Temperature and cysteine addition effect on formation of sunflower hydrolysate Maillard reaction products and corresponding influence on sensory characteristics assessed by partial least square regression

2014 ◽  
Vol 57 ◽  
pp. 242-258 ◽  
Author(s):  
Karangwa Eric ◽  
Linda Virginie Raymond ◽  
Shabbar Abbas ◽  
Shiqing Song ◽  
Yating Zhang ◽  
...  
Keyword(s):  
Maillard Reaction ◽  
Partial Least Square ◽  
Least Square ◽  
Sensory Characteristics ◽  
Partial Least Square Regression ◽  
Maillard Reaction Products ◽  
Reaction Products ◽  
Least Square Regression

Modeling CO2 exchange and meteorological factors of an apple orchard using partial least square regression

2020 ◽  
Vol 27 (35) ◽  
pp. 43439-43451 ◽  
Author(s):  
Jianfeng Yang ◽  
Yumin Duan ◽  
Xiaoni Yang ◽  
Mukesh Kumar Awasthi ◽  
Huike Li ◽  
...  
Keyword(s):  
Meteorological Factors ◽  
Co2 Exchange ◽  
Apple Orchard ◽  
Partial Least Square ◽  
Least Square ◽  
Partial Least Square Regression ◽  
Least Square Regression

QSPR MODELLING OF STABILITY CONSTANTS OF METAL-THIOSEMICARBAZONE COMPLEXES USING MULTIVARIATE REGRESSION METHODS AND ARTIFICIAL NEURAL NETWORK

2021 ◽  
Vol 36 (06) ◽  
Author(s):  
NGUYEN MINH QUANG ◽  
TRAN NGUYEN MINH AN ◽  
NGUYEN HOANG MINH ◽  
TRAN XUAN MAU ◽  
PHAM VAN TAT
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Stability Constants ◽  
Partial Least Square ◽  
Least Square ◽  
Partial Least Square Regression ◽  
Structure Property ◽  
Least Square Regression ◽  
Thiosemicarbazone Complexes ◽  
Artificial Neural

In this study, the stability constants of metal-thiosemicarbazone complexes, logb11 were determined by using the quantitative structure property relationship (QSPR) models. The molecular descriptors, physicochemical and quantum descriptors of complexes were generated from molecular geometric structure and semi-empirical quantum calculation PM7 and PM7/sparkle. The QSPR models were built by using the ordinary least square regression (QSPROLS), partial least square regression (QSPRPLS), primary component regression (QSPRPCR) and artificial neural network (QSPRANN). The best linear model QSPROLS (with k of 9) involves descriptors C5, xp9, electric energy, cosmo volume, N4, SsssN, cosmo area, xp10 and core-core repulsion. The QSPRPLS, QSPR PCR and QSPRANN models were developed basing on 9 varibles of the QSPROLS model. The quality of the QSPR models were validated by the statistical values; The QSPROLS: R2train = 0.944, Q2LOO = 0.903 and MSE = 1.035; The QSPRPLS: R2train = 0.929, R2CV = 0.938 and MSE = 1.115; The QSPRPCR: R2train = 0.934, R2CV = 0.9485 and MSE = 1.147. The neural network model QSPRANN with architecture I(9)-HL(12)-O(1) was presented also with the statistical values: R2train = 0.9723, and R2CV = 0.9731. The QSPR models also were evaluated externally and got good performance results with those from the experimental literature.

scholarly journals Estimation of carcass weight of Hanwoo (Korean native cattle) as a function of body measurements using statistical models and a neural network

2020 ◽  
Vol 33 (10) ◽  
pp. 1633-1641
Author(s):  
Dae-Hyun Lee ◽  
Seung-Hyun Lee ◽  
Byoung-Kwan Cho ◽  
Collins Wakholi ◽  
Young-Wook Seo ◽  
...  
Keyword(s):  
Neural Network ◽  
Multiple Regression ◽  
Partial Least Square ◽  
Least Square ◽  
Partial Least Square Regression ◽  
Carcass Weight ◽  
Body Measurements ◽  
Least Square Regression ◽  
Data Points ◽  
Hanwoo Cattle

