Instrumentation
Near Infrared Reflectance Spectrophotometric Analysis ofAgriculturalProducts C. A. Watson ARS—USDA, North Dakota State University, Fargo, N.D. 58102
There has long existed in the agricultural industries a need for a rapid and accurate measurement of the chemical composition of the raw and processed products—for example, the measurement of the protein content of grains, oilseeds, and forages and their processed products; the oil content of oilseeds; and the moisture content of nearly every product including grains, oilseeds, and forages. With this information, a more refined marketing and processing system of agricultural products would be realized from a quality and nutritional standpoint. Developments in near infrared spectrophotometry hold many promises of fulfilling these needs. Until recently the near infrared (NIR) region of the spectrum has been neglected for the analysis of agricultural products. In 1962 Hart et al. (i) reported a method for determining moisture content of seeds based on the NIR spectrophotometric analysis of their methanol extracts. Later, Norris and Hart (2) and Ben-Gera and Norris (3) published additional research on improvements or modifications in the method. In 1963 Hoffman (4) pointed out the advantages of NIR diffuse reflectance spectrophotometry for the determination of moisture content in solids. He showed the relation of re-
flectance at 1930 nm to moisture content of several solids including flour and starch. Massie and Norris (5) reported the reflectance properties of several grains in the NIR region of the spectra. This work was primarily related to drying of grain. Ben-Gera and Norris (6) showed the effect of fat concentration in milk on the absorption spectrum in the NIR region and in 1968 (7) reported on a method for determining fat and moisture content of meat products by using direct NIR spectrophotometry. The correlation coefficients between the NIR method and standard methods were +0.974 and +0.977 for fat and moisture, respectively. Karl Norris and associates at the Agricultural Research Service, USDA, Beltsville, Md., have done considerable research since the mid-1960's in developing and perfecting NIR reflectance spectrophotometry for determining the quality constituents of agricultural products. Much of this early developmental work was not published. One of the primary aspects of the technique was the use of measurements at two wavelengths for each constituent analyzed as a way to correct for spectral changes not related to concentration of the constituent. One measurement was taken at a wave-
Infrared Source
Lens·
Wide Band Energy ' (Visible Light and Infrared Radiation)
Aperture Infrared Filter *. (Six Used) Narrow Band Infrared Radiation
Motion of Filter Reflected Narrow Band Infrared Radiation Impinging on Photocell
Photocell Ground Solid Sample
-,
/
Figure 1. Schematic of source, aperture, interference filters, photocell, and s a m ple for InfraAlyzer
IR Lamp
A ^P"
Based on the research and develop ment of Norris and colleagues, two commercial companies introduced NIR reflectance instruments to the grain industries in 1971 (11). These instruments were designed to estimate protein, oil, and moisture contents on a ground sample of grain or oilseed without the use of any solvent.
Lens
Wide Band Light Band
Tilting Filter Wavelength varies as filter turns
Narrow Band Light Beam
Photodetector Calibration Standard (drawer out)
Sample (drawer in)
0
Sample Drawer
Figure 2. Schematic of source, aperture, interference filters, photocells, and s a m ple for grain quality analyzer 836 A • ANALYTICAL CHEMISTRY, VOL. 49, NO. 9, AUGUST 1977
length where absorbance was most af fected by changes in the concentration of the constituent being measured. A second measurement was taken at a closely adjacent wavelength that was not absorbed or was little affected by the specific constituent or by other constituents in the sample. The ab sorbance was measured by reflectance (β) and then converted by a minicom puter to log l/R. The difference in ab sorbance as measured by reflectance A(log l/R) between the two wave lengths was more accurately and pre cisely related to the concentration of the constituent than a measurement at one wavelength (8). Other impor tant factors in the technique were de velopment of minicomputers, which could be designed as an integral part of an instrument, for storing and ana lyzing relatively large amounts of data; developments of techniques to make precise optical filters, which per mit high energy illumination in the NIR spectrum, at a reasonable cost; development of electronic circuitry ca pable of detecting and amplifying minute signals; and development of scanning optical filter arrangements that allow precision multiple wave length measurements (9). Norris and colleagues expanded their early work (10) on determination of moisture in grains by NIR reflectance spectropho tometry to include protein and oil.
