 Open Access
 Total Downloads : 211
 Authors : S. Sumitra, Dr. (Mrs). Ananthi Sheshasaayee
 Paper ID : IJERTV5IS040311
 Volume & Issue : Volume 05, Issue 04 (April 2016)
 DOI : http://dx.doi.org/10.17577/IJERTV5IS040311
 Published (First Online): 05042016
 ISSN (Online) : 22780181
 Publisher Name : IJERT
 License: This work is licensed under a Creative Commons Attribution 4.0 International License
Data Preprocessing for Quality Analysis of Contact Lens Material in Ophthalmology using Factor Analysis
S. Sumitra
Research Scholar, Bharathair University, Coimbatore.
Dr. (Mrs). Ananthi Sheshasaayee, Head & Associate Professor, Dept. of Computer Science,
Quaid E Millath Government College for Women, (A) Anna Salai, Chennai 600002.
Abstract This paper focuses on Factor analysis performed on contact lens material. The Factor Analysis on the contact lens material and interpretation are the central steps in this research process. The extracted contact lens data are comprehended and interpreted to trigger the research. Analysis based on a wide range of variables can be tedious and time consuming. The contact lens materials such as RGP, Soft and Hybrid are considered as the source of dataset for factor analysis. Principle Component Analysis is the method adopted to do Factor Analysis. From the result it is found, out of 10 parameters 5 parameters are considered to be the key parameters for identifying the quality contact lens material in the field of ophthalmology.
Keywords Contact Lens, RGP Lens, Soft Lens , Hybrid Lens, Factor Analysis, PCA, Ophathalmology.

INTRODUCTION
Factor analysis is commonly known as a common factor model or theoretical model. Factor analysis is a statistical procedure to study the interrelationship among the variables in an effort to find a new set of factors, fewer in number, than the original variables so that the factors are common among the original variables. It explains the dimensions or factors for complex events. It is a mathematical procedure to simplify the interrelated measure to discover the pattern in the set of variables [1] .
Factor analysis allows testing theories involving variables which are hard to measure directly. On the prosaic level of analysis, factor analysis helps to establish the observed variables if the measuring facts underlying the same factors with varying reliability. Computing methods of factor analysis are stated below:
OB1 = k11 F1 + k12 F2 + k13 F3 ++ k1m Fm + k1 U1 OB2 = k21 F1 + k22 F2 + k23 F3 +.+ k2m Fm + k2 U2
..
.
OBn = kn1 F1 + kn2 F2 + kn3 F3 ++ knm Fm + kn Um where
OB1, OB2.. OBn are observed variables F1, F2.. Fm are common factors
U1, U2. Un are unique factors expressed in linear function
k11 , k21 .. knm are the observed variables from the factors. The coefficients of these factors are the weights in the same way as regression coefficient.
Factor analysis basically follows four steps. They are:

Computing the correlation of all variables

Determining the required factors or factor extraction

Rotating factors or interpreting the factors

Calculating the score of each factor.
Factor analysis has its applications in varied fields like medicine, economics, marketing, geography and in various other technological advancements of computers. Factor analysis is required to check the absence of univariate and multivariate outliers [2]. More likelihood of the samples can be identified using factor analysis [3].


