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pca lda classification performance  (Genovis Inc)


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    Genovis Inc pca lda classification performance
    Pca Lda Classification Performance, supplied by Genovis Inc, used in various techniques. Bioz Stars score: 93/100, based on 92 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
    https://www.bioz.com/product/pca+lda+classification+performance/OpeRATOR+Lyophilized/frost_jonathan_a__2019__vibrational_spectroscopy_for_the_assessment_of_vulval_disease-2581-232-239
    Average 93 stars, based on 92 article reviews
    pca lda classification performance - by Bioz Stars, 2026-09
    93/100 stars

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    Related Articles

    Microscopy:

    Article Title: Vibrational Spectroscopy for the Assessment of Vulval Disease
    Article Snippet: .. Figure 7-10 — SNR for increasing acquisition time for spectra taken from normal vulval skin ................................................................................................... 211 Figure 7-11 — PTFE spectrum, x50 objective, 10 sec integration time .................... 214 Figure 7-12 — Normalised counts of the 732 cm1 peak as the microscope focus advances towards the PTFE through different depths of Intralipid 2.48%, x50 objective, 605 integration time ................................................................................. 214 Figure 7-13 — Marking of punch biopsies (ink dot on left of the biopsy allows the pathologist to cut the specimen along the solid line, mount section A in paraffin and cut sections for staining and histology at the level marked with the dashed line) ...217 Figure 7-14 — First alternative method for selecting principal components within leave one sample out cross validated PCA LDA classification ......................................... 219 Figure 7-15 — Sub loop for determining the number of biological relevant PCs in the first alternative method for selecting PCs within leave one sample out cross validated PCA LDA classification ........................................................................................... 220 Figure 7-16 — Second alternative method for selecting principal components within the leave one sample out cross validated PCA LDA classification ......................... 222 Figure 7-17 — Sub loop for identifying the biological relevant PCs in the second alternative method for selecting PCs within leave one sample out cross validated PCA LDA classification .................................................................................. 222 Figure 7-18 — Histogram of score values from linear discriminant function ............ 225 Figure 7-19 — PCA LDA classification performance (area under receiver operator curve) using spectra obtained from different depths of focus. .. ................................ 226 Figure 7-20 — A - Composite of all principal component loadings weighted by contribution to the linear discriminant classification model; B — Collagen type | (Sigma C7774) and elastin (Sigma E1625) spectra; C - Most significant principal component loading; D - Second most significant principal component loading.

    Staining:

    Article Title: Vibrational Spectroscopy for the Assessment of Vulval Disease
    Article Snippet: .. Figure 7-10 — SNR for increasing acquisition time for spectra taken from normal vulval skin ................................................................................................... 211 Figure 7-11 — PTFE spectrum, x50 objective, 10 sec integration time .................... 214 Figure 7-12 — Normalised counts of the 732 cm1 peak as the microscope focus advances towards the PTFE through different depths of Intralipid 2.48%, x50 objective, 605 integration time ................................................................................. 214 Figure 7-13 — Marking of punch biopsies (ink dot on left of the biopsy allows the pathologist to cut the specimen along the solid line, mount section A in paraffin and cut sections for staining and histology at the level marked with the dashed line) ...217 Figure 7-14 — First alternative method for selecting principal components within leave one sample out cross validated PCA LDA classification ......................................... 219 Figure 7-15 — Sub loop for determining the number of biological relevant PCs in the first alternative method for selecting PCs within leave one sample out cross validated PCA LDA classification ........................................................................................... 220 Figure 7-16 — Second alternative method for selecting principal components within the leave one sample out cross validated PCA LDA classification ......................... 222 Figure 7-17 — Sub loop for identifying the biological relevant PCs in the second alternative method for selecting PCs within leave one sample out cross validated PCA LDA classification .................................................................................. 222 Figure 7-18 — Histogram of score values from linear discriminant function ............ 225 Figure 7-19 — PCA LDA classification performance (area under receiver operator curve) using spectra obtained from different depths of focus. .. ................................ 226 Figure 7-20 — A - Composite of all principal component loadings weighted by contribution to the linear discriminant classification model; B — Collagen type | (Sigma C7774) and elastin (Sigma E1625) spectra; C - Most significant principal component loading; D - Second most significant principal component loading.



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    93
    Genovis Inc pca lda classification performance
    Pca Lda Classification Performance, supplied by Genovis Inc, used in various techniques. Bioz Stars score: 93/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
    https://www.bioz.com/product/pca+lda+classification+performance/OpeRATOR+Lyophilized/frost_jonathan_a__2019__vibrational_spectroscopy_for_the_assessment_of_vulval_disease-2581-232-239
    Average 93 stars, based on 1 article reviews
    pca lda classification performance - by Bioz Stars, 2026-09
    93/100 stars
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