Studies to Measure Cotton Fibre Length, Strength, Micronaire and Colour by vis/NIR Reflectance Spectroscopy. Part II: Principal Components Regression

1994 ◽  
Vol 2 (4) ◽  
pp. 185-198 ◽  
Author(s):  
Joseph G. Montalvo ◽  
Steven E. Buco ◽  
Harmon H. Ramey
Keyword(s):  
Principal Components ◽  
Fibre Length ◽  
Reflectance Spectroscopy ◽  
Cotton Fibre ◽  
Reflectance Spectra ◽  
Principal Components Regression ◽  
Validation Data ◽  
Data Set ◽  
Fibre Property ◽  
The Press

In Part I of this series, both cotton fibre property and reflectance spectra data on 185 US cottons including four Pimas were analysed by descriptive statistics. In this paper, principal components regression (PCR) models for measuring six properties from the cotton's vis/NIR reflectance spectra are critically examined. These properties are upper-half mean length (UHM), uniformity index (UI), bundle strength (STR), micronaire (MIC) and colour (Rd and +b). The spectra were recorded with a scanning spectrophotometer in the wavelength range from 400 to 2498 nm. A variety of spectral processing options, some of which give improved PCR analysis results, were applied prior to the regressions and allowed for testing of over 100 PCR models. All PCR model results are based on the PRESS statistic by one-out-rotation, a fast approximation of the PRESS statistic (to reduce computer time) or on cluster analysis using separate calibration and validation data sets. The standard error of prediction (SEP) of all the properties except UHM compared well to the reference method precision. The precision of the UHM measure by reflectance spectroscopy was strongly influenced by the sample repack error. The SEP of UHM, UI and STR was improved by excluding the Pimas from the data set.

Studies to Measure Cotton Fibre Length, Strength, Micronaire and Colour by Vis/NIR Reflectance Spectroscopy. Part I: Descriptive Statistics of Fibre Properties and Reflectance Spectra

1993 ◽  
Vol 1 (3) ◽  
pp. 153-173 ◽  
Author(s):  
Joseph G. Montalvo ◽  
Sherman E. Faught ◽  
Harmon H. Ramey ◽  
Steven E. Buco
Keyword(s):  
Standard Error ◽  
Near Infrared ◽  
A Priori ◽  
Fibre Length ◽  
High Volume ◽  
Reflectance Spectroscopy ◽  
Reflectance Spectra ◽  
Fibre Property ◽  
Property Data ◽  
Nir Reflectance Spectroscopy

Fibre property data representing the 1989 and 1990 crop years and its reflectance spectra are analysed using standard error, regression and correlation analysis. The six properties of interest are upper-half mean length, uniformity index, strength and micronaire measured on two high volume instrument systems placed side-by-side, and colour (Rd and +b) measured by the traditional lab system. Visible (vis) and near infrared (NIR) reflectance spectra are observed on a scanning spectrophotometer, and span the 400–2500 nm range. Three findings highlight the research. One, a diagnostic test is presented to decide, a priori of reflectance spectroscopy, the degree to which the mean property values have reduced random error. Two, the standard error of replicate spectra provides a way to probe the fibre mass in the diffuse reflectance optical path. The spectral error is strongly influenced by both how the cotton is packed into the spectrophotometric cell and the non-homogeneity of the sample. And three, correlations between the spectra confirm that some visible and NIR wavelength regions contain mutually exclusive information about the properties of this natural staple.

scholarly journals Evaluation of the solubility of 11-keto-β-boswellic acid and its histological effect on the diabetic mice liver using a novel technique

2021 ◽  
pp. 1797-1803
Author(s):  
Issa Al Amri ◽  
Fazal Mabood ◽  
Isam T. Kadim ◽  
Abdulaziz Alkindi ◽  
A. Al-Harrasi ◽  
...  
Keyword(s):  
Liver Tissue ◽  
Diabetic Control ◽  
Reflectance Spectroscopy ◽  
Reflectance Spectra ◽  
Control Group ◽  
Diabetic Mice ◽  
Boswellic Acid ◽  
Data Set ◽  
Diabetic Control Group ◽  
Liver Tissues