Objective: The objective of this study was to develop a model for estimating the carcass weight of Hanwoo cattle as a function of body measurements using three different modeling approaches: i) multiple regression analysis, ii) partial least square regression analysis, and iii) a neural network.Methods: Data from a total of 134 Hanwoo cattle were obtained from the National Institute of Animal Science in South Korea. Among the 372 variables in the raw data, 20 variables related to carcass weight and body measurements were extracted to use in multiple regression, partial least square regression, and an artificial neural network to estimate the cold carcass weight of Hanwoo cattle by any of seven body measurements significantly related to carcass weight or by all 19 body measurement variables. For developing and training the model, 100 data points were used, whereas the 34 remaining data points were used to test the model estimation.Results: The R2 values from testing the developed models by multiple regression, partial least square regression, and an artificial neural network with seven significant variables were 0.91, 0.91, and 0.92, respectively, whereas all the methods exhibited similar R2 values of approximately 0.93 with all 19 body measurement variables. In addition, relative errors were within 4%, suggesting that the developed model was reliable in estimating Hanwoo cattle carcass weight. The neural network exhibited the highest accuracy.Conclusion: The developed model was applicable for estimating Hanwoo cattle carcass weight using body measurements. Because the procedure and required variables could differ according to the type of model, it was necessary to select the best model suitable for the system with which to calculate the model.

Ironmaking Process Parameter Analysis and Optimization for the Blast Furnace

2009 ◽  
Vol 620-622 ◽  
pp. 21-24
Author(s):  
Shuang Ping Yang ◽  
Yong Hui Song ◽  
Liu Hua Xin
Keyword(s):  
Optimal Operation ◽  
Partial Least Square ◽  
Least Square ◽  
Parameter Analysis ◽  
Partial Least Square Regression ◽  
Evaluation Indicator ◽  
Least Square Regression ◽  
Calculation Results ◽  
Set Up ◽  
Reasonable Description

With practical data of the BF ironmaking from Jiuquan Iron&Steel Cooperation Ltd. (JISC), taking the quality of pig iron as evaluation indicator, mathematical models based on the least square regression and partial least square regression were set up respectively by co-relation analysis of feeding-to-product interval of the BF processing. The calculation results showed that the reasonable description can be obtained by the partial least square regression model; and 10 of 29 parameters with obvious impact on the BF operation were listed accordingly. Meanwhile, an optimal group of parameters was found by genetic algorism calculation method. The optimal index of the group was 99.13％. This study is beneficial to the improvement of feeding adjustment and optimal operation of BF ironmaking.

Measurement of the Irganox Content in Polypropylene Polymers during Extrusion

2005 ◽  
Vol 13 (3) ◽  
pp. 147-154 ◽  
Author(s):  
Wolfgang Becker ◽  
Norbert Eisenreich
Keyword(s):  
Near Infrared ◽  
Prediction Models ◽  
Partial Least Square ◽  
Least Square ◽  
Partial Least Square Regression ◽  
Process Conditions ◽  
Transmission Spectra ◽  
Mean Square ◽  
Score Plot ◽  
Least Square Regression

Near infrared spectroscopy was used as an in-line control system for the measurement of polypropylene filled with different amounts of Irganox additives. For this purpose transmission probes were installed in an extruder. The probes can withstand temperatures up to 300°C and pressures up to 60 MPa. Transmission spectra of polypropylene mixed with an Irganox additive were recorded. PCA score plot was carried out revealing the influence of varying conditions for the mixing of the sample preparation. Prediction models were generated with partial least square regression which resulted in a model which estimated Irganox with a coefficient of detremination of 0.984 and a root mean square error of prediction of 0.098%. Furthermore the possibilities for controlling process conditions by measuring transmission at a specific wavelength were shown.

scholarly journals Model Partial Least Square Regression (PLSR) Pengaruh Bidang Pendidikan dan Ekonomi Terhadap Tingkat Kemiskinan di Indonesia

2017 ◽  
Vol 7 (1) ◽  
pp. 67
Author(s):  
I Gusti Ayu Made Srinadi
Keyword(s):  
School Enrollment ◽  
Partial Least Square ◽  
Least Square ◽  
Partial Least Square Regression ◽  
Poor People ◽  
Model Estimation ◽  
Poverty Level ◽  
Model Estimate ◽  
Literacy Rate ◽  
Least Square Regression

Partial Least Square Regression (PLSR)  is one of the methods applied in the estimation of multiple linear regression models when the ordinary least square method  (OLS) can not be used. OLS generates an invalid model estimate when multicollinearity occurs or when the number of independent variables is greater than the number of data observations. In conditions that OLS can be applied in obtaining model estimation, want to know the performance of PLSR method. This study aims to determine the model of PLSR the influence of literacy rate, the average of school duration,  school enrollment rate, Income per capita, and open unemployment rate to the level of poverty seen from the percentage of poor people in Indonesia by 2015. Estimated model with OLS , Only variable of literacy rate  are included in the model with the coefficient of determination R2 = 32.52%. PLSR model estimation of cross-validation, leave-one-out method with one selected component has R2 of 33,23%. Both models shows  a negative relationship between poverty and literacy rate. The higher literacy rate will reduce the poverty level, indicating that the success of the Indonesian government in the development of education will support the government's success in reducing poverty level.

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