquisition and analysis are relatively simple when compared to front-face technique.

scholarly journals Total luminescence spectroscopy for differentiating between brandies and wine distillates

2009 ◽  
Vol 27 (No. 6) ◽  
pp. 425-432 ◽  
Author(s):  
J. Tóthová ◽  
J. Sádecká ◽  
P. Májek
Keyword(s):  
Multivariate Analysis ◽  
Emission Spectra ◽  
Principal Component ◽  
Hierarchical Cluster ◽  
Luminescence Spectroscopy ◽  
Luminescence Spectra ◽  
Classification Error ◽  
Excitation Wavelength ◽  
Data Sets ◽  
Excitation Spectra

In this study, the differentiation was investigated between brandy and wine distillate samples by fluorescence spectroscopy in combination with multivariate analysis. The samples corresponding to eight brandies from three producers and sixteen wine distillates from five producers were acquired in the local supermarkets. Total luminescence spectra of diluted and undiluted samples were recorded. In order to extract reliable information from the data sets, two multivariate analysis methods, Principal Component Analysis (PCA) and Hierarchical Cluster Analysis (HCA), were applied separately on the excitation and emission spectra. The best differentiation was achieved using the emission spectra (400–470 nm) recorded at the excitation wavelength of 340 nm, or the excitation spectra (240–380 nm) recorded at the emission wavelength of 450 nm. The similarity map defined by the PC1 and PC2 of the PCA performed on the excitation spectra accounted for 94.9% of the total variance (PC1 90.3%, PC2 4.6%) and allowed a good discrimination between the beverages. Although the PCA similarity map defined by the PC1 (84.2%) and PC2 (13.0%) performed on the emission spectra did not lead to a clear discrimination between the beverages, a general trend pointing out the brandies and wine distillates was observed on the map. HCA performed on the excitation spectra provided a better differentiation between the two classes, without any classification error, while HCA performed on the emission spectra allowed 95.8% correct classification.

scholarly journals Detection and classification of Bacteria using Raman Spectroscopy Combined with Multivariate Analysis

2017 ◽  
Vol 2 (4) ◽  
pp. 435 ◽  
Author(s):  
S. Sil ◽  
R. Mukherjee ◽  
N. S. Kumar ◽  
Aravind S. ◽  
J. Kingston ◽  
...  
Keyword(s):  
Raman Spectroscopy ◽  
Multivariate Analysis ◽  
Principal Component ◽  
Hierarchical Cluster ◽  
Spectroscopic Techniques ◽  
Hybrid Technique ◽  
Chemometric Method ◽  
Linear Discriminant ◽  
Linear Discrimination

<p class="p1">Vibrational spectroscopic techniques have advantages over conventional microbiological approaches towards identification &amp; detection of pathogens. Since unique spectral fingerprint is obtained, one can identify very closely related bacteria using such methods. In this study Raman microspectroscopy in combination with chemometric method has been used to classify four strains of <em>E</em>. <em>coli </em>(two pathogenic &amp; two non-pathogenic). Different multivariate approaches such as hierarchical cluster analysis, principal component analysis &amp; linear discriminant analysis were explored to obtain efficient classification of the Raman signals obtained from the four strains of <em>E.coli</em>. It was observed that multivariate analysis was able to classify the bacteria at strain level. Linear discrimination analysis using PC scores (PC-LDA) was found to give very good result with as high as 100% accuracy. This hybrid technique (Raman spectroscopy &amp; multivariate analysis) has tremendous potential to be developed as a tool for bacterial identification.<span class="Apple-converted-space"> </span></p>

scholarly journals Rapid and nondestructive identification of Belgian and Netherlandish Trappist beers by front-face synchronous fluorescence spectroscopy coupled with multiple statistical analysis

2021 ◽  
Vol 13 (1) ◽  
pp. 83-92
Author(s):  
Jin Tan ◽  
Ming-Fen Li
Keyword(s):  
Discriminant Analysis ◽  
Fluorescence Spectroscopy ◽  
Cross Validation ◽  
Discrimination Performance ◽  
Principal Component ◽  
Synchronous Fluorescence ◽  
Front Face ◽  
True Negative ◽  
Synchronous Fluorescence Spectroscopy ◽  
Linear Discriminant

