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.

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.

scholarly journals Histamine Control in Raw and Processed Tuna: A Rapid Tool Based on NIR Spectroscopy

Foods ◽  
2021 ◽  
Vol 10 (4) ◽  
pp. 885
Author(s):  
Sergio Ghidini ◽  
Luca Maria Chiesa ◽  
Sara Panseri ◽  
Maria Olga Varrà ◽  
Adriana Ianieri ◽  
...  
Keyword(s):  
Root Mean Square ◽  
Near Infrared ◽  
Nir Spectroscopy ◽  
Partial Least Square ◽  
Least Square ◽  
Partial Least Square Regression ◽  
Coefficient Of Determination ◽  
Mean Square ◽  
Tuna Fish ◽  
Mean Square Errors

The present study was designed to investigate whether near infrared (NIR) spectroscopy with minimal sample processing could be a suitable technique to rapidly measure histamine levels in raw and processed tuna fish. Calibration models based on orthogonal partial least square regression (OPLSR) were built to predict histamine in the range 10–1000 mg kg−1 using the 1000–2500 nm NIR spectra of artificially-contaminated fish. The two models were then validated using a new set of naturally contaminated samples in which histamine content was determined by conventional high-performance liquid chromatography (HPLC) analysis. As for calibration results, coefficient of determination (r2) > 0.98, root mean square of estimation (RMSEE) ≤ 5 mg kg−1 and root mean square of cross-validation (RMSECV) ≤ 6 mg kg−1 were achieved. Both models were optimal also in the validation stage, showing r2 values > 0.97, root mean square errors of prediction (RMSEP) ≤ 10 mg kg−1 and relative range error (RER) ≥ 25, with better results showed by the model for processed fish. The promising results achieved suggest NIR spectroscopy as an implemental analytical solution in fish industries and markets to effectively determine histamine amounts.

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.

scholarly journals Transferability and scaling of soil total carbon prediction models in Florida

2014 ◽  
Author(s):  
Sabine Grunwald ◽  
Congrong Yu ◽  
Xiong Xiong
Keyword(s):  
Prediction Error ◽  
Near Infrared ◽  
Prediction Models ◽  
Regional Scale ◽  
Partial Least Square ◽  
Least Square ◽  
Partial Least Square Regression ◽  
Total Carbon ◽  
Support Vector ◽  
Domain Iii

The applicability, transfer, and scalability of visible/near-infrared (VNIR)-derived soil models are still poorly understood. The objectives of this study in Florida, U.S. were to: (i) compare three methods to predict soil total carbon (TC) using five fields (local scale) and a pooled (regional scale) VNIR spectral dataset, (ii) assess the model’s transferability among fields, and (iii) evaluate the up- and down-scaling behavior of TC prediction models. A total of 560 TC-spectral sets were modeled by Partial Least Square Regression (PLSR), Support Vector Machine (SVM), and Random Forest. The transferability and up- and down-scaling of models were limited by the following factors: (i) the spectral data domain, (ii) soil attribute domain, (iii) methods that describe the internal model structure of VNIR-TC relationships, and (iv) environmental domain space of attributes that control soil carbon dynamics. All soil logTC models showed excellent performance based on all three methods with R2 > 0.86, bias < 0.01%, root mean square prediction error (RMSE) = 0.09%, residual predication deviation (RPD) > 2.70% , and ratio of prediction error to inter-quartile range (RPIQ) > 4.54. PLSR performed substantially better than SVM to scale and transfer models. Upscaled soil TC models performed somewhat better in terms of model fit (R2), RPD, and RPIQ, whereas downscaled models showed less bias and smaller RMSE based on PLSR. Given the many factors that can impinge on empirically derived soil spectral prediction models, as demonstrated by this study, more focus on the applicability and scaling of them is needed.

Determination of routine chemicals, physical indices and macromolecular substances in reconstituted tobacco using near infrared spectroscopy combined with sample set partitioning

2020 ◽  
Vol 28 (3) ◽  
pp. 153-162
Author(s):  
Lijun Wu ◽  
Baoxing Wang ◽  
Lei Zhang ◽  
Rumin Duan ◽  
Rui Gao ◽  
...  
Keyword(s):  
Infrared Spectroscopy ◽  
Near Infrared Spectroscopy ◽  
Near Infrared ◽  
Nir Spectroscopy ◽  
Set Partitioning ◽  
Partial Least Square ◽  
Least Square ◽  
Partial Least Square Regression ◽  
Least Square Regression ◽  
Sample Set

Near infrared spectroscopy coupled with sample set partitioning based on joint X-Y distances combined with partial least square regression was applied to the quantitative analysis of six routine chemicals, five physical indices and four macromolecular substances in reconstituted tobacco. The quantitative regression models of these indices were established by joint X-Y distances combined with partial least square regression. Results showed remarkable correlation between predicted and measured values of the 15 indices. The root mean square error of prediction of all the indices was low, and the correlation coefficients of these PLS models were all greater than 0.85. This was the first study in which NIR spectroscopy had been used to determine the macromolecular substances as well as certain physical indices in reconstituted tobacco. Results showed that this method could be feasibly applied for rapid detection of these properties of industrial products.

