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Maize Starch Yield Calibrations with Near Infrared Reflectance
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Biosystems Engineering (2003) 85 (4), 455–460 doi:10.1016/S1537-5110(03)00082-5 PH}Postharvest Technology
Maize Starch Yield Calibrations with Near Infrared Reflectance Marvin R. Paulsen1; Lester O. Pordesimo2; Mukti Singh1; Steven W. Mbuvi3; Binying Ye1 1
Agricultural Engineering Department, University of Illinois, 1304 W. Pennsylvania Avenue, Urbana, IL 61801, USA; e-mail of corresponding author: [email protected] 2 Agricultural and Biosystems Engineering Department, University of Tennessee, P.O. Box 1071, Knoxville, TN 37901, USA; e-mail: [email protected] 3 Deceased Former Supervisor, Identity Preserved Grain Laboratory, Illinois Crop Improvement Association, Champaign, IL 61820, USA (Received 28 March 2002; accepted in revised form 15 April 2003; published online 11 June 2003)
Maize starch yield is affected by variety, environmental growing conditions, and drying conditions. Onehundred gram starch yield tests that predict actual wet milling starch yield were used as a reference method for developing an extractable starch calibration on a NIRSystems Model 6500 spectrophotometer. A maize starch yield calibration was developed from 940 samples and used to predict a validation set of 304 samples. It had a standard error of prediction (SEP) of 106, a coefficient of determination r2 of 077 and a ratio of performance to deviations (rpd) of 21. This indicates about 95% of similar samples could have starch yield predicted by near-infrared reflectance within about21%. The calibration should be successful in segregating maize lots for high and low starch yield percentages. # 2003 Silsoe Research Institute. All rights reserved Published by Elsevier Science Ltd
1. Introduction One of the newly emerging enhanced-value markets is for high-starch maize. Basically, high starch means that maize will have higher than average extractable starch when used for wet milling, the primary user of highstarch maize. Wet milling is the process of steeping maize and chemically separating and removing starch, protein (gluten), oil and fibre from the maize kernel. High-quality and high starch yielding maize provides two benefits: (1) it enables higher starch recovery and improved coproduct separation and (2) enables higher process efficiencies in gluten–starch separation, fibre separation and gluten feed drying. Based on a study of the Japanese wet milling market by Hill et al. (1993), these two benefits can amount to as much as $936 tonne 1 added advantage over average US No. 3 yellow dent maize. The ability to obtain high starch extractability or high starch yield depends on maize variety, environmental growing conditions, and drying conditions. Starch yield differences among hybrids can range from 58 to 72% based on 100 g laboratory-scale wet milling tests, Eckhoff (1995) and Eckhoff et al. (1996). Since most maize 1537-5110/03/$30.00
grown in the US has to be dried for safe storage, heatedair drying is often used. Most drying in the US is done with crossflow dryers operating at temperatures of 93– 1048C. When high-temperature drying is used, it is usually more fuel efficient to use as high as possible drying air temperature for a given airflow rate. Unfortunately, if kernel temperatures exceed 608C during drying when the grain is sufficiently wet, the heating may denature protein, causing protein to bind with starch, resulting in a more difficult starch–gluten separation. Most maize averages about 62–70% in starch yield in laboratory-scale wet milling tests. Maize varieties range from 58 to 72% d.b. in starch yield; and it is estimated that up to a 5–6% point reduction in starch yield could occur due to drying methods. A 2% point change in starch yield is estimated to be worth about $157–235 tonne 1. MacMasters et al. (1959) reported that shelled maize that was dried in air at 828C or higher had reduced starch yield and had higher protein in the recovered starch. Vojnovich et al. (1975) harvested maize at 32, 25 and 20% moisture content and dried it in a fluidised bed dryer with 504 m3 min 1 tonne 1 at temperatures of 49, 455
# 2003 Silsoe Research Institute. All rights reserved Published by Elsevier Science Ltd
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82 and 1498C. Starch content was initially 724% d.b. and starch recovery ranged from 505 to 866%. Averaged over all harvest moistures, they found starch recovery decreased significantly as drying temperature increased (correlation coefficient r of 093) and their regression coefficient was 027% loss in starch recovery per 8C increase in fluidised bed drying air temperature. Weller et al. (1988) harvested four varieties of maize at 30, 24 and 18% and dried them to 14% w.b. moisture content at an airflow of 20 m3 min m 3 of grain at temperatures of 22, 49, 71 and 938C. Starch recovery decreased significantly from 990, 987, 969 to 924% as drying air temperature increased from 22, 49, 71 to 938C, respectively. Also as harvest moistures decreased from 30 to 18%, starch recoveries increased from 960 to 977%. Lasseran (1991) reported that for maize harvested at 32% and dried to 16%, starch yield stayed constant at about 67% d.b. for drying air temperatures up to 908C and then decreased linearly to about 59% at 1558C. Singh et al. (1998) tested nine varieties in 1994 and in 1995 for starch yield after various drying treatments. In 1994, maize was harvested at about 34 and 22% moisture and dried to 14% moisture content at 25 and 1108C drying air temperatures with airflow of 20 m3 min m 3 of maize. For all nine varieties, the 1108C drying treatment combined with the highest harvest moisture produced the lowest starch yield. These starch yield reductions ranged from 6 to 18% points compared to the 258C treatment. The interesting result is that drying at 1108C when harvest moisture was 22% had consistently lower starch yield than the 258C drying treatments; however, the amount of starch reduction ranged from 1 to 8% points of starch depending on the variety. At 258C drying temperatures, the harvest level had little effect on starch yield and these conditions had the highest starch yield, ranging from 66 to 71% depending on variety. In 1995, Singh et al. (1998) harvested nine varieties at about 27 and 19% moisture content and dried them with 25 and 808C drying air. Only one of these nine varieties had a statistically significant reduction in starch yield due to drying at 808C compared to 258C. This could be because at 808C, the starch reduction due to temperature is not as great as at 1008C or because the harvest moistures of 27% were not as high as the 34% in the preceding year. Haros and Suraez (1997) determined starch yield and starch recovery for a dent and a flint maize variety that was harvested at 143% w.b. moisture and dried at various temperatures. The flint maize was rewetted to 217 and 256% w.b. moisture, while the dent maize was
