Nondestructive Detection of Nitrogen in Chinese Cabbage Leaves Using VIS NIR Spectroscopy

HORTSCIENCE 41(1):162 166. 2006. Nondestructive Detection of Nitrogen in Chinese Cabbage Leaves Using VIS NIR Spectroscopy Min Min and Won Suk Lee 1 Agricultural and Biological Engineering Department, University of Florida, Gainesville, FL, 32611-0570 Yong Hyeon Kim Division of Bioresource Systems Engineering, Chonbuk National University, Jeonju, Korea Ray A. Bucklin Agricultural and Biological Engineering Department, University of Florida, Gainesville, FL, 32611-0570 Additional index words. B-coefficient, diffuse reflectance, nitrogen band, nondestructive, PLS, SMLR Abstract. Proper nutrient management is essential to increase yield, quality and profit. This study was conducted to estimate the N concentrations of chinese cabbage (Brassica campestris L. ssp. pekinensis Norangbom ) plug seedlings using visible and near infrared spectroscopy for nondestructive N detection. Chinese cabbage seeds were sown and raised in three 200-cell plug trays filled with growing mixture in a plant growth chamber with three different levels (40%, 80%, and 100%) of required N. Reflectance for leaves of chinese cabbage seedlings was measured with a spectrophotometer 15 days after the experiment started. Reflectance was measured in the 400 to 2500 nm wavelength range at 1.1-nm increments. The leaves were dried afterwards to measure their water content and were analyzed for their actual N contents. The experiment was repeated twice (group I and II). Correlation coefficient spectrum, standard deviation spectrum, stepwise multiple linear regression (SMLR), and partial least squares (PLS) regression were used to determine wavelengths for N prediction models. Performances of SMLR and PLS were similar. For the validation data set (group II), SMLR produced an r 2 of 0.846 and PLS yielded r 2 of 0.840. The most significant wavelength 710 nm, which was identified by all methods, was correlated to chlorophyll. Water content positively correlated with N concentration (r = 0.76). Wavelengths of 1467, 1910, and 1938 nm selected by SMLR from both groups also showed that water had a strong effect on N prediction. Wavelengths near 2136 nm indicated that protein had potential use in N prediction. Wavelengths near 550 and 840 nm could also contribute to N prediction. Chinese cabbage is the most important leafy vegetable in Korea. An estimated 1,242 million plants of its plug seedlings are transplanted yearly (Kim and Lee, 2000). Chinese cabbage benefits from fertilizers with high N content. Inappropriate nutrient management may lead to undesirable effects on total or marketable yield, environmental contamination and profit. Application of too little N causes reduced yields, shortens storage life, and delays maturity, while excess N may cause rapid growth leading to coarse, loose heads, cracking, tipburn, poor processing, and low storage quality (Peet, 2004). This situation creates a need for monitoring the N status of crops. Visible and near infrared spectroscopy (VIS NIR) have been largely used to detect nutrient status for crops, due to their suitability Received for publication 20 Aug. 2005. Accepted for publication 22 Sept. 2005. This research was supported by the Florida Agricultural Experiment Station, and approved for publication as journal series R-10693. 1 To whom reprint requests should be addressed; e-mail wslee@ufl.edu. 162 for rapid and nondestructive determination of nutrient concentrations (Miller and Thomas, 2003). Two main N sources generally exist in green leaves, i.e., chlorophyll and proteins. Chlorophyll contains 5% to 10% N. Chlorophyll exhibits strong absorption in the visible region arising from conjugated carbon carbon single and double bonds of the porphyrin ring and the magnesium (Mg) ion. The infrared spectra of chlorophyll show strong absorption due to C H bonds in the phytol tail of the molecule (Katz et al., 1966). Chlorophyll absorbs light at wavelengths of 430, 450, 650, and 660 nm and reflects light at 550 nm which makes leaves look green (Farabee, 2001). Proteins, which are the primary nitrogenous constitute in leaves, typically hold 70% to 80% of total N. Spectral bands for N related to protein absorptions at 2054 and 2172 nm are due to N in the molecular