scholarly journals Morphological Characteristics of Grapevine Cultivars and Closed Contour Analysis with Elliptic Fourier Descriptors

Plants ◽  
2021 ◽  
Vol 10 (7) ◽  
pp. 1350
Author(s):  
Muhammed Kupe ◽  
Bahadır Sayıncı ◽  
Bunyamin Demir ◽  
Sezai Ercisli ◽  
Mojmir Baron ◽  
...  
Keyword(s):  
Image Processing ◽  
Water Drop ◽  
Geometric Mean ◽  
Morphological Characteristics ◽  
Principal Component ◽  
Plant Morphology ◽  
Elliptic Fourier Analysis ◽  
Contour Analysis ◽  
Linear Discriminant ◽  
Grape Cultivars

Morphology is the most visible and distinct character of plant organs and is accepted as one of the most important tools for plant biologists, plant breeders and growers. A number of methods based on plant morphology are applied to discriminate in particular close cultivars. In this study, image processing analysis was used on 20 grape cultivars (“Amasya beyazı“, “Antep karası“, “Bahçeli karası”, “Çavuş“, “Cevşen“, “Crimson“, “Dimrit“, “Erenköy beyazı“, “Hafızali“, “Karaşabi“, “Kırmızı“, “İzabella (Isabella) “, “Morşabi“, “Müşgüle“, “Nuniya“, “Royal“, “Sultani çekirdeksiz (Sultanina)“, “Yalova incisi“, “Yerli beyazv“, “Yuvarlak çekirdeksiz“) to classify them. According to image processing analysis, the longest and the greatest projected area values were observed in “Antep karası“ cultivar. The “Sultani çekirdeksiz“ cultivar had the least geometric mean diameter. The greatest sphericity ratios were observed in “Yerli beyaz“, “Erenköy beyazı“ and “Amasya beyazı“ cultivars. According to principal component analysis, dimensional attributes were identified as the most significant source of variation discriminant grape cultivars from each other. Morphological differences between the cultivars were explained by sphericity and elongation variables. According to elliptic Fourier analysis (EFA) results, grape morphology largely looks like ellipse and sphere. However, there are some cultivars that look similar to a water drop. The cultivars with similar morphology were identified by a pair-wise comparison test conducted with the use of linear discriminant analysis, and they were presented in a scatter plot. According to cluster analysis, present grape cultivars were classified into seven sub-groups, which indicated great diversity.

scholarly journals Biochemical composition and shape-dimensional traits of rosehip genotypes

2021 ◽  
Vol 0 (0) ◽  
Author(s):  
Bünyamin Demir ◽  
Bahadır Sayıncı ◽  
Mehmet Yaman ◽  
Ahmet Sümbül ◽  
Ercan Yıldız ◽  
...  
Keyword(s):  
Biochemical Composition ◽  
Paired Comparison ◽  
Geometric Mean ◽  
Fruit Shape ◽  
Image Processing Technique ◽  
Shape Variation ◽  
Elliptic Fourier Analysis ◽  
Projected Area ◽  
Vertical Orientation ◽  
Linear Discriminant

Abstract In the present study, the biochemical composition and shape and dimensional traits of 25 rosehip (Rosa canina) genotypes were investigated. The shape and dimensional traits were determined by image processing technique. Seed-propagated rosehip genotypes belonging to R. canina were collected from the natural flora of Mesudiye (Ordu) and Talas (Kayseri) districts. Antioxidant activity (39.510–72.673 mmol · kg−1), total flavonoids (287.80–1,686.20 mg quercetin equivalent (QE) · kg−1) and total phenolics (38,519.40–79,080.60 mg gallic acid equivalent · kg−1) of the genotypes exhibited large variations. Width (12.2 mm) and thickness (12.5 mm) of fruits averages were found to be close to each other. The genotypes exhibited fruit lengths between 12.0 mm and 29.5 mm. Average projected area at horizontal orientation (179.7 mm2) was greater than the projected area at vertical orientation (120.4 mm2). Sphericity average was calculated as 71.4%. According to principal component (PC) analysis, the most important dimensional traits discriminating genotypes from each other were identified as surface area, geometric mean diameter and volume. In terms of shape attributes, distinctive differences were observed in sphericity, circularity, elongation and surface closure rates (SCR) of the genotypes. According to elliptic Fourier analysis (EFA), genotypes look like a sphere. In terms of shape, there were long, spherical, flat bottomed, pointed bottomed and asymmetric-looking genotypes indicating how environment and genotype affect the fruit shape. The greatest shape variation was transverse contraction and expansion. According to the clustering analysis for shape attributes, rosehip genotypes were classified into six groups. Dendrogram, scatter plots of linear discriminant analysis and paired comparison test results put forth the shape differences of the genotype successfully.