rewetted to 197 and 231% w.b. moisture. Samples of about 150 g were dried at 70, 90 and 1108C with 5–8% relative humidity air with an air velocity of 5 m s 1. The flint maize was dried to 114% and the dent maize was dried to 109% w.b. moisture content. The dried maize was laboratory wet milled using 100 g of sample steeped in 500 ml of 025% SO2 solution. Samples were steeped at 528C for 48 h. The yields of the wet milling fractions were determined on dry basis and are shown in Table 1. Haros and Suraez (1997) reported that starch yields were reduced for both initial moistures and for both dent and flint maize for the 1108C drying air temperature when compared to the 90 and 708C drying temperature treatments. From all of these drying studies, the start of drying conditions detrimental to starch yield in maize varied considerably but was generally between 71 and 1108C. This wide variation in temperature is likely due in part to varietal differences as well as differences in initial drying moistures. In general, as initial drying moisture increases, the chance for reducing starch yield increases also. Dijkhuizen et al. (1998) measured oil, protein and starch using a Dickey-john (GAC) III near-infrared reflectance (NIR) analyser for Illinois High Protein (IHP) and Illinois Low Protein (ILP) maize. Samples were also milled according to the 100 g laboratory wet milling procedure by Eckhoff et al. (1996). Starch yields from the 100 g procedure ranged from 390 to 688% d.b. for the IHP and ILP maize, respectively, and were significantly correlated positively with NIR starch
Table 1 Starch yields for maize as a function of drying air temperature (from: Haros & Suraez, 1997) Moisture content, % w.b (d.b.) Flint maize 217 (277) 217 (277) 217 (277) 256 (344) 256 (344) 256 (344) Undried Dent maize 197 (245) 197 (245) 197 (245) 231 (300) 231 (300) 231 (300) Undried
Drying air temperature, 8C
Starch yields, % d.b. (Mean of two replications)
70 90 110 70 90 110 }
658 659 585 641 634 612 688
70 90 110 70 90 110 }
738 744 693 743 721 679 740
ARTICLE IN PRESS MAIZE STARCH YIELD CALIBRATIONS
(r 5 +082) and were significantly negatively correlated with NIR protein (r 5 083). Gluten concentrations based on the 100-g test ranged from 241 to 106% d.b. for the IHP and ILP maize, respectively, and were significantly positively correlated with NIR protein (r 5 +072). Fibre concentrations based on the 100 g test ranged from 263 to 161% d.b. for the IHP and ILP maize, respectively, and were significantly negatively correlated with starch yield (r 5 081). Thus, samples with high protein had higher gluten concentrations, higher fibre and lower starch yields. NIR spectroscopy can provide whole kernel analysis of starch, moisture, protein, oil and fibre percentages in less than a minute. With present calibrations, NIR provides starch content, rather than starch yield. Content refers to the amount of starch present, while yield refers to the amount of starch that can actually be extracted. The use of technology such as near infrared reflectance/transmittance (NIR/NIT) for predicting starch yields for wet milling would markedly improve the ability to select the most ideally suited maize lots and hybrids for wet milling use.
2. Objective The objective of this research was to develop a calibration for starch yield using NIR for maize that could be marketed for wet milling.
3. Method of approach Maize samples from soft, medium and hard genotypes were grown and harvested in 1996 on the Agricultural Engineering Research Farm at the University of Illinois. They were harvested at moisture contents of 30, 25 and 20% w.b. and dried to 15% in laboratory crossflow dryers at temperatures of 25, 50, 60 and 708C. Additional samples were obtained from commercial elevators and breeder plots in 1996. In the autumn of 1997, samples of a soft, medium and hard genotype were harvested at about 25% moisture content from the Agricultural Engineering Research Farm and dried at temperatures of about 50, 60, 70 and 908C to about 14% moisture content. Other samples were tested that had been harvested in 1995 and dried at 25, 60, 70 and 808C from initial harvest moistures of about 27 and 19% to 14% moisture content (Singh et al., 1998). Additional samples were obtained from commercial elevators and maize breeder plots in 1997 and later years. In total, 1290 samples were included in the data base.
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A Foss NIRSystems Model 6500 scanning spectrophotometer was used to scan maize samples from 400 to 2500 nm by 2 nm increments. The spectrophotometer was interfaced to a Pentium computer which was equipped with NIRS2, version 4.01 Infrasoft International LLC software which enabled data set merging, statistical treatments such as elimination of noncontributing spectra based on a standardised H statistic (scaled Mahalanobis distance), and calibration development using partial least squares (PLS), modified PLS, or principal components methods (Mark & Workman, 1991). Later, WINISI v 15 NIRS2 was used for making the calibrations and presentation of the data. The instrument is capable of doing both NIR and NIT measurements on whole grain samples of 25–100 g. As stronger signals are obtained with NIR than with NIT, the spectrophotometer was used in the reflectance mode. With few exceptions, sample sizes scanned were 100 g. The Model 6500 was standardised to the 00001272 master instrument and periodic instrument diagnostics were run after warm-up. The diagnostic tests included instrument response, wavelength accuracy, and NIR repeatability. Instrument response is a measure of the absolute reflectance from a ceramic reference. The maximum value could be 65 535 and for a auto-gain instrument the visible range should be above 50 000 and the NIR range should be above 35 000 (WinisiII Manual, 1998). Wavelength accuracy uses a didymium and polystyrene standard for the visible and NIR regions, respectively. NIR repeatability determines noise or deviations in the optical data (log 1/R, where R is reflectance in nm) at each wavelength by scanning the internal ceramic as a reference and then again as a sample. Maize samples that were scanned were tested for starch yield using the 100 g laboratory procedure (Eckhoff et al., 1996). The standard error for starch yield for the 100 g laboratory wet milling procedure is about 05–07% points of starch based on means of about 66–67% d.b. Having a reference procedure with a relatively low standard deviation, is extremely important for making calibrations using NIR or NIT. Generally, the best achievable standard error of cross validation (SECV) for NIR is estimated to be about 13 times the laboratory standard error. Thus, a NIR standard error of cross validation of 065–091% would be considered good. The 100 g laboratory milling procedure determined starch yield, steepwater, germ, gluten, and total fibre (fine fibre + coarse fibre). All of the constituents were expressed in per cent dry basis. Moisture content was also determined using 20 g in a 1038C air oven for 72 h. Dried samples were placed in a desiccator and allowed to cool for 30 min before weighing.
