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Eigenvector Research Inc pls toolbox 8.0
Discrimination between wheat leaves with Rhopalosiphum padi (Rp), Sitobion avenae (Sa) and controls with no aphids. PLSDA (top), ROC curves (middle) and VIP scores versus regression vectors (bottom) from PLSDA. ( A ) control vs. Rp; ( B ) control vs. Sa; ( C ) Rp vs. Sa; and ( D ) control vs. (Rp + Sa). Blue lines represent estimated PLSDA ROC curves (calibration set); green lines represent estimated PLSDA ROC curves (cross-validation); dashed lines represent 50% lines; and circles indicate model thresholds. PLSDA, <t>partial</t> <t>least</t> <t>square</t> discriminant analysis; ROC, receiver operating characteristic; VIP, variable importance in projection.
Pls Toolbox 8.0, supplied by Eigenvector Research Inc, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
https://www.bioz.com/product/pls+toolbox+8%2E0/pls+toolbox/pmc10221955-196-15-18
Average 90 stars, based on 1 article reviews
pls toolbox 8.0 - by Bioz Stars, 2026-09
90/100 stars

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1) Product Images from "Wheat Oxylipins in Response to Aphids, CO 2 and Nitrogen Regimes"

Article Title: Wheat Oxylipins in Response to Aphids, CO 2 and Nitrogen Regimes

Journal: Molecules

doi: 10.3390/molecules28104133

Discrimination between wheat leaves with Rhopalosiphum padi (Rp), Sitobion avenae (Sa) and controls with no aphids. PLSDA (top), ROC curves (middle) and VIP scores versus regression vectors (bottom) from PLSDA. ( A ) control vs. Rp; ( B ) control vs. Sa; ( C ) Rp vs. Sa; and ( D ) control vs. (Rp + Sa). Blue lines represent estimated PLSDA ROC curves (calibration set); green lines represent estimated PLSDA ROC curves (cross-validation); dashed lines represent 50% lines; and circles indicate model thresholds. PLSDA, partial least square discriminant analysis; ROC, receiver operating characteristic; VIP, variable importance in projection.
Figure Legend Snippet: Discrimination between wheat leaves with Rhopalosiphum padi (Rp), Sitobion avenae (Sa) and controls with no aphids. PLSDA (top), ROC curves (middle) and VIP scores versus regression vectors (bottom) from PLSDA. ( A ) control vs. Rp; ( B ) control vs. Sa; ( C ) Rp vs. Sa; and ( D ) control vs. (Rp + Sa). Blue lines represent estimated PLSDA ROC curves (calibration set); green lines represent estimated PLSDA ROC curves (cross-validation); dashed lines represent 50% lines; and circles indicate model thresholds. PLSDA, partial least square discriminant analysis; ROC, receiver operating characteristic; VIP, variable importance in projection.

Techniques Used: Control, Biomarker Discovery

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Article Snippet: A portable near-infrared (NIR) spectrometer coupled with chemometrics for the detection of fumonisin B1 and B2 (FBs) in ground corn samples was proposed in the present work.. A total of 173 corn samples were collected, and their FB contents were determined by HPLC–MS/MS.. Partial least squares (PLS), support vector machine (SVM) and local PLS based on global PLS score (LPLS-S) algorithms were employed to construct quantitative models.

Article Title: Wheat Oxylipins in Response to Aphids, CO 2 and Nitrogen Regimes
Article Snippet: Further data analysis was carried out in MATLAB R2019b (MathWorks, Inc., Natick, MA, USA), and PLS Toolbox 8.0 (Eigenvector Research, Inc., Wenatchee, WA, USA) was used for principal component analysis (PCA) and partial least square discriminant analysis (PLSDA) models of the autoscaled data.

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Biomarker Discovery:

Article Title: Rapid detection of fumonisin B 1 and B 2 in ground corn samples using smartphone-controlled portable near-infrared spectrometry and chemometrics.
Article Snippet: A portable near-infrared (NIR) spectrometer coupled with chemometrics for the detection of fumonisin B1 and B2 (FBs) in ground corn samples was proposed in the present work.. A total of 173 corn samples were collected, and their FB contents were determined by HPLC–MS/MS.. Partial least squares (PLS), support vector machine (SVM) and local PLS based on global PLS score (LPLS-S) algorithms were employed to construct quantitative models.

