Moisture Determination by Near-Infrared Spectrometry P. F. Vornheder and W. J. Brabbs The Procter & Gamble Company, Winton Hill Technical Center, 6000 Center Hill Road, Cincinnati, Ohio 45224 PROBABLY NO SINGLE compound has received as much analytical attention as water, and considering the number of moisture analyses performed, the continuing search for better methodology is usually Justified. In his summary paper in 1966, Roth ( I ) discussed ten distinct automated methods for moisture determination ranging from conventional oven weight loss and Karl Fischer techniques to the more exotic and specialized approaches like nuclear deflection, vapor pressure equalization, and electrolytic capacitance. Only a few of the available techniques, however, have had sufficient breadth of application and simplicity to gain widespread acceptance. Classical oven methods, including the automated moisture balance version, are the most universal but are not applicable t o materials which contain volatiles other than water. In addition, some carbohydrates undergo thermal decomposition, with attendant weight loss, at the temperature necessary for oven moisture determination (2). Using a vacuum oven overcomes the latter problem, but adds complexity and time to the analysis. Karl Fischer titration is more specific than oven methods, but is not applicable to lipid-containing materials without a prior extraction of the water, usually with methanol. This extraction adds significantly to the overall analysis time and presents an added opportunity for atmospheric moisture to interfere. In addition, many oxidizing and reducing materials interfere with the analysis (3). Two methods are described in the literature for measuring moisture in food materials using near-infrared. Rader ( 4 ) determined moisture in dried vegetables and spices by nearinfrared using a dimethyl formamide extraction with heating at 90 “C for one hour. By this procedure it is necessary to establish the heating time for each product that will ensure complete extraction of the moisture without decomposing the sample. Gold (5) has described a moisture method applicable to fruits and vegetables which utilizes a methanol extraction of the sample followed by near-infrared quantitation of the water in the extract. This approach eliminates problems of carbohydrate decomposition and volatility associated with the oven temperatures, but the use of methanol requires highspeed blending under a nitrogen blanket for efficient extraction, Even then, methanol is inadequate when the sample contains more than 2-3 lipid material, as is the case in many natural products. This paper describes a greatly simplified extraction approach, based on the use of dimethylsulfoxide as the extraction solvent, which is precise, accurate, and applicable to an extremely broad range of sample types.
EXPERIMENTAL
Specificity. The absorption of near-infrared energy is primarily from overtone and combination vibrations of CH, ~~~
___
(1) M. Roth, Chem. Eng., 73, 83-88 (1966). (2) I. S. Fagerson, J . Agr. Food Chem., 17,747 (1969). (3) J. Mitchell and D. M. Smith, “Aquametry,” Vol. V, Interscience Publishers, Inc., New York, 1948. (4) B. R. Rader, J. Ass. Ofic. A n d . Chem.. 49,726-730 (1966). ( 5 ) H. J. Gold, Food Technol. (Chicago), 18, 184-185 (1964). 1454
NH, and OH functional groups (6). The absorption maxima at 1.94 pm is more useful than the one at 1.45 pm, in that other hydroxyl-containing compounds (e.g. alcohols) do not absorb infrared radiation strongly in this region. DMSO has minimal absorption in this area, providing a “transmission window” which allows its direct use as a near-infrared solvent as well as an extraction solvent. The range of sample materials examined did not exhibit any interfering bands at 1.94 pm, nor did they show interference due to the dispersed solids present. Other solvents, such as methanol and chloroform will change the extinction coefficient of water in DMSO (7), and often show peaks of their own in the spectral range of interest. Instrumentation. Two instruments were used in this study: a Perkin-Elmer 221 recording infrared spectrophotometer with a LiF prism interchange, and a Beckman DK-2 ultraviolet, visible, near-infrared recording spectrophotometer. Both instruments were operated using standard instrumental conditions described in their respective instruction manuals. Matched quartz I-cm cells were used for all analyses. Comparable results were obtained at the analytical wavelength (1.94 pm) from both instruments. Most of the experimental work was done with the Beckman DK-2. Extraction Solvent. Dimethylsulfoxide (DMSO), methanol, ethanol, chloroform, acetone, and carbon disulfide were evaluated as extraction solvents, both alone and in combinations. Dimethylsulfoxide proved to be an excellent extraction solvent for this work, The excellent transmission characteristics of DMSO in the near-infrared region of interest is shown in a spectrum of DMSO (1-cm pathlength) us. air in Figure 1. In addition, it is powerfully hygroscopic and a very aggressive solvent, making it extremely efficient at either dissolving or dispersing a wide range of samples and quantitatively removing the water. Furthermore, the molar absorptivity for water in DMSO is approximately twice that of water in methanol. Because of the hygroscopic character of DMSO, it must be protected from atmospheric moisture during use. This is accomplished by using a dispensing buret with a drying tube (containing Drierite) attached to the air intake, and minimizing atmospheric contact during handling of the sample solution. The reference cell should be sealed and periodically checked for moisture pickup against the solvent being used. Since some of the physiological properties are still undetermined for DMSO, due care should be taken to minimize skin contact during handling of the solvent. Sample Type. Both solid and liquid samples can be analyzed by this procedure, providing they can be dissolved or dispersed by the DMSO. Products analyzed included instant and regular grind coffees, green coffee beans, sugar, honey, syrup, vegetable fats and oils, flavor components with high levels of volatiles, flours, potato chips, paper products, and glycol. Instant coffee, sugar, honey, and syrups are all completely soluble in DMSO and can be analyzed immediately. The remainder of products are readily dispersed and the moisture is extracted by DMSO. Preparation of Standard Curve. For calibration purposes, (6) R. F. Goddu, “Near-Infrared Spectrophotometry, Advances in Analytical Chemistry and Instrumentation,” Vol. 1, Academic Press, New York, 1960. (7) J. E. Sinsheimer and N. M. Poswalk, J. Pharm. Sci., 57, 20072010 (1968).