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4. Results Maize samples were scanned with the NIRSystems spectrophotometer and were also tested using the 100-g laboratory test for starch yield.
4.1. Spectra A plot of the averaged spectra for the 940 maize samples in the calibration data set is shown in Fig. 1. The ordinate axis represents absorption, denoted as log10 (1/R). Locations with peaks represent high absorbtion of visible light or NIR electromagnetic spectrum energy. The abscissa goes from 400 to 2500 nm, with a non-continuity at 1100 nm. The visible region is from 400 to 700 nm while the NIR continues from 700 up to 2500 nm. For NIR spectroscopy in general, the WinisiII Manual (1998) states that the region from 700 to 1900 nm is made up of primarily first, second and third overtones. Beyond 1900 and up to 2500 nm, a combination of one or more overtone bands provides the absorptions. There are CH2 bands that indicate fat or oil that are usually found at 890, 1162, 1720, 1760, 2308 and 2340 nm. There are OH bands that indicate water at 960, 1150, 1405 and 1905 to 2000 nm. Important bands for NH that indicate protein are at 1040, 1210, 1496, 2050 and 2140 to 2180 nm. The height and shape of the spectra are affected by scatter due to particle size differences, surface reflectance from the sample, and the baseline of the spectrum. Spectrum baseline is usually dictated by the optics of the instrument, sample cup type, and distance from the detector to the sample cup (WinisiII Manual, 1998). In reflectance measurements with a finely ground sample, a beam of radiation strikes the sample and penetrates a few millimetres, diffuses out around the illumination point and is reflected back to the detector. Since the 1.4 1.2
Log 1/R
1 0.8 0.6 0.4 0.2 0 400
800
1200 1600 Wavelength, nm
2000
2400
Fig. 1. Averaged spectral reflectance log10 (1/R) from 400 to 2500 nm for 940 maize samples
radiation penetrated and interacted with the sample, it carries absorption information. If radiation reflects from the surface only, it carries no information. The value of the log (1/R) tends to be lower for finely ground tightly packed samples, ranging from 005 to 070 for lowmoisture samples. As particle sizes become bigger, the sample appears darker and the log (1/R) values become larger. For whole grain kernels in rectangular cups at 10–12% moisture, log (1/R) values typically range from 02 to 12. In addition, if the kernels appear shiny, the surface reflection has the effect of compressing the higher peaks that occur in the 1900–2500 nm region. Samples with large particles that are also high in moisture, 25–60%, will have very high log (1/R) values, ranging from 02 up to 20 (Winisi II, 1998). If log (1/R) values go above 15, there is a problem that reflectance data becomes non-linear due to stray light. A standardisation process can correct for linear differences between instruments, but not for non-linear differences, so it is better to avoid conditions that allow log (1/R) values to go above 15. Our samples were generally in the range of 10–15% moisture content and the log (1/R) values were in the range of 01–14 as shown in Fig. 1. 4.2. Laboratory values for starch yield Laboratory reference values for starch yield were determined by the 100 g laboratory test. In a given year, the standard deviation of the 100 g laboratory procedure is about 05–07% points of starch yield (Eckhoff et al., 1996). A program routine called Center was used to identify sample outliers based on a statistical determination of a global H distance greater than 25 from the mean. This caused the 1290 samples to be reduced to 1267 samples. Using Select procedure, every fourth sample was removed and placed in the validation set. This provided 950 samples for the calibration data set and 317 samples for the validation data set. Principal component analysis (PCA) scores were created for the calibration set using an H distance from the mean of 25 as the cutoff. Creating the scores reduced the 950 samples to 940 samples. A histogram of the values for the 940 samples used in the extractable starch calibration is shown in Fig. 2. Laboratory values ranged from 549 to 709% expressed at 0% moisture. The mean value was 652% with a standard deviation of 256. Laboratory values were added to the spectral file for development of the calibration for starch yield. 4.3. Starch yield calibration WinisiII software was used to develop the calibration from the 940 samples in the calibration data set and the
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MAIZE STARCH YIELD CALIBRATIONS
73 NIT extractable starch, % d.b.
200 180 Number of samples
160 140 120 100 80 60 40 20
69 67 65 63 61 59 57
54
56
68 60 62 64 66 58 100 g extractable starch, % d.b.
57
59
70
Fig. 2. Starch yield reference values for 940 samples in the calibration data set [mean 5 652%, standard deviation (SD) 5 256]
equation obtained was developed using modified PLS. The standard normal variate (SNV) and Detrend option were used and outliers were downweighted. The critical value for the T statistic was set to the default value of 25 and that for the H statistic was set to the default value of 200. Four cross validations groups and a maximum of 14 terms were used. A mathematical treatment of 1,4,4,1 (derivative, gap, smooth, smooth2) was used. Derivative of 1 indicates the first derivative was used; gap of 4 means the derivative was calculated over a narrow gap of 4 nm (gaps could go up to 20 nm), a smooth value of 4 means smoothing occurred over a small number of data points (20 is maximum), and smooth2 is seldom used and the recommended value is 1. After two elimination passes, the 940 samples were reduced to 907 samples. The extractable starch equation with 907 samples had a standard error of calibration (SEC) of 103, a standard error of cross validation (SECV) of 110 and a coefficient of determination of 078. The ratio of performance to deviations (rpd) is calculated as the standard deviation of laboratory values divided by the standard error of prediction (SEP) or SECV, and was 22. A high rpd is desirable and if greater than 10 it indicates the equation is predicting more than that due to random chance. The prediction equation was next used to predict the calibration data set. A critical T with lower and upper limits of 25–30, a critical value for the global neighbourhood (GH) of 3–4.0, and a critical value for the local neighbourhood (NH) of 06–10 was used. With one elimination pass, these limits caused the 940 samples in the calibration data set to have 905 samples. Figure 3 shows the equation used to predict the 905 samples from the calibration data set. The starch range was from 572 to 709% with a standard deviation of 222. The SEP was 102, the coefficient of determination was 079, and the rpd was 22 for the 905 samples. Figure 3 shows the
61 63 65 67 69 71 100 g extractable starch, % d.b.
