Infrared Spectroscopic Quantification of Methacrylation of Hyaluronic Acid: A Scaffold for Tissue Engineering Applications

2018 ◽  
Vol 72 (10) ◽  
pp. 1455-1466 ◽  
Author(s):  
Farzad Yousefi ◽  
Shital Kandel ◽  
Nancy Pleshko
Keyword(s):  
Tissue Engineering ◽  
Hyaluronic Acid ◽  
Ir Spectroscopy ◽  
Regression Model ◽  
Multiple Linear Regression Model ◽  
Total Reflection ◽  
Attenuated Total Reflection ◽  
Reaction Process ◽  
Independent Variables ◽  
H Nmr

Methacrylated hyaluronic acid (MeHA) has been used extensively in tissue engineering and drug delivery applications. The degree of methacrylation (DM) of HA impacts hydrogel crosslinking, which is of pivotal importance for cell interactions. The methacrylation reaction occurs over several hours, and DM is currently assessed post reaction and after dialysis of the solution, using nuclear magnetic resonance (1H NMR) data. Thus, there is little control over exact DM in a specific reaction. Here, infrared (IR) spectroscopy in attenuated total reflection (ATR) mode was investigated as an alternate modality for assessment of the DM of HA hydrogels, including during the reaction progression. Attenuated total reflection is a low-cost technique that is widely available in research and industry labs that can be used online during the reaction process. Strong correlations were achieved with IR-derived peak heights from dialyzed and lyophilized samples at 1708 cm−1 (from the methacrylic ester carbonyl vibration), and 1H NMR values ( R = 0.92, P = 6.56E-11). Additional IR peaks of importance were identified using principal component analysis and resulted in significant correlations with the 1H NMR DM parameter: 1454 cm−1 ( R = 0.85, P = 2.81E-8), 1300 cm−1 ( R = 0.95, P = 4.50E-14), 950 ( R = 0.85, P = 3.55E-8), 856 cm−1 ( R = 0.94, P = 1.20E-12), and 809 cm−1 ( R = 0.93, P = 3.54E-12). A multiple linear regression model to predict 1H NMR-derived DM using the 1708, 1300, and 1200 cm−1 peak heights as independent variables resulted in prediction with an error of 3.2% using dialyzed and lyophilized samples ( P < 0.001). Additionally, a multilinear regression model to predict the DM in undialyzed liquid MeHA samples obtained during the reaction process using similar peak height positions as independent variables resulted in a prediction error of 0.81% ( P < 0.05). Thus, IR spectroscopy can be utilized as an alternate modality to 1H NMR for quantification of the DM of MeHA while sampling either on-line during the methacrylation reaction as well as in post-lyophilized products. This could greatly simplify workflow for tissue engineering and other applications.

scholarly journals Empirical α–β runout modelling of snow avalanches in the Catalan Pyrenees

2021 ◽  
pp. 1-12
Author(s):  
Pere Oller ◽  
Cristina Baeza ◽  
Glòria Furdada
Keyword(s):  
Comparative Analysis ◽  
Regression Model ◽  
Extreme Values ◽  
Multiple Linear Regression Model ◽  
Present Knowledge ◽  
Snow Avalanches ◽  
Independent Variables ◽  
Horizontal Length ◽  
Length And Area ◽  
Runout Modelling

Abstract A variation in the α−β model which is a regression model that allows a deterministic prediction of the extreme runout to be expected in a given path, was applied for calculating avalanche runout in the Catalan Pyrenees. Present knowledge of major avalanche activity in this region and current mapping tools were used. The model was derived using a dataset of 97 ‘extreme’ avalanches that occurred from the end of 19th century to the beginning of 21st century. A multiple linear regression model was obtained using three independent variables: inclination of the avalanche path, horizontal length and area of the starting zone, with a good fit of the function (R2 = 0.81). A larger starting zone increases the runout and a larger length of the path reduces the runout. The new updated equation predicts avalanche runout for a return period of ~100 years. To study which terrain variables explain the extreme values of the avalanche dataset, a comparative analysis of variables that influence a longer or shorter runout was performed. The most extreme avalanches were treated. The size of the avalanche path and the aspect of the starting zone showed certain association between avalanches with longer or shorter runouts.

Dampak Brand Image dan Harga terhadap Keputusan Pembelian Produk Miniso

2021 ◽  
Vol 2 (2) ◽  
pp. 75-87
Author(s):  
Kardinah Indrianna Meutia ◽  
Hadita Hadita ◽  
Wirawan Widjarnarko
Keyword(s):  
Linear Regression ◽  
Regression Model ◽  
Linear Regression Model ◽  
Brand Image ◽  
Multiple Linear Regression Model ◽  
Purchasing Decisions ◽  
Business World ◽  
Independent Variables ◽  
Spss Software ◽  
Fierce Competition

The economy in the current era of globalization has fierce competition, especially in the business world, where each company moves to continue to make products primarily to meet what is needed by consumers and companies are always innovating to make products that are different from before and from  competitors and strive to be superior to other products.  This study was conducted with the aim of analyzing the independent variables which include brand image and price variables on their influence on the dependent variable, namely purchasing decisions.  This study uses multiple linear regression model and with classical assumption test using SPSS software version 24. Data were obtained primarily by distributing questionnaires to 162 students at Bhayangkara University, Jakarta Raya.  This study states that brand image and price variables can partially and significantly influence consumer purchasing decisions positively. The F test explains that the brand image and price variables together can influence purchasing decisions with results showing f-count>f-table.