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Table I. Precision Data for Near-Infrared Moisture No. of Product samples Range, H 2 0 Std dev, Vegetable oils 10 Oto0.3 0.002 10 1 to 5 0.09 Potato chips 20 2to5 0.08 Instant coffee 4 40 to 50 0.14 Doughs
z
Table 11. Comparison of Results by Four Different Moisture Methods Oven, 105 "C, Toluene Near-infrared five hours distillation Vacuum oven
w 0.8 0
z a m
K
5: m
z
3.78 3.74 3.97 3.85 3.71
0.7
a
0.6
4.87
--
5.20
3.97 3.85 4.08 Av., 3.97
Av 3.81
0.5
0.4
0.3
Table 111. Comparison of Near-Infrared and Karl Fischer Results water
Type of sample Potato chips
2.0 2.1 WAVEL E N G T H ( Mi crome t e r 8 1 1.8
1.9
Honey Soybean oils
Figure 1. Near-infrared DMSO spectrum
o'60
1 a
1.60
1.80
2.00
2.20
240
WAVELENGTH (Micrometers)
Figure 2. H20 in DMSO 0.1-0.5 ml of water are pipetted (lambda pipet) directly into 100-ml volumetric flasks containing approximately 50 ml of DMSO, and then brought to volume. Each of these solutions is scanned from 2.1-1.8 pm in a 1-cm quartz cell using a cell filled with DMSO as a reference blank. The plot of absorbance (1.94 pm) us. ml water/100 ml solution is linear from 0.00 ml water/100 ml solution to 0.70 ml water/100 ml solution. Method. In all cases, sample size should be adjusted according to estimated moisture content, so that the final
z
Near-infrared
Karl Fischer
2.32 2.24 17.25 0.15 0.13 0.03 0.06 0.02 0.05 0.03 0.05 0.07 0.03 0.07
2.22a 2. 1 5 a 17.34 0.18 0.16 0.05 0.09 0.05 0.06 0.07 0.07 0.09 0.06 0.08
Run on methanol extract from overnight Soxhlet extraction.
moisture absorbance measured is within the range of the standard curve. For soluble materials the sample is weighed directly into a 100-ml volumetric flask, approximately 75 ml of DMSO is added, the flask is purged with dry nitrogen, and then tightly stoppered. The flask is again purged with dry nitrogen and tightly stoppered. A portion of the DMSO solution is then transferred to a 1-cm quartz cell and scanned from 2.1-1.8 pm against a DMSO blank. The absorbance maximum is corrected for background by drawing a base-line tangent to the peak shoulders (Figure 2). Per cent water is calculated from the calibration curve [(grams water + grams sample) X 1001. For insoluble but dispersible materials, the sample is weighed directly into a 4-oz. glass sample jar, 100 ml of DMSO is added, and the jar is capped tightly and shaken until the sample is dispersed. For these samples, a standing period of 2 to 4 hours is suggested to obtain extraction of the moisture. Using 4-02, glass sample jars minimizes the exposure of the DMSO to the atmosphere and makes for simplicity of operation, particularly if the jars are considered disposable. RESULTS AND DISCUSSION
Precision and Accuracy. Standard deviations determined on four food products by near-infrared are reported in Table I. To establish the accuracy of the near-infrared procedure
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independent of existing methods, additional water was added to two food products-instant coffee and vegetable oils. For instant coffee, 94% of the added moisture was quantitatively recovered while on vegetable oils, 98% was recovered. Comparison with Other Moisture Methods. Table I1 shows the differences in moisture level determined by nearinfrared, standard five-hour-105 “C oven procedure, toluene distillation, and vacuum oven a t 60 “C and 27-inch Hg, on a sample of instant coffee. The high values for both 105 “C oven and toluene distillation methods show the effects of thermal decomposition of carbohydrates. Under time and ternperature conditions, either method is capable of acceptable precision, but neither will give true moisture values. - Table 111 demonstratesthe ability of the near-infrared procedure t o be a n acceptable replacement for the Karl Fischer
method on other food materials. A consistent high bias was noted on all moisture results of soybean oils determined by Karl Fischer as compared to near-infrared. The natural oil color presented interferences that made visual recognition of the true Karl Fischer end point difficult, and resulted in over-titration of the sample. As mentioned above, vegetable oil components such as peroxides and free fatty acids may also interfere in the Karl Fischer determinations. ACKNOWLEDGMENT
The authors thank C. C. Stophlet, W. L. Jasper, and R. W. Sanders for their suggestions and assistance in obtaining much of the analytical data. RECEIVED for review April 23,1970. Accepted July 10, 1970.