73
Fig. 3. Near infrared reflectance predicted starch yield for the calibration data set using the derived equation [sample size N 5 905;standard deviation (SD) 5 222; standard error of prediction (SEP) 5 102;coefficient of determination r2 5 079; ratio of performance to deviations (rpd) 5 22; slope 5 103; bias 5 00] 70 60 Number of samples
0
71
50 40 30 20 10 0 54
56
58 60 62 64 66 68 100 g extractable starch, % d.b.
70
Fig. 4. Starch yield reference values for 317 samples in the validation data set [mean 5 652; standard deviation (SD) 5 259]
predicted calibration line with a slope of 103 and a bias of 0.
4.4. Starch yield prediction A prediction of starch yield on 317 samples for the validation set of samples having a laboratory starch yield distribution as shown in Fig. 4 was run. Figure 4 indicates starch yields ranged from 546 to 705% with a mean of 652% which was the same as the calibration set mean as shown in Fig. 2. The standard deviation was 259. Using the same control limits and one elimination pass, this prediction with 304 samples as shown in Fig. 5, had a slope of 102 and a bias of 066. The standard error of prediction (SEP) was 106, R2 was 077, and the rpd was 21. The relatively wide band of data about the prediction line indicates the range of variability in samples having the same starch yield could be expected
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Oslo, Norway, 24–27 August 1998. This study was a part of Project No. 95077 and 95168 of the Illinois Agricultural Experiment Station, College of Agricultural, Consumer and Environmental Sciences, University of Illinois at Urbana-Champaign. The research was funded in part by the Illinois Council on Food and Agricultural Research.
NIT extractable starch, %d.b.
72 70 68 66 64 62
References 60 60
62
64 66 68 70 100 g extractable starch, % d.b.
72
Fig. 5. Near infrared reflectance predicted starch yield for the validation data set using the derived calibration equation [sample size N 5 304; standard deviation (SD) 5 220; standard error of prediction (SEP) 5 106; coefficient of determination r2 5 077; ratio of performance to deviations (rpd) 5 21; slope 5 102; bias 5 066)
to fall within about 21% points of the predicted value about 95% of the time.
5. Conclusions Maize starch yield is affected by variety, environmental growing conditions, and drying conditions. Onehundred gram starch yield tests that predict actual wet milling starch yield were used as a reference method for developing an extractable starch calibration on a NIRSystems Model 6500 spectrophotometer. A maize starch yield calibration was developed from 940 samples and it had a standard error of cross validation (SECV) of 110%. In predicting the validation data set, the equation had values for the standard error of prediction (SEP) of 106, the coefficient of determination r2 of 077, and the ratio of performance to deviations (rpd) of 21. This indicates about 95% of similar samples could have starch yield predicted by near infrared reflectance within about 21%. The calibration should be successful in segregating maize lots for high and low starch yield percentages.
Acknowledgements Presented as Paper No. 98-F-065 at AgEng Oslo 98 an International Conference on Agricultural Engineering,
Dijkhuizen A; Dudley J W ; Rocheford T R; Haken A E; Eckhoff S R (1998). Near-infrared reflectance correlated to 100-g wet milling analysis in maize. Cereal Chemistry, 75(2), 266–270 Eckhoff S R (1995). The future of commodity maize. Wet Milling Notes. No. 11, University of Illinois, Agricultural Engineering Dept., Urbana-Champaign, IL, USA Eckhoff S R; Singh K; Zehr B E; Rausch K D; Fox E J; Mistry A K; Haken A E; Niu Y X; Zou S H; Buriak P; Tumbleson M E; Keeling P L (1996). A 100 g laboratory maize wet milling procedure. Cereal Chemistry, 73(1) 54–57 Haros M; Suraez C (1997). Effect of drying, initial moisture and variety in maize wet milling. Journal of Food Engineering, 34, 473–481 Hill L D; Bender K; Eckhoff S R; Paulsen M R; Snyder K (1993). Economic evaluation of air dried maize. AE-4698. Dept. of Agricultural Economics. IL Agricultural Experiment Station, University of Illinois, Urbana-Champaign, IL, USA Lasseran J C (1991). Chemical and physical changes in maize components affecting quality for the wet-milling industry. In: Uniformity by 2000, An International Workshop on Maize and Soybean Quality (Hill L D, ed) pp 197–216. Scherer Communications, Urbana, IL, USA MacMasters M M; Earle F R; Hall H H; Ramser J H; Dungan G H (1959). A study of the effect of drying conditions on the composition and suitability for wet milling of artificially dried maize. Cereal Chemistry, 31, 451–461 Mark H; Workman J (1991). Statistics in Spectroscopy. Academic Press, San Diego, CA, USA Singh V; Haken A E; Paulsen M R; Eckhoff S R (1998). Starch yield sensitivity of maize hybrids to drying temperature and harvest moisture content. Starch/Starke, 50(5), 181–183 Vojnovich C; Anderson R A; Griffin Jr E L (1975). Wet milling properties of maize after field shelling and artificial drying. Cereal Foods World, 20, 333–335 Weller C L; Paulsen M R; Steinberg M P (1988). Correlation of starch recovery with assorted quality factors of four maize hybrids. Cereal Chemistry, 65(5), 392–397 WinisiII Manual (1998). Infrasoft International, LLC. Version 1.0. Foss NIRSystems/ Tecator. Port Matilda, PA 16870, USA
Biosystems Engineering (2003) 85 (4), 455–460 doi:10.1016/S1537-5110(03)00082-5 PH}Postharvest Technology
Maize Starch Yield Calibrations with Near Infrared Reflectance Marvin R. Paulsen1; Lester O. Pordesimo2; Mukti Singh1; Steven W. Mbuvi3; Binying Ye1 1
Agricultural Engineering Department, University of Illinois, 1304 W. Pennsylvania Avenue, Urbana, IL 61801, USA; e-mail of corresponding author: [email protected] 2 Agricultural and Biosystems Engineering Department, University of Tennessee, P.O. Box 1071, Knoxville, TN 37901, USA; e-mail: [email protected] 3 Deceased Former Supervisor, Identity Preserved Grain Laboratory, Illinois Crop Improvement Association, Champaign, IL 61820, USA (Received 28 March 2002; accepted in revised form 15 April 2003; published online 11 June 2003)
