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Journal articles on the topic 'Generalized Procrustes Analysis'

Author: Grafiati

Published: 4 June 2021

Last updated: 29 January 2023

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1

Igual, Laura, Xavier Perez-Sala, Sergio Escalera, Cecilio Angulo, and Fernando De la Torre. "Continuous Generalized Procrustes analysis." Pattern Recognition 47, no. 2 (February 2014): 659–71. http://dx.doi.org/10.1016/j.patcog.2013.08.006.

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2

Bartoli, Adrien, Daniel Pizarro, and Marco Loog. "Stratified Generalized Procrustes Analysis." International Journal of Computer Vision 101, no. 2 (September 7, 2012): 227–53. http://dx.doi.org/10.1007/s11263-012-0565-0.

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3

Bennani Dosse, Mohammed, Henk A. L. Kiers, and Jos M. F. Ten Berge. "Anisotropic generalized Procrustes analysis." Computational Statistics & Data Analysis 55, no. 5 (May 2011): 1961–68. http://dx.doi.org/10.1016/j.csda.2010.11.027.

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4

Adachi, Kohei. "Generalized joint Procrustes analysis." Computational Statistics 28, no. 6 (March 27, 2013): 2449–64. http://dx.doi.org/10.1007/s00180-013-0413-x.

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Meyners, Michael, Joachim Kunert, and El Mostafa Qannari. "Comparing generalized procrustes analysis and statis." Food Quality and Preference 11, no. 1-2 (January 2000): 77–83. http://dx.doi.org/10.1016/s0950-3293(99)00038-5.

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6

Langron, S. P., and A. J. Collins. "Perturbation Theory for Generalized Procrustes Analysis." Journal of the Royal Statistical Society: Series B (Methodological) 47, no. 2 (January 1985): 277–84. http://dx.doi.org/10.1111/j.2517-6161.1985.tb01356.x.

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7

Xiong, R., K. Blot, J. F. Meullenet, and J. M. Dessirier. "Permutation tests for Generalized Procrustes Analysis." Food Quality and Preference 19, no. 2 (March 2008): 146–55. http://dx.doi.org/10.1016/j.foodqual.2007.03.003.

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8

Dijksterhuis, Garmt, and Pieter Punter. "Interpreting generalized procrustes analysis ‘analysis of variance’ tables." Food Quality and Preference 2, no. 4 (January 1990): 255–65. http://dx.doi.org/10.1016/0950-3293(90)90017-o.

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9

Wilkinson, Clare, Maarten Schipper, and Tina Leguijt. "Weighted analysis for missing values in generalized procrustes analysis." Food Quality and Preference 11, no. 1-2 (January 2000): 85–90. http://dx.doi.org/10.1016/s0950-3293(99)00027-0.

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10

ten Berge, Jos M. F., and Paul A. Bekker. "The isotropic scaling problem in Generalized Procrustes Analysis." Computational Statistics & Data Analysis 16, no. 2 (August 1993): 201–4. http://dx.doi.org/10.1016/0167-9473(93)90114-9.

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11

Kroonenberg, P. M., W. J. Dunn, and J. J. F. Commandeur. "Consensus Molecular Alignment Based on Generalized Procrustes Analysis‖." Journal of Chemical Information and Computer Sciences 43, no. 6 (November 2003): 2025–32. http://dx.doi.org/10.1021/ci0302916.

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12

Alcalde, María Jesús, Isabel Moreno-Indias, Alberto Horcada, Antonio Molina, and Manuel Juárez. "Generalized procrustes analysis (GPA) as a tool to discriminate among sheep breeds." Archives Animal Breeding 57, no. 1 (October 29, 2014): 1–10. http://dx.doi.org/10.7482/0003-9438-57-028.

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Abstract. Forty male lambs of five Southern Spanish breeds were used to study the effects of the breed in their sensorial characteristics. The used breeds were: Segureña, Spanish Merino, Grazalema Merino, Churra Lebrijana and Montesina breeds. Milk lambs were slaughtered at 12 kg of live weight. A descriptive sensory evaluation was developed using the longissimus lumborum from each animal by a panel of 12 experts and a Generalized Procrustes Analysis (GPA) was used to discriminate among them. Generalized Procrustes Analysis clearly differentiated Churra Lebrijana of out the rest breeds. Churra Lebrijana was defined as more tender, juicier and with less lamb odour than the rest of the Southern Spanish lamb breeds. Thus, GPA is able to discriminate among breeds.