RELATED WORK
In the field of Astrophysics an algorithm proposed for strong, galaxy scale gravitational lenses in the residual image using basic vectors obtained from principal component analysis. PCA (Principal Component Analysis) based galaxy subtraction acts better than the traditional subtraction model for fitting data in astrophysics[4].The exploration of consumption pattern in food and nutrients was analysed using factor analysis methods among the rural adult population in India[5] .
The research work focused on the PCA process for extraction of factors. Principle Component Analyses are extraction of components selected at maximum variation in the original data set. By the implementation of factor analysis the parameters opted to identify the quality contact lens material of a larger number is reduced to fewer dimensions with the available data. PCA has vital application in the field of material science, were PCA was used to study of SWNTs (Singlewalled Carbon Nano Tubes) on coarse grained simulations and atomistic fine grained simulation. In PCA was implemented to identify the dynamic factors[6]. The prevalence patient to reach vision centers in rural area was found using factor analysis. According to the result the patient found it very difficult to travel to Vision care centers[7]. PCA was a classical statistical method for image processing applications and this method further dimensionally reduces the feature vectors[8].
In the field of epidemiology the development of an instrument for Eye Care Expectation Survey (ECES) was found by the cross section study using factor analysis[9]. In the area of investigation, ophthalmology and visual science PCA was used as a diagnostic tool for checking the loss of
terpenoids in meibum and the plethora of positive biological functions. In this the lower level of cholesterol esters are determined between the lipid layer deposits of the eye with the patient age[10]. The psychology field also implemented factor analysis and exploratory factor analysis to check the happy medium accuracy and completeness with over helming technical complexity[11] .
PCA was also implemented in the field of climatology to check the performance of meteorological variability of the surface precipitation and temperature by using daily cumulative rain showered in the winter season in the region of Sardinia[12].
Ioannis Tranoudis, and Nathan Efron [13] determined that the water content and diameter is reduced significantly when the temperature is raised to 20350 C. The lens quality remained unchanged with the lens made of HEMA/MAA (HEMA: 2hydoxylethyl methacrylate, MAA: methacrylic acid) at 70%. High water content material does not dehydrate easily because of its low relative change in oxygen transmissibility for a period of 6 hours. Oxygen permeability (OP) is a parameter of contact lens, which permits the oxygen reach the eye by diffusion, while Dk/t is a parameter which denotes the transmissibility level. The Dk/t and Dk values significantly depend on the measurement of the physiological values given for the evaluation of contact lens performance. The high transmission capabilities of varied Dk/t values have low impact on the physiological performance of the lenses. It is also understood that the Dk value above 70 are better. Using the stack method the characterization and the accurate measurement of oxygen transmissibility and permeability are measured. It resulted that the significance level of physiological data remains significant by using BOAT (Biological Oxygen Apparent Transmissibility) and EOP (Equivalent Oxygen Percentage) methods.[14]. Factor analysis shows the relationship and pattern which can be easily interpreted among the variables[15].
Factor analysis was done on the parameters of contact lens types such as RGP (Rigid Gas Permeable), Soft and Hybrid which are identified using ID3 algorithm. Principal component regression analysis was performed in identifying the water content of contact lenses. The sample taken for the analysis was implemented with PerkinElmer Spectrum Quant and PCA tool to produce the calibration of the contact lenses[16] .

PROPOSED WORK

Factor Analysis For Quality Analysis Of Contact Lens (QACL) Material In Ophthalmology
The contact lens in which the collected data set consists of 9 parameters is processed further to identify the factors essential to construct an algorithm need for quality enhanceent of contact lens material. This essential parameter provides a way for the designing and development of quality lens material for primary refractive errors.

Parameters Adopted
There are three types of contact lenses, namely RGP lenses, Soft lenses and Hybrid lenses. These lenses are used to reduce the power for the patients who are affected from the preliminary eye problems. In the concept of quality inspection of the lens material, contact lenses can be grouped according to the following key factors:

Lens Material

Lens Design

Period of comfort while wearing lenses

Parameters required designing a lens

Side effects due to lens wear
The questionnaires are obtained from the contact lens article [17] which promoted to carry away the research. The above mentioned processes are basically performed manually. The shape, the size of the contact lens is designed using lathecut and molding. The quality of the contact lens analysed using machines. The side effects when wearing the contact lenses are not analyzed initially, instead they checked the lens for correction of vision; fitment of the contact lens to the cornea and reduction of power after wearing the contact lens are analyzed. Therefore it was necessary to apply a condition to minimize the side effects and provide a stable vision to the eyes. The research is to analyze and study the parameters that are required to design a quality lens. The parameters are classified as:

Lens dimensions

Diameter

Base curve radius

Thickness

Power


Optical properties

Water content

Dk value

Refractive index

Specific gravity

Light Transmittance

A brief examination of the types of materials and the plethora of lens designs will demonstrate that care needs to be taken when selecting lens material whether for offthepeg lenses or customdesigned. The four main differences to bear in mind are[18]:

Oxygen permeability: the higher the OP, the lower the refractive index of the material and, as with spectacle lenses, the thicker the final lens

Polymeric mix: silicone, fluorine, polymethylmethacrylate or other backbone components, crosslinking agents

Refractive index: The refracting power of a lens is directly related to its refractive index and determines the thickness and curvature of the optical device.

Wettability:Wettability refers to adherence of two materials. It refers to the adhesive and cohesive force balance between lens surfaces.
The FDA gives each contact lens material a generic name. In general, all hydrogel and silicone hydrogel lens generic names end in the suffix filcon and all nonhydrogel lenses ends in focon[19].