Maize starch yield is affected by variety, environmental growing conditions, and drying conditions. Onehundred gram starch yield tests that predict actual wet milling starch yield were used as a reference method for developing an extractable starch calibration on a NIRSystems Model 6500 spectrophotometer. A maize starch yield calibration was developed from 940 samples and used to predict a validation set of 304 samples. It had a standard error of prediction (SEP) of 106, a coefficient of determination r2 of 077 and a ratio of performance to deviations (rpd) of 21. This indicates about 95% of similar samples could have starch yield predicted by near-infrared reflectance within about21%. The calibration should be successful in segregating maize lots for high and low starch yield percentages. # 2003 Silsoe Research Institute. All rights reserved Published by Elsevier Science Ltd
1. Introduction One of the newly emerging enhanced-value markets is for high-starch maize. Basically, high starch means that maize will have higher than average extractable starch when used for wet milling, the primary user of highstarch maize. Wet milling is the process of steeping maize and chemically separating and removing starch, protein (gluten), oil and fibre from the maize kernel. High-quality and high starch yielding maize provides two benefits: (1) it enables higher starch recovery and improved coproduct separation and (2) enables higher process efficiencies in gluten–starch separation, fibre separation and gluten feed drying. Based on a study of the Japanese wet milling market by Hill et al. (1993), these two benefits can amount to as much as $936 tonne 1 added advantage over average US No. 3 yellow dent maize. The ability to obtain high starch extractability or high starch yield depends on maize variety, environmental growing conditions, and drying conditions. Starch yield differences among hybrids can range from 58 to 72% based on 100 g laboratory-scale wet milling tests, Eckhoff (1995) and Eckhoff et al. (1996). Since most maize 1537-5110/03/$30.00
grown in the US has to be dried for safe storage, heatedair drying is often used. Most drying in the US is done with crossflow dryers operating at temperatures of 93– 1048C. When high-temperature drying is used, it is usually more fuel efficient to use as high as possible drying air temperature for a given airflow rate. Unfortunately, if kernel temperatures exceed 608C during drying when the grain is sufficiently wet, the heating may denature protein, causing protein to bind with starch, resulting in a more difficult starch–gluten separation. Most maize averages about 62–70% in starch yield in laboratory-scale wet milling tests. Maize varieties range from 58 to 72% d.b. in starch yield; and it is estimated that up to a 5–6% point reduction in starch yield could occur due to drying methods. A 2% point change in starch yield is estimated to be worth about $157–235 tonne 1. MacMasters et al. (1959) reported that shelled maize that was dried in air at 828C or higher had reduced starch yield and had higher protein in the recovered starch. Vojnovich et al. (1975) harvested maize at 32, 25 and 20% moisture content and dried it in a fluidised bed dryer with 504 m3 min 1 tonne 1 at temperatures of 49, 455
# 2003 Silsoe Research Institute. All rights reserved Published by Elsevier Science Ltd
ARTICLE IN PRESS 456
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82 and 1498C. Starch content was initially 724% d.b. and starch recovery ranged from 505 to 866%. Averaged over all harvest moistures, they found starch recovery decreased significantly as drying temperature increased (correlation coefficient r of 093) and their regression coefficient was 027% loss in starch recovery per 8C increase in fluidised bed drying air temperature. Weller et al. (1988) harvested four varieties of maize at 30, 24 and 18% and dried them to 14% w.b. moisture content at an airflow of 20 m3 min m 3 of grain at temperatures of 22, 49, 71 and 938C. Starch recovery decreased significantly from 990, 987, 969 to 924% as drying air temperature increased from 22, 49, 71 to 938C, respectively. Also as harvest moistures decreased from 30 to 18%, starch recoveries increased from 960 to 977%. Lasseran (1991) reported that for maize harvested at 32% and dried to 16%, starch yield stayed constant at about 67% d.b. for drying air temperatures up to 908C and then decreased linearly to about 59% at 1558C. Singh et al. (1998) tested nine varieties in 1994 and in 1995 for starch yield after various drying treatments. In 1994, maize was harvested at about 34 and 22% moisture and dried to 14% moisture content at 25 and 1108C drying air temperatures with airflow of 20 m3 min m 3 of maize. For all nine varieties, the 1108C drying treatment combined with the highest harvest moisture produced the lowest starch yield. These starch yield reductions ranged from 6 to 18% points compared to the 258C treatment. The interesting result is that drying at 1108C when harvest moisture was 22% had consistently lower starch yield than the 258C drying treatments; however, the amount of starch reduction ranged from 1 to 8% points of starch depending on the variety. At 258C drying temperatures, the harvest level had little effect on starch yield and these conditions had the highest starch yield, ranging from 66 to 71% depending on variety. In 1995, Singh et al. (1998) harvested nine varieties at about 27 and 19% moisture content and dried them with 25 and 808C drying air. Only one of these nine varieties had a statistically significant reduction in starch yield due to drying at 808C compared to 258C. This could be because at 808C, the starch reduction due to temperature is not as great as at 1008C or because the harvest moistures of 27% were not as high as the 34% in the preceding year. Haros and Suraez (1997) determined starch yield and starch recovery for a dent and a flint maize variety that was harvested at 143% w.b. moisture and dried at various temperatures. The flint maize was rewetted to 217 and 256% w.b. moisture, while the dent maize was
