scholarly journals Possibility of Human Gender Recognition Using Raman Spectra of Teeth

Molecules ◽  
2021 ◽  
Vol 26 (13) ◽  
pp. 3983
Author(s):  
Ozren Gamulin ◽  
Marko Škrabić ◽  
Kristina Serec ◽  
Matej Par ◽  
Marija Baković ◽  
...  
Keyword(s):  
Raman Spectra ◽  
Principal Component ◽  
Support Vector ◽  
Gender Recognition ◽  
Proof Of Concept ◽  
Male And Female ◽  
Tooth Type ◽  
Tooth Apex ◽  
The Difference

Gender determination of the human remains can be very challenging, especially in the case of incomplete ones. Herein, we report a proof-of-concept experiment where the possibility of gender recognition using Raman spectroscopy of teeth is investigated. Raman spectra were recorded from male and female molars and premolars on two distinct sites, tooth apex and anatomical neck. Recorded spectra were sorted into suitable datasets and initially analyzed with principal component analysis, which showed a distinction between spectra of male and female teeth. Then, reduced datasets with scores of the first 20 principal components were formed and two classification algorithms, support vector machine and artificial neural networks, were applied to form classification models for gender recognition. The obtained results showed that gender recognition with Raman spectra of teeth is possible but strongly depends both on the tooth type and spectrum recording site. The difference in classification accuracy between different tooth types and recording sites are discussed in terms of the molecular structure difference caused by the influence of masticatory loading or gender-dependent life events.

Detecting protagonists and antagonists in the voice quality of American cartoon characters: a quantitative LTAS-based analysis

Phonetica ◽  
2021 ◽  
Vol 78 (4) ◽  
pp. 345-384
Author(s):  
Ke Hui Tong ◽  
Scott Reid Moisik
Keyword(s):  
A Priori ◽  
Voice Quality ◽  
Principal Component ◽  
Acoustic Properties ◽  
Support Vector ◽  
Cartoon Character ◽  
Quantitative Examination ◽  
The Difference ◽  
Cartoon Characters ◽  
Dominance Pattern

Abstract The voices of heroes and villains in cartoons contribute to their uniqueness and helps shape how we perceive them. However, not much research has looked at the acoustic properties of character voices and the possible contributions these have to cartoon character archetypes. We present a quantitative examination of how voice quality distinguishes between characters based on their alignment as either protagonists or antagonists, performing Principal Component Analysis (PCA) on the Long-term Average Spectra (LTAS) of concatenated passages of the speech of various characters obtained from four different animated cartoons. We then assessed if the categories of “protagonists” and “antagonists” (determined via an a priori classification) could be distinguished using a classification algorithm, and if so, what acoustic characteristics could help distinguish the two categories. The overall results support the idea that protagonists and antagonists can be distinguished by their voice qualities. Support Vector Machine (SVM) analysis yielded an average classification accuracy of 96% across the cartoons. Visualisation of the spectral traits constituting the difference did not yield consistent results but reveals a low-versus-high frequency energy dominance pattern segregating antagonists and protagonists. Future studies can look into how other variables might be confounded with voice quality in distinguishing between these categories.

Principal Component Regression for Forensic Age Determination Using the Raman Spectra of Teeth

2020 ◽  
Vol 74 (12) ◽  
pp. 1473-1485
Author(s):  
Aziz Osmani ◽  
Matej Par ◽  
Marko Škrabić ◽  
Marin Vodanović ◽  
Ozren Gamulin
Keyword(s):  
Raman Spectra ◽  
Age Determination ◽  
Principal Component Regression ◽  
Principal Component ◽  
Root Apex ◽  
Predictive Capability ◽  
Forensic Application ◽  
Specimen Handling ◽  
Male And Female ◽  
Tooth Surfaces

Raman spectra of mineralized tooth tissues were used to build a principal component regression (PCR) age determination model for forensic application. A sample of 71 teeth was obtained from donors aging from 11 to 76 years. No particular selection criteria were applied; teeth affected with various pathological processes were deliberately included to simulate a realistic forensic scenario. In order to comply with the nondestructive specimen handling, Raman spectra were collected from tooth surfaces without any previous preparation. Different tooth tissues were evaluated by collecting the spectra from three distinct sites: tooth crown, tooth neck, and root apex. Whole recorded spectra (3500–200 cm−1) were used for principal component analysis and building of the age determination model using PCR. The predictive capabilities of the obtained age determination models varied according to the spectra collection site. Optimal age determination was attained by using Raman spectra collected from cementum at root apex (R2 values of 0.84 and 0.71 for male and female donors, respectively). For optimal performance of that model, male and female donors had to be analyzed separately, as merging both genders into a single model considerably diminished its predictive capability (R2 = 0.29).