structure, in particular to C N and N H bonds (Kokaly, 2001). Water, taking up to 90% of the total weight, is the major component of chinese cabbage. It is a good absorber of middle-infrared energy, so the greater the water inside leaves, the lower the middle-infrared reflectance. Experiments by Curcio and Petty (1951) showed that five prominent absorption bands for pure water were at 760, 970, 1190, 1450, and 1940 nm. When the water content of the plant decreases to 50%, the reflectance at any portion of the visible, near- and middle-infrared regions will largely increase. Many studies have shown a strong relationship between N concentration and leaf reflectance spectra. Thomas and Oerther (1972) found a non-linear relationship between reflectance at 550 nm and leaf N content of sweet pepper leaves with a correlation coefficient of 0.93. Card et al. (1988) found that N in dried and ground foliage leaves could be determined accurately from reflectance with a laboratory spectrometer (r 2 = 0.90). Katayama et al. (1996) found that the correlation coefficient (r) between absorption spectra and starch of sweet potato was 0.949. Lee et al. (1999) found that SPAD (Soil and Plant Analyzer Development, Minolta Inc.) readings, which were based on 659 and 940 nm by transmittance, were well correlated with N content in corn ear leaves (r 2 = 0.96). Carter and Knapp (2001) reported that wavelengths near 700 nm were crucial for estimating leaf chlorophyll stress. Bell et al. (2004) found that VIS NIR was effective for turfgrass N estimation (r 2 = 0.76). Some wavelengths of 448, 669, 719, 1377, 1773, and 2231 nm were identified by Min and Lee (2005) for citrus leaves as significant wavelengths for N detection. They also reported that VIS NIR has potential as a rapid method for citrus leaf N prediction (r 2 = 0.85). The success of calibration model development and wavelength selection largely relies on statistical solutions. The partial least squares (PLS) procedure, used as a powerful tool in chemometrics, works by extracting successive linear combinations of the predictors, which optimally explain response variation and predictor variation. PLS has been described as a two-step method where the first step reduces data matrix dimensions and the second step identifies latent structure models in the data matrix (Helland, 2001; Lingjaerde and Christophersen, 2000). In contrast to principal component regression (PCR), which chooses factors that explain the maximum variance in predictor variables without considering the response variables, the PLS method balances the two objectives, seeking the factors that explain both response and predictor variations (SAS, 1990). The predicted residual sum of squares (PRESS) statistic in PLS measures how well the regression equation fits the data set. An optimal number of factors is generally obtained when PRESS is minimized (Sundberg, 1999), and a smaller PRESS value indicates a better model prediction. However, selecting the number of factors where the absolute minimum PRESS exists may not be the best choice. By using the CVTEST cross-validation option in SAS PLS, a statistical comparison can be performed to test the significance of differences in the PRESS value for each number of factors, thus determining how many factors should be selected for a calibration model. The overall objective of this research was to explore the feasibility of using near-infrared

Table 1. Nutrient formulations used for different N level. Three nutrient solutions had the same microelements (in mg L 1 ) of 2.0 Fe, 0.5 B, 0.5 Mn, 0.05 Zn, 0.01 Cu, and 0.01 Mo. Inorganic Nutrient formulations (mg L 1 ) salt Normal z N80 y N40 x KNO 3 242 242 242 Ca(NO 3 ) 2 4H 2 O 566 378 0 NH 4 H 2 PO 4 92 92 92 MgSO 4 7H 2 O 197 197 197 CaCl 2 2H 2 O 0 88 350 Fe-EDTA 20 20 20 H 3 BO 3 3 3 3 MnSO 4 4H 2 O 2 2 2 ZnSO 4 7H 2 O 0.22 0.22 0.22 CuSO 4 5H 2 O 0.05 0.05 0.05 Na 2 MoO 4 2H 2 O 0.02 0.02 0.02 z Nutrient solution containing 100% of the N requirements. y Nutrient solution containing 80% of the N requirements. x Nutrient solution containing 40% of the N requirements. spectroscopy for nondestructive N detection of chinese cabbage seedlings. More specifically, the objectives were to investigate characteristics of reflectance spectra for the leaves of chinese cabbage