rewetted to 197 and 231% w.b. moisture. Samples of about 150 g were dried at 70, 90 and 1108C with 5–8% relative humidity air with an air velocity of 5 m s 1. The flint maize was dried to 114% and the dent maize was dried to 109% w.b. moisture content. The dried maize was laboratory wet milled using 100 g of sample steeped in 500 ml of 025% SO2 solution. Samples were steeped at 528C for 48 h. The yields of the wet milling fractions were determined on dry basis and are shown in Table 1. Haros and Suraez (1997) reported that starch yields were reduced for both initial moistures and for both dent and flint maize for the 1108C drying air temperature when compared to the 90 and 708C drying temperature treatments. From all of these drying studies, the start of drying conditions detrimental to starch yield in maize varied considerably but was generally between 71 and 1108C. This wide variation in temperature is likely due in part to varietal differences as well as differences in initial drying moistures. In general, as initial drying moisture increases, the chance for reducing starch yield increases also. Dijkhuizen et al. (1998) measured oil, protein and starch using a Dickey-john (GAC) III near-infrared reflectance (NIR) analyser for Illinois High Protein (IHP) and Illinois Low Protein (ILP) maize. Samples were also milled according to the 100 g laboratory wet milling procedure by Eckhoff et al. (1996). Starch yields from the 100 g procedure ranged from 390 to 688% d.b. for the IHP and ILP maize, respectively, and were significantly correlated positively with NIR starch
Table 1 Starch yields for maize as a function of drying air temperature (from: Haros & Suraez, 1997) Moisture content, % w.b (d.b.) Flint maize 217 (277) 217 (277) 217 (277) 256 (344) 256 (344) 256 (344) Undried Dent maize 197 (245) 197 (245) 197 (245) 231 (300) 231 (300) 231 (300) Undried
Drying air temperature, 8C
Starch yields, % d.b. (Mean of two replications)
70 90 110 70 90 110 }
658 659 585 641 634 612 688
70 90 110 70 90 110 }
738 744 693 743 721 679 740
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(r 5 +082) and were significantly negatively correlated with NIR protein (r 5 083). Gluten concentrations based on the 100-g test ranged from 241 to 106% d.b. for the IHP and ILP maize, respectively, and were significantly positively correlated with NIR protein (r 5 +072). Fibre concentrations based on the 100 g test ranged from 263 to 161% d.b. for the IHP and ILP maize, respectively, and were significantly negatively correlated with starch yield (r 5 081). Thus, samples with high protein had higher gluten concentrations, higher fibre and lower starch yields. NIR spectroscopy can provide whole kernel analysis of starch, moisture, protein, oil and fibre percentages in less than a minute. With present calibrations, NIR provides starch content, rather than starch yield. Content refers to the amount of starch present, while yield refers to the amount of starch that can actually be extracted. The use of technology such as near infrared reflectance/transmittance (NIR/NIT) for predicting starch yields for wet milling would markedly improve the ability to select the most ideally suited maize lots and hybrids for wet milling use.
2. Objective The objective of this research was to develop a calibration for starch yield using NIR for maize that could be marketed for wet milling.
3. Method of approach Maize samples from soft, medium and hard genotypes were grown and harvested in 1996 on the Agricultural Engineering Research Farm at the University of Illinois. They were harvested at moisture contents of 30, 25 and 20% w.b. and dried to 15% in laboratory crossflow dryers at temperatures of 25, 50, 60 and 708C. Additional samples were obtained from commercial elevators and breeder plots in 1996. In the autumn of 1997, samples of a soft, medium and hard genotype were harvested at about 25% moisture content from the Agricultural Engineering Research Farm and dried at temperatures of about 50, 60, 70 and 908C to about 14% moisture content. Other samples were tested that had been harvested in 1995 and dried at 25, 60, 70 and 808C from initial harvest moistures of about 27 and 19% to 14% moisture content (Singh et al., 1998). Additional samples were obtained from commercial elevators and maize breeder plots in 1997 and later years. In total, 1290 samples were included in the data base.
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A Foss NIRSystems Model 6500 scanning spectrophotometer was used to scan maize samples from 400 to 2500 nm by 2 nm increments. The spectrophotometer was interfaced to a Pentium computer which was equipped with NIRS2, version 4.01 Infrasoft International LLC software which enabled data set merging, statistical treatments such as elimination of noncontributing spectra based on a standardised H statistic (scaled Mahalanobis distance), and calibration development using partial least squares (PLS), modified PLS, or principal components methods (Mark & Workman, 1991). Later, WINISI v 15 NIRS2 was used for making the calibrations and presentation of the data. The instrument is capable of doing both NIR and NIT measurements on whole grain samples of 25–100 g. As stronger signals are obtained with NIR than with NIT, the spectrophotometer was used in the reflectance mode. With few exceptions, sample sizes scanned were 100 g. The Model 6500 was standardised to the 00001272 master instrument and periodic instrument diagnostics were run after warm-up. The diagnostic tests included instrument response, wavelength accuracy, and NIR repeatability. Instrument response is a measure of the absolute reflectance from a ceramic reference. The maximum value could be 65 535 and for a auto-gain instrument the visible range should be above 50 000 and the NIR range should be above 35 000 (WinisiII Manual, 1998). Wavelength accuracy uses a didymium and polystyrene standard for the visible and NIR regions, respectively. NIR repeatability determines noise or deviations in the optical data (log 1/R, where R is reflectance in nm) at each wavelength by scanning the internal ceramic as a reference and then again as a sample. Maize samples that were scanned were tested for starch yield using the 100 g laboratory procedure (Eckhoff et al., 1996). The standard error for starch yield for the 100 g laboratory wet milling procedure is about 05–07% points of starch based on means of about 66–67% d.b. Having a reference procedure with a relatively low standard deviation, is extremely important for making calibrations using NIR or NIT. Generally, the best achievable standard error of cross validation (SECV) for NIR is estimated to be about 13 times the laboratory standard error. Thus, a NIR standard error of cross validation of 065–091% would be considered good. The 100 g laboratory milling procedure determined starch yield, steepwater, germ, gluten, and total fibre (fine fibre + coarse fibre). All of the constituents were expressed in per cent dry basis. Moisture content was also determined using 20 g in a 1038C air oven for 72 h. Dried samples were placed in a desiccator and allowed to cool for 30 min before weighing.
