- Non-Destructive Measurement of Gelatin Content of European Papers: 1400-1800 An Honors Thesis (HONRS 499) by Brenda S. Fuller Patricia Lang, Advisor Ball State University Muncie, Indiana Date of Graduation May 4, 1996 - - Purpose of Thesis The broad purpose of our research is to determine the effect of gelatin concentration on paper permanence, in collaboration with paper scientist Tim Barrett of the University of Iowa. Our specific research goal is to use attenuated total reflectance (ATR) infrared spectroscopy and various factor analysis methods to characterize historical papers in a non-destructive manner. We have tested and developed the infrared sampling techniques and have compared several factor analysis methods to determine a method which can best be used to analyze properties of the paper samples. The method that proved most useful allows gelatin properties to be estimated by their infrared spectras, provides accurate predictions of sulfur and potassium content, and estimates the paper's date. .- INTRODUCTION HISTORY OF PAPER-MAKING IN EUROPE In Europe between fourteen hundred and eighteen hundred, gelatin was commonly used in the paper-making process for several reasons. Gelatin was used as a sizing agent, and it helped prevent bleeding of the ink. Gelatin also increased the strength of the paper and made the paper more resistant to abrasion. Some studies suggest gelatin acts as a buffering system against environmental pollutants that catalyze the acidic degradation of paper. It is still unclear what effect, if any, gelatin has on paper permanence. FIRST STUDIES OF GELATIN CONTENT OF HISTORICAL PAPERS Tim Barrett of the University of Iowa originated the study of the effect of gelatin on paper permanence. He took forty historical papers and divided them into two groups according to their light values that were measured using a Minolta Chroma-meter. Surface pH, calcium content, iron content, sulfur content, and potassium content, as well as the gelatin content were all measured. The gelatin content, or percent hide glue, was determined by the concentration of 4-hydroxyproline. A significant correlation between these values and the light values was found, indicating that these properties aid in the preservation of paper. This also means that gelatin may aid in the preservation of paper. However, in order to validate this notion, a nondestructive method for determining gelatin for a larger number of samples would need to be employed. This is the primary purpose for our study. EXPERIMENTAL METHODS PRINCIPLES OF ATR We have chosen an infrared sampling process known as attenuated total reflectance (ATR). ATR is a non-destructive method of internal reflectance spectroscopy, meaning that the paper sample is not destroyed when the spectrum is taken. The paper sample is placed directly .1 .....- against two sides of a crystal with a high refractive index (See Figure 1). A KRS-5 element was used in this research. Complete internal reflection occurs at the interface of the sample and element, given that the angle of incidence is less than the critical angle. Some radiation inevitably penetrates the surface of the sample, and the sample absorbs particular frequencies. These absorptions provide the infrared spectra and characteristic peaks. Gelatin has several characteristic infrared absorption bands (See Spectrum 1). The main two bands are the amide I band and the amide II band. The amide I band is caused by the C=O stretching of the amide bonds (found at approximately 1650 cm- 1), and the amide II band is a result of the C-N-H bending (found at approximately 1550 cm- 1). PROCEDURE First, acceptable spectra from both sides of each sample had to be taken using the ATR accessory coupled to a Perkin-Elmer 1760X FT-I R spectrometer. Care had to be taken so that enough pressure was applied to the sample to obtain a good spectrum without fracturing the crystal. The spectra from the two sides of the sample were averaged together to decrease the effect of the inhomogeneities of the paper. The spectra of 25 of the 40 historical paper samples were then run through the statistical package (Quant +, Perkin-Elmer software), principle components regression (PCR) as a calibration set. (The remaining 15 samples were not used because visible evidence showed damage sustained during previously performed pH measurements.) The PCR algorithm attempts to express the variation in the spectral data in as few terms as possible. PCR takes the raw spectral data and breaks it down into a set of weighted factors. Then, the computer drops those factors that do not correlate to the given properties. This produces a set of factors that are then compared to the properties of the standard set. Factors may be either positive or negative. The first factor is approximately the weighted average of all the original spectra. The rest of the - factors account for the residual variation in the data set (See Figure 2). 