Instruments for Grains and Oilseeds Two companies manufacture instru ments for the determination of pro tein, oil, and moisture in grains, oil seeds, and feedstuffs by NIR reflec tance spectrophotometry. Technicon Industrial Systems, Tarrytown, N.Y. 10591, markets the InfraAlyzer made by the Dickey-john Co., Auburn, 111.; and Neotec Instruments, Inc., Rockville, Md. 20852, markets several mod els of their grain quality analyzer (GQA). The basic principles of the instru ments (InfraAlyzer and GQA) are the same. The difference in the log l/R at two wavelengths (maxima and adja cent minima) is determined for each constituent analyzed. Protein, oil, and moisture exhibit different peaks of major absorbance in the NIR spec trum; however, each substance also absorbs appreciably at other wave lengths (9). By making log l/R mea surements at all three maxima and at
the adjacent minima on samples hav ing a known range of all three compo nents similar to t h a t in the unknowns, equations can be solved by use of mul tiple linear regression analyses for constants t h a t relate the A(log l / β ) to the contents of protein, oil, and mois ture. These constants are used to cali brate the instruments for the various constituents. T h e measuring system includes interference filters to isolate selected wavebands of infrared energy, a photosensor and associated signalconditioning amplifier, and a mini computer or microprocessor for ana lyzing the data; all units are built into a compact instrument. T h e schematics of the light source, lenses, filters, de tectors, etc., for the InfraAlyzer and GQA are shown in Figures 1 and 2, re spectively. T h e main difference between the two instruments is t h a t the InfraAlyz er has six interference filters to isolate precise wavelengths, whereas the GQA has three filters mounted in a rotating paddle wheel. Each of the three filters passes a variable wavelength depend ing on the angle between the filter and the light source. An encoder attached to t h e paddle wheel permits selection of the six precise wavelengths to be used in the analysis. Only two wave lengths are obtainable from any one filter at any one time. T h e bandpass increases as the filter tilts from the normal to the light beam which de creases the precision of the filter. With both instruments the reflected light is detected, and the data pro duced are introduced into a built-in computer which reads out percentage protein, oil, or moisture. T h e wave lengths used by the InfraAlyzer and the GQA-31 are compared below: InfraAlyzer, nm 1680 1940 2100 2180 2230 2310
GQA-31, nm 1867 1920 2118 2167 2250 2297
T h e GQA-41 allows for convenient changes in wavelength if necessary by a push-button control system of its tilt-filter wheel. This has certain ad vantages if the determination of cer tain constituents require slightly dif ferent wavelengths for maximum sen sitivity, but there is no published in formation which shows this arrange ment increases the accuracy of the in strument. T h e GQA-41 does not cover the full range of the near infrared spectrum. T h e computation processes for the instruments differ slightly. T h e log l / β values (referred to as L\, L2, etc.) for the six wavelengths of the Infra Alyzer are combined with a set of Κι
Table I. Coefficients of Correlation Between Constituents in Several Products Determined by NIR Reflectance Spectrophotometry and by Accepted Methods Constituent
Product
Ash
+ K4mL4
+ K5mL5
Correlation coefficient
Soybeans Oats Soybeans Rice (whole grain)
74 14 15 19
0.969 0.978 0.977 0.783
Flour
18
0.968
values (constants) which are deter mined by a set of three equations as follows: % moisture = ΚimLx
Ret
+
K2mL2 + K3mL3 + KemL6 + X 7 m
% oil = K1OL1 + K2oL2 + K3oLa + KnoLiKsoLc, + KeoLe + Κ η0 % protein = KipLi + K2pL2 + K3pL3 + KipLi + K5pLg + KepLe + K7p where L\, L2, etc. = Log 1/fli, Log 1/R2, etc. T h e values of log 1/R measured by the GQA-31 at six wavelengths are combined to give three values of log 1/R differences as follows: A(Log 1/Rm) = Log 1/β 1 9 2 0 — Log l/i?i 8 67 (moisture) A(Log l / « 0 ) = Log l/#2297 - Log Ι/Λ2250 (oil) A(Log 1/β ρ ) = Log 1/Λ2ΐ67 - Log Ι/Λ2118 (protein) These A(Log 1/R) values, which are labeled on the GQA-31 as " C " values, are then combined into equations to give the percentage of each constitu ent as follows: % moisture = K0 + Kx A(Log 1/Rm) + K2 A(Log 1/β 0 ) + K3 A(Log 1/βρ) % oil = K'0 + K\ A(Log 1/Rm) + K2 A(Log 1/β 0 ) + K3 A(Log 1/βρ) % protein = K"0 + K\ A(Log 1/Rm) + K\ A(Log 1/β 0 ) + K"3 A(Log 1/βρ)
T h e Κι values determined from the above equations are set in the instru ments by 10k potentiometers, seven for each constituent in the InfraAlyzer and four for each constituent in the GQA-31. T h e "K" values are con stants characteristic of the component being measured in the sample. For determination of constituents, the instruments must be calibrated against other analytical laboratory re sults, e.g., Kjeldahl nitrogen content for protein. Calibration is based on analyses of about 50 samples covering the range in content of the constitu ents expected in the samples to be an alyzed. Other constituents (i.e., mois ture and lipid) t h a t affect the specific constituent being analyzed (protein) must exhibit a range t h a t would be ex pected in the unknown; otherwise, constants for multiplying certain absorbances may be weighted in error. T h e Model GQA-41 analyzes the data differently from the other models of the GQA and the InfraAlyzer. T h e GQA-41 computes the approximate slope of the reflectance curve by using the first derivative, or dR/R, divided by the reflectance ( β ) . This computa tion has been termed "normalization" of the slope.