scholarly journals Gender Determination with Ischiopubic Index and Upper Pelvic Index in Iranian Adult Radiographic Stereotypes

2021 ◽  
Vol 19 (2) ◽  
Author(s):  
Hossein Ahmadi Kahjoogh ◽  
Maryam Vasheghani Farahani ◽  
Babak Shekarchi ◽  
Beheshteh Abouhamzeh ◽  
Mahdi Isazadeh
Keyword(s):  
Adult Population ◽  
Male And Female ◽  
Imaging Center ◽  
Study Population ◽  
The Mean ◽  
Spss Software ◽  
The Difference ◽  
The Individual ◽  
Female Subjects

Background: Different parts of the skeletal system have been studied in different studies to determine gender. The pelvis is one of the most important skeletal components for the determination of gender and identification of humans. Objectives: The current study aimed to determine gender using the ischiopubic index and upper pelvic index in the adult population of Iran. Methods: In this study, 140 individuals (70 male and 70 female subjects) referring to Athari Imaging Center in Tehran, Iran, for various medical reasons entered the study by giving written and oral consent using the convenience sampling method. Measurements were performed on digital anteroposterior pelvic radiographs (with standard conditions and radiographic stencil distance from the light source as 100 cm). The obtained data were analyzed using SPSS software (version 22). Results: The mean ischemic index of the whole population was 108.88 ± 13.75. The difference in the ischiopubic index between male and female subjects was significant (P < 0.05). The mean upper pelvic index of the whole study population was 81.70 ± 10.09; however, the difference in the upper pelvic index between the two groups of male and female subjects was not significant (P < 0.05). Conclusions: The pelvic ischemic index in Iranian women is higher than that reported for men. If the ischiopubic index is higher than 103, with 93% sensitivity and 73% specificity, it would be female. According to the study of graphs, if there is a special identity for diagnosis, there will be the possibility to match the graph before the death of the individual with the graph taken from skeletal remains.

scholarly journals A Detailed Analysis on Kidney and Heart Disease Prediction using Machine Learning

2021 ◽  
pp. 9-14
Author(s):  
Claire Salkar
Keyword(s):  
Machine Learning ◽  
Chronic Kidney Disease ◽  
Logistic Regression ◽  
Heart Disease ◽  
Kidney Disease ◽  
Principal Component ◽  
Support Vector ◽  
Computationally Efficient ◽  
Optimal Method ◽  
The Difference

Detection of disease at earlier stages is the most challenging one. Datasets of different diseases are available online with different number of features corresponding to a particular disease. Many dimensionalities reduction and feature extraction techniques are used nowadays to reduce the number of features in dataset and finding the most appropriate ones. This paper explores the difference in performance of different machine learning models using Principal Component Analysis dimensionality reduction technique on the datasets of Chronic kidney disease and Cardiovascular disease. Further, the authors apply Logistic Regression, K Nearest Neighbour, Naïve Bayes, Support Vector Machine and Random Forest Model on the datasets and compare the performance of the model with and without PCA. A key challenge in the field of data mining and machine learning is building accurate and computationally efficient classifiers for medical applications. With an accuracy of 100% in chronic kidney disease and 85% for heart disease, KNN classifier and logistic regression were revealed to be the most optimal method of predictions for kidney and heart disease respectively.

scholarly journals Determination of Phase Transition by Principal Component Analysis Applied to Raman Spectra of Polycristalline BATIO3 at Low and High Temperature

2012 ◽  
Vol 10 (1) ◽  
Author(s):  
E.V. Mejía-Uriarte ◽  
R.Y. Sato-Berrú ◽  
M. Navarrete ◽  
O. Kolokoltsev ◽  
J. M. Saniger
Keyword(s):  
Phase Transition ◽  
Principal Component Analysis ◽  
High Temperature ◽  
Raman Spectra ◽  
Principal Component ◽  
Component Analysis ◽  
Para Determinar

Este trabajo describe el análisis de las componentes principales (PCA) aplicados a los espectros Raman del BaTiO3como función de la temperatura. Durante el experimento las muestras fueron calentadas continuamente y losespectros Raman fueron registrados cada 0.5 °C a una razón de 0.1 °C por minuto. Este procedimiento es aplicadopara muestras de polvo compacto de BaTiO3 para obtener su comportamiento térmico de -190 °C a 230 °C. Latécnica del PCA fue capaz de distinguir características espectrales para determinar la temperatura de la transición defase y la historia térmica completa, incluyendo las transiciones de fase estructural: romboédrica a ortorrómbica at -100 °C, ortorrómbica a tetragonal at -5°C y tetragonal a cúbica at 121 °C.

scholarly journals Powerful processing to three-dimensional facial recognition using triple information