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4. Results Maize samples were scanned with the NIRSystems spectrophotometer and were also tested using the 100-g laboratory test for starch yield.
4.1. Spectra A plot of the averaged spectra for the 940 maize samples in the calibration data set is shown in Fig. 1. The ordinate axis represents absorption, denoted as log10 (1/R). Locations with peaks represent high absorbtion of visible light or NIR electromagnetic spectrum energy. The abscissa goes from 400 to 2500 nm, with a non-continuity at 1100 nm. The visible region is from 400 to 700 nm while the NIR continues from 700 up to 2500 nm. For NIR spectroscopy in general, the WinisiII Manual (1998) states that the region from 700 to 1900 nm is made up of primarily first, second and third overtones. Beyond 1900 and up to 2500 nm, a combination of one or more overtone bands provides the absorptions. There are CH2 bands that indicate fat or oil that are usually found at 890, 1162, 1720, 1760, 2308 and 2340 nm. There are OH bands that indicate water at 960, 1150, 1405 and 1905 to 2000 nm. Important bands for NH that indicate protein are at 1040, 1210, 1496, 2050 and 2140 to 2180 nm. The height and shape of the spectra are affected by scatter due to particle size differences, surface reflectance from the sample, and the baseline of the spectrum. Spectrum baseline is usually dictated by the optics of the instrument, sample cup type, and distance from the detector to the sample cup (WinisiII Manual, 1998). In reflectance measurements with a finely ground sample, a beam of radiation strikes the sample and penetrates a few millimetres, diffuses out around the illumination point and is reflected back to the detector. Since the 1.4 1.2
Log 1/R
1 0.8 0.6 0.4 0.2 0 400
800
1200 1600 Wavelength, nm
2000
2400
Fig. 1. Averaged spectral reflectance log10 (1/R) from 400 to 2500 nm for 940 maize samples
radiation penetrated and interacted with the sample, it carries absorption information. If radiation reflects from the surface only, it carries no information. The value of the log (1/R) tends to be lower for finely ground tightly packed samples, ranging from 005 to 070 for lowmoisture samples. As particle sizes become bigger, the sample appears darker and the log (1/R) values become larger. For whole grain kernels in rectangular cups at 10–12% moisture, log (1/R) values typically range from 02 to 12. In addition, if the kernels appear shiny, the surface reflection has the effect of compressing the higher peaks that occur in the 1900–2500 nm region. Samples with large particles that are also high in moisture, 25–60%, will have very high log (1/R) values, ranging from 02 up to 20 (Winisi II, 1998). If log (1/R) values go above 15, there is a problem that reflectance data becomes non-linear due to stray light. A standardisation process can correct for linear differences between instruments, but not for non-linear differences, so it is better to avoid conditions that allow log (1/R) values to go above 15. Our samples were generally in the range of 10–15% moisture content and the log (1/R) values were in the range of 01–14 as shown in Fig. 1. 4.2. Laboratory values for starch yield Laboratory reference values for starch yield were determined by the 100 g laboratory test. In a given year, the standard deviation of the 100 g laboratory procedure is about 05–07% points of starch yield (Eckhoff et al., 1996). A program routine called Center was used to identify sample outliers based on a statistical determination of a global H distance greater than 25 from the mean. This caused the 1290 samples to be reduced to 1267 samples. Using Select procedure, every fourth sample was removed and placed in the validation set. This provided 950 samples for the calibration data set and 317 samples for the validation data set. Principal component analysis (PCA) scores were created for the calibration set using an H distance from the mean of 25 as the cutoff. Creating the scores reduced the 950 samples to 940 samples. A histogram of the values for the 940 samples used in the extractable starch calibration is shown in Fig. 2. Laboratory values ranged from 549 to 709% expressed at 0% moisture. The mean value was 652% with a standard deviation of 256. Laboratory values were added to the spectral file for development of the calibration for starch yield. 4.3. Starch yield calibration WinisiII software was used to develop the calibration from the 940 samples in the calibration data set and the
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73 NIT extractable starch, % d.b.
200 180 Number of samples
160 140 120 100 80 60 40 20
69 67 65 63 61 59 57
54
56
68 60 62 64 66 58 100 g extractable starch, % d.b.
57
59
70
Fig. 2. Starch yield reference values for 940 samples in the calibration data set [mean 5 652%, standard deviation (SD) 5 256]
equation obtained was developed using modified PLS. The standard normal variate (SNV) and Detrend option were used and outliers were downweighted. The critical value for the T statistic was set to the default value of 25 and that for the H statistic was set to the default value of 200. Four cross validations groups and a maximum of 14 terms were used. A mathematical treatment of 1,4,4,1 (derivative, gap, smooth, smooth2) was used. Derivative of 1 indicates the first derivative was used; gap of 4 means the derivative was calculated over a narrow gap of 4 nm (gaps could go up to 20 nm), a smooth value of 4 means smoothing occurred over a small number of data points (20 is maximum), and smooth2 is seldom used and the recommended value is 1. After two elimination passes, the 940 samples were reduced to 907 samples. The extractable starch equation with 907 samples had a standard error of calibration (SEC) of 103, a standard error of cross validation (SECV) of 110 and a coefficient of determination of 078. The ratio of performance to deviations (rpd) is calculated as the standard deviation of laboratory values divided by the standard error of prediction (SEP) or SECV, and was 22. A high rpd is desirable and if greater than 10 it indicates the equation is predicting more than that due to random chance. The prediction equation was next used to predict the calibration data set. A critical T with lower and upper limits of 25–30, a critical value for the global neighbourhood (GH) of 3–4.0, and a critical value for the local neighbourhood (NH) of 06–10 was used. With one elimination pass, these limits caused the 940 samples in the calibration data set to have 905 samples. Figure 3 shows the equation used to predict the 905 samples from the calibration data set. The starch range was from 572 to 709% with a standard deviation of 222. The SEP was 102, the coefficient of determination was 079, and the rpd was 22 for the 905 samples. Figure 3 shows the
61 63 65 67 69 71 100 g extractable starch, % d.b.
