scholarly journals MODEL OUTPUT STATISTICS DENGAN PRINCIPAL COMPONENT REGRESSION, PARTIAL LEAST SQUARE REGRESSION, DAN RIDGE REGRESSION UNTUK KALIBRASI PRAKIRAAN CUACA JANGKA PENDEK

2021 ◽  
Vol 10 (3) ◽  
pp. 355
Author(s):  
NISWATUL QONA’AH ◽  
HASIH PRATIWI ◽  
YULIANA SUSANTI
Keyword(s):  
Ridge Regression ◽  
Principal Component Regression ◽  
Principal Component ◽  
Partial Least Square ◽  
Least Square ◽  
Partial Least Square Regression ◽  
Model Output ◽  
Mean Square ◽  
Least Square Regression ◽  
Model Output Statistics

Penelitian ini merupakan upaya pengembangan Model Output Statistics (MOS) yang akan digunakan sebagai alat kalibrasi prakiraan cuaca jangka pendek. Informasi mengenai prakiraan cuaca yang akurat diharapkan dapat meminimalkan risiko kecelakaan yang disebabkan oleh cuaca, khususnya dalam bidang transportasi udara dan laut. Metode yang akan dikembangkan mencakup beberapa stasiun pengamatan cuaca di Indonesia. MOS merupakan sebuah metode berbasis regresi yang mengoptimalkan hubungan antara observasi cuaca dan luaran model Numerical Weather Predictor (NWP). Beberapa masalah yang muncul kaitannya dengan MOS adalah; mereduksi dimensi luaran NWP, mendapatkan variabel prediktor yang mampu menjelaskan variabilitas variabel respon, dan menentukan metode statistik yang sesuai dengan karakteristik data, sehingga dapat menggambarkan hubungan antara variabel respon dan variabel prediktor. Tujuan dari penelitian ini yaitu untuk mendapatkan pemodelan MOS yang sesuai untuk variabel respon suhu maksimum, suhu minimum, dan kelembapan udara. Metode regresi yang digunakan adalah Principal Component Regression (PCR), Partial Least Square Regression (PLSR), dan ridge regression. Selanjutnya, model MOS yang terbentuk divalidasi dengan kriteria Root Mean Square Error (RMSE) dan Percentage Improval (IM%). MOS mampu mengoreksi bias prakiraan NWP hingga lebih dari 50%. Berdasarkan RMSE terkecil pada penelitian ini, suhu maksimum lebih akurat diprakirakan menggunakan model PLSR, sementara suhu minimum dan kelembapan udara lebih akurat diprakirakan menggunakan ridge regression.Kata Kunci: cuaca, MOS, NWP.

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 Selecting Milk Spectra to Develop Equations to Predict Milk Technological Traits

Foods ◽  
2021 ◽  
Vol 10 (12) ◽  
pp. 3084
Author(s):  
Maria Frizzarin ◽  
Isobel Claire Gormley ◽  
Alessandro Casa ◽  
Sinéad McParland
Keyword(s):  
Principal Component ◽  
Fixed Number ◽  
Prediction Performance ◽  
Partial Least Square ◽  
Least Square ◽  
Partial Least Square Regression ◽  
Local Approach ◽  
Mahalanobis Distances ◽  
Least Square Regression ◽  
Selection Of

Including all available data when developing equations to relate midinfrared spectra to a phenotype may be suboptimal for poorly represented spectra. Here, an alternative local changepoint approach was developed to predict six milk technological traits from midinfrared spectra. Neighbours were objectively identified for each predictand as those most similar to the predictand using the Mahalanobis distances between the spectral principal components, and subsequently used in partial least square regression (PLSR) analyses. The performance of the local changepoint approach was compared to that of PLSR using all spectra (global PLSR) and another LOCAL approach, whereby a fixed number of neighbours was used in the prediction according to the correlation between the predictand and the available spectra. Global PLSR had the lowest RMSEV for five traits. The local changepoint approach had the lowest RMSEV for one trait; however, it outperformed the LOCAL approach for four traits. When the 5% of the spectra with the greatest Mahalanobis distance from the centre of the global principal component space were analysed, the local changepoint approach outperformed the global PLSR and the LOCAL approach in two and five traits, respectively. The objective selection of neighbours improved the prediction performance compared to utilising a fixed number of neighbours; however, it generally did not outperform the global PLSR.