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13

Verboon, Peter, and K. Ruben Gabriel. "Generalized Procrustes analysis with iterative weighting to achieve resistance." British Journal of Mathematical and Statistical Psychology 48, no. 1 (May 1995): 57–73. http://dx.doi.org/10.1111/j.2044-8317.1995.tb01050.x.

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14

Chen, Yisong, Antoni B. Chan, Zhouchen Lin, Kenji Suzuki, and Guoping Wang. "Efficient tree-structured SfM by RANSAC generalized Procrustes analysis." Computer Vision and Image Understanding 157 (April 2017): 179–89. http://dx.doi.org/10.1016/j.cviu.2017.02.005.

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15

Fusiello, Andrea, and Fabio Crosilla. "Solving bundle block adjustment by generalized anisotropic Procrustes analysis." ISPRS Journal of Photogrammetry and Remote Sensing 102 (April 2015): 209–21. http://dx.doi.org/10.1016/j.isprsjprs.2015.02.002.

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16

Dijksterhuis, G. B., and J. C. Gower. "The interpretation of Generalized Procrustes Analysis and allied methods." Food Quality and Preference 3, no. 2 (January 1991): 67–87. http://dx.doi.org/10.1016/0950-3293(91)90027-c.

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17

van Ginkel, Joost R., and Pieter M. Kroonenberg. "Using Generalized Procrustes Analysis for Multiple Imputation in Principal Component Analysis." Journal of Classification 31, no. 2 (July 2014): 242–69. http://dx.doi.org/10.1007/s00357-014-9154-y.

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18

Křepela, M., J. Sequens, and D. Zahradník. "A contribution to the knowledge of Scots pine (Pinus sylvestris L.) stem shape." Journal of Forest Science 50, No. 5 (January 11, 2012): 211–18. http://dx.doi.org/10.17221/4617-jfs.

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This is a study on the evaluation of stem shape in Scots pine. The experiments were carried out on 430 stems without bark, taken from 30 fully analysed sample trees that grew in 5 different locations in the Czech Republic. Generalized Procrustes analysis was used as the method of study. The stems were described by the use of landmarks. Full Procrustes coordinates were calculated for all stems, and the full Procrustes mean shape was set for individual sample trees, locations and all stems. For Procrustes tangent coordinates, variability was examined using the method of principal components. The two most important principal components were diagrammatized and described. Furthermore, statistical tests of mean shape vectors for individual locations were carried out.

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19

Grice, James W., and Kimberly K. Assad. "Generalized Procrustes Analysis: A Tool for Exploring Aggregates and Persons." Applied Multivariate Research 13, no. 1 (August 24, 2009): 93. http://dx.doi.org/10.22329/amr.v13i1.2836.

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Gower (1975) introduced Generalized Procrustes Analysis (GPA) as a multivariate statistical technique for analyzing three-dimensional data matrices. The current paper presents a non-technical introduction to the logic underlying GPA and then presents a completely worked example using genuine data. Specifically,self and peer ratings obtained from students attending a Summer Science Academy are analyzed and discussed. It is shown that GPA offers a powerful set of tools for exploring data at both the aggregate and individual level. A number of issues regarding the current analysis methods are also discussed.

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20

Wöber, Wilfried, Lars Mehnen, Manuel Curto, Papius Dias Tibihika, Genanaw Tesfaye, and Harald Meimberg. "Investigating Shape Variation Using Generalized Procrustes Analysis and Machine Learning." Applied Sciences 12, no. 6 (March 20, 2022): 3158. http://dx.doi.org/10.3390/app12063158.

