SPECIES SEPARATION IN THE DECLARATA GROUP OF THE GENUS EUXOA, A COMPUTER ANALYSIS BASED ON STRUCTURAL CHARACTERS

1973 ◽  
Vol 105 (3) ◽  
pp. 501-508 ◽  
Author(s):  
D. F. Hardwick ◽  
L. P. Lefkovitch
Keyword(s):  
Discriminant Analysis ◽  
Computer Analysis ◽  
Principal Components ◽  
Species Separation ◽  
Discriminant Analyses

AbstractA sample of 205 specimens of the declarata group of the genus Euxoa was subjected to clustering, principal components, and discriminant analyses. The analyses were based largely on male genitalic characters. Only the discriminant analysis was successful in segregating the three nominal components of the group.

scholarly journals Detecting Bacterial Biofilms Using Fluorescence Hyperspectral Imaging and Various Discriminant Analyses

Sensors ◽  
2021 ◽  
Vol 21 (6) ◽  
pp. 2213
Author(s):  
Ahyeong Lee ◽  
Saetbyeol Park ◽  
Jinyoung Yoo ◽  
Jungsook Kang ◽  
Jongguk Lim ◽  
...  
Keyword(s):  
Discriminant Analysis ◽  
Hyperspectral Imaging ◽  
Food Processing ◽  
Nearest Neighbor ◽  
Hyperspectral Image ◽  
Uv Light ◽  
Hyperspectral Images ◽  
K Nearest Neighbor ◽  
E Coli ◽  
Discriminant Analyses

Biofilms formed on the surface of agro-food processing facilities can cause food poisoning by providing an environment in which bacteria can be cultured. Therefore, hygiene management through initial detection is important. This study aimed to assess the feasibility of detecting Escherichia coli (E. coli) and Salmonella typhimurium (S. typhimurium) on the surface of food processing facilities by using fluorescence hyperspectral imaging. E. coli and S. typhimurium were cultured on high-density polyethylene and stainless steel coupons, which are the main materials used in food processing facilities. We obtained fluorescence hyperspectral images for the range of 420–730 nm by emitting UV light from a 365 nm UV light source. The images were used to perform discriminant analyses (linear discriminant analysis, k-nearest neighbor analysis, and partial-least squares discriminant analysis) to identify and classify coupons on which bacteria could be cultured. The discriminant performances of specificity and sensitivity for E. coli (1–4 log CFU·cm−2) and S. typhimurium (1–6 log CFU·cm−2) were over 90% for most machine learning models used, and the highest performances were generally obtained from the k-nearest neighbor (k-NN) model. The application of the learning model to the hyperspectral image confirmed that the biofilm detection was well performed. This result indicates the possibility of rapidly inspecting biofilms using fluorescence hyperspectral images.

scholarly journals Evaluation of crisphead lettuce cultivars in different cover types by manova and discriminant analysis

2013 ◽  
Vol 31 (3) ◽  
pp. 439-444
Author(s):  
Izabela Regina C de Oliveira ◽  
Marcelo T Rezende ◽  
Carlos Tadeu dos S Dias ◽  
Daniela de S Gomes ◽  
Élberis P Botrel ◽  
...  
Keyword(s):  
Discriminant Analysis ◽  
Cover Crops ◽  
Environmental Sustainability ◽  
Cover Crop ◽  
Univariate Analysis ◽  
Polyethylene Film ◽  
Standard Errors ◽  
Multivariate Techniques ◽  
Discriminant Analyses ◽  
Plot Design

In many agricultural experiments the variables are biologically correlated and it is inappropriate to study them only under univariate analysis. Therefore, we evaluated commercial characteristics of crisphead lettuce cultivars and covers under a multivariate approach, using canonical discriminant analysis. We used a split plot design and we tested the cover crop, cultivar and interaction effects by using MANOVA (α= 5%). Means and its standard errors were obtained for average total weight, weight of the head, volume and density of plants. Canonical discriminant analyses were performed using PROC CANDISC procedure in SAS (SAS Institute, 2008) system. Canonical plots were obtained using JMP 9.0 (SAS Institute, 2010) linked to SAS database. With these plots it was possible to note the differences among factors levels. When polyethylene film was used as cover the plants had inferior commercial characteristics than plants in which cover crops were used. Thus, the cover with polyethylene film can be discouraged in the cultivation of crisphead lettuce, promoting environmental sustainability. We suggest these multivariate techniques in horticulture studies.

scholarly journals Morphological evaluation of common bean diversity in Bosnia and Herzegovina using the discriminant analysis of principal components (DAPC) multivariate method