Front-face synchronous fluorescence spectroscopy (FFSFS) was applied for the rapid and noninvasive recognition of Belgian and Netherlandish Trappist beers against non-Trappist beers. The front-face synchronous fluorescence spectra at wavelength intervals (??) of 30 and 60 nm for 80 bottles of beer, including 41 Trappist and 39 non-Trap-pist beers, were acquired in a 5 × 10 mm fused-quartz cuvette settled in a traditional right-angle sample compartment. The discrimination model was constructed by either principal component analysis (PCA) combined with linear discriminant analysis (LDA) or partial least squares-discriminant analysis (PLS-DA). Both PCA–LDA and PLS-DA models were validated by full (leave-one-out) cross-validation and k-fold cross-validation (k = 5). The PCA–LDA model presents reliable discrimination performance, with the cross-validated sensitivity (true positive rate) and specificity (true negative rate) in the range of 82.9–85.4% and 71.8–76.9%, respectively. The misclassification mainly occurs to a small portion of ambiguous Trappist and non-Trappist samples such as Abbey beers, which are rather similar to Trappist beers.

scholarly journals The Use of Right Angle Fluorescence Spectroscopy to Distinguish the Botanical Origin of Greek Common Honey Varieties

2021 ◽  
Vol 11 (9) ◽  
pp. 4047
Author(s):  
Marinos Xagoraris ◽  
Panagiota-Kyriaki Revelou ◽  
Eleftherios Alissandrakis ◽  
Petros A. Tarantilis ◽  
Christos S. Pappas
Keyword(s):  
Fluorescence Spectroscopy ◽  
External Validation ◽  
Three Dimensional ◽  
Excitation Wavelength ◽  
Botanical Origin ◽  
Linear Discriminant ◽  
Classification Score ◽  
Hydroxybenzoic Acids ◽  
Unifloral Honey ◽  
Novel Method

The standardization of the botanical origin of honey reflects the commercial value and quality of honey. Nowadays, most consumers are looking for a unifloral honey. The aim of the present study was to develop a novel method for honey classification using chemometric models based on phenolic compounds analyzed with right angle fluorescence spectroscopy, coupled with stepwise linear discriminant analysis (LDA). The deconstructed spectrum from three-dimensional-emission excitation matrix (3D-EEM) spectra provided a correct classification score of 94.9% calibration and cross-validation at an excitation wavelength (λex) of 330 nm. Subsequently, a score of 81.4% and 79.7%, respectively, at an excitation wavelength (λex) of 360 nm was achieved. Each chemometric model confirmed its power through the external validation with a score of 82.1% for both. Differentiation could be correlated with hydroxycinnamic and hydroxybenzoic acids, which absorb in this region of the spectrum. Fluorescence spectroscopy constitutes a rapid and sensitive technique, which, when combined with the stepwise algorithm and LDA method, can be used as a reliable and predictive authentication tool for honey. This study indicates that the developed methodology is a promising technique for determination of the botanical origin of common Greek honey varieties. Our long-term ambition is to support producers and suppliers to remain in a competitive national and international market.

scholarly journals Harnessing intrinsic fluorescence for typing of secondary structures of DNA

2020 ◽  
Author(s):  
Michela Zuffo ◽  
Aurélie Gandolfini ◽  
Brahim Heddi ◽  
Anton Granzhan
Keyword(s):  
High Throughput ◽  
Conformational Changes ◽  
Emission Spectra ◽  
Principal Component ◽  
Secondary Structures ◽  
Intrinsic Fluorescence ◽  
Label Free ◽  
Linear Discriminant ◽  
Effective Manner