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The biological investigation of a population’s shape diversity using digital images is typically reliant on geometrical morphometrics, which is an approach based on user-defined landmarks. In contrast to this traditional approach, the progress in deep learning has led to numerous applications ranging from specimen identification to object detection. Typically, these models tend to become black boxes, which limits the usage of recent deep learning models for biological applications. However, the progress in explainable artificial intelligence tries to overcome this limitation. This study compares the explanatory power of unsupervised machine learning models to traditional landmark-based approaches for population structure investigation. We apply convolutional autoencoders as well as Gaussian process latent variable models to two Nile tilapia datasets to investigate the latent structure using consensus clustering. The explanatory factors of the machine learning models were extracted and compared to generalized Procrustes analysis. Hypotheses based on the Bayes factor are formulated to test the unambiguity of population diversity unveiled by the machine learning models. The findings show that it is possible to obtain biologically meaningful results relying on unsupervised machine learning. Furthermore we show that the machine learning models unveil latent structures close to the true population clusters. We found that 80% of the true population clusters relying on the convolutional autoencoder are significantly different to the remaining clusters. Similarly, 60% of the true population clusters relying on the Gaussian process latent variable model are significantly different. We conclude that the machine learning models outperform generalized Procrustes analysis, where 16% of the population cluster was found to be significantly different. However, the applied machine learning models still have limited biological explainability. We recommend further in-depth investigations to unveil the explanatory factors in the used model.

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21

WAKELING, IAN N., MONIQUE M. RAATS, and HALLIDAY J. H. MacFIE. "A NEW SIGNIFICANCE TEST FOR CONSENSUS IN GENERALIZED PROCRUSTES ANALYSIS." Journal of Sensory Studies 7, no. 2 (June 1992): 91–96. http://dx.doi.org/10.1111/j.1745-459x.1992.tb00526.x.

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22

Keskin, S., A. Kor, and S. Karaca. " Evaluation of sensory characteristics of sheep and goat meat by Procrustes Analysis." Czech Journal of Animal Science 57, No. 11 (November 5, 2012): 516–21. http://dx.doi.org/10.17221/6384-cjas.

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Relationships between sensory variables, species, feeding systems, and panelists were examined by Procrustes Analysis. Six meat groups considering species and feeding type combinations were evaluated by ten panelists for five sensory characteristics (colour, texture, odour, taste, and acceptability). These characteristics were assessed using a nine-point hedonic scale (9 = extremely enjoy, 1 = extremely dislike). Generalized Procrustes Analysis was performed and 61.11% of the total variability was explained by the first two dimensions that correspond to Factor 1 and Factor 2. The first one accounted for 33.99% and the second for 27.12%. Results showed that pasture conditions were reflected in all sensory characteristics of meat of Hair goat as well as Karakas sheep. This study concluded that meat of sheep and goats reared in pasture conditions was more preferable as concerns sensory characteristics than that of fattening animals or those reared in intensive conditions.  

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23

Kor, Askin, and Siddik Keskin. "Quality and Sensory Evaluation for Goat Meat Using Generalized Procrustes Analysis." Journal of Animal and Veterinary Advances 10, no. 10 (October 1, 2011): 1313–16. http://dx.doi.org/10.3923/javaa.2011.1313.1316.

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24

Arnold, Gillian M., John C. Gower, Sugnet Gardner-Lubbe, and Niël J. le Roux. "Biplots of free-choice profile data in generalized orthogonal Procrustes analysis." Journal of the Royal Statistical Society: Series C (Applied Statistics) 56, no. 4 (August 2007): 445–58. http://dx.doi.org/10.1111/j.1467-9876.2007.00586.x.

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25

Xiong, Huiling, Dapeng Zhang, Christopher J. Martyniuk, Vance L. Trudeau, and Xuhua Xia. "Using Generalized Procrustes Analysis (GPA) for normalization of cDNA microarray data." BMC Bioinformatics 9, no. 1 (2008): 25. http://dx.doi.org/10.1186/1471-2105-9-25.

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26

KUNERT, JOACHIM, and EL MOSTAFA QANNARI. "A SIMPLE ALTERNATIVE TO GENERALIZED PROCRUSTES ANALYSIS: APPLICATION TO SENSORY PROFILING DATA." Journal of Sensory Studies 14, no. 2 (June 1999): 197–208. http://dx.doi.org/10.1111/j.1745-459x.1999.tb00112.x.