Genetika ◽  
2013 ◽  
Vol 45 (3) ◽  
pp. 963-977 ◽  
Author(s):  
Jasmin Grahic ◽  
Fuad Gasi ◽  
Mirsad Kurtovic ◽  
Lutvija Karic ◽  
Mirha Djikic ◽  
...  
Keyword(s):  
Discriminant Analysis ◽  
Common Bean ◽  
Bosnia And Herzegovina ◽  
Principal Components ◽  
Morphological Characteristics ◽  
Principal Component ◽  
Discriminant Functions ◽  
Factor Loadings ◽  
Geographical Regions ◽  
High Level

In order to analyze morphological characteristics of locally cultivated common bean landraces from Bosnia and Herzegovina (B&H), thirteen quantitative and qualitative traits of 40 P. vulgaris accessions, collected from four geographical regions (Northwest B&H, Northeast B&H, Central B&H and Sarajevo) and maintained at the Gene bank of the Faculty of Agriculture and Food Sciences in Sarajevo, were examined. Principal component analysis (PCA) showed that the proportion of variance retained in the first two principal components was 54.35%. The first principal component had high contributing factor loadings from seed width, seed height and seed weight, whilst the second principal component had high contributing factor loadings from the analyzed traits seed per pod and pod length. PCA plot, based on the first two principal components, displayed a high level of variability among the analyzed material. The discriminant analysis of principal components (DAPC) created 3 discriminant functions (DF), whereby the first two discriminant functions accounted for 90.4% of the variance retained. Based on the retained DFs, DAPC provided group membership probabilities which showed that 70% of the accessions examined were correctly classified between the geographically defined groups. Based on the taxonomic distance, 40 common bean accessions analyzed in this study formed two major clusters, whereas two accessions Acc304 and Acc307 didn?t group in any of those. Acc360 and Acc362, as well as Acc324 and Acc371 displayed a high level of similarity and are probably the same landrace. The present diversity of Bosnia and Herzegovina?s common been landraces could be useful in future breeding programs.

scholarly journals Linear discriminant analysis based on gas chromatographic measurements for geographical prediction of USA medical domestic cannabis

2020 ◽  
Author(s):  
Ramia Z. Al Bakain ◽  
Yahya S. Al-Degs ◽  
James V. Cizdziel ◽  
Mahmoud A. Elsohly
Keyword(s):  
Discriminant Analysis ◽  
Linear Discriminant Analysis ◽  
Principal Components ◽  
Supervised Classification ◽  
Geographical Origin ◽  
Active Components ◽  
Linear Discriminant ◽  
Sample Extraction ◽  
Analytical Range

AbstractFifty four domestically produced cannabis samples obtained from different USA states were quantitatively assayed by GC–FID to detect 22 active components: 15 terpenoids and 7 cannabinoids. The profiles of the selected compounds were used as inputs for samples grouping to their geographical origins and for building a geographical prediction model using Linear Discriminant Analysis. The proposed sample extraction and chromatographic separation was satisfactory to select 22 active ingredients with a wide analytical range between 5.0 and 1,000 µg/mL. Analysis of GC-profiles by Principle Component Analysis retained three significant variables for grouping job (Δ9-THC, CBN, and CBC) and the modest discrimination of samples based on their geographical origin was reported. PCA was able to separate many samples of Oregon and Vermont while a mixed classification was observed for the rest of samples. By using LDA as a supervised classification method, excellent separation of cannabis samples was attained leading to a classification of new samples not being included in the model. Using two principal components and LDA with GC–FID profiles correctly predict the geographical of 100% Washington cannabis, 86% of both Oregon and Vermont samples, and finally, 71% of Ohio samples.

scholarly journals Cultivar Classification of Single Sweet Corn Seed Using Fourier Transform Near-Infrared Spectroscopy Combined with Discriminant Analysis

2019 ◽  
Vol 9 (8) ◽  
pp. 1530 ◽  
Author(s):  
Guangjun Qiu ◽  
Enli Lü ◽  
Ning Wang ◽  
Huazhong Lu ◽  
Feiren Wang ◽  
...  
Keyword(s):  
Fourier Transform ◽  
Discriminant Analysis ◽  
Near Infrared ◽  
Seed Quality ◽  
Sweet Corn ◽  
Full Range ◽  
Classification Model ◽  
Support Vector ◽  
Corn Seed ◽  
Discriminant Analyses