scholarly journals Changes in the Morphological Characteristics of Potato Plants Attributed to Seasonal Variability

Agriculture ◽  
2020 ◽  
Vol 10 (4) ◽  
pp. 95
Author(s):  
Olga Escuredo ◽  
Ana Seijo-Rodríguez ◽  
M. Shantal Rodríguez-Flores ◽  
Laura Meno ◽  
M. Carmen Seijo
Keyword(s):  
Potato Crop ◽  
Linear Regression Analysis ◽  
Morphological Characteristics ◽  
Principal Component ◽  
Leaf Size ◽  
Plant Morphology ◽  
Multiple Linear Regression Analysis ◽  
Morphological Data ◽  
Crop Cycle ◽  
Lower Temperature

The development of a potato crop differs according to the environmental conditions and growing season of an area. Periods of high temperatures and drought have been frequent in recent years, and this has affected crops worldwide. The effect of meteorological factors on the plant morphology of potato cultivars growing in A Limia was analyzed for three consecutive years. The crop cycle with the highest temperatures and least accumulated rainfall (2016) showed plants with a higher number of leaflets, which were shorter in length. The crop cycle (2014) with a lower temperature and more rainfall had the tallest plants, the highest degree of flowering, fewer pairs of leaflets and the highest length of the floral peduncle. Kennebec and Fontane were the varieties that showed the least variability in morphological characteristics during the seasons analyzed. Considering the meteorological and morphological data, a principal component analysis was carried out, which explained 80.1% of the variance of the data. Spearman rank correlations showed higher significant coefficients between the temperature and foliar characteristics. The leaf size of plants was estimated using a multiple linear regression analysis, which included the mean temperature, explaining 64% of the variability of the data.

scholarly journals To Weight or Not to Weight, That Is the Question: The Design of a Composite Indicator of Landscape Fragmentation

2021 ◽  
Vol 11 (7) ◽  
pp. 3208
Author(s):  
Andrea De Montis ◽  
Vittorio Serra ◽  
Giovanna Calia ◽  
Daniele Trogu ◽  
Antonio Ledda
Keyword(s):  
Principal Component Analysis ◽  
Geometric Mean ◽  
Composite Index ◽  
Weighted Average ◽  
Principal Component ◽  
Expert Judgment ◽  
Landscape Fragmentation ◽  
Composite Indicator ◽  
Per Se ◽  
Higher Sensitivity

Composite indicators (CIs), i.e., combinations of many indicators in a unique synthetizing measure, are useful for disentangling multisector phenomena. Prominent questions concern indicators’ weighting, which implies time-consuming activities and should be properly justified. Landscape fragmentation (LF), the subdivision of habitats in smaller and more isolated patches, has been studied through the composite index of landscape fragmentation (CILF). It was originally proposed by us as an unweighted combination of three LF indicators for the study of the phenomenon in Sardinia, Italy. In this paper, we aim at presenting a weighted release of the CILF and at developing the Hamletian question of whether weighting is worthwhile or not. We focus on the sensitivity of the composite to different algorithms combining three weighting patterns (equalization, extraction by principal component analysis, and expert judgment) and three indicators aggregation rules (weighted average mean, weighted geometric mean, and weighted generalized geometric mean). The exercise provides the reader with meaningful results. Higher sensitivity values signal that the effort of weighting leads to more informative composites. Otherwise, high robustness does not mean that weighting was not worthwhile. Weighting per se can be beneficial for more acceptable and viable decisional processes.

scholarly journals Real Time Breath Analysis Using Portable Gas Chromatography for Adult Asthma Phenotypes

