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 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.

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 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.

scholarly journals Determination of minced meat quality using machine learning

2017 ◽  
Author(s):  
Αθηνά Ροπόδη
Keyword(s):  
Discriminant Analysis ◽  
Multispectral Imaging ◽  
Principal Component ◽  
Hierarchical Cluster ◽  
Support Vector ◽  
Linear Discriminant ◽  
Vector Machines ◽  
Ftir Spectrometry ◽  
Minced Meat

Τη σημερινή εποχή, οι καταναλωτές απαιτούν συνεχή επιβεβαίωση της προέλευσης, της ποιότητας και της συμμόρφωσης με την ετικέτα των τροφίμων που αγοράζουν. Για το λόγο αυτό, οι βιομηχανίες τροφίμων, οι έμποροι και οι αρχές είναι αναγκαίο να αναπτύξουν προηγμένες, αποτελεσματικές και χαμηλού κόστους λύσεις για τη διασφάλιση της ποιότητας και τον εντοπισμό δόλιων πρακτικών. Σε αυτό το πλαίσιο, η μελέτη αυτή εστιάζει (α) στην πολυφασματική απεικόνιση (Multispectral Imaging-MSI), (β) την φασματοσκοπία υπέρυθρου με μετασχηματισμό Fourier (Fourier Transform Infrared -FTIR spectrometry) και (γ) την εφαρμογή προηγμένων μεθόδων ανάλυσης δεδομένων και μηχανικής μάθησης. Όσον αφορά την ποιότητα, εξετάστηκε η αποτελεσματικότητα των προαναφερόμενων μεθόδων σε σχέση με (α) τον εντοπισμό της μη-συμμόρφωσης με την ετικέτα ή/και δόλιων πρακτικών και (β) τη μικροβιολογική αλλοίωση. Οι ακόλουθες αναλύσεις, έλαβαν χώρα:Στην 1η περίπτωση, εξετάστηκε η νοθεία του μοσχαρίσιου κιμά με χοιρινό. Χρησιμοποιήθηκαν 220 πολυφασματικές εικόνες δειγμάτων από 4 ανεξάρτητες πειραματικές διαδικασίες (κομμάτια κρέατος διαφορετικής προέλευσης). Η νοθεία έγινε με βήμα 10% w/w, δημιουργώντας 11 κατηγορίες (συμπεριλαμβανομένων των ανόθευτων χοιρινών και μοσχαρίσιων δειγμάτων). Μετά από ένα στάδιο προεπεξεργασίας της εικόνας, εφαρμόστηκαν η Ιεραρχική Ανάλυση Συστάδων (Hierarchical Cluster Analysis - HCA) και Ανάλυση Κυρίων Συνιστωσών (Principal Component Analysis - PCA). Παρατηρήθηκαν δε σημαντικές διαφορές μεταξύ των διαφορετικών κομματιών κρέατος και των διαφορετικών κλάσεων όταν και τα τρία πρώτα ζευγάρια κομματιών κρέατος συμπεριλήφθηκαν στην ανάλυση. Μετά την κατάτμηση των δεδομένων σε σετ εκπαίδευσης και επικύρωσης, τα δεδομένα του τέταρτου ζεύγους χρησιμοποιήθηκαν για ανεξάρτητη επικύρωση και εφαρμόστηκαν οι μέθοδοι Γραμμικής Διακριτικής Ανάλυσης και Μερικών Ελαχίστων Τετραγώνων Linear Discriminant Analysis - LDA, Partial least-squares discriminant analysis – PLSDA) για 11 και για 3 (ανόθευτα χοιρινά, μοσχαρίσια και νοθευμένα) κλάσεις. Στην περίπτωση των 11 κλάσεων, 98.48% και 96.97% των δειγμάτων κατηγοριοποιήθηκαν εντός μιας ±10% κατηγορίας για LDA και PLSDA αντίστοιχα, ενώ στην περίπτωση των τριών επιτεύχθηκε σωστή κατηγοριοποίηση 98.48%. Τα αποτελέσματα της ανεξάρτητης επικύρωσης ήταν λιγότερο ακριβή για την LDA, αλλά με την PLSDA όλα τα δείγματα κατηγοριοποιήθηκαν σωστά, αποδεικνύοντας ότι το ποσοστό 10% είναι εντός των ορίων ανίχνευσης.