2 - When the significant factors are recognized, their contributions to the calibration spectra are calculated. These contributions are called the factor loadings. A set of loadings corresponds to the contributions of each factor to its calibration spectrum. Multiple linear regression is then used to identify correlations between the factor loadings and the properties of the samples. Factors that show little correlation to a property are eliminated until the remaining factors show a significant correlation. By using graphs, known values of properties in the calibration set are compared with those predicted by the regression equation. The linear regression of the graph provides a correlation factor for the set. To generate the best model possible, the expert assist option was used for our models. Expert assist automatically rejects abnormal or eschewed standards until the best possible model is generated. RESULTS AND DISCUSSION Graph 1 shows the correlation for the computer's predictions of gelatin concentration from the regression equation vs. the "known" gelatin concentration obtained by the wet chemical hydroxyproline method. From a calibration set of 25 historic paper samples, we have a multiple correlation of 0.8057, a significant R value considering the calibration set was constructed with a wide variety of paper samples of different natural origin. In addition, we find that five factors were extracted from the set of 25 spectra and three correlate with gelatin. In addition, since our collaborators had characterized each of these papers with regard to surface pH, calcium, sulfur, iron, potassium, fluorescence, and light, we entered these values for those properties into our statistical package and did a regression to see if any extracted factors correlated with them. This is shown in the lower portion of Table 1. We can see that sulfur, potassium, and date correlate with an R value of about 0.7 or higher. In addition, or perhaps more interesting, some extracted factors are common among different properties. For example - 3 - factor four and five correlate with gelatin, sulfur, and potassium. We speculate that this relationship might be related to the fact that alum (unalum potassium sulfate) was often used in conjunction with gelatin sizing. To validate this method, the factor analysis package performed a full cross-validation test on the data set. Using information from the validation report, graphs of estimated validation values vs. specified values were constructed for each property (See graphs 2-10). Gelatin, sulfur, and potassium concentrations showed high correlations, indicating that this method showed promise for the predictions of these properties. In order to evaluate the effect aging had on our prediction capabilities, we analyzed a set of three papers from an earlier study: a Whatman filter paper, the same paper after gelatin sizing, and the same paper gelatin sized after accelerated aging (exposure to auto exhaust followed by heat). We obtained spectra using the same procedure as outlined for the historical papers in our calibration set, then used the factor analysis model previously developed. The results can be seen in Table 2: IR Predicted Properties of Artificially Aged Filter Papers. There is essentially no difference between the aged and unaged samples when we estimate the error in our ability to calculate gelatin concentration as being approximately equal to the concentration calculated for the 0% gelatin sample. This implies that the spectral characteristics detected in our method that are not connected with gelatin are not changed by the aging method. The properties of L*, pH, date, and calcium all show little differences between aged and unaged samples as well. It should be noted that there is a significant difference in the L* values of the paper with no gelatin and the papers with gelatin. In an attempt to increase prediction capabilities, a new calibration set was created by adding known flax standards. The flax samples included flax with 0% , 0.5%, 1.0%, 2.0%, 4.0%, 6.0%, and 8.0% gelatin. After the new calibration set was run through the factor analysis program defined earlier, factors one and two were plotted against each other since they 4 .- are the two factors that show the most variance. Circles were drawn around each cluster of data points, and only one data point was chosen from each cluster. (See figure 3). This provided a new calibration set, with a mor-e even distribution of values, making the data less weighted. Graph 11 shows an estimated vs. specified plot for gelatin concentration after the flax samples were combined with the 25 paper samples, and after the "clustering" procedure was performed on the new set. The R value for gelatin was found to be 0.9298. The R values for the other properties are as follows: potassium, 0.5391; pH, 0.4560; iron, 0.9985; L', 0.6020; calcium, 0.7227; sulfur, 0.8665; fluorescence, 0.9687; and date, 0.8165. (See Table 3 for a breakdown of each of the contributing factors.) The samples deleted as a result of the clustering method were then used as test samples to validate the computer's ability to accurately predict gelatin content and the other properties using our new calibration set. This information was converted into graphs showing estimated vs. specified values for each of the ,- properties. In some of the graphs, outliers decreased R values, so they were removed. The R value of gelatin for this validation set was 0.27925 (See Graph 12). After the outlier (Sample 117T) was removed, an R value of 0.62835 was obtained (See Graph 12B). The other R values are as follows: pH, 0.17543 (See Graph 13); L', 0.20006 (See Graph 14) - After the outlier (Sample 82T) was removed, an R value of 0.50562 was obtained (See Graph 14B); calcium, 0.23225 (See Graph 15) - After the outlier (Sample 82T) was removed, an R value of 0.37593 was obtained (See Graph 15B); sulfur, 0.43382 (See Graph 16); 0.55501 (See Graph 17); potassium, iron, 0.30236 (See Graph 18); fluorescence, 0.55588 (See Graph 19); and date, 0.29721 (See Graph 20) - After the outlier (Sample 157W) was removed, an R value of 0.70115 was obtained (See Graph 20B). The flax samples were then removed from the "clustered" calibration set to observe the effect of the flax samples in our new method. The R value of