ABSTRACTDNA is polymorphic since, despite its ubiquitous presence as a double-stranded helix, it is able to fold into a plethora of other secondary structures both in vitro and in cells. Despite the considerable advances that have been made in understanding this structural diversity, its high-throughput investigation still faces severe limitations. This mainly stems from the lack of suitable label-free methods, combining a fast and cheap experimental workflow with high information content. Here, we explore the use of intrinsic fluorescence emitted by nucleic acids for this scope. After a preliminary assessment of the suitability of this phenomenon for tracking the conformational changes of DNA, we examined the intrinsic steady-state emission spectra of an 89-membered set of synthetic oligonucleotides with reported conformation (G-quadruplexes, i-motifs, single- and double stranded DNA) by means of multivariate analysis. Specifically, principal component analysis of emission spectra resulted in successful clustering of oligonucleotides into three corresponding conformational groups, albeit without discrimination between single- and double-stranded structures. Linear discriminant analysis of the same training set was exploited for the assessment of new sequences, allowing the evaluation of their G4-forming propensity. Our method does not require any labelling agent or dye, avoiding the related intrinsic bias, and can be utilized to screen novel sequences of interest in a high-throughput and cost-effective manner. In addition, we observed that left-handed (Z-) G4 structures were systematically more fluorescent than most other G4 structures, almost reaching the quantum yield of 5′-d[(G3T)3G3]-3′ (G3T), the most fluorescent G4 structure reported to date. This property is likely to arise from the similar base-stacking geometry in both types of structures.

scholarly journals Discrimination of the Geographical Origin of the Lateral Roots of Aconitum carmichaelii Using the Fingerprint, Multicomponent Quantification, and Chemometric Methods

Molecules ◽  
2019 ◽  
Vol 24 (22) ◽  
pp. 4124 ◽  
Author(s):  
Lu-Lin Miao ◽  
Qin-Mei Zhou ◽  
Cheng Peng ◽  
Chun-Wang Meng ◽  
Xiao-Ya Wang ◽  
...  
Keyword(s):  
High Performance ◽  
Geographical Origin ◽  
Lateral Roots ◽  
Principal Component ◽  
Hierarchical Cluster ◽  
Similarity Analysis ◽  
Diterpenoid Alkaloids ◽  
Linear Discriminant ◽  
Hplc Fingerprint ◽  
Aconitum Carmichaelii

Fuzi is a well-known traditional Chinese medicine developed from the lateral roots of Aconitum carmichaelii Debx. It is rich in alkaloids that display a wide variety of bioactivities, and it has a strong cardiotoxicity and neurotoxicity. In order to discriminate the geographical origin and evaluate the quality of this medicine, a method based on high-performance liquid chromatography (HPLC) was developed for multicomponent quantification and chemical fingerprint analysis. The measured results of 32 batches of Fuzi from three different regions were evaluated by chemometric analysis, including similarity analysis (SA), hierarchical cluster analysis (HCA), principal component analysis (PCA), and linear discriminant analysis (LDA). The content of six representative alkaloids of Fuzi (benzoylmesaconine, benzoylhypaconine, benzoylaconine, mesaconitine, hypaconitine, and aconitine) were varied by geographical origin, and the content ratios of the benzoylmesaconine/mesaconitine and diester-type/monoester-type diterpenoid alkaloids may be potential traits for classifying the geographical origin of the medicine. In the HPLC fingerprint similarity analysis, the Fuzi from Jiangyou, Sichuan, was distinguished from the Fuzi from Butuo, Sichuan, and the Fuzi from Yunnan. Based on the HCA and PCA analyses of the content of the six representative alkaloids, all of the batches were classified into two categories, which were closely related to the plants’ geographical origins. The Fuzi samples from Jiangyou were placed into one category, while the Fuzi samples from Butuo and Yunnan were put into another category. The LDA analysis provided an efficient and satisfactory prediction model for differentiating the Fuzi samples from the above-mentioned three geographical origins. Thus, the content of the six representative alkaloids and the fingerprint similarity values were useful markers for differentiating the geographical origin of the Fuzi samples.