scholarly journals Prediction Is The Determination Of The Levels Of Total Flavonoid Extract Of 70% Ethanol On The Leaves Of Rose Guava (Syzigium Jambos (L.) Alston) By Using A Combination Of FTIR And Chemometrics Plsr (Partial Least Square Regression)

2019 ◽  
Author(s):  
Nur Tsalits Fahman Mughni
Keyword(s):  
Root Mean Square Error ◽  
Mean Square Error ◽  
Root Mean Square ◽  
Partial Least Square ◽  
Least Square ◽  
Total Flavonoid ◽  
Partial Least Square Regression ◽  
Mean Square ◽  
Syzygium Jambos ◽  
Least Square Regression

Rose Guava (Syzygium jambos (L.) Alston) is known to have flavonoid compounds. Where flavonoids are natural product compounds that have uses as a treatment. An alternative method used to determine the prediction of total flavonoid levels is a combination of FTIR and Chemometrics (Partial least square regression) through the parameter RMSEC value (Root mean square error of calibration), RMSECV (Root mean square error of validation), PRESS (Predicted residual error sum of squares) and R2. The results of the combination of FTIR and CEMOMETRICS (Partial least square regression) on the prediction of total flavonoid levels can provide a good model with calibration obtained R2 value0.9999, RMSEC 0.0000637 and the results of vaid obtained PRESS value0.19225, R2 0.978 and RMSECV 0.041 . Based on the literature, the model can be said to be good if the RMSEC and RMSECV values are smaller than R2.

Mechanical Parameter Analysis of Rock Material via PLSR Model

2011 ◽  
Vol 467-469 ◽  
pp. 1826-1831 ◽  
Author(s):  
Zao Bao Liu ◽  
Wei Ya Xu ◽  
Fei Xu ◽  
Lin Wei Wang
Keyword(s):  
Regression Model ◽  
Principal Component ◽  
Partial Least Square ◽  
Rock Material ◽  
Least Square ◽  
Parameter Analysis ◽  
Partial Least Square Regression ◽  
Mechanical Parameter ◽  
Least Square Regression

Mechanical parameter analysis is a complicated issue since it is influenced by many factors. Closely related with the influencing factors of compressibility coefficients of rock material (sandstone), this article first introduces the way to process partial least square regression (PLSR) analysis. The process of carrying out PLSR is divided into six steps as for analysis and prediction of the regression model, which are data preparation, principle collection, regression model for first principle component, secondary principle analysis, establishment of final regression model and number determination of principal component l. And then introduces PLSR for application of analysis and prediction of compressibility coefficients with 30 experiment samples. Seven prediction samples are carried out by PLSR with the training process of 30 samples. The result shows PLSR has good accuracy in prediction under the condition that the model is properly deprived based on certain experimental samples. Finally, some conclusions are made for further study on both mechanical parameters and partial least square regression method.

APPLICATION OF NEAR INFRARED SPECTROSCOPY FOR ENDPOINT DETERMINATION OF BLENDING AND INFLUENCE OF LOADING ORDER

2020 ◽  
pp. 191-197
Author(s):  
PATTEERA SODATA ◽  
JOMJAI PEERAPATTANA
Keyword(s):  
Infrared Spectroscopy ◽  
Root Mean Square Error ◽  
Near Infrared ◽  
Active Pharmaceutical Ingredient ◽  
Partial Least Square ◽  
Least Square ◽  
Partial Least Square Regression ◽  
Mean Square ◽  
Least Square Regression ◽  
Blending Process

Objective: This study aimed to apply near-infrared spectroscopy along with a thief as a tool to determine the endpoint of the blending process. Methods: The calibration model was constructed by partial least square regression. The best model was applied to determine the endpoint of the blending process, also the effect of loading order on the endpoint for the blending of the formulation containing a low concentration of the active pharmaceutical ingredient. Results: The best partial least square regression model yielded the lowest root mean square error of calibration of 1.4004, the lowest root mean square error of prediction of 1.4108 and the highest correlation coefficient of 0.9921. Validation study revealed the reference values were not statistically different from those of the predicted values. The model could predict the endpoint of the blending process with acceptable precision and accuracy. Standard deviation of the content of active pharmaceutical ingredients was ≤ 3% of the target after eighteen minutes of the blending process, which indicated the uniformity of powder blends. Additionally, the model revealed the order of powder loading slightly affected the blending time. The protocol that loaded the active pharmaceutical ingredient first or last needed a longer time to achieve the uniformity of blend. Conclusion: NIR spectroscopy is the rapid and effective tools that could be applied to study the blending process in the pharmaceutical manufacturing.

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