Background and Aim: The literature is scant on the effect of 11-keto-β-boswellic acid (KBA) on the liver of diabetes-induced mice. This study was designed to develop a rapid, sensitive, accurate, and inexpensive detection technique for evaluating the solubility of KBA obtained from the gum resin of Omani frankincense (Boswellia sacra) in the liver of streptozotocin-induced diabetic mice using Fourier transform infrared (FTIR) reflectance spectroscopy coupled with principal components analysis (PCA). It also aimed to investigate the effect of KBA on histological changes in the hepatocytes of diabetic mice. Materials and Methods: Eighteen mice were assigned to the healthy control group, the diabetic control group, or the KBA-treated diabetic group. Liver tissue samples from all groups were scanned using an FTIR reflectance spectrophotometer in reflection mode. FTIR reflectance spectra were collected in the wavenumber range of 400-4000 cm-1 using an attenuated total reflectance apparatus. Results: FTIR reflectance spectra were analyzed using PCA. The PCA score plot, which is an exploratory multivariate data set, revealed complete segregation among the three groups' liver samples based on changes in the variation of wavenumber position in the FTIR reflectance spectra, which indicated a clear effect of KBA solubility on treatments. Histological analysis showed an improvement in the liver tissues, with normal structures of hepatocytes exhibiting mild vacuolation in their cytoplasm. Conclusion: KBA improved the morphology of liver tissues in the diabetic mice and led to complete recovery of the damage observed in the diabetic control group. FTIR reflectance spectroscopy coupled with PCA could be deployed as a rapid, low-cost, and non-destructive detection method for evaluating treatment effects in diseased liver tissue based on the solubility of KBA.

scholarly journals AUTOMATIC KERNEL REGRESSION MODELLING USING COMBINED LEAVE-ONE-OUT TEST SCORE AND REGULARISED ORTHOGONAL LEAST SQUARES

2004 ◽  
Vol 14 (01) ◽  
pp. 27-37 ◽  
Author(s):  
X. HONG ◽  
S. CHEN ◽  
P. M. SHARKEY
Keyword(s):  
Least Squares ◽  
Test Score ◽  
Identification Algorithm ◽  
Validation Data ◽  
Data Set ◽  
Structure Selection ◽  
Orthogonal Least Squares ◽  
The Press ◽  
Recursive Computation ◽  
Leave One Out

This paper introduces an automatic robust nonlinear identification algorithm using the leave-one-out test score also known as the PRESS (Predicted REsidual Sums of Squares) statistic and regularised orthogonal least squares. The proposed algorithm aims to achieve maximised model robustness via two effective and complementary approaches, parameter regularisation via ridge regression and model optimal generalisation structure selection. The major contributions are to derive the PRESS error in a regularised orthogonal weight model, develop an efficient recursive computation formula for PRESS errors in the regularised orthogonal least squares forward regression framework and hence construct a model with a good generalisation property. Based on the properties of the PRESS statistic the proposed algorithm can achieve a fully automated model construction procedure without resort to any other validation data set for model evaluation.

Multivariate statistical approaches as applied to environmental physics studies

Open Physics ◽  
2006 ◽  
Vol 4 (2) ◽  
Author(s):  
Vasil Lovchinov ◽  
Stefan Tsakovski
Keyword(s):  
Surface Waters ◽  
Principal Components ◽  
Coastal Sediments ◽  
Principal Components Regression ◽  
Latent Factors ◽  
Multivariate Statistical ◽  
Data Set ◽  
Components Analysis ◽  
Substantial Interest ◽  
Environmental Physics

AbstractThe present communication deals with the application of the most important environmetric approaches like cluster analysis, principal components analysis and principal components regression (apportioning models) to environmental systems which are of substantial interest for environmental physics — surface waters, aerosols, and coastal sediments. Using various case studies we identify the latent factors responsible for the data set structure and construct models showing the contribution of each identified source (anthropogenic or natural) to the total measure of the pollution. In this way the information obtained by the monitoring data becomes broader and more intelligent, which help in problem solving in environmental physics.

scholarly journals Development and temporal external validation of a simple risk score tool for prediction of outcomes after severe head injury based on admission characteristics from level-1 trauma centre of India using retrospectively collected data

BMJ Open ◽  
2021 ◽  
Vol 11 (1) ◽  
pp. e040778
Author(s):  
Vineet Kumar Kamal ◽  
Ravindra Mohan Pandey ◽  
Deepak Agrawal
Keyword(s):  
Hospital Mortality ◽  
External Validation ◽  
Trauma Centre ◽  
Unfavourable Outcome ◽  
Motor Score ◽  
Validation Data ◽  
Data Set ◽  
Development Data ◽  
Level 1 ◽  
Pupillary Reactivity