A Mathematical Comparison of the Lachance - Traill Matrix Correction Procedure with Statistical Multiple Linear Regression Analysis in XRF Applications

1992 ◽  
Vol 36 ◽  
pp. 1-10
Author(s):  
Anthony J. Klimasara
Keyword(s):  
Linear Regression ◽  
Regression Model ◽  
Multiple Linear Regression ◽  
Linear Regression Model ◽  
Linear Regression Analysis ◽  
Multiple Linear Regression Model ◽  
Mathematical Meaning ◽  
Matrix Correction ◽  
Independent Variables ◽  
Xrf Analysis

AbstractIt will be shown that the Lachance-Traill XRF matrix correction equations can be derived from the statistical multiple linear regression model. By selecting and properly transforming the independent variables and then applying the statistical multiple linear regression model, the following form of the matrix correction equation is obtained:Furthermore, it will be shown that the Lachance-Traill influence coefficients have a deeper mathematical meaning. They can be related to the multiple regression coefficients of the transformed system:Finally, it will be proposed that the Lachance-Traill model is equivalent to the statistical multiple linear regression model with the transformed independent variables. Knowing these facts will simplify correction subroutines in Quantitative/Empirical XRF Analysis programs. These mathematical facts have already been implemented and presented in a paper: “Automated Quantitative XRF Analysis Software in Quality Control Applications” (Pacific-International Congress on X-ray Analytical Methods, Hawaii, 1991).This demonstrates that the Lachance-Traill model has a strong mathematical foundation and is naturally justified mathematically.

Degradation of MEH-PPV due to Oxygen/Moisture Traps through C-V Analysis and Attenuated Total Reflection IR Spectroscopy

2004 ◽  
Vol 814 ◽  
Author(s):  
R. K. Khillan ◽  
Y. Su ◽  
K. Varahramyan
Keyword(s):  
Ir Spectroscopy ◽  
Negative Ions ◽  
Optical Microscope ◽  
Total Reflection ◽  
Attenuated Total Reflection ◽  
Weak Inversion ◽  
Inversion Region ◽  
Optical Images ◽  
Strong Inversion ◽  
Capacitance Voltage

AbstractWe present the studying of oxygen and moisture traps in MEH-PPV through the MIS Capacitance – Voltage (C-V) analysis, and the Attenuated Total Reflection Infrared (ATR IR) spectroscopy technique. The presence of oxygen studied by ATR IR has also been verified by optical images from high resolution optical microscope. In quasi-static C-V measurements of the MIS (Al/MEH-PPV/p-Si) capacitors made, an extension of the weak inversion region was measured before strong inversion, which becomes more pronounced with aging. This increase in the weak inversion region is attributed to electron trapping by oxygen to form negative ions in the MEH-PPV layer. ATR IR spectroscopy shows the formation of carbonyl peak at 1651 cm−1 with aging, which is due to the presence of oxygen. Both the C-V analysis and Attenuated Total Reflection IR Spectroscopy are powerful tools for investigating the degradation of MEH-PPV polymer.

scholarly journals The Effect of Phase on Paper Thickness Optimization in the Paper Recycling Process Using Recycling Machine

2020 ◽  
Vol 2 (1) ◽  
pp. 19
Author(s):  
Fauzhia Rahmasari
Keyword(s):  
Regression Analysis ◽  
Linear Regression ◽  
Regression Model ◽  
Multiple Linear Regression ◽  
Multiple Linear Regression Model ◽  
Thickness Optimization ◽  
Recycling Process ◽  
Paper Recycling ◽  
Independent Variables ◽  
The Relationship

AbstractEfforts to manage the recycling of paper waste into new paper have been carried out in recent times. It takes a tool or machine that is able to effectively and efficiently recycle used paper into new paper. There are several factors that affect the effectiveness of paper recycling machines, one of which is the paper thickness. One method that can be used to analyze the factors that influence paper thickness in the paper production process using a paper recycling machine is regression analysis. Regression analysis is data analysis techniques in statistics that is used to examine the relationship between several independent variables and dependent variable. However, if we want to examine the relationship or effect of two or more independent variables on a dependent variable, the regression model used is a multiple linear regression model. This study purposes are to analyze the factors that influence paper thickness using a paper recycling machine using multiple linear regression and to inform the modeling about that. The results showed that the factors that affect the paper thickness optimization are destruction and press phase. AbstractUpaya pengelolaan daur ulang sampah kertas menjadi kertas baru telah banyak dilakukan pada jaman sekarang. Dibutuhkan suatu alat atau mesin yang mampu secara efektif dan efisien dalam mendaur ulang kertas bekas menjadi kertas baru. Terdapat beberapa faktor yang mempengaruhi tingkat efektifitas mesin daur ulang kertas diantaranya adalah ketebalan kertas. Salah satu metode yang dapat digunakan untuk menganalisis faktor-faktor yang mempengaruhi ketebalan kertas pada proses produksi kertas menggunakan mesin daur ulang kertas adalah analisis regresi. Analisis regresi merupakan teknik analisis data dalam statistika yang digunakan untuk mengkaji hubungan antara beberapa variabel bebas dengan variabel tidak bebas. Namun, jika ingin mengkaji hubungan atau pengaruh dua atau lebih variabel bebas terhadap satu variabel tidak bebas, maka model regresi yang digunakan adalah model regresi linier berganda. Tujuan dalam penelitian ini yaitu menganalisis faktor-faktor yang mempengaruhi ketebalan kertas menggunakan mesin daur ulang kertas menggunakan regresi linier berganda serta memberikan informasi pemodelan mengenai hal tersebut. Hasil penelitian menunjukkan bahwa faktor yang mempengaruhi keoptimalan ketebalan kertas adalah fase penghancuran dan pemadatan kertas

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