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27

de Jong, Sijmen, Johannes Heidema, and Henk C. M. van der Knaap. "Generalized procrustes analysis of coffee brands tested by five European sensory panels." Food Quality and Preference 9, no. 3 (May 1998): 111–14. http://dx.doi.org/10.1016/s0950-3293(97)00041-4.

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28

Cho, Jungchan, Minsik Lee, Chong-Ho Choi, and Songhwai Oh. "EM-GPA: Generalized Procrustes analysis with hidden variables for 3D shape modeling." Computer Vision and Image Understanding 117, no. 11 (November 2013): 1549–59. http://dx.doi.org/10.1016/j.cviu.2013.07.009.

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29

Ser, Gazel. "Using Generalized Procrustes Analysis for Evaluation of Sensory Characteristic Data of Lamb Meat." Turkish Journal of Agriculture - Food Science and Technology 7, no. 6 (June 25, 2019): 840. http://dx.doi.org/10.24925/turjaf.v7i6.840-844.2214.

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Generalized Procrustes Analysis (GPA) is a multivariate statistic method that is used at the evaluation of sensory analyses in the food industry. GPA provides benefit in terms of decreasing the difference between the panellists and bringing the data obtained from different panellists together. In this study, the aim was to determine the effect of a pre-slaughter fasting period on sensory characteristics of lambs fed with different rations using GPA. Semi-trained panellists formed from twenty-six persons were requested for evaluation of the meat samples such as tenderness, juiciness, flavour and overall liking rated on a scale of 1(extremely dislike) to 9 (extremely good). The first two factors obtained by GPA explained 66.74% of total variability. As a result of the analysis, it was determined that 12 h and 24 h fasting of lambs fed barley supplemented with alfalfa hay were less preferable when compared to lambs fed alfalfa hay only. In addition, lambs in both groups with 48 h fasting were preferred less by the panellists. In conclusion, GPA analysis provides useful data concerning the sensitivity of each panellist in a sensory panel test.

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Vega-Hernández, María Concepción, and Carmen Patino-Alonso. "Comparing COSTATIS and Generalized Procrustes Analysis with Multi-Way Public Education Expenditure Data." Mathematics 9, no. 15 (July 31, 2021): 1816. http://dx.doi.org/10.3390/math9151816.

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Governments serve a variety of purposes, and where governments spend their money has always been of concern to society. In particular, spending on public education is of great interest. However, the volume of this information can be difficult to manage. Therefore, the purpose of this work is to compare the COSTATIS method and generalized Procrustes analysis (GPA) when working with multi-way data. Despite the particular characteristics of each of them, they present similarities and differences that, when analyzed together, can provide complementary results to researchers. The COSTATIS consists of a co-inertia analysis of the compromise of two k-table analyses. The GPA method provides an optimal superimposed representation of individual configurations, and a common consensus configuration is constructed as the mean of all transformed configurations. In addition, the GPA method includes the translation, rotation and scaling of coordinates. In this study, both methods were applied, and the advantages and disadvantages of each are presented. The treated data are a sequence of tables from various countries where different public expenditures on education have been measured over time.

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KING, BONNIE M., and PAUL ARENTS. "A STATISTICAL TEST OF CONSENSUS OBTAINED FROM GENERALIZED PROCRUSTES ANALYSIS OF SENSORY DATA." Journal of Sensory Studies 6, no. 1 (April 1991): 37–48. http://dx.doi.org/10.1111/j.1745-459x.1991.tb00500.x.

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32

Case, F., A. Beinat, F. Crosilla, and I. M. Alba. "Virtual trial assembly of a complex steel structure by Generalized Procrustes Analysis techniques." Automation in Construction 37 (January 2014): 155–65. http://dx.doi.org/10.1016/j.autcon.2013.10.013.

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33

Karaca, Serhat, and Gazel Ser. "Evaluation of the Physiological and Behavioural Relationships of Yearling Bucks by Generalized Procrustes Analysis." Proceedings of the Bulgarian Academy of Sciences 75, no. 8 (August 31, 2022): 1234–43. http://dx.doi.org/10.7546/crabs.2022.08.17.