Preprocessing
Preprocessing steps involve identifying the observed variable (parameters) represents the original variables which are used to identify how much the observed variable correlates and interpreted with the original variables. These observed variables are grouped into factors which underlying input variables that are named as independent groups which are dependent with each other.
Table 1.1 : Contact Lens Manufacturers and Brands
Manufacturers
Brands
Bausch and Lomb
Soft lens Toric and Pure Vision
Johnson and Johnson
Acuvue Oasys , Acuvue 2
CIBA Vision
O2 optix, Air optix and Focus Dailies
Cooper Vision
Proclear, Proclear EP and Proclear Multifocal
From table 1.1, the dataset of the contact lens material are taken from various brands of Bausch and Lomb, Air optrix, Johnson and Johnson and Cooper vision. The optical and physical properties of the contact lenses are the observed variables used in this analysis. The analysis study motivates to identify a unified model which mainly corresponds to reduce the side effects of wearing contact lens of refractive error patients.
Tables 1.2,1.3 and 1.4 illustrate the dataset required for factor analysis of contact types such as RGP lens, soft lens and hybrid lens respectively.
Table 1.2 : RGP Contact Lens materials
Table 1.3 : Soft Contact Lens material
Soft Lens Material
Hioxifilcon B
Lotrafilcon A
Lotrafilcon B
Galyfilcon A
Narafilcon A
Senofilcon A
ComfilconA
Enfilcon A
Alphafilcon A
Hioxifilcon A
Hioxifilcon D
Nelfilcon A
Balafilcon A
Etafilcon A
Ocufilcon C
Ocufilcon D
Phemfilcon A
Methfilcon A
Methafilcon A
Methafilcon B
Vilfilcon A
Hilafilcon B
Polymacon
Table 1.4 : Hybrid Contact Lens materials
Hybrid Lens Name
Paflufocon D hemiberfilcon A
Petrafocon A hemiberfilcon A
The contact lens materials like RGP, Soft and Hybrid considered as the source and the parameters such as Water content, Dk value, Diameter, Base Curve Radius, Thickness, Power, Refractive Index, Specific gravity and Light transmittance are collected. Using these parameters factorization was performed. Table 1.5 shows the parameters list.
RGP Lens Materials
Boston II Itafocon A
Boston IV Itafocon B
Boston Equalens Itaflourofocon A
Flosi Kolfocon A
Fluorex 700 Flusilfocon A
Fluofocon 60 paflufocon B
Fluoroperm 30 paflufocon C
Fluoroperm 92 paflufocon A
Fluoroperm 151 paflufocon D
Menicon Z
ONSI 56 Onsifocon A
OP2 Lotifocon B
OP3 Lotifocon A
OP6 Lotifocon C
Optacryl60
Paraperm EW Pasifocon C
Paraperm O2 Pasifocon A
Tyro 97 Hofocon A
RGP Lens Materials
Boston II Itafocon A
Boston IV Itafocon B
Boston Equalens Itaflourofocon A
Flosi Kolfocon A
Fluorex 700 Flusilfocon A
Fluofocon 60 paflufocon B
Fluoroperm 30 paflufocon C
Fluoroperm 92 paflufocon A
Fluoroperm 151 paflufocon D
Menicon Z
ONSI 56 Onsifocon A
OP2 Lotifocon B
OP3 Lotifocon A
OP6 Lotifocon C
Optacryl60
Paraperm EW Pasifocon C
Paraperm O2 Pasifocon A
Tyro 97 Hofocon A
Table 1.5 : Parameters obtained for factor analysis
Parameters
Water Content
Dk
Diameter
Base Curve Radius
Thickness Power
Refractive Index
Specific Gravity
Light Transmittance
The Factor analyses are performed among the data referred in table 1.2, 1.3 and 1.4. These data on the mentioned table have different range of values in diameter, base curve radius and thickness. From the values in diameter, base curve radius and thickness is expanded for each data set and taken for processing. The sample data set taken for analysis is about 46,313. Table 1.6 illustrates the samples used for analysis.
Level 3: This level of factor analysis performs Correlation for the data. Correlation was performed with the eight parameters which represents the relationship between the factors and variables. From table 1.8 it is clear that the design element of the matrix will have the value of 1.
Table 1.8 : Correlation Matrix
Table 1.6 Sample dataset
Lens Name
RGP Lens Material Boston II Itafocon A
Soft Lens Material Lotrafilcon A
Hybrid Lens Material Paflufocon D hemiberfilcon A
Water Content
20
24
27
Dk
12
140
100
Diameter
7.0 to 11.5
13.8
14.5
Base curve radius
5.0 to 9.00
8.6
7.1 to 8.54
Thickness
0.07 to 0.65
0.08
0.12 to 0.3
Power
3.00
3.00
3.00
Refractive Index
1.47
1.43
1.53
Specific Gravity
1.13
1.08
1.1
Light Transmittance
91
96
91
The parameters such as water content, Dk, Diameter, base curve radius, thickness, refractive index, power, specific gravity and light transmittance are shown with their recorded values of each contact lens brand. Using this dataset of 46,313 samples factor analysis was performed. Power is considered as constant with 3.00D during manufacturing and it's a dependent parameter, hence not used in factor analysis. SPSS tool was used to perform factor analysis. The evaluation reports of factor analysis are followed as per the steps given below.
Level 1: Construction of Data involves formation of parameters with its corresponding inputs are collected as shown in table 1.6.
Level 2: This level of process performs descriptive statistics with 46,313 data. In this the mean and standard deviation are found. Table 1.7 shows the descriptive statistics of the given data.
Level 4: After finding the correlation among the matrix data, the component matrix is identified. The data used here are subjected to perform factor analysis in two stages. Though the stages are two both the outputs can be requested at the same time. To perform the analysis, SPSS tool was used. In stage 1, SPSS was used to extract factors with an Eigen value of one or higher. The method used here is principal component method (PCA).
Table 1.9 Extraction of Components
Table 1.7 : Descriptive Statistics
Mean
Std. Deviation
Analysis N
Water Content
23.67