Metabolites ◽  
2021 ◽  
Vol 11 (5) ◽  
pp. 265
Author(s):  
Ruchi Sharma ◽  
Wenzhe Zang ◽  
Menglian Zhou ◽  
Nicole Schafer ◽  
Lesa A. Begley ◽  
...  
Keyword(s):  
Gas Chromatography ◽  
Real Time ◽  
Rapid Assessment ◽  
Principal Component ◽  
Corticosteroid Treatment ◽  
Breath Analysis ◽  
Blood Eosinophil ◽  
Exhaled Breath ◽  
Respiratory Illnesses ◽  
Linear Discriminant

Asthma is heterogeneous but accessible biomarkers to distinguish relevant phenotypes remain lacking, particularly in non-Type 2 (T2)-high asthma. Moreover, common clinical characteristics in both T2-high and T2-low asthma (e.g., atopy, obesity, inhaled steroid use) may confound interpretation of putative biomarkers and of underlying biology. This study aimed to identify volatile organic compounds (VOCs) in exhaled breath that distinguish not only asthmatic and non-asthmatic subjects, but also atopic non-asthmatic controls and also by variables that reflect clinical differences among asthmatic adults. A total of 73 participants (30 asthma, eight atopic non-asthma, and 35 non-asthma/non-atopic subjects) were recruited for this pilot study. A total of 79 breath samples were analyzed in real-time using an automated portable gas chromatography (GC) device developed in-house. GC-mass spectrometry was also used to identify the VOCs in breath. Machine learning, linear discriminant analysis, and principal component analysis were used to identify the biomarkers. Our results show that the portable GC was able to complete breath analysis in 30 min. A set of nine biomarkers distinguished asthma and non-asthma/non-atopic subjects, while sets of two and of four biomarkers, respectively, further distinguished asthmatic from atopic controls, and between atopic and non-atopic controls. Additional unique biomarkers were identified that discriminate subjects by blood eosinophil levels, obese status, inhaled corticosteroid treatment, and also acute upper respiratory illnesses within asthmatic groups. Our work demonstrates that breath VOC profiling can be a clinically accessible tool for asthma diagnosis and phenotyping. A portable GC system is a viable option for rapid assessment in asthma.

scholarly journals A model for spectroscopic food sample analysis using data sonification

2021 ◽  
Author(s):  
Hsein Kew
Keyword(s):  
Dimensionality Reduction ◽  
Classification Accuracy ◽  
Reduction Method ◽  
Real Life ◽  
Principal Component ◽  
Relevant Information ◽  
Analysis Model ◽  
Linear Discriminant ◽  
Audio Output ◽  
Dimensionality Reduction Method

AbstractIn this paper, we propose a method to generate an audio output based on spectroscopy data in order to discriminate two classes of data, based on the features of our spectral dataset. To do this, we first perform spectral pre-processing, and then extract features, followed by machine learning, for dimensionality reduction. The features are then mapped to the parameters of a sound synthesiser, as part of the audio processing, so as to generate audio samples in order to compute statistical results and identify important descriptors for the classification of the dataset. To optimise the process, we compare Amplitude Modulation (AM) and Frequency Modulation (FM) synthesis, as applied to two real-life datasets to evaluate the performance of sonification as a method for discriminating data. FM synthesis provides a higher subjective classification accuracy as compared with to AM synthesis. We then further compare the dimensionality reduction method of Principal Component Analysis (PCA) and Linear Discriminant Analysis in order to optimise our sonification algorithm. The results of classification accuracy using FM synthesis as the sound synthesiser and PCA as the dimensionality reduction method yields a mean classification accuracies of 93.81% and 88.57% for the coffee dataset and the fruit puree dataset respectively, and indicate that this spectroscopic analysis model is able to provide relevant information on the spectral data, and most importantly, is able to discriminate accurately between the two spectra and thus provides a complementary tool to supplement current methods.