seedlings, to find the optimal number of factors (or wavelengths) that could best describe chinese cabbage leaf properties, and to develop a calibration model for predicting N concentrations of unknown chinese cabbage samples using diffuse reflectance spectroscopy in the visible (VIS) and near infrared (NIR) regions for better N management. Materials and Methods Chinese cabbage (Brassica campestris L. ssp. pekinensis Norangbom ) seeds were sown and raised in 200-cell plug trays (Bumnong Co., Ltd., Korea) filled with growing mixture (BM2, Berger Peat Moss, Canada) in a plant growth chamber. Two metal halide lamps (MT400DL/BH; EYE Lighting International of North America, Inc.) were used to provide illumination with photoperiod of 12 h d 1 after seeds were germinated. Photosynthetic photon flux on the plug trays was 250 ± 12 µmol m 2 s 1. Air temperature was controlled to 25/13 C for light/dark periods. Nutrient solutions with three different levels (Normal, N80, and N40) of N requirements were prepared (Table 1). The normal nutrient solution for optimal chinese cabbage growth suggested by the National Horticultural Research Institute in Korea, which is composed of 8.0N 2.4P 2.4K 4.8Ca 1.6Mg (mg L 1 ), was formulated by Lee et al. (2000). Normal, N80 and N40 treatments were the nutrient solutions containing 100%, 80%, and 40% of the N requirements, which was in both ammonium and nitrate forms. N80 and N40 treatments were used to create N deficiency conditions for this experiment. A conductivity meter (model YSI31; YSI Inc., Yellow Springs, Ohio) was used to measure electric conductivity of the nutrient solution. Electric conductivities in the treatments were shown as 1.1 ms cm 1 for Normal, 1.2 ms cm 1 for N80, and 1.0 ms cm -1 for N40. The ph of the nutrient solutions measured by a ph meter (model SA720; ORION Research Inc.) showed ph 6.2 for normal, ph 6.2 for N80, and ph 6.3 for N40. The nutrient solution was supplied once every 2 d during 10 d after germination and then once everyday by sub-irrigation. Typically it would take about 15 d for the chinese cabbage seedlings to develop three to four true leaves after germination in a growth chamber, which would be considered to be optimal for transplanting to a field in Korea. Therefore, the third true leaf showing the biggest leaf in a plant was sampled and prepared for reflectance measurement 15 d after germination started. The experiment to estimate the N concentration of leaves for chinese cabbage plug seedlings was replicated once in this study. Forty seedlings were randomly selected from each N treatment for reflectance measurement, which yielded 120 samples (= 3 N treatments 40 samples per N treatment) for each experiment. The data set from the first experiment was labeled as group I, and the data set from the replicated experiment was labeled as group II. After reflectance measurements, water content of the leaves was measured. Tukey s procedure was used to compare means between each N treatment. A spectrophotometer (Cary 500 Scan UV-VIS NIR; Varian Inc.) equipped with an integrating sphere (DRA-CA-5500; Labsphere) was used to collect spectral reflectance data from 400 to 2500 nm with an increment of 1.1 nm. The diameter of the sample measurement port was 38 mm. A 50-mm-diameter polytetrafluoroethylene (PTFE) disk was used to obtain the optical reference standard for the system each day before spectral measurement of the leaf samples. Spectral reflectance of the central part of the fully developed third leaves was measured relative to the optical reference standard. Dark signal was subtracted for each measurement. After measuring reflectance, leaves were dried in an oven at 60 C for 72 h, and were then ground. Chemical analyses of actual N concentrations were conducted by the AOAC (Association of Analytical Communities) official method 990.03 (AOAC, 1995) which had a repeatability of 99%. The reflectance (R) of all samples was converted into absorbance (Log(1/R)) before any further analysis. The data were smoothed using a 15-point Savitzky-Golay polynomial convolution filter to remove the noise in the signal using the PLS Toolbox (Eigenvector Research Inc., Manson, Wash.). For wavelength selection and calibration model development, four statistical methods, i.e., correlation coefficient