8.294
46313
Dk
52.63
42.973
46313
Diameter
8.636
2.1481
46313
Base curve Radius
7.329
1.0330
46313
Thickness
.9103
2.17947
46313
Refractive Index
1.4568
.01843
46313
Specific Gravity
1.134
.0388
46313
Light Transmittance
93.15
4.792
46313
After implementing Principal component matrix, table 1.9 shows the extraction of the components. The result shows that 4 components have been extracted with given data. PCA extracted maximum variance from the data set with each component, thus reduces the large number of variables into a smaller number of variables.
Level 5: This level is an important process of the factor analysis which known as interpretation. In this level the first step is to interpret the output from the factors extracted, with their Eigen value and the cumulative percentage of the variance. The Cumulative percentage statistics are as shown in table 1.10.
Table 1.10: Total Variance Obtained using PCA
At this level of the process using PCA the initial solution of each variable is standardized to have a mean of
0.0 and a standard deviation of + 1.0. Thus, the variance of each variable is 1.0. The total variance obtained is 8. Since the single variable can account for 1.0 unit of variance, or should have the Eigen value greater than1.0.
Further, the four factors have been extracted using the Eigen value. The Eigen value considered based on the criteria that the Eigen value should be 1 or higher. According to the cumulative percentage of variance, the four factors have been extracted with a cumulative percentage of 75.3% of the total variance (information contained in the original variables).
This is an ideal method to reduce the number of variables from 8 to 4 underlying factors. While the loss is only about 24.7% of the information content 75.3% is retained by the 4 factors extracted out of the 8 original variables. This represents a reasonable good solution for the above mentioned problem. Now, interpreting the 4 extracted factors are justified with rotated and unrotated matrices as shown in figure 1.1
Level 6: After finding the factors the next level is to plot the graph using Cattells screen plot using the Eigen value associated with each of the extracted factors against each of the other factors. Figure 1.1 shows the extracted factors.
Level 7: At this level, called rotation of factors, where the rows correspond to the original variables and the columns to the factors are compared and the required factors are obtained as shown in table 1.11.
Table 1.11: Rotated Component Matrix using PCA
From Table 1.11, the rotated factor matrix, it is noticed that variable numbered 1 and 2 have the loading .866 and .840 on factor 1 which has the highest loading nearest to
1.000. This suggests that factor 1 is the combination of two original variables.
Table 1.12 suggests a similar grouping. Therefore, there is no problem in interpreting factor 1 of column 1, as a combination of refractive index and water content which are the properties of contact lens material named as optical 1. Interpretation for factor 2 was done and the result obtained from the table 1.11 is .735 and .725 of column 2 with high loadings. The values obtained as factor 2 are specific gravity and diameter which are named opticalphysical1. Factor 3 was obtained from table 1.12 has the values as .766, .676 and
.609 from column 3 of the rotated matrix which are Dk values, light transmittance and base curve radius respectively. This group is named as opticalphysical2 based on their internal properties. Factor 4 obtained had the value 0.922 of column 4 which determine the thickness of the contact lens. This factor 4 was named as optical2.
Level 8: This level is mainly implemented to find the communality of the variables taken from the data set. The data set of the lens material was taken for communality, which fins proportion of the variance by summing of its squared factor loading. The component matrix indicates the correlation of each variable with each factor. The communality table is shown in Table 1.13
The communalities of the 8 variables are given in table 1.13. As is evident from the table, the proportion of variance in each variable accounted for by the four factors which are not same.
Using Factor analysis in analysis of QCLA material gave 75% of the result about the factor required for designing quality lens analysis algorithms. The key parameters required for designing the QLAA were obtained.
Initial
Extraction
Water Content
1.000
.808
Dk
1.000
.811
Diameter
1.000
.703
Base curve Radius
1.000
.654
Thickness
1.000
.861
Refractive Index
1.000
.789
Specific Gravity
1.000
.661
Light Transmittance
1.000
.737
Initial
Extraction
Water Content
1.000
.808
Dk
1.000
.811
Diameter
1.000
.703
Base curve Radius
1.000
.654
Thickness
1.000
.861
Refractive Index
1.000
.789
Specific Gravity
1.000
.661
Light Transmittance
1.000
.737
Table 1.13: Communalities obtained using PCA
Extraction Method: Principal Component Analysis.
Factor 4 contains one variable which is the thickness of the material that possesses physical properties of the lens. The factor loadings and the communality are high which is taken as the key parameters for QLAA.
From the factor analysis, dependent variables such as Specific Gravity, Light Transmittance, BCR are eliminated from the process of quality analysis. The reason is that the above variables are constructed based on the key parameters such as Water Content, Dk, Diameter, Refractive Index and Thickness. If the value of the key parameters either increase or decrease, the value of the dependent variables also increase or decrease to make the quality analysis process very efficient.