A Computer System for Use in Quantifying Liquid Scintillation Data Bruce E. Haissig and Arthur L. Schipper, Jr. North Central Forest Experiment Station, Forest Service-U. Folwell Auenue, S t . Paul, Minn. 55101
S. Department of Agriculture,
CONVERSION OF THE SEMIQUANTITATIVE DATA obtained by liquid scintillation spectrometry t o disintegrations per minute (DPM) or microcuries (pC) per sample entails laborious hand calculations. When two radionuclides such as 3Hand l4C are present in the same sample, and many samples are counted, the calculations become time consuming to the point of impracticality. Count data can be quantified, with varying degrees of automation, by means of programmable desk-top calculators (1-4) and on- ( 2 , 5 )and off-line ( 2 , 5 1 4 ) electronic computers. Although individual circumstances dictate use, the advantages of desk-top calculators and on-line computers (1) are generally outweighed by those of a n off-line computer and supplemental support services (2). Simply, the quantification and statistical analysis of large volumes of count data are most cheaply and accurately accomplished with an off-line electronic com(1) M. F. Grower and E. D. Bransome, Jr., Anal. Bioclieni., 31,
159 (1969). (2) Y. Kobayashi and D. V. Maudsley. “Methods of Biochemical Analysis,” D. Glick, Ed., Vol. 17. Interscience Publishers, New York. 1969, p 55. (3) J. G. Manns and E. P. MacKenzie, Can. J . Physiol. P/iarmacol., 47,490 (1969). (4) B. F. Scott, J . Radioarid. Chem., 1,61 (1968). (5) J. H. Parmentier and F. E. L. Ten Haaf, Irit. J . Appl. Radiat. Isotopes, 20, 305 (1969). (6) J. M. Felts and P. A. Mayes, Biochem. J . , 105,735 (1967). (7) F. A. Blanchard, Int. J . Appl. Radiat. Isotopes, 14, 213 (1963). (8) M. I. Kiichevsky, S. A. Zaveler, and J. Bulkeley, Anal. Biochem., 22, 442 (1968). (9) C. Matthijssen, h d . , 15, 382 (1966). (10) R . Ninomiya, Ijit. J . Appl. Radiat. Isotopes, 17, 355 (1966). (11) J. J. O’Toole and J. 0. Oshurn, ibid., 19, 821 (1968). (12) E. D. Plotka, E. G. Stant, Jr., F. A. Waltz, V. A. Garwood, and R. E. Erb, ibid., 17,637 (1966). (13) J . L. Spratt, ibid., 16, 439 (1965). (14) J. L. Spratt and G. L. Lage, ibid., 18, 247 (1967). 1456
puter. This is especially true when a good interface is established between the scintillation spectrometer and computer (69). Several off-line computer programs that simplify the quantification of count data have appeared since 1963 (6-14). We were particularly interested in a program that would quantify, within established levels of error, both single- and doublelabel count data obtained from samples with a wide range of quench and varied radionuclide ratios. Anticipated low count rates dictated use of a program that would subtract variable levels of background, the result of differential quench among samples (15, 16). Simplicity in obtaining and using program “control” data was also desired because we have found that computer output is reliable only when control data are obtained anew for each experiment, even when counting parameters are standardized. The control data must protect the user against inherent instrument instability (which we have noted even over short periods), and against the changes in counting parameters resulting after recalibration of an instrument that has been operated in a different mode. Finally, a program was needed that would accept data input as a single numerical expression of external standardization ( 2 , 5 , 17), such as dual-channels ratio (external standard ratio, ESR), and gross counts per minute (CPM) per sample. Available off-line computer programs did not meet all the above requirements regarding program capability, predictability of error, control data format, background subtraction, and mode of data input. We therefore developed a computer (15) I. T. Takahashi and F. A. Blanchard, Anal. Biochem., 29, 154 (1969). (16) D. L. Horrocks, “Survey of Progress in Chemistry,” A. F. Scott, Ed., Vol. 5. Academic Press, New York. 1969, p 185. (17) C. H. Wang, “The Current Status of Liquid Scintillation Counting,” E. D. Branson, Jr., Ed.. Grune and Stratton, New York, 1970, p 305 (in press).
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