73
Fig. 3. Near infrared reflectance predicted starch yield for the calibration data set using the derived equation [sample size N 5 905;standard deviation (SD) 5 222; standard error of prediction (SEP) 5 102;coefficient of determination r2 5 079; ratio of performance to deviations (rpd) 5 22; slope 5 103; bias 5 00] 70 60 Number of samples
0
71
50 40 30 20 10 0 54
56
58 60 62 64 66 68 100 g extractable starch, % d.b.
70
Fig. 4. Starch yield reference values for 317 samples in the validation data set [mean 5 652; standard deviation (SD) 5 259]
predicted calibration line with a slope of 103 and a bias of 0.
4.4. Starch yield prediction A prediction of starch yield on 317 samples for the validation set of samples having a laboratory starch yield distribution as shown in Fig. 4 was run. Figure 4 indicates starch yields ranged from 546 to 705% with a mean of 652% which was the same as the calibration set mean as shown in Fig. 2. The standard deviation was 259. Using the same control limits and one elimination pass, this prediction with 304 samples as shown in Fig. 5, had a slope of 102 and a bias of 066. The standard error of prediction (SEP) was 106, R2 was 077, and the rpd was 21. The relatively wide band of data about the prediction line indicates the range of variability in samples having the same starch yield could be expected
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Oslo, Norway, 24–27 August 1998. This study was a part of Project No. 95077 and 95168 of the Illinois Agricultural Experiment Station, College of Agricultural, Consumer and Environmental Sciences, University of Illinois at Urbana-Champaign. The research was funded in part by the Illinois Council on Food and Agricultural Research.
NIT extractable starch, %d.b.
72 70 68 66 64 62
References 60 60
62
64 66 68 70 100 g extractable starch, % d.b.
72
Fig. 5. Near infrared reflectance predicted starch yield for the validation data set using the derived calibration equation [sample size N 5 304; standard deviation (SD) 5 220; standard error of prediction (SEP) 5 106; coefficient of determination r2 5 077; ratio of performance to deviations (rpd) 5 21; slope 5 102; bias 5 066)
to fall within about 21% points of the predicted value about 95% of the time.
5. Conclusions Maize starch yield is affected by variety, environmental growing conditions, and drying conditions. Onehundred gram starch yield tests that predict actual wet milling starch yield were used as a reference method for developing an extractable starch calibration on a NIRSystems Model 6500 spectrophotometer. A maize starch yield calibration was developed from 940 samples and it had a standard error of cross validation (SECV) of 110%. In predicting the validation data set, the equation had values for the standard error of prediction (SEP) of 106, the coefficient of determination r2 of 077, and the ratio of performance to deviations (rpd) of 21. This indicates about 95% of similar samples could have starch yield predicted by near infrared reflectance within about 21%. The calibration should be successful in segregating maize lots for high and low starch yield percentages.
Acknowledgements Presented as Paper No. 98-F-065 at AgEng Oslo 98 an International Conference on Agricultural Engineering,
Dijkhuizen A; Dudley J W ; Rocheford T R; Haken A E; Eckhoff S R (1998). Near-infrared reflectance correlated to 100-g wet milling analysis in maize. Cereal Chemistry, 75(2), 266–270 Eckhoff S R (1995). The future of commodity maize. Wet Milling Notes. No. 11, University of Illinois, Agricultural Engineering Dept., Urbana-Champaign, IL, USA Eckhoff S R; Singh K; Zehr B E; Rausch K D; Fox E J; Mistry A K; Haken A E; Niu Y X; Zou S H; Buriak P; Tumbleson M E; Keeling P L (1996). A 100 g laboratory maize wet milling procedure. Cereal Chemistry, 73(1) 54–57 Haros M; Suraez C (1997). Effect of drying, initial moisture and variety in maize wet milling. Journal of Food Engineering, 34, 473–481 Hill L D; Bender K; Eckhoff S R; Paulsen M R; Snyder K (1993). Economic evaluation of air dried maize. AE-4698. Dept. of Agricultural Economics. IL Agricultural Experiment Station, University of Illinois, Urbana-Champaign, IL, USA Lasseran J C (1991). Chemical and physical changes in maize components affecting quality for the wet-milling industry. In: Uniformity by 2000, An International Workshop on Maize and Soybean Quality (Hill L D, ed) pp 197–216. Scherer Communications, Urbana, IL, USA MacMasters M M; Earle F R; Hall H H; Ramser J H; Dungan G H (1959). A study of the effect of drying conditions on the composition and suitability for wet milling of artificially dried maize. Cereal Chemistry, 31, 451–461 Mark H; Workman J (1991). Statistics in Spectroscopy. Academic Press, San Diego, CA, USA Singh V; Haken A E; Paulsen M R; Eckhoff S R (1998). Starch yield sensitivity of maize hybrids to drying temperature and harvest moisture content. Starch/Starke, 50(5), 181–183 Vojnovich C; Anderson R A; Griffin Jr E L (1975). Wet milling properties of maize after field shelling and artificial drying. Cereal Foods World, 20, 333–335 Weller C L; Paulsen M R; Steinberg M P (1988). Correlation of starch recovery with assorted quality factors of four maize hybrids. Cereal Chemistry, 65(5), 392–397 WinisiII Manual (1998). Infrasoft International, LLC. Version 1.0. Foss NIRSystems/ Tecator. Port Matilda, PA 16870, USA