Anatomization of the systems of dimension relaxation for facial recognition

2020 ◽  
pp. 1-11
Author(s):  
Mayamin Hamid Raha ◽  
Tonmoay Deb ◽  
Mahieyin Rahmun ◽  
Tim Chen
Keyword(s):  
Face Recognition ◽  
Main Idea ◽  
Principal Component ◽  
Feature Space ◽  
Projection Methods ◽  
Data Representation ◽  
Linear Discriminant ◽  
Class Differences ◽  
Underlying Space ◽  
Maximum Variance

Face recognition is the most efficient image analysis application, and the reduction of dimensionality is an essential requirement. The curse of dimensionality occurs with the increase in dimensionality, the sample density decreases exponentially. Dimensionality Reduction is the process of taking into account the dimensionality of the feature space by obtaining a set of principal features. The purpose of this manuscript is to demonstrate a comparative study of Principal Component Analysis and Linear Discriminant Analysis methods which are two of the highly popular appearance-based face recognition projection methods. PCA creates a flat dimensional data representation that describes as much data variance as possible, while LDA finds the vectors that best discriminate between classes in the underlying space. The main idea of PCA is to transform high dimensional input space into the function space that displays the maximum variance. Traditional LDA feature selection is obtained by maximizing class differences and minimizing class distance.

scholarly journals A Generic Block-Level Error Confinement Technique for Memory Based on Principal Component Analysis

2019 ◽  
Vol 9 (22) ◽  
pp. 4733
Author(s):  
Cuiping Shao ◽  
Huiyun Li ◽  
Zheng Wang ◽  
Jiayan Fang
Keyword(s):  
Image Processing ◽  
Principal Component Analysis ◽  
Principal Component ◽  
Error Resilience ◽  
Component Analysis ◽  
Cmos Technology ◽  
Error Correcting Codes ◽  
Memory Storage ◽  
Protection Scheme ◽  
Bit Flip

Nanoscale CMOS technology has encountered severe reliability issues especially in on-chip memory. Conventional word-level error resilience techniques such as Error Correcting Codes (ECC) suffer from high physical overhead and inability to correct increasingly reported multiple bit flip errors. On the other hands, state-of-the-art applications such as image processing and machine learning loosen the requirement on the levels of data protection, which result in dedicated techniques of approximated fault tolerance. In this work, we introduce a novel error protection scheme for memory, based on feature extraction through Principal Component Analysis and the modular-wise technique to segment the data before PCA. The extracted features can be protected by replacing the fault vector with the averaged confinement vectors. This approach confines the errors with either single or multi-bit flips for generic data blocks, whilst achieving significant savings on execution time and memory usage compared to traditional ECC techniques. Experimental results of image processing demonstrate that the proposed technique results in a reconstructed image with PSNR over 30 dB, while robust against both single bit and multiple bit flip errors, with reduced memory storage to just 22.4% compared to the conventional ECC-based technique.

scholarly journals Multiscale Supervised Classification of Point Clouds with Urban and Forest Applications

Sensors ◽  
2019 ◽  
Vol 19 (20) ◽  
pp. 4523 ◽  
Author(s):  
Carlos Cabo ◽  
Celestino Ordóñez ◽  
Fernando Sáchez-Lasheras ◽  
Javier Roca-Pardiñas ◽  
and Javier de Cos-Juez
Keyword(s):  
Random Forest ◽  
Laser Scanning ◽  
Supervised Classification ◽  
Computing Time ◽  
Principal Component ◽  
Point Clouds ◽  
Support Vector ◽  
Linear Discriminant ◽  
Vector Machines ◽  
Input Variables

We analyze the utility of multiscale supervised classification algorithms for object detection and extraction from laser scanning or photogrammetric point clouds. Only the geometric information (the point coordinates) was considered, thus making the method independent of the systems used to collect the data. A maximum of five features (input variables) was used, four of them related to the eigenvalues obtained from a principal component analysis (PCA). PCA was carried out at six scales, defined by the diameter of a sphere around each observation. Four multiclass supervised classification models were tested (linear discriminant analysis, logistic regression, support vector machines, and random forest) in two different scenarios, urban and forest, formed by artificial and natural objects, respectively. The results obtained were accurate (overall accuracy over 80% for the urban dataset, and over 93% for the forest dataset), in the range of the best results found in the literature, regardless of the classification method. For both datasets, the random forest algorithm provided the best solution/results when discrimination capacity, computing time, and the ability to estimate the relative importance of each variable are considered together.