gelatin was significantly lower (R=0.3614. See Graph 21) for this data set than the set with the flax samples added .5 - band (See Spectrum 1). The relative pathlength of the standards were then determined from the reference band. CONCLUSIONS The use of factor analysis allows an automated, objective method of extracting the spectral features which are responsible for the variance among the samples in the calibration set. These features may not be apparent visually to the analyst, are often overlapped, or are affected by baseline variations, noise and impurities. Once a method's parameters have been established, sample prediction is simplified greatly. Now, the analysis can be performed repeatedly to analyze other samples. The multiple linear regression allows more than one factor to be used in the calculation of a property value. This usually improves the results. Overall, factor analysis has the potential for use as a non-destructive and simultaneous method of determining several properties: pH, calcium, etc. Factor analysis also shows promise for providing insights on the relationship between various paper constituents. It can be concluded that instruments that would analyze the pH of papers without causing as much damage as current methods would improve the results. Portable instruments for measuring ultrasonic wave propagation, infrared, and x-ray fluorescence would be very helpful in the non-destructive determination of these properties of historical papers as well. The horizontal ATR accessory shows promise for a more non-destructive method of analyzing the papers. The paper can be placed directly on the crystal, and clamped into place. This means that the paper does not need to be cut. Also, the clamp is calibrated, so the pressure on each sample can be equal. This allows for better reproducibility. We have not done intensive research using this accessory, but a few samples were examined, and the spectrum obtained appear to be acceptable as well. Therefore, this study deserves further research. - 7 (R=0.9298). The R values for pH, calcium, and date were lower than the previous set as well (See Table 4 for a breakdown of each of the contributing factors). This method was then evaluated in the same manner as with the method that included flax. The information obtained was converted into graphs showing estimated vs. specified values for each of the properties. The R value for gelatin for this calibration set was 0.23975 (See Graph 22). The other R values are as follows: pH, 0.08476 (See Graph 23); 0.0013642 (See Graph 25); Graph 27); L*, 0.10065 (See Graph 24); sulfur, 0.35944 (See Graph 26); iron, 0.35923 (See Graph 28); calcium, potassium, 0.53877 (See fluorescence, 0.45948 (See Graph 29); and date, 0.38299 (See Graph 30). The testing of the three methods shows that there is not a significant difference in the first two methods. The validation tests indicate that both of these methods allow a reasonable estimated of gelatin, sulfur, and potassium concentrations. The clustering seems to improve on the date predictions, while decreasing the accuracy of the sulfur and potassium predictions. The last method showed a significant decrease in the prediction capabilities of most all the properties. Overall, our validation tests show that the first method is generally the most useful. Throughout our research, inhomogeneity within a given sample has limited the reproducibility of our predictions. Since one side of a sample might have a higher gelatin content than the other, we compensated by averaging the spectrum from each side together. The other factor that affects reproducibility is the penetration depth the radiation. This penetration depth differs between samples for two reasons. The surfaces of the samples are different, and the pressure applied to the sample against the crystal is also variable. Since the path lengths of the standards are unknown, normalization parameters were adjusted to overcome thickness variations by using the cellulose absorptions from 900 t01250 cm- 1 as a reference 6 ACKNOWLEDGEMENTS I would like to take this opportunity to thank everyone who has helped me in any way with my thesis. First, I would like to thank my advisor Dr. Lang for all of the guidance she has given me. Her excellent teaching abilities triggered my interest in research. Timothy Barrett for initiating the research on historical papers. I am grateful to I would also like to thank the Honors college undergraduate fellowship program for providing funding for our research. Finally, I would like to thank my parents and my family for supporting me in every way, and for always believing in me. - 8 .Attenuated Total Reflection (A.T.R.) A-----+- SOURCE MIR CRYSTAL Figure 1 - ) ) ) 100.06 99.5 99.0 98.5 98.0 97.5 97.0_ ~ 96.5 J I 96.0 %T 95.5 tI1 '" \. 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Clustering) Graph 11 CORRELATIONS AFTER FLAX SAMPLES WERE ADDED TO CALIBRATION AND AFTER CLUSTERING PROPERTY PCR FACTOR CORRELATION COEFFICIENT GELATIN 1 3 4 5 -0.3132 -0.4562 0.5464 0.5096 pH LIGHT CALCIUM I I I IRON FLUORESCENCE DATE 5 5 I I I 3 4 SULFUR POTASSIUM 4 I 4 -0.4560 0.6020 0.7227 MULTIPLE CORRELATION (IF APPLICABLE) 0.9298 I I I 0.5105 0.4919 I -0.5391 0.8665 I 1 2 4 5 -0.0409 -0.9937 -0.1925 -0.0378 2 4 5 0.8262 0.3358 0.3825 0.9687 1 2 3 0.4376 -0.2422 0.4407 0.8165 0.9985 Table 3 Validation Test of Gelatin (After Flax I After Clustering) 3 . ----------------,----------------------.----------------------.----------------------,--------.-------------.----------------------,------------.-----------. .' 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