RESULTS
Identification of Key Parameters
Factor 1 consists of two variables, namely refractive error and water content. Both the variables are having high loadings; factor 1 denotes the measured optical values and these variables are independent variables required for QLAA. Factor 2 is the measure of optical and physical properties of the contact lenses. The variables have high loadings according to the communalities of specific gravity and diameter. The values are .661 and .703 respectively.
For the quality analysis, diameter had high variance accounted by the 4 Factors. Specific Gravity is dependent variable of water content which possesses optical properties where density of water is high. Specific Gravity is also high and vice versa. Therefore Specific Gravity can be discarded for further process in quality analysis. Diameter which is the physical properties of the lens is the key parameters for QLAA.
Factor 3 is the combination of three variables namely Dk, Light Transmittance and Base curve radius, it possess both the physical and optical properties of the contact lenses. Dk, Light Transmittance and Base Curve Radius had high factor loadings where their communalities are .811, .737 and .654. In this, Dk is the optical property of the contact lenses which is an independent variable where communality was high when compared to Light Transmittance and BCR. This paves the way that the Dk variable considered as a key parameter of Quality analysis.
The Variables Light Transmittance, Base curve Radius are dependent variable. Light Transmittance is the optical property which is dependent on the refractive index of the material that is, the amount of light that enters into the lens material is specified. When Refractive index is high, then Light Transmittance will also be high and vice versa. Light Transmittance was considered to be low factor, which will not reflect much for the next level of process hence it was not considered for QLAA.
Base curve radius (BCR) is a dependent variable of the diameter which possesses physical property of the contact lenses that specifies the shape of the lens material which fit to the cornea. Based on the diameter of the lens BCR is calculated for the lenses, BCR and diameter are dependent variables. If the diameter is known the radius of the lens can be noted efficiently. So it is possible to eliminate the factor BCR. Therefore BCR was eliminated from QLAA.
The key parameters identified using factor analysis are the building blocks for designing the quality lens material which helps in reducing side effects when using the contact lenses for the patients. It also helps to improve comfort and visualize objects clearly when wearing the contact lenses.