Application to Grains and Oilseeds Several articles (12-19, 25) have re ported-the relation between constitu ents determined by the NIR instru ments and by standard procedures. These results are summarized in Table I. For more detail, the reader should consult the cited references. T h e d a t a in Table I show excellent
ANALYTICAL CHEMISTRY, VOL. 49, NO. 9, AUGUST 1977 • 837 A
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relations between the accepted methods and the NIR methods. Note that differences between the two methods can be attributed to errors in both methods and not just the NIR method. Other Applications
Reports of the determination of constituents in agricultural products other than grains and grain products have been published. Massie (20) reported a correlation coefficient of +0.82 between fat content of ground beef determined with a gallium arsenide infrared emitter and by a chemical method. However, there were problems with the life of the emitter and accuracy of the determination. Neotec Instruments, Inc., markets a ground meat analyzer (GM/A) for the estimation of percentage fat in ground meat. The GM/A uses a low intensity light source to illuminate the meat sample. An optical scanning system measures the amount of light reflected by the sample at the appropriate absorption wavelengths of the fat. The instrument is calibrated to a sample of known fat content. The author is not aware of any published information regarding the precision and accuracy of the GM/A. However, Weiner Associates, Inc. (21 ) , evaluated the GM/A, and their unpublished report shows that the standard deviation band for measurements of fat content of ground beef differs by less than 1 wt % fat content from the standard deviation of measurements made by the official AOAC procedure. Norris et al. (22) showed the potential of NIR reflectance for prediction of the quality of forages. They correlated the second derivative of log (1/R) vs. wavelength with compositional and nutritional data of 87 forage samples. When up to nine wavelength points were used for the prediction equations, correlation coefficients ranged from +0.80 for digestible matter intake to +0.99 for crude protein and +0.98 for neutral detergent fiber. Norris (personal communication) also found excellent correlations between the contents of certain amino acids in agricultural products and NIR reflectance values. Problems
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The primary problem of NIR analysis of grains and oilseeds for protein, oil, and moisture is preparation of the sample. These problems have been discussed (23) and are summarized here. Because the samples in most cases must be ground, uniform grinding is a major problem. Hymowitz et al. (14) showed that grinding time affected protein and oil results of soybeans and oats determined by NIR. A
Legal Rights of Chemists and Engineers A d v a n c e s in Chemistry Series N o . 1 6 1 Warren D. Niederhauser, Editor Rohm and Haas Company E. Gerald Meyer, Editor University of Wyoming A symposium cosponsored by the Division of Professional Relations and the Council Committee on Professional Relations of the American Chemical Society. Here is the first volume of its kind to cover an entire spectrum of legal rights of chemists and engineers and the problems they encounter as professionals. Included in twelve highly instructive papers are well-established legal precedents, legislation currently before Congress, suggested new legislation, and extensive bibliographies which permit the reader to pursue particular areas of interest. The collection contains a great deal of new information and data useful to university chemistry and engineering departments, law schools, patent attorneys, medical scientists, architects and technicians, chemical process companies, and professional associations. CONTENTS Careers Combining Chemistry and The Law · Patent Law Revision · Special Compensation for Salaried Chemists and Rewards for Inventors · Confidentiality, Secrecy Agreements, and Trade Secrets · Employment Contracts · Legalization of Employment Guidelines · Layoffs and Serious Grievances · Union's Effect on Legal Obligations • Societal Responsibilities of Chemists · Equal Opportunity Employment · Tax Effects of Retirement Plans · Federal Tax Law
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significant grinding X genotype inter action was shown for protein determi nations of corn. Williams (13) showed t h a t grinding samples on seven differ ent grinders affected N I R d e t e r m i n a tions. Accuracy of t h e calibration of the N I R i n s t r u m e n t s was affected by season and location where wheat was grown. Watson et al. (12) reported particle size distribution of five classes of wheat ground on three types of mills. Differences in particle size be tween classes of wheat were large re gardless of t h e mill used. T h e type of grinder a p p e a r e d to affect the N I R de t e r m i n a t i o n s , b u t t h e r e was no consis t e n t relation between particle size dis tribution a n d N I R results. P o m e r a n z a n d Moore (16) reported significant differences due to variety a n d location on t h e protein content of wheat deter mined by several m e t h o d s including N I R . W a t s o n et al. (24) reported t h a t protein content d e t e r m i n e d by N I R was influenced to a large e x t e n t by w h e a t class b u t could not relate these differences to particle size differences of t h e ground wheat.