ObjectiveTo develop and validate a simple risk scores chart to estimate the probability of poor outcomes in patients with severe head injury (HI).DesignRetrospective.SettingLevel-1, government-funded trauma centre, India.ParticipantsPatients with severe HI admitted to the neurosurgery intensive care unit during 19 May 2010–31 December 2011 (n=946) for the model development and further, data from same centre with same inclusion criteria from 1 January 2012 to 31 July 2012 (n=284) for the external validation of the model.Outcome(s)In-hospital mortality and unfavourable outcome at 6 months.ResultsA total of 39.5% and 70.7% had in-hospital mortality and unfavourable outcome, respectively, in the development data set. The multivariable logistic regression analysis of routinely collected admission characteristics revealed that for in-hospital mortality, age (51–60, >60 years), motor score (1, 2, 4), pupillary reactivity (none), presence of hypotension, basal cistern effaced, traumatic subarachnoid haemorrhage/intraventricular haematoma and for unfavourable outcome, age (41–50, 51–60, >60 years), motor score (1–4), pupillary reactivity (none, one), unequal limb movement, presence of hypotension were the independent predictors as its 95% confidence interval (CI) of odds ratio (OR)_did not contain one. The discriminative ability (area under the receiver operating characteristic curve (95% CI)) of the score chart for in-hospital mortality and 6 months outcome was excellent in the development data set (0.890 (0.867 to 912) and 0.894 (0.869 to 0.918), respectively), internal validation data set using bootstrap resampling method (0.889 (0.867 to 909) and 0.893 (0.867 to 0.915), respectively) and external validation data set (0.871 (0.825 to 916) and 0.887 (0.842 to 0.932), respectively). Calibration showed good agreement between observed outcome rates and predicted risks in development and external validation data set (p>0.05).ConclusionFor clinical decision making, we can use of these score charts in predicting outcomes in new patients with severe HI in India and similar settings.

scholarly journals A novel ferroptosis related gene signature is associated with prognosis in patients with ovarian serous cystadenocarcinoma

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Zhixiang Yu ◽  
Haiyan He ◽  
Yanan Chen ◽  
Qiuhe Ji ◽  
Min Sun
Keyword(s):  
Cox Regression ◽  
Expression Profiles ◽  
Critical Role ◽  
Gene Signature ◽  
Cancer Genome ◽  
Training Data ◽  
Hub Genes ◽  
Validation Data ◽  
Data Set ◽  
Cox Regression Analysis

AbstractOvarian cancer (OV) is a common type of carcinoma in females. Many studies have reported that ferroptosis is associated with the prognosis of OV patients. However, the mechanism by which this occurs is not well understood. We utilized Genotype-Tissue Expression (GTEx) and The Cancer Genome Atlas (TCGA) to identify ferroptosis-related genes in OV. In the present study, we applied Cox regression analysis to select hub genes and used the least absolute shrinkage and selection operator to construct a prognosis prediction model with mRNA expression profiles and clinical data from TCGA. A series of analyses for this signature was performed in TCGA. We then verified the identified signature using International Cancer Genome Consortium (ICGC) data. After a series of analyses, we identified six hub genes (DNAJB6, RB1, VIMP/ SELENOS, STEAP3, BACH1, and ALOX12) that were then used to construct a model using a training data set. The model was then tested using a validation data set and was found to have high sensitivity and specificity. The identified ferroptosis-related hub genes might play a critical role in the mechanism of OV development. The gene signature we identified may be useful for future clinical applications.

scholarly journals Metastatic heterogeneity of the consensus molecular subtypes of colorectal cancer

2021 ◽  
Vol 6 (1) ◽  
Author(s):  
Peter W. Eide ◽  
Seyed H. Moosavi ◽  
Ina A. Eilertsen ◽  
Tuva H. Brunsell ◽  
Jonas Langerud ◽  
...  
Keyword(s):  
Gene Expression ◽  
Colorectal Cancer ◽  
Principal Components ◽  
Prognostic Value ◽  
Tumor Heterogeneity ◽  
Molecular Subtypes ◽  
R Package ◽  
Data Set ◽  
Primary Tumors ◽  
External Data

AbstractGene expression-based subtypes of colorectal cancer have clinical relevance, but the representativeness of primary tumors and the consensus molecular subtypes (CMS) for metastatic cancers is not well known. We investigated the metastatic heterogeneity of CMS. The best approach to subtype translation was delineated by comparisons of transcriptomic profiles from 317 primary tumors and 295 liver metastases, including multi-metastatic samples from 45 patients and 14 primary-metastasis sets. Associations were validated in an external data set (n = 618). Projection of metastases onto principal components of primary tumors showed that metastases were depleted of CMS1-immune/CMS3-metabolic signals, enriched for CMS4-mesenchymal/stromal signals, and heavily influenced by the microenvironment. The tailored CMS classifier (available in an updated version of the R package CMScaller) therefore implemented an approach to regress out the liver tissue background. The majority of classified metastases were either CMS2 or CMS4. Nonetheless, subtype switching and inter-metastatic CMS heterogeneity were frequent and increased with sampling intensity. Poor-prognostic value of CMS1/3 metastases was consistent in the context of intra-patient tumor heterogeneity.