CONCLUSION
In this process, the necessary factors were found among the group of parameters used in manufacturing of contact lenses. By using this analysis the next phase of framing a prototype for Quality analysis of contact lens material is generated. From the eight parameters such as water content, Dk, diameter, base curve radius, thickness, and refractive index, specific gravity and light transmittance five parameters are obtained for performing QLAA of contact lens material. The five parameters identified are Water Content, Dk, Diameter, Refractive Index and Thickness.

REFERENCES

Child I D. (2006), The essentials of factor analysis. (3rd ed.). New York, NY: Continuum International Publishing Group.

Field A. (2009). Discovering Statistics using SPSS. Sage: London. / Field, A. (2009). Discovering Statistics Using SPSS: Introducing Statistical Method (3rd ed.). Thousand Oaks, CA: Sage Publications.

Tabachnick, B. G., & Fidell, L. S. (2007), Using multivariate statistics (5th ed.). Boston: Allyn and Bacon.

Joseph, R.; Courbin, F.; Metcalf, R.B.; Giocoli, C.; Hartley, P.; Jackson, N.; Bellagamba, F.; Kneib, J.P.; Koopmans, L.; Lemson, G.; Meneghetti, M.; Meylan, G.; Petkova, M.; Pires, S. (2014), A PCA based automated finder for galaxyscale strong lenses, Astronomy & Astrophysics, Vol. 566, id.A63, pp. 110

Venkaiah K, Brahmam G.N.V. Vijayaraghavan K (2011), Application of Factor Analysis to Identify Dietary Patterns and Use of Factor Scores to Study Their Relationship with Nutritional Status of Adult Rural Populations, J Health Popul Nutr. Aug 2011; 29(4): 327338.
PMCID: PMC3190363

Prathamesh M. Shenai, Zhiping Xu, Yang Zhao(2012), Applications of Principal Component Analysis (PCA) in Materials Science, Principal Component Analysis – Engineering Applications, Dr. Parinya Sanguansat (Ed.), ISBN: 9789535101826, InTech, DOI: 10.5772/37523.

Vilas Kovai, Gullapalli N Rao, Brien Holden (2013), Key factors determining success of primary eye care through vision centres in rural India: Patients perspectives, Indian J Ophthalmol. 2012 Sep Oct;60(5):48791. doi: 10.4103/03014738.100558.

Abhishek Banerjee (2012), Impact of Principal Component Analysis in the Application of Image Processing, International Journal of Advanced Research in Computer Science and Software Engneering, Vol. 2, Issue 1, January 2012.

Aerlyn G. Dawn, Gerald McGwin,Paul P. Lee (2005), Patient Expectations Regarding Eye Care Development and Results of the Eye Care Expectations Survey (ECES), (2005), Arch Ophthalmol. 2005 Apr;Vol. 123(4):53441.

Douglas Borchman, Gary N. Foulks, Marta C. Yappert, and Sarah E. Milliner (2012), Differences in Human Meibum Lipid Composition with Meibomian Gland Dysfunction Using NMR and Principal Component Analysis, Invest Ophthalmol Vis Sci. Jan 2012; 53(1): 337347.

Understanding concepts and applications, American Psychological Association, 195 pp, ISBN: 9781591470939.

Benzi R, Deidda R,Marrocu M (1997), Characterization of Temperature and Precipitation fields over Sardinia with Principal Component Analysis and Singular Spectrum Analysis, International Journal of Climatology Vol.17 , 12311262 (1997).

Ioannis Tranoudis , Nathan Efron(2004), Tensile properties of soft contact lens materials, Contactlens & Anterior Eye, The Journal of the British Contact Lens Association,Vol. 27, Issue 4, pp. 177191.

J. M. GonzalezMeijome , V. CompaÃ±Moreno, E. Riande (2008), Determination of Oxygen Permeability in Soft Contact Lenses Using a Polarographic Method: Estimation of Relevant Physiological Parameters, Ind. Eng. Chem. Res., 2008, Vol. 47 (10), pp 36193629.

An Gie Yong , Sean Pearce (2013), A Beginners Guide to Factor Analysis: Focusing on Exploratory Factor Analysis,Tutorials in Quantitative Methods for Psychology 2013, Vol. 9(2), pp. 7994.

Brown H Dean (2008), Determination of Water Content of Contact Lenses Using FTNIR, Field Application Report FTIR, PerkinElmer.

Contact Lenses – American Medical Association.

Donald Cameron (2002), Are all RGP lenses basically same?, Opotometry Today, March 8, 2002 OT.

Johnson & Johnson Vision Care, Inc. 2012. ACU27967 April 2012.