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The aim of this study is to evaluate the relationships between some sexual behaviours and physiological traits such as serum testosterone concentration, body weight loss and presence of horns of yearling bucks using Generalized Procrustes Analysis (GPA). In the study, 11 yearling bucks being sexually in- experienced were used. The sexual behaviours were determined with a total of four tests, which consisted of exposing the yearling bucks individually to three unrestrained estrous ewes for 15 min. In these tests, some behavioural characteristics such as number of ejaculations, flehmen response, number of mounts and serum testosterone concentration were determined. In the GPA analysis, the first two factors explained approximately 98% of the total variation between animals (Dimension 1 = 95.57%, Dimension 2 = 2.68%). It was determined that there was a positive and high correlation between tests and the number of mounts, which had a high correlation with the first dimension. Ejaculation frequency, flehmen response and testosterone levels decreased in line with in- creases in the number of mounts. As a result, it can be suggested that GPA can be effectively evaluated quantitative and qualitative behavioural characteristics without requiring any prior assumptions and reducing variability among animals.

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Ahn, Mi So, Sang Min Shin, Te-Ju Wu, Dong Joon Lee, Ching-Chang Ko, Chooryung J. Chung, and Yong-Il Kim. "Correlation between the cross-sectional morphology of the mandible and the three-dimensional facial skeletal pattern: A structural equation modeling approach." Angle Orthodontist 89, no. 1 (August 3, 2018): 78–86. http://dx.doi.org/10.2319/122117-879.1.

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ABSTRACT Objective: To clarify the relationship between the cross-sectional morphology of the mandible and vertical, transverse, and anteroposterior facial skeletal patterns using statistical shape analysis and structural equation modeling (SEM). Materials and Methods: We used 150 cone beam computed tomography (CBCT) images to obtain three-dimensional (3D) facial landmarks and cross-sectional images of the mandible. The morphology of the inner and outer cortices of the mandible was analyzed using statistical shape analysis, including generalized Procrustes analysis and principal component analysis (PCA). Factor analysis was performed to determine factors pertaining to the skeletal measurements and shape variations for the inner and outer cortices, following which a structural equation model was constructed. Results: Using factor analysis, characteristics of the vertical, transverse, and anteroposterior facial skeletal patterns were determined. PCA of the cross-sectional morphology of the mandible revealed 70% of the cumulative proportion by PC1 and PC2 after generalized Procrustes superimpositions. SEM showed complex relationships between the facial skeletal patterns and variations in the cross-sectional morphology of the mandibular cortices. The influence of the transverse factors on the outer cortex as a latent variable was relatively significant (P = .057). However, the influence of the vertical factors on the outer and inner cortices was not significant. Conclusions: The transverse skeletal pattern is associated with the morphology of the outer cortex of the mandible.

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Groth, Andreas, and Michael Ghil. "Monte Carlo Singular Spectrum Analysis (SSA) Revisited: Detecting Oscillator Clusters in Multivariate Datasets." Journal of Climate 28, no. 19 (September 29, 2015): 7873–93. http://dx.doi.org/10.1175/jcli-d-15-0100.1.

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Abstract Singular spectrum analysis (SSA) along with its multivariate extension (M-SSA) provides an efficient way to identify weak oscillatory behavior in high-dimensional data. To prevent the misinterpretation of stochastic fluctuations in short time series as oscillations, Monte Carlo (MC)–type hypothesis tests provide objective criteria for the statistical significance of the oscillatory behavior. Procrustes target rotation is introduced here as a key method for refining previously available MC tests. The proposed modification helps reduce the risk of type-I errors, and it is shown to improve the test’s discriminating power. The reliability of the proposed methodology is examined in an idealized setting for a cluster of harmonic oscillators immersed in red noise. Furthermore, the common method of data compression into a few leading principal components, prior to M-SSA, is reexamined, and its possibly negative effects are discussed. Finally, the generalized Procrustes test is applied to the analysis of interannual variability in the North Atlantic’s sea surface temperature and sea level pressure fields. The results of this analysis provide further evidence for shared mechanisms of variability between the Gulf Stream and the North Atlantic Oscillation in the interannual frequency band.

