scholarly journals Sensory and instrumental texture assessment of roasted pistachio nut/kernel by partial least square (PLS) regression analysis: effect of roasting conditions

2015 ◽  
Vol 53 (1) ◽  
pp. 370-380 ◽  
Author(s):  
Toktam Mohammadi Moghaddam ◽  
Seyed M.A. Razavi ◽  
Masoud Taghizadeh ◽  
Ameneh Sazgarnia
Keyword(s):  
Regression Analysis ◽  
Partial Least Square ◽  
Least Square ◽  
Pls Regression ◽  
Pistachio Nut ◽  
Instrumental Texture ◽  
Pls Regression Analysis

scholarly journals Towards an accurate Ground-Level Ozone Prediction

2018 ◽  
Vol 8 (2) ◽  
pp. 1131
Author(s):  
Eiman Tamah Alshammari
Keyword(s):  
Ground Level ◽  
Absolute Error ◽  
Partial Least Square ◽  
Least Square ◽  
Partial Least Square Regression ◽  
Polynomial Kernel ◽  
Support Vector ◽  
Pls Regression ◽  
Ground Level Ozone ◽  
Least Square Regression

This paper motivation is to find the most accurate technique to predict the ground level ozone at Al Jahra station, Kuwait. The data on the meteorological variables (air temperature, relative humidity, solar radiation, direction and speed of wind) and concentration of seven pollutants of environment (SO2, NO2, NO, CO2, CO, NMHC, and CH4) were applied to forecast the ozone concentration in atmosphere. In this report, three methods (PLS regression, support vector machine (SVM), and multiple least-square regression) were used to predict ground-level ozone. We used Fifteen parameters to evaluate the performance of methods. Multiple least-square regression, partial least square regression (PLS regression), and SVM using linear and radial kernels were the best performers with MAE (mean absolute error) of 9.17x 10-03, 9.72 x 10-03, 9.64 x 10-03, and 9.12 x 10-03, respectively. SVM with polynomial kernel had MAE of 5.46 x 10-02. These results show that these methods could be used to predict ground-level ozone concentrations at Al Jahra station in Kuwait.

scholarly journals Identifying maternal and infant factors associated with newborn size in rural Bangladesh by partial least squares (PLS) regression analysis

PLoS ONE ◽  
2017 ◽  
Vol 12 (12) ◽  
pp. e0189677 ◽  
Author(s):  
Alamgir Kabir ◽  
Md. Jahanur Rahman ◽  
Abu Ahmed Shamim ◽  
Rolf D. W. Klemm ◽  
Alain B. Labrique ◽  
...  
Keyword(s):  
Regression Analysis ◽  
Least Squares ◽  
Partial Least Squares ◽  
Pls Regression ◽  
Rural Bangladesh ◽  
Factors Associated ◽  
Newborn Size ◽  
Pls Regression Analysis

scholarly journals Detection, Purity Analysis, and Quality Assurance of Adulterated Peanut (Arachis hypogaea) Oils

Foods ◽  
2018 ◽  
Vol 7 (8) ◽  
pp. 122 ◽  
Author(s):  
Shayla Smithson ◽  
Boluwatife Fakayode ◽  
Siera Henderson ◽  
John Nguyen ◽  
Sayo Fakayode
Keyword(s):  
Quality Assurance ◽  
Edible Oils ◽  
Limit Of Detection ◽  
Safety Concern ◽  
Low Cost ◽  
Partial Least Square ◽  
Least Square ◽  
Pls Regression ◽  
Purity Analysis ◽  
Significant Public Health

The intake of adulterated and unhealthy oils and trans-fats in the human diet has had negative health repercussions, including cardiovascular disease, causing millions of deaths annually. Sadly, a significant percentage of all consumable products including edible oils are neither screened nor monitored for quality control for various reasons. The prospective intake of adulterated oils and the associated health impacts on consumers is a significant public health safety concern, necessitating the need for quality assurance checks of edible oils. This study reports a simple, fast, sensitive, accurate, and low-cost chemometric approach to the purity analysis of highly refined peanut oils (HRPO) that were adulterated either with vegetable oil (VO), canola oil (CO), or almond oil (AO) for food quality assurance purposes. The Fourier transform infrared spectra of the pure oils and adulterated HRPO samples were measured and subjected to a partial-least-square (PLS) regression analysis. The obtained PLS regression figures-of-merit were incredible, with remarkable linearity (R2 = 0.994191 or better). The results of the score plots of the PLS regressions illustrate pattern recognition of the adulterated HRPO samples. Importantly, the PLS regressions accurately determined percent compositions of adulterated HRPOs, with an overall root-mean-square-relative-percent-error of 5.53% and a limit-of-detection as low as 0.02% (wt/wt). The developed PLS regressions continued to predict the compositions of newly prepared adulterated HRPOs over a period of two months, with incredible accuracy without the need for re-calibration. The accuracy, sensitivity, and robustness of the protocol make it desirable and potentially adoptable by health departments and local enforcement agencies for fast screening and quality assurance of consumable products.

scholarly journals Multiclass Prediction with Partial Least Square Regression for Gene Expression Data: Applications in Breast Cancer Intrinsic Taxonomy

2013 ◽  
Vol 2013 ◽  
pp. 1-9 ◽  
Author(s):  
Chi-Cheng Huang ◽  
Shih-Hsin Tu ◽  
Ching-Shui Huang ◽  
Heng-Hui Lien ◽  
Liang-Chuan Lai ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Gene Expression ◽  
Molecular Subtypes ◽  
Expression Profiles ◽  
Weighted Kappa ◽  
Partial Least Square ◽  
Least Square ◽  
Partial Least Square Regression ◽  
Training Dataset ◽  
Pls Regression

Multiclass prediction remains an obstacle for high-throughput data analysis such as microarray gene expression profiles. Despite recent advancements in machine learning and bioinformatics, most classification tools were limited to the applications of binary responses. Our aim was to apply partial least square (PLS) regression for breast cancer intrinsic taxonomy, of which five distinct molecular subtypes were identified. The PAM50 signature genes were used as predictive variables in PLS analysis, and the latent gene component scores were used in binary logistic regression for each molecular subtype. The 139 prototypical arrays for PAM50 development were used as training dataset, and three independent microarray studies with Han Chinese origin were used for independent validation (n=535). The agreement between PAM50 centroid-based single sample prediction (SSP) and PLS-regression was excellent (weighted Kappa: 0.988) within the training samples, but deteriorated substantially in independent samples, which could attribute to much more unclassified samples by PLS-regression. If these unclassified samples were removed, the agreement between PAM50 SSP and PLS-regression improved enormously (weighted Kappa: 0.829 as opposed to 0.541 when unclassified samples were analyzed). Our study ascertained the feasibility of PLS-regression in multi-class prediction, and distinct clinical presentations and prognostic discrepancies were observed across breast cancer molecular subtypes.

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