Seed purity is a key indicator of crop seed quality. The conventional methods for cultivar identification are time-consuming, expensive, and destructive. Fourier transform near-infrared (FT-NIR) spectroscopy combined with discriminant analyses, was studied as a rapid and nondestructive technique to classify the cultivars of sweet corn seeds. Spectra with a range of 1000–2500 nm collected from 760 seeds of two cultivars were used for the discriminant analyses. Thereafter, 126 feature wavelengths were identified from 1557 wavelengths using a genetic algorithm (GA) to build simplified classification models. Four classification algorithms, namely K-nearest neighbor (KNN), soft independent method of class analogy (SIMCA), partial least-squares discriminant analysis (PLS-DA), and support vector machine discriminant analysis (SVM-DA) were tested on full-range wavelengths and feature wavelengths, respectively. With the full-range wavelengths, all four algorithms achieved a high classification accuracy range from 97.56% to 99.59%, and the SVM-DA worked better than other models. From the feature wavelengths, no significant decline in accuracies was observed in most of the models and a high accuracy of 99.19% was still obtained by the PLS-DA model. This study demonstrated that using the FT-NIR technique with discriminant analyses could be a feasible way to classify sweet corn seed cultivars and the proper classification model could be embedded in seed sorting machinery to select high-purity seeds.

Assessing the Validity of the Semantic Differential Portion of the Apperceptive Personality Test

2001 ◽  
Vol 88 (3_suppl) ◽  
pp. 1235-1244 ◽  
Author(s):  
David J. Palmiter ◽  
David E. Silber
Keyword(s):  
Discriminant Analysis ◽  
Social Desirability ◽  
Semantic Differential ◽  
Personality Test ◽  
Ideal Self ◽  
Social Desirability Scale ◽  
Strong Negative Correlation ◽  
Discriminant Analyses ◽  
The Difference ◽  
The Ideal

This study investigated the validity of the semantic differential portion of the Apperceptive Personality Test with 225 undergraduates who completed the Marlowe-Crowne Social Desirability scale, actual-self and ideal-self semantic differential scales (e.g., Actual-self and Ideal-self), and either the Apperceptive Personality Test or a modified version. A projected-self score was calculated using the semantic differential ratings of the hero(ine) character on the test, e.g., Projected-self. A strong negative correlation indicated that, as the difference between the Ideal-self and Actual-self decreased, the difference between the Actual-self and Projected-self increased. Discriminant analyses indicated that highly guarded participants, e.g., high Social Desirability scores, showed more congruency between Ideal-self and Actual-self and less congruency between Actual-self ratings and Projected-self on the APT than did less guarded participants. When the difference scores incorporated only those semantic differential items that loaded on an Evaluative factor, the same result of discriminant analysis was found when participants who completed the modified version were included. These findings support the validity of the test's semantic differential items and suggest that guardedness tends to promote more similarity between Actual-self and Ideal-self and less similarity between Actual-self and Projected-self.

Amphibian Distribution with Respect to Pond Water Chemistry near Sudbury, Ontario

1992 ◽  
Vol 49 (S1) ◽  
pp. 114-121 ◽  
Author(s):  
V. Glooschenko ◽  
W. F. Weller ◽  
P. G. R. Smith ◽  
R. Alvo ◽  
J. H. G. Archbold
Keyword(s):  
Discriminant Analysis ◽  
Water Chemistry ◽  
Principal Components ◽  
Rana Sylvatica ◽  
Pond Water ◽  
Bufo Americanus ◽  
Breeding Sites ◽  
Metal Levels ◽  
Components Analysis ◽  
Detailed Chemical

Amphibians were present in 118 potential breeding sites 9–66 km northeast and southwest of Sudbury, Ontario. Detailed chemical analyses were done for 38 ponds, and 23 variables were subjected to principal components analysis to summarize the main gradients in pond chemistry. Discriminant analysis using scores of the first three principal components showed that the presence of Rana pipiens, R. clamitans and Hyla crucifer was positively related to buffering status (alkalinity, pH, and other correlated variables); the presence of H. crucifer was also negatively related to atmospheric deposition status (cadmium, nickel, other correlated metals, and sulphate). Discriminant analysis using the original water chemistry variables confirms these general patterns. Two species show relationships with buffering status variables: Rana sylvatica with conductivity and R. clamitans with alkalinity. Three species also show negative correlation with metal levels in pond water: Bufo americanus with nickel, R. clamitans with aluminum, and R. pipiens with zinc. Although most of the species expected do occur in the Sudbury area, the distributions of several species appear related to buffering status and metals present in their immediate environemnt. There were only two observations of Ambystoma maculatum, and low numbers of egg masses were noted for R. sylvatica.

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