Future F u t u r e use of N I R reflectance spec t r o p h o t o m e t r y for t h e determination of various constituents in agricultural p r o d u c t s is promising. Scientists and technicians m u s t realize t h a t the in s t r u m e n t a t i o n and methodology have not reached their ultimate levels of re finement. Additional research should be able to overcome the problems now encountered with N I R reflectance spectrophotometry. We need to learn a b o u t t h e commodities a n d properties t h a t can be measured, t h e traits t h a t are actually measured a t the chemical a n d molecular levels, t h e factors t h a t affect t h e m e a s u r e m e n t s , a n d the best m e a n s to minimize errors. Compre hensive information on these aspects of the technique should be considered before t h e role of N I R reflectance in agricultural science can be fully estab lished. Despite its problems, the sys t e m has already gained considerable acceptance. T h e rapid acceptance of N I R reflectance m e t h o d s is a t t r i b u t e d to t h e ease and cleanliness of t h e oper ation (i.e., no chemicals are required), t h e short time a n d low cost per sample if large n u m b e r s are analyzed, and the relative accuracy of t h e method.
(6) I. Ben-Gera and K. H. Norris, Israel J. Agric. Res., 18,117(1968). (7) I. Ben-Gera and K. H. Norris, J. Food Sci., 33, 64 (1968). (8) Technicon Industrial Systems, Techni cal Publication No. TA5-0340-00, 1975. (9) R. D. Rosenthal, presented to Milling & Baking Div., American Assoc, of Cere al Chemists 1973 Annual Mtg., Denver, Colo., 1973. (10) Κ. Η. Norris, Agric. Eng., 45, 370 (1964). (11) R. D. Rosenthal, Annual Mtg. Kansas Assoc. Wheat Growers and Kansas Wheat Comm., Hutchinson, Kan., Dec. 10-11, 1971. (12) C. A. Watson, D. Carville, E. Dikeman, G. Daigger, and G. D. Booth, Cere al Chem., 53, 214 (1976). (13) P. C. Williams, ibid., 52, 561 (1975). (14) T. Hymowitz, J. W. Dudley, F. I. Col lins, and C. M. Brown, Crop Sci., 14, 713 (1974). (15) R. W. Rinne, S. Gibbons, J. Bradley, R. Seif, and C. A. Brim, Agric. Res. Ser., USDA, ARS-NC-26, July 1975. (16) Y. Pomeranz and R. B. Moore, Bakers Dig., 50,44(1975). (17) R. A. Stermer, Y. Pomeranz, and R. J. McGinty, Cereal Chem., in press (1977). (18) C. A. Watson, G. Etchevers, and W. C. Shuey, ibid., 53, 803 (1976). (19) R. A. Stermer, C. A. Watson, and E. Dikeman, Annual Mtg. ASAE, Paper No. 76-3030, 1976. (20) D. R. Massie, reprinted from ASAE Pub. 1-76, 1973. (21) Weiner Assoc. Inc., unpublished re port prepared for Alexander Grant & Co., July 29, 1974. (22) K. H. Norris, R. F. Barnes, J. E. Moore, and J. S. Shenk, J. Animal Sci., 43, 889 (1976). (23) Technicon Industrial Systems, Tarrytown, N.Y., TIS News 2, 1976. (24) C. A. Watson, W. C. Shuey, O. J. Banasik, and J. W. Dick, Cereal Chem., sub mitted for publication. (25) Y. Pomeranz, R. B. Moore, and F. S. Lai, ASBC J., 35, 86 (1977). Mention of a t r a d e m a r k n a m e or proprietary p r o d u c t does not constitute a guarantee or war ranty of t h e product by the U.S. D e p a r t m e n t of Agriculture and does not imply its approval to the exclusion of other products t h a t may also be suitable.
References (1) J. R. Hart, K. H. Norris, and C. Golumbic, Cereal Chem., 39, 94 (1962). (2) K. H. Norris and J. R. Hart, "Humidity Moisture", P. N. Winn, Jr., Ed., Vol IV, ρ 19, Reinhold, New York, N.Y., 1965. (3) I. Ben-Gera and K. H. Norris, Israel J. Agric. Res., 18, 125 (1968). (4) K. Hoffman, Feuchtemessung durch Infrarotreflexion, Chem. Ing. Tech., 35, 55 (1963). (5) D. R. Massie and Κ. Η. Norris, Trans. ASAE. 8.598(1965).
840 A • ANALYTICAL CHEMISTRY, VOL. 49, NO. 9, AUGUST 1977
C. A. Watson is a research chemist with the North Central Region, Agri cultural Research Service, U.S. De partment of Agriculture, Department of Cereal Chemistry and Technology, North Dakota State University, Fargo