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36

Maranho, Rúben, Maria Teresa Ferreira, and Francisco Curate. "Sexual Dimorphism of the Human Scapula: A Geometric Morphometrics Study in Two Portuguese Reference Skeletal Samples." Forensic Sciences 2, no. 4 (November 23, 2022): 780–94. http://dx.doi.org/10.3390/forensicsci2040056.

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The estimation of biological sex is of paramount relevance in the analysis of skeletal remains recovered in forensic contexts. This study aims to assess sexual dimorphism for identification purposes, from two reference samples of the Portuguese population, and a depiction of the size- and shape-related sexual dimorphism of the human scapula using geometric morphometrics approaches. The sample comprised 211 individuals (100 males and 111 females). A generalized Procrustes analysis (GPA) was performed for shape analyses, a principal component analysis (PCA) and a Procrustes ANOVA were implemented on the GPA transformed variables, and a discriminant analysis was used to assess the cross-validated accuracy of sex estimates. The data showed that male scapulae were larger, with medial and lateral curves more pronounced and an inferior angle more acute than females. The males and females were classified with low accuracy (66.82% and 65.88% for landmarks and semi-landmarks data) based on shape. Combining size and shape variables improved the accuracy of the prediction using landmarks data (80.09%). A combination of both variables might improve the chances of the geometric morphometrics methodology in correctly estimating the sex of unidentified individuals, especially if the skeletal elements show low sexual shape dimorphism.

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Lavalle, Andrea Lina, Raquel Defacio, Mariano De Leo, and Sergio Jorge Bramardi. "Methodological proposal for the characterization of accessions in Germplasm Banks using Generalized Procrustes Analysis applied to incomplete but connected trials." Revista de la Facultad de Ciencias Agrarias UNCuyo 53, no. 1 (July 7, 2021): 35–45. http://dx.doi.org/10.48162/rev.39.004.

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Characterization of plant material conserved in germplasm banks allows the study and analysis of the genetic variability within a collection. When germplasm banks have a large number of accessions, field evaluation should be performed using assays with manageable accession subsets. Common checks connecting the different assays are required to compare these accession subsets. In this study, the Generalized Procrustes Analysis was proposed as a basis for obtaining a factorial plane where all individuals are projected. This technique is applied to genotypes common to all assays, iteratively generating scale factors and rotation matrices. Accessions only belonging to a given assay are considered supplementary elements. This proposal was illustrated using datasets of 54 maize accessions from the Pergamino Active Germplasm Bank of the Experimental Station at the Instituto Nacional de Tecnología Agropecuaria (INTA) in Argentina. The proposal achieved highly satisfactory results. Highlights: In field evaluation of large germplasm collections, the material must be divided into manageable experimental trials, in which different accession subsets are evaluated in different environments. A new algorithm based on Generalized Procrustes Analysis (GPA) allowed to find the consensus of several configurations of individuals connected by common checks. The characterization data analysis strategy was illustrated using a set of accessions from the Argentine Maize Germplasm Bank. The new proposal stands as a useful tool for evaluate germplasm collections, providing good results with easy implementation and considering the multivariate structure of the data set.

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Russell, Anthony, and Heather Jamniczky. "A geometric morphometric assessment of the 'batagurine process' of testudinoid turtles." Amphibia-Reptilia 25, no. 4 (2004): 369–79. http://dx.doi.org/10.1163/1568538042788906.

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AbstractThe batagurine process and associated rearward extension of the pterygoid, putative diagnostic characters of the turtle taxon Bataguridae, are evaluated using geometric morphometrics. Visual examination of 28 testudinoid turtles, representing each of the three clades within the taxon, showed the presence of a possible batagurine process and rearward extension of the pterygoid in the majority of specimens, but identification of the batagurine process sensu stricto was not able to be objectively established. Six landmarks on the palatal surface and sets of semi-landmarks on the basioccipital and pterygoid were digitized on photographs of the ventral surface of each skull for geometric morphometric analysis, and subjected to Generalized Procrustes and Relative Warps Analysis. No significant differences in basioccipital, pterygoid or palate shape between the three taxa were found, and the Relative Warps Analysis of the Procrustes coordinates revealed no separation of taxa on relative warps one or two for any of the data sets. The batagurine process and accompanying posterior extension of the pterygoid are thus of questionable phylogenetic utility in the separation of the Bataguridae from other testudinoid turtles.