Στην δεύτερη περίπτωση, 110 δείγματα κιμά τριών διαφορετικών κομματιών κρέατος από μοσχάρι και άλογο και επιπλέον εικόνες που ελήφθησαν μετά από 6, 24 και 48 ώρες χρησιμοποιήθηκαν για την ανίχνευση νοθείας. Η PCA χρησιμοποιήθηκε για οπτικοποίηση των δεδομένων, ενώ οι μέθοδοι PLSDA και Random Forest (RF) για κατηγοριοποίηση μεταξύ διαφορετικών ποσοστών νοθείας (4 κλάσεις), ανόθευτων μοσχαρίσιων, ανόθευτων αλογίσιων και νοθευμένων, ανόθευτων και νοθευμένων, και τέλος μεταξύ φρέσκων και συντηρημένων δειγμάτων. Τα μοντέλα κατά την ανεξάρτητη επικύρωση δεν είχαν υψηλή ακρίβεια. Στο τέλος, προτιμήθηκε η χρήση μηχανών διανυσμάτων υποστήριξης (Support Vector Machines – SVMs) σε δύο στάδια προκειμένου να διαχωριστούν τα φρέσκα από τα συντηρημένα δείγματα και μετά τα νοθευμένα από τα ανόθευτα. Έτσι, επιτεύχθηκε ποσοστό σωστής κατηγοριοποίησης 95.31% στο ανεξάρτητο σετ επικύρωσης.Στην τρίτη περίπτωση, ελήφθησαν πολυφασματικές εικόνες και φάσματα FTIR από κιμά επτά διαφορετικών κομματιών και από αντίστοιχα αποψυγμένα δείγματα που είχαν καταψυχθεί στους -20°C για 7 και 32 μέρες (συνολικά 105 εικόνες και φάσματα). Η PCA χρησιμοποιήθηκε για τη διερεύνηση των δεδομένων, ενώ PLSDA και SVM πέτυχαν 100% σωστή κατηγοριοποίηση μεταξύ φρέσκων και αποψυγμένων κατά την επικύρωση και την ανεξάρτητη επικύρωση με χρήση πολυφασματικών εικόνων. Η FTIR ήταν λιγότερο ακριβής με 93.3 και 96.7% αντίστοιχα.Στην 4η και 5η περίπτωση, διερευνήθηκε η αλλοίωση του βοδινού κιμά. Στην 4η περίπτωση, χρησιμοποιώντας τη διαδικτυακή εφαρμογή “MeatReg”, χρησιμοποιήθηκαν επτά διαφορετικές μέθοδοι για την εκτίμηση του μικροβιακού πληθυσμού. Τα δεδομένα αποτελούνταν από 105 δείγματα συντηρημένα σε δυο διαφορετικές συσκευασίες -αέρας και modified air packaging (MAP - 20% CO2/ 80% O2)- και δύο θερμοκρασίες (4 και 10°C), μικροβιολογικές αναλύσεις (Pseudomonads, Lactobacilli, B. thermosphacta and Enterobacteriaceae, Ολική Μεσόφιλη Χλωρίδα - ΟΜΧ). Τα δεδομένα πολυφασματικής απεικόνισης και FTIR συγκρίθηκαν με αυτά από ηλεκτρονική μύτη, υγρή χρωματογραφία υψηλής απόδοσης (HPLC) και αέρια χρωματογραφία/ φασματοσκοπία μάζας (GC-MS). Τα αποτελέσματα διαφοροποιήθηκαν αρκετά ανάλογα το είδος του οργάνου και της ομάδας μικροοργανισμών. Παρόλα αυτά υπήρξε καλή ακρίβεια, με την μέθοδο RF να δίνει τα καλύτερα αποτελέσματα.Ομοίως στην 5η περίπτωση, 168 δείγματα βοδινού κιμά αναλύθηκαν ως προς την ΟΜΧ, ενώ παράλληλα έγιναν μετρήσεις FTIR. Τα δείγματα είχαν συντηρηθεί σε αέρα και MAP στους 4 και 10°C. Χρησιμοποιήθηκε μία προσέγγιση βασισμένη στην μεθοδολογία των ensemble μοντέλων, όπου η εκτίμηση της αλλοίωσης έγινε βάσει μίας μίξης αποτελεσμάτων επιμέρους νευρωνικών δικτύων (artificial neural networks). Το μέσο τετραγωνικό σφάλμα της πρόβλεψης ήταν 0.16 (log CFU/g)^2.