scholarly journals A fast and low-cost approach to quality control of alcohol-based hand sanitizer using portable near infrared spectrometer and chemometrics

2021 ◽  
pp. 096703352098731
Author(s):  
Adenilton C da Silva ◽  
Lívia PD Ribeiro ◽  
Ruth MB Vidal ◽  
Wladiana O Matos ◽  
Gisele S Lopes
Keyword(s):  
Quality Control ◽  
Discriminant Analysis ◽  
Near Infrared ◽  
Nir Spectroscopy ◽  
Principal Component ◽  
Partial Least Square ◽  
Least Square ◽  
Chemical Components ◽  
Linear Discriminant ◽  
Hand Sanitizer

The use of alcohol-based hand sanitizers is recommended as one of several strategies to minimize contamination and spread of the COVID-19 disease. Current reports suggest that the virucidal potential of ethanol occurs at concentrations close to 70%. Traditional methods of verifying the ethanol concentration in such products invite potential errors due to the viscosity of chemical components or may be prohibitively expensive to undertake in large demand. Near infrared (NIR) spectroscopy and chemometrics have already been used for the determination of ethanol in other matrices and present an alternative fast and reliable approach to quality control of alcohol-based hand sanitizers. In this study, a portable NIR spectrometer combined with classification chemometric tools, i.e., partial least square discriminant analysis (PLS–DA) and linear discriminant analysis with successive algorithm projection (SPA–LDA) were used to construct models to identify conforming and non-conforming commercial and laboratory synthesized hand sanitizer samples. Principal component analysis (PCA) was applied in an exploratory data study. Three principal components accounted for 99% of data variance and demonstrate clustering of conforming and non-conforming samples. The PLS–DA and SPA–LDA classification models presented 77 and 100% of accuracy in cross/internal validation respectively and 100% of accuracy in the classification of test samples. A total of 43% commercial samples evaluated using the PLS–DA and SPA–LDA presented ethanol content non-conforming for hand sanitizer gel. These results indicate that use of NIR spectroscopy and chemometrics is a promising strategy, yielding a method that is fast, portable, and reliable for discrimination of alcohol-based hand sanitizers with respect to conforming and non-conforming ethanol concentrations.

Encasement in plant morphology: an integrative approach from genes to organisms

2005 ◽  
Vol 83 (10) ◽  
pp. 1207-1221 ◽  
Author(s):  
Christian Lacroix ◽  
Bernard Jeune ◽  
Denis Barabé
Keyword(s):  
Growth Rate ◽  
Morphological Characteristics ◽  
Plant Morphology ◽  
Integrative Approach ◽  
Structural Level ◽  
Classical Approach ◽  
Developmental Genes ◽  
Qualitative And Quantitative ◽  
New Concepts ◽  
Dynamic Morphology

Recent advances in molecular genetics are prompting developmental plant morphologists to refine the theoretical context of their field. For example, at the level of the action of certain developmental genes, the distinction between recognized structural categories (i.e., stem and leaf) are not obvious. This issue has also been analyzed by morphologists from qualitative and quantitative perspectives and has lead to similar conclusions. Consequently, the classical approach to morphology with a typological view of organ categories is no longer sufficient to explain the set of all possible forms. However, within the context of a dynamic morphology, where processes of development such as growth rate, duration, and distribution are considered, a more encompassing view of the generation of form can be achieved. We therefore propose that classical morphology is a subset of dynamic morphology. The main goal of this paper is to show how new concepts and methods of viewing plant morphology allow us to build a conceptual theoretical framework that may have a predictive value with respect to morphological characteristics as well as molecular properties of organs. The main premise of this commentary, within the context of dynamic morphology, is that the plant consists of an encasement of structures or a nesting of partially similar units. Common developmental processes are in operation at each structural level and variations in the modalities of these processes lead to the development of specific structures. Repeating polymorphic sets (RPS) represent an extension of this perspective on plant development and have the potential to predict the existence of new, perhaps unknown forms. The idea of repeating polymorphic sets can also be extended to outline the activity of specific developmental genes to explain how a wide variety of those genes are interrelated during development to specify form.

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