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van Wingerde, Bart, and Joost van Ginkel. "SPSS Syntax for Combining Results of Principal Component Analysis of Multiply Imputed Data Sets using Generalized Procrustes Analysis." Applied Psychological Measurement 45, no. 3 (February 4, 2021): 231–32. http://dx.doi.org/10.1177/0146621621990757.

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40

Ferreira, E. L., C. Lencioni, M. T. Benassi, M. O. Barth, and D. H. M. Bastos. "Descriptive Sensory Analysis and Acceptance of Stingless Bee Honey." Food Science and Technology International 15, no. 3 (June 2009): 251–58. http://dx.doi.org/10.1177/1082013209341136.

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Stingless bee honey samples were evaluated by sensory descriptive analysis using free choice profile methodology. Appearance, flavor, and aroma were described and the data were treated with Generalized Procrustes Analysis. Individual descriptive terms ranged from 8 to 20. Plotting the samples in a bidimensional plan indicated that appearance attributes (color and viscosity) and sweet, sour and acid flavor were strongly correlated with x axis (Dimension 1) while coconut, wood, acid, sour, and sweet flavor aroma attributes were correlated with y axis (Dimension 2). The affective test was also performed and with the exception of the Melipona scutellaris honey, all the other samples showed good acceptance. Honeys that were described as sweeter and less acid were preferred by nontrained assessors, indicating that the regular consumer recognizes honey produced by Apis mellifera bee as a standard.

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41

Osis, Sean T., Blayne A. Hettinga, Shari L. Macdonald, and Reed Ferber. "A novel method to evaluate error in anatomical marker placement using a modified generalized Procrustes analysis." Computer Methods in Biomechanics and Biomedical Engineering 18, no. 10 (January 27, 2014): 1108–16. http://dx.doi.org/10.1080/10255842.2013.873034.

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42

Pastor, M. Vicenta, Elvira Costell, Luis Izquierdo, and Luis Durán. "III-9. Sensory profile of peach nectars: Evaluation of attributes and assessors by generalized procrustes analysis." Food Quality and Preference 7, no. 3-4 (July 1996): 347. http://dx.doi.org/10.1016/s0950-3293(96)90255-4.

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Mauricio, A. A., A. B. Palazzo, V. M. Caselato, and H. M. A. Bolini. "Generalized Procrustes Analysis and External Preference Map Used to Consumer Drivers of Diet Gluten Free Product." Food and Nutrition Sciences 07, no. 09 (2016): 711–23. http://dx.doi.org/10.4236/fns.2016.79072.

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Rutland, John W., Bradley N. Delman, Christopher Bellaire, James G. Napoli, William Shuman, Emily A. Rutland, Daniel Ranti, et al. "Craniofacial Dysmorphology in Unilateral Coronal Synostosis Using Three-Dimensional Landmark-Based Analysis With Generalized Procrustes Superimposition." Journal of Craniofacial Surgery 32, no. 1 (September 2, 2020): 16–20. http://dx.doi.org/10.1097/scs.0000000000006787.

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Druml, Thomas, Maximilian Dobretsberger, and Gottfried Brem. "Ratings of equine conformation – new insights provided by shape analysis using the example of Lipizzan stallions." Archives Animal Breeding 59, no. 2 (June 28, 2016): 309–17. http://dx.doi.org/10.5194/aab-59-309-2016.