Use of Gas Chromatograms of the Essential Leaf Oils of the Genus Eucalyptus for Taxonomic Purposes

1997 ◽  
Vol 45 (1) ◽  
pp. 1 ◽  
Author(s):  
Peter J. Dunlop ◽  
Caroline M. Bignell ◽  
D. Brynn Hibbert
Keyword(s):  
Discriminant Analysis ◽  
Linear Discriminant Analysis ◽  
Vacuum Distillation ◽  
Principal Component ◽  
Analytical Techniques ◽  
Hierarchical Cluster ◽  
Eucalyptus Species ◽  
Linear Discriminant ◽  
Genus Eucalyptus

Using morphological observations, botanists have classified Eucalyptus species into characteristic series. A new vacuum distillation technique has been employed to obtain the characteristic leaf oils, which are very close to their in vivo compositions, from 35 species belonging to series Tetrapterae, series Torquatae and series Rufispermae. Accurate gas chromatograms have been obtained for each species and three analytical techniques (principal component analysis (PCA), hierarchical cluster analysis (CA) and linear discriminant analysis (LDA)) have been used to process these chromatograms to see if agreement with these classifications could be achieved without using any auxiliary morphometric data. For the species chosen for the present study, linear discriminant analysis was the most successful in assigning species to their present botanic classifications. These analytical methods were also used with some success in searching for groupings within a series and within a species.

The Role of Multivariant Analysis on the Interpretation of FTIR and Raman Spectra

2020 ◽  
pp. 104-129 ◽  
Author(s):  
Sefa Celik ◽  
Ali Tugrul Albayrak ◽  
Sevim Akyuz ◽  
Aysen E. Ozel
Keyword(s):  
Biological Systems ◽  
Principal Component ◽  
Biological Macromolecules ◽  
Spectroscopic Techniques ◽  
Linear Discriminant ◽  
Wide Range ◽  
Vibrational Bands ◽  
Multivariant Analysis

FTIR and Raman spectroscopy are complementary spectroscopic techniques that play an important role in the analysis of molecular structure and the determination of characteristic vibrational bands. Vibrational spectroscopy has a wide range of applications including mainly in physics and biology. Its applications have gained tremendous speed in the field of biological macromolecules and biological systems, such as tissue, blood, and cells. However, the vibrational spectra obtained from the biological systems contain a large number of data and information that make the interpretation difficult. To facilitate the analysis, multivariant analysis comprising the reduction of the dimension of spectrum data and classification of them by eliminating redundancy data, which are obtained from the spectra and does not have any role, becomes critical. In this chapter, the applications of Principal Component Analysis (PCA), Linear Discriminant Analysis (LDA), and their combination PCA-LDA, which are widely used among multivariant techniques on biological systems will be disclosed.

scholarly journals Morphometric Evaluation of Preeclamptic Placenta Using Light Microscopic Images

2014 ◽  
Vol 2014 ◽  
pp. 1-9 ◽  
Author(s):  
Rashmi Mukherjee
Keyword(s):  
Principal Component ◽  
Placental Tissue ◽  
Hierarchical Cluster ◽  
Trophoblast Invasion ◽  
Placental Development ◽  
Linear Discriminant ◽  
Microscopic Images ◽  
Tissue Sections ◽  
Morphometric Features ◽  
Microscopic Evaluation

Deficient trophoblast invasion and anomalies in placental development generally lead to preeclampsia (PE) but the inter-relationship between placental function and morphology in PE still remains unknown. The aim of this study was to evaluate the morphometric features of placental villi and capillaries in preeclamptic and normal placentae. The study included light microscopic images of placental tissue sections of 40 preeclamptic and 35 normotensive pregnant women. Preprocessing and segmentation of these images were performed to characterize the villi and capillaries. Fisher’s linear discriminant analysis (FLDA), hierarchical cluster analysis (HCA), and principal component analysis (PCA) were applied to identify the most significant placental (morphometric) features from microscopic images. A total of 10 morphometric features were extracted, of which the villous parameters were significantly altered in PE. FLDA identified 5 highly significant morphometric features (>90% overall discrimination accuracy). Two large subclusters were clearly visible in HCA based dendrogram. PCA returned three most significant principal components cumulatively explaining 98.4% of the total variance based on these 5 significant features. Hence, quantitative microscopic evaluation revealed that placental morphometry plays an important role in characterizing PE, where the villous is the major component that is affected.

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.

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