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.

scholarly journals Proximate composition determination in goat cheese whey by near infrared spectroscopy (NIRS)

PeerJ ◽  
2020 ◽  
Vol 8 ◽  
pp. e8619
Author(s):  
Isadora Kaline Camelo Pires de Oliveira Galdino ◽  
Hévila Oliveira Salles ◽  
Karina Maria Olbrich dos Santos ◽  
Germano Veras ◽  
Flávia Carolina Alonso Buriti
Keyword(s):  
Infrared Spectroscopy ◽  
Near Infrared Spectroscopy ◽  
Proximate Composition ◽  
Near Infrared ◽  
Prediction Models ◽  
Partial Least Square ◽  
Least Square ◽  
Partial Least Square Regression ◽  
Composition Analysis ◽  
Reference Methods

Background In Brazil, over the last few years there has been an increase in the production and consumption of goat cheeses. In addition, there was also a demand to create options to use the whey extracted during the production of cheeses. Whey can be used as an ingredient in the development of many products. Therefore, knowing its composition is a matter of utmost importance, considering that the reference methods of food analysis require time, trained labor and expensive reagents for its execution. Methods Goat whey samples produced in winter and summer were submitted to proximate composition analysis (moisture, total solids, ashes, proteins, fat and carbohydrates by difference) using reference methods and near infrared spectroscopy (NIRS). The spectral data was preprocessed by baseline correction and the Savitzky–Golay derivative. The models were built using Partial Least Square Regression (PLSR) with raw and preprocessed data for each dependent variable (proximate composition parameter). Results The average whey composition values obtained using the referenced methods were in accordance with the consulted literature. The composition did not differ significantly (p > 0.05) between the summer and winter whey samples. The PLSR models were made available using the following figures of merit: coefficients of determination of the calibration and prediction models (R2cal and R2pred, respectively) and the Root Mean Squared Error Calibration and Prediction (RMSEC and RMSEP, respectively). The best models used raw data for fat and protein determinations and the values obtained by NIRS for both parameters were consistent with their referenced methods. Consequently, NIRS can be used to determine fat and protein in goat whey.

scholarly journals Kernel Partial Least Square Regression with High Resistance to Multiple Outliers and Bad Leverage Points on Near-Infrared Spectral Data Analysis

Symmetry ◽  
2021 ◽  
Vol 13 (4) ◽  
pp. 547
Author(s):  
Divo Dharma Silalahi ◽  
Habshah Midi ◽  
Jayanthi Arasan ◽  
Mohd Shafie Mustafa ◽  
Jean-Pierre Caliman
Keyword(s):  
Near Infrared ◽  
Partial Least Square ◽  
Least Square ◽  
Partial Least Square Regression ◽  
Data Matrix ◽  
High Dimensional ◽  
Leverage Points ◽  
Multiple Outliers ◽  
Data Space ◽  
Least Square Regression

Multivariate statistical analysis such as partial least square regression (PLSR) is the common data processing technique used to handle high-dimensional data space on near-infrared (NIR) spectral datasets. The PLSR is useful to tackle the multicollinearity and heteroscedasticity problem that can be commonly found in such data space. With the problem of the nonlinear structure in the original input space, the use of the classical PLSR model might not be appropriate. In addition, the contamination of multiple outliers and high leverage points (HLPs) in the dataset could further damage the model. Generally, HLPs contain both good leverage points (GLPs) and bad leverage points (BLPs); therefore, in this case, removing the BLPs seems relevant since it has a significant impact on the parameter estimates and can slow down the convergence process. On the other hand, the GLPs provide a good efficiency in the model calibration process; thus, they should not be eliminated. In this study, robust alternatives to the existing kernel partial least square (KPLS) regression, which are called the kernel partial robust GM6-estimator (KPRGM6) regression and the kernel partial robust modified GM6-estimator (KPRMGM6) regression are introduced. The nonlinear solution on PLSR was handled through kernel-based learning by nonlinearly projecting the original input data matrix into a high-dimensional feature mapping that corresponded to the reproducing kernel Hilbert spaces (RKHS). To increase the robustness, the improvements on GM6 estimators are presented with the nonlinear PLSR. Based on the investigation using several artificial dataset scenarios from Monte Carlo simulations and two sets from the near-infrared (NIR) spectral dataset, the proposed robust KPRMGM6 is found to be superior to the robust KPRGM6 and non-robust KPLS.

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