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Abstract. The quality of individual ratings of conformation traits can commonly be evaluated by calculating inter-rater correlations and repeatability coefficients. We present an approach in which we associate the individual rating scores with the underlying horse shapes derived from standardized images, performing a shape regression. Therefore, we analyzed the shape of 102 Lipizzan stallions from the Spanish Riding School in Vienna, defined by 246 shape-correlated two-dimensional coordinates using techniques from the field of image analysis and geometric morphometrics. In addition we examined the differences in the conformation classifiers' perceptions of type traits and functional traits. In this study part, the rating scores of eight conformation classifiers were tested for agreement, yielding inter-rater correlations ranging from 0.30 to 0.55 and kappa coefficients ranging from 0.08 to 0.42. From the 12 scoring traits assessed on a valuating scale, type traits with a mean kappa coefficient (κ) of 0.27 demonstrated a higher agreement than functional traits (κ = 0.14). Based on 246 two-dimensional anatomical and somatometric landmarks, the shape variation was analyzed by the use of generalized orthogonal least-squares Procrustes (generalized Procrustes analysis – GPA) procedures. Shape variables were regressed into the results from visually scored linear type trait classifications (shape regressions). From the 48 performed shape regressions (eight classifiers, six traits), 42 % resulted in a significant equation. In 58 % of the ratings, no association between scores and the phenotype of the horses was found. Phenotypic differences of model horses along significant regression curves of mean ratings and individual ratings were exemplarily visualized and compared by warped and averaged images. Finally, we demonstrated that the method of shape regression offers the possibility to evaluate the association of individual ratings from expert conformation classifiers with the shapes of horses. The detected bias in classifiers' rankings have not been considered in breeding programs, and its impact on selection procedures still needs further research.

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Peters, James R., Robert M. Campbell, and Sriram Balasubramanian. "Characterization of the age-dependent shape of the pediatric thoracic spine and vertebrae using generalized procrustes analysis." Journal of Biomechanics 63 (October 2017): 32–40. http://dx.doi.org/10.1016/j.jbiomech.2017.07.030.

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Joardar, Sandip, and Amitava Chatterjee. "Palm Dorsa Vein Pattern based biometric verification system using Anisotropic Generalized Procrustes Analysis on weighted training dictionary." Applied Soft Computing 85 (December 2019): 105562. http://dx.doi.org/10.1016/j.asoc.2019.105562.

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Coetzer, R. L. J., R. F. Rossouw, and N. J. Le Roux. "Reference set selection with generalized orthogonal Procrustes analysis for multivariate statistical process monitoring of multiple production processes." Chemometrics and Intelligent Laboratory Systems 132 (March 2014): 52–62. http://dx.doi.org/10.1016/j.chemolab.2014.01.006.

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Pastor, M. V., E. Costell, L. Izquierdo, and L. Durán. "Perfil descriptivo de néctares de melocotón. Evaluación de jueces y de atributos con el análisis de Procrustes generalizado/Sensory profile of peach nectars. Evaluation of assessors and attributes by generalized Procrustes analysis." Food Science and Technology International 2, no. 4 (August 1996): 219–30. http://dx.doi.org/10.1177/108201329600200404.

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A trained panel evaluated eight peach nectars by conventional descriptive profile analysis. Two- way analysis of variance showed significant (α ≤ 0.05) differences between samples for the 13 attributes evaluated. For nine of them, interaction between samples and assessors was also signif icant. The internal structure of the data was analysed by generalized Procrustes analysis. It was found that two attributes (granularity and aftertaste) were not good discriminators between samples and that two assessors (1 and 2) differed considerably from the average response. Principal component analysis was applied to the mean scores of samples, considering all attributes and computing means for all assessors and after removing the data for two attributes and two asses sors. Similar sample configurations were obtained in both cases. The two first axes explained more of the total variability in the second case (78.8% vs 69.6%).

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Sinesio, F., L. Guerrero, A. Romero, E. Moneta, and J. C. Lombard. "Sensory Evaluation of Walnut: An Interlaboratory Study." Food Science and Technology International 7, no. 1 (February 2001): 37–47. http://dx.doi.org/10.1106/0848-6b59-dtl3-a3ya.

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This work summarizes an interlaboratory study on full sensory profiling designed with the aim of finding some key attributes relevant to describe the sensory quality of walnuts. The study compared descriptive sensory profiling by different laboratories in different countries (Spain, France and Italy) Three trained panels in those European countries in the characterization of walnuts were used. Samples of walnuts belonging to different cultivars were harvested from experimental fields and some commercial lots. Multivariate methods (generalized procrustes analysis and varclus procedure) of analysis were used for extracting useful sensory information about the products and the variability between the panels. The descriptive profiles generated by the panels independently showed some analogies and provided similar product classification.

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