2020 ◽  
Vol 9 (4) ◽  
pp. 326
Author(s):  
Mohammad Karimi Moridani ◽  
Ahad Karimi Moridani ◽  
Mahin Gholipour
Keyword(s):  
Face Recognition ◽  
Three Dimensional ◽  
Laser Scanner ◽  
Principal Component ◽  
Support Vector ◽  
The Face ◽  
Principal Component Analysis Algorithm ◽  
The Difference ◽  
Security Surveillance ◽  
The Trinity

<p><span>Face Detection plays a crucial role in identifying individuals and criminals in Security, surveillance, and footwork control systems. Face Recognition in the human is superb, and pictures can be easily identified even after years of separation. These abilities also apply to changes in a facial expression such as age, glasses, beard, or little change in the face. This method is based on 150 three-dimensional images using the Bosphorus database of a high range laser scanner in a Bogaziçi University in Turkey. This paper presents powerful processing for face recognition based on a combination of the salient information and features of the face, such as eyes and nose, for the detection of three-dimensional figures identified through analysis of surface curvature. The Trinity of the nose and two eyes were selected for applying principal component analysis algorithm and support vector machine to revealing and classification the difference between face and non-face. The results with different facial expressions and extracted from different angles have indicated the efficiency of our powerful processing.</span></p>

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 Carotenoid Analysis of Ripe Banana Flesh and Peel from Three Cultivars of Banana

2019 ◽  
Vol 1 (2) ◽  
pp. 60
Author(s):  
Gabriela Johana Septiany ◽  
Widya Dwi Rukmi Putri ◽  
Ida Nur Panca ◽  
Heriyanto Heriyanto ◽  
Assoc. Prof. Dr. Leenawaty Limantara
Keyword(s):  
Principal Component Analysis ◽  
Total Concentration ◽  
Principal Component ◽  
Mass Spectrometric ◽  
Provitamin A ◽  
Banana Peel ◽  
The Difference ◽  
Promising Source ◽  
Β Carotene

Banana peel is a promising source to be utilized in fortification of food products due to the high content of carotenoids (Cars) which are active as provitamin A. Dominant, total and relative concentrations of Cars were determined from fresh flesh and peel in three cultivars of banana obtained from Raja, Ambon Kuning, and Kepok Kuning. To evaluate the difference in Car compositions principal component analysis (PCA) was also performed. Based on chromatographic, spectroscopic, and mass spectrometric analyses, dominant Cars were separated and identified to be lutein, α-carotene and β-carotene. Lutein was the major Car of fresh peel, while other two Cars were found in fresh flesh in addition to lutein. Raja banana had the highest total concentration of Cars among three banana samples used. PCA results generated from the absorption spectra showed three clusters of the different banana samples. PCA results are correlated to their Car compositions and this method might be applicable for the determination of dominant Cars.

Raman Spectroscopy for Adipose Tissue Assessment in Rat Models of Obesity and Type 1 Diabetes

2021 ◽  
pp. 000370282199035
Author(s):  
Guadalupe Donjuán-Loredo ◽  
Ricardo Espinosa-Tanguma ◽  
Fabiola León-Bejarano ◽  
Jordi A. Ramírez-Elías ◽  
Roberto Salgado-Delgado ◽  
...  
Keyword(s):  
Raman Spectroscopy ◽  
Type 1 Diabetes ◽  
Adipose Tissue ◽  
Raman Spectra ◽  
Principal Component ◽  
Nondestructive Method ◽  
Support Vector ◽  
Noninvasive Methods ◽  
Functional Changes

Adipose tissue presents structural and functional changes in obesity and type 1 diabetes mellitus (T1DM). In obesity, the size and number of adipocytes and adipokine secretion increases. In T1DM, a loss of adipose tissue suggests changes in the metabolic activity of this tissue. A significant challenge is to find alternative noninvasive methods to evaluate molecular changes in adipose tissue related to obesity and T1DM. Recently, Raman spectroscopy and chemometrics techniques have emerged as a tool for biological tissue analysis. In this work, we propose the use of Raman spectroscopy to characterize spectral differences in adipose tissue from different rat groups (control, obese, and T1DM). The Raman spectra were analyzed using direct band analysis, ratiometric analysis, and chemometric methods (principal component analysis (PCA) and support vector machines (SVMs)). We found that the Raman spectra of obese rats showed significant spectral differences compared to control and diabetic groups related to fatty acids Raman bands. Also, the obese group has a significant decrease in the degree of unsaturation of lipids. The PCA–SVM models showed classification performance ranging from 71.43% to 71.79% accuracy for brown and white adipose tissue samples, respectively. In conclusion, the results demonstrate that Raman spectroscopy can be used as a nondestructive method to assess adipose tissue according to a metabolic condition.

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