Article Title: Wheat Oxylipins in Response to Aphids, CO 2 and Nitrogen Regimes
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Article Title: Urine metabolomic analysis for monitoring internal load in professional football players.
Article Snippet: Introduction The design of training programs for football players is not straightforward due to intraand inter-individual variability that leads to different physiological responses under similar training loads.. Objective To study the association between the external load, defined by variables obtained using electronic performance tracking systems (EPTS), and the urinary metabolome as a surrogate of the metabolic adaptation to training.. Methods Urine metabolic and EPTS data from 80 professional football players collected in an observational longitudinal study were analyzed by ultra-performance liquid chromatography coupled to electrospray ionization quadrupole time-offlight mass spectrometry and assessed by partial least squares (PLS) regression.

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Article Snippet: Mould and aflatoxin contamination remains a serious threat to food quality and safety.. In this study, simultaneous detection of Aspergillus moulds and aflatoxin B1 (AFB1) contamination in rice was investigated by laser induced fluorescence (LIF) technology for the first time.. Different Aspergillus strains belong to toxigenic and nontoxigenic species were artificially inoculated in rice samples and storage for a period of time to evolve into various mould and AFB1 infection levels.

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Discrimination between wheat leaves with Rhopalosiphum padi (Rp), Sitobion avenae (Sa) and controls with no aphids. PLSDA (top), ROC curves (middle) and VIP scores versus regression vectors (bottom) from PLSDA. ( A ) control vs. Rp; ( B ) control vs. Sa; ( C ) Rp vs. Sa; and ( D ) control vs. (Rp + Sa). Blue lines represent estimated PLSDA ROC curves (calibration set); green lines represent estimated PLSDA ROC curves (cross-validation); dashed lines represent 50% lines; and circles indicate model thresholds. PLSDA, <t>partial</t> <t>least</t> <t>square</t> discriminant analysis; ROC, receiver operating characteristic; VIP, variable importance in projection.
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Discrimination between wheat leaves with Rhopalosiphum padi (Rp), Sitobion avenae (Sa) and controls with no aphids. PLSDA (top), ROC curves (middle) and VIP scores versus regression vectors (bottom) from PLSDA. ( A ) control vs. Rp; ( B ) control vs. Sa; ( C ) Rp vs. Sa; and ( D ) control vs. (Rp + Sa). Blue lines represent estimated PLSDA ROC curves (calibration set); green lines represent estimated PLSDA ROC curves (cross-validation); dashed lines represent 50% lines; and circles indicate model thresholds. PLSDA, <t>partial</t> <t>least</t> <t>square</t> discriminant analysis; ROC, receiver operating characteristic; VIP, variable importance in projection.
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Image Search Results


Discrimination between wheat leaves with Rhopalosiphum padi (Rp), Sitobion avenae (Sa) and controls with no aphids. PLSDA (top), ROC curves (middle) and VIP scores versus regression vectors (bottom) from PLSDA. ( A ) control vs. Rp; ( B ) control vs. Sa; ( C ) Rp vs. Sa; and ( D ) control vs. (Rp + Sa). Blue lines represent estimated PLSDA ROC curves (calibration set); green lines represent estimated PLSDA ROC curves (cross-validation); dashed lines represent 50% lines; and circles indicate model thresholds. PLSDA, partial least square discriminant analysis; ROC, receiver operating characteristic; VIP, variable importance in projection.

Journal: Molecules

Article Title: Wheat Oxylipins in Response to Aphids, CO 2 and Nitrogen Regimes

doi: 10.3390/molecules28104133

Figure Lengend Snippet: Discrimination between wheat leaves with Rhopalosiphum padi (Rp), Sitobion avenae (Sa) and controls with no aphids. PLSDA (top), ROC curves (middle) and VIP scores versus regression vectors (bottom) from PLSDA. ( A ) control vs. Rp; ( B ) control vs. Sa; ( C ) Rp vs. Sa; and ( D ) control vs. (Rp + Sa). Blue lines represent estimated PLSDA ROC curves (calibration set); green lines represent estimated PLSDA ROC curves (cross-validation); dashed lines represent 50% lines; and circles indicate model thresholds. PLSDA, partial least square discriminant analysis; ROC, receiver operating characteristic; VIP, variable importance in projection.

Article Snippet: Further data analysis was carried out in MATLAB R2019b (MathWorks, Inc., Natick, MA, USA), and PLS Toolbox 8.0 (Eigenvector Research, Inc., Wenatchee, WA, USA) was used for principal component analysis (PCA) and partial least square discriminant analysis (PLSDA) models of the autoscaled data.

Techniques: Control, Biomarker Discovery