Evaluation of the CONSUME and FOFEM fuel consumption models in pine and mixed hardwood forests of the eastern United States

2014 ◽  
Vol 44 (7) ◽  
pp. 784-795 ◽  
Author(s):  
Susan J. Prichard ◽  
Eva C. Karau ◽  
Roger D. Ottmar ◽  
Maureen C. Kennedy ◽  
James B. Cronan ◽  
...  
Keyword(s):  
United States ◽  
Fuel Consumption ◽  
Prescribed Burning ◽  
Forest Type ◽  
Fire Effects ◽  
Eastern United States ◽  
Validation Data ◽  
Data Set ◽  
Predicted Values ◽  
Fine Fuels

Reliable predictions of fuel consumption are critical in the eastern United States (US), where prescribed burning is frequently applied to forests and air quality is of increasing concern. CONSUME and the First Order Fire Effects Model (FOFEM), predictive models developed to estimate fuel consumption and emissions from wildland fires, have not been systematically evaluated for application in the eastern US using the same validation data set. In this study, we compiled a fuel consumption data set from 54 operational prescribed fires (43 pine and 11 mixed hardwood sites) to assess each model’s uncertainties and application limits. Regions of indifference between measured and predicted values by fuel category and forest type represent the potential error that modelers could incur in estimating fuel consumption by category. Overall, FOFEM predictions have narrower regions of indifference than CONSUME and suggest better correspondence between measured and predicted consumption. However, both models offer reliable predictions of live fuel (shrubs and herbaceous vegetation) and 1 h fine fuels. Results suggest that CONSUME and FOFEM can be improved in their predictive capability for woody fuel, litter, and duff consumption for eastern US forests. Because of their high biomass and potential smoke management problems, refining estimates of litter and duff consumption is of particular importance.

scholarly journals 127 Predicting Dry Matter Intake of Gestating and Lactating Beef Cows

2020 ◽  
Vol 98 (Supplement_2) ◽  
pp. 58-58
Author(s):  
Megan A Gross ◽  
Claire Andresen ◽  
Amanda Holder ◽  
Alexi Moehlenpah ◽  
Carla Goad ◽  
...  
Keyword(s):  
Beef Cattle ◽  
Milk Yield ◽  
Feed Intake ◽  
Beef Cows ◽  
Multiple Regression Equation ◽  
Validation Data ◽  
Data Set ◽  
Lactating Cows ◽  
Downward Bias ◽  
Protein Supply

Abstract In 1996, the NASEM beef cattle committee developed and published an equation to estimate cow feed intake using results from studies conducted or published between 1979 and 1993 (Nutrient Requirements of Beef Cattle). The same equation was recommended for use in the most recent version of this publication (2016). The equation is sensitive to cow weight, diet digestibility and milk yield. Our objective was to validate the accuracy of this equation using more recent published and unpublished data. Criteria for inclusion in the validation data set included projects conducted or published within the last ten years, direct measurement of forage intake, adequate protein supply, and pen feeding (no tie stall or metabolism crate data). The validation data set included 29 treatment means for gestating cows and 26 treatment means for lactating cows. Means for the gestating cow data set was 11.4 ± 1.9 kg DMI, 599 ± 77 kg BW, 1.24 ± 0.14 Mcal/kg NEm per kg of feed and lactating cow data set was 14.5 ± 2.0 kg DMI, 532 ± 116.3 kg BW, and 1.26 ± 0.24 Mcal NEm per kg feed, respectively. Non intercept models were used to determine equation accuracy in predicting validation data set DMI. The slope for linear bias in the NASEM gestation equation did not differ from 1 (P = 0.07) with a 3.5% positive bias. However, when the NASEM equation was used to predict DMI in lactating cows, the slope for linear bias significantly differed from 1 (P < 0.001) with a downward bias of 13.7%. Therefore, a new multiple regression equation was developed from the validation data set: DMI= (-4.336 + (0.086427 (BW^.75) + 0.3 (Milk yield)+6.005785(NEm)), (R-squared=0.84). The NASEM equation for gestating beef cows was reasonably accurate while the lactation equation underestimated feed intake.

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