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 Ambient air particles: The use of ion chromatography and multivariate techniques in analysis of water-soluble ions

2020 ◽  
pp. 77-77
Author(s):  
Zaklina Todorovic ◽  
Jelena Radulovic ◽  
Ivana Sredovic-Ignjatovic ◽  
Ljubisa Ignjatovic ◽  
Antonije Onjia
Keyword(s):  
Ion Chromatography ◽  
Mineral Dust ◽  
Principal Component ◽  
Hierarchical Cluster ◽  
Ambient Air ◽  
Water Soluble ◽  
Multivariate Techniques ◽  
K Nearest Neighbors ◽  
Traffic Emission ◽  
Linear Discriminant

Seventeen water-soluble substances (sodium, ammonium, potassium, magnesium, calcium, formate, methanesulfonate, glyoxylate, chloride, nitrite, nitrate, glutarate, succinate, malate, malonate, sulfate, and oxalate) in 94 samples of particle matter in the ambient air, collected for ten months, in a suburb of Belgrade (Serbia), were determined by ion chromatography. To apportion the sources of the air pollution, the log-transformed data were processed by applying multivariate techniques. Principal component and factor analysis identified three main factors controlling the data variability: stationary combustion processes with the highest loadings of oxalate, malonate, and malate; landfill emission and secondary inorganic aerosol characterized by high levels of ammonium, nitrate, and sulfate; and a contribution of mineral dust composed of magnesium, calcium, and chloride. The hierarchical cluster analysis pointed out a differentiation of the samples into five groups belonging to different variables inputs. For the classification of ambient air samples using nine selected ions, the recognition ability of linear discriminant analysis, k-nearest neighbors, and soft independent modeling of class analogy were 87.0, 94.6, and 97.8 %, respectively. Time-series analysis showed that the traffic emission is more pronounced in winter in contrast to the mineral dust influence, while the effect of waste combustion exhibits no trend.

scholarly journals A Screening Method Based on Headspace-Ion Mobility Spectrometry to Identify Adulterated Honey

Sensors ◽  
2019 ◽  
Vol 19 (7) ◽  
pp. 1621 ◽  
Author(s):  
María José Aliaño-González ◽  
Marta Ferreiro-González ◽  
Estrella Espada-Bellido ◽  
Miguel Palma ◽  
Gerardo F. Barbero
Keyword(s):  
Ion Mobility Spectrometry ◽  
Ion Mobility ◽  
Screening Method ◽  
Principal Component ◽  
Hierarchical Cluster ◽  
High Price ◽  
Intermediate Precision ◽  
Linear Discriminant ◽  
High Fructose ◽  
Novel Method

Nowadays, adulteration of honey is a frequent fraud that is sometimes motivated by the high price of this product in comparison with other sweeteners. Food adulteration is considered a deception to consumers that may have an important impact on people’s health. For this reason, it is important to develop fast, cheap, reliable and easy to use analytical methods for food control. In the present research, a novel method based on headspace-ion mobility spectrometry (HS-IMS) for the detection of adulterated honey by adding high fructose corn syrup (HFCS) has been developed. A Box–Behnken design combined with a response surface method have been used to optimize a procedure to detect adulterated honey. Intermediate precision and repeatability studies have been carried out and coefficients of variance of 4.90% and 4.27%, respectively, have been obtained. The developed method was then tested to detect adulterated honey. For that purpose, pure honey samples were adulterated with HFCS at different percentages (10–50%). Hierarchical cluster analysis (HCA) and principal component analysis (PCA) showed a tendency of the honey samples to be classified according to the level of adulteration. Nevertheless, a perfect classification was not achieved. On the contrary, a full classification (100%) of all the honey samples was performed by linear discriminant analysis (LDA). This is the first time the technique of HS-IMS has been applied for the determination of adulterated honey with HFCS in an automatic way.

Quality evaluation of tugaksheeree samples by ATR-FTIR spectroscopy using multicomponent analysis

2020 ◽  
Vol 10 ◽  
Author(s):  
Nikunj D. Patel ◽  
Niranjan S. Kanaki
Keyword(s):  
Principal Component Analysis ◽  
Cluster Analysis ◽  
Quality Evaluation ◽  
Principal Component ◽  
Hierarchical Cluster ◽  
Attenuated Total Reflectance ◽  
Chemometric Methods ◽  
Total Reflectance ◽  
Fingerprint Region ◽  
Ftir Technique

Background: Numerous Ayurvedic formulations contains tugaksheeree as key ingredient. Tugaksheereeis the starch gained from the rhizomes of two plants, Curcuma angustifoliaRoxb. (Zingiberaceae) and Marantaarundinacea (MA) Linn. (Marantaceae). Objective: The primary concerns in quality assessment of Tugaksheeree occur due to adulteration or substitution. Method: In current study, Fourier transform infrared (FTIR) technique with attenuated total reflectance (ATR) facility was used to evaluate tugaksheeree samples. Total 10 different samples were studied and transmittance mode was kept to record the spectra devoid of pellets of KBR. Further treatment was given with multi component tools by considering fingerprint region of the spectra. Multivariate analysis was performed by various chemometric methods. Result: Multi component methods like Principal Component Analysis (PCA), and Hierarchical Cluster Analysis (HCA)were used to discriminate the tugaksheeree samples using Minitab software. Conclusion: This method can be used as a tool to differentiate samples of tugaksheeree from its adulterants and substitutes.

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