spectrum, standard deviation spectrum, stepwise multiple linear regression (SMLR), and PLS regression procedures were used. The B-coefficient in PLS, which could be decomposed to X-loading and X-weight, directly reflects the relationship between predictors and responses by regression equation as Y = XB. X-loadings represent the common variations in the spectral data, and X-weights represent the changes in the spectra that correspond to the regression constituents. Wavelengths with high B-coefficients contributed more to a calibration model, and could be considered as significant wavelengths (Esbensen, 2002). SAS (SAS, 1990) was used for these multivariate data analyses. The data sets collected from each group of chinese cabbage leaves were separated into calibration and validation data sets. The calibration data set included 60 samples (20 samples from each N treatment), which were randomly selected from the total of 120 samples. The remaining 60 samples were used as a validation data set. A combined data set of both groups was also used for analysis. For the combined data set, a calibration data set was created with 120 samples, 60 randomly selected samples from each group, and the remaining 120 samples were used as a validation data set. The coefficient of determination (r 2 ) between predicted N concentration and true concentration, standard error of calibration (SEC), standard error of prediction (SEP), and root mean square difference (RMSD) were used to evaluate reliability of the calibration model. Results and Discussion MISCELLANEOUS The analysis results of actual N concentration, water content, and Tukey s mean grouping analysis of the chinese cabbage leaf samples are shown in Table 2. Tukey s means tests were conducted among the six subgroups. The two groups showed different data characteristics due to variances introduced by replicated experiment, cabbage seeds and reflectance Table 2. Results of N concentration analysis and water content of the samples from three different N treatments. N application Avg N SD N range Water content Sample (mg L 1 ) (g kg 1 ) (g kg 1 ) (g kg 1 ) (g g 1 wb) Group I Normal 111.9 33.7 a z 5.1 24.7 48.4 0.936 N80 89.6 29.0 b 4.0 15.4 36.2 0.936 N40 44.7 19.1 c 2.4 15.7 28.7 0.918 Group II Normal 111.9 43.3 d 4.0 36.2 52.5 0.940 N80 89.6 31.6 a 3.4 25.7 40.5 0.931 N40 44.7 19.3 c 1.8 16.5 23.6 0.921 z Means within a row followed by the same letter are not significantly different (P > 0.05). 163

Fig. 1. Absorbance spectra of two chinese cabbage samples with different N treatments. Fig. 2. Correlation coefficient spectra between absorbance of each wavelength and actual N concentration for all the samples in group I, group II and combined data sets. Fig. 3. Standard deviation of absorbance for all the samples in group I, group II and combined data sets. 164 measurements. The actual N range for group I was 15.4 to 48.4 g kg 1, and 16.5 to 52.5 g kg 1 for group II. With increasing N application rate, the N concentration in chinese cabbage leaves increased. Average N contents of the three N treatments in each group were found to be significantly different from each other at 95% significance level. Water content of the chinese cabbage leaves ranged from 0.919 to 0.940 g g 1 wet basis. Water contents for Normal and N80 treatments were higher than those for N40 treatment. Different N treatments caused different average N concentrations in chinese cabbage leaves. The leaf sample with N concentration of 45.4 g kg 1 had a higher absorbance than leaf samples with N concentration of 17.2 g kg 1 at an N band of 550 nm (Fig. 1). Water had absorption bands at 1450 and 1940 nm. Three correlation coefficient spectra between absorbance of each wavelength and actual N concentrations are described in Fig. 2. In the visible range, wavelengths near 550 and 710 nm have high correlations for both groups. Wavelength regions showing high correlation indicated important wavelengths. Correlation coefficients (r) for group II in visible region and wavelengths near 1940 nm were higher than those for group I. At water band 1940 nm, r for group I was 0.1, while for group II, r was 0.8. Noise in group I was larger than group II. Combined data set averaged the effect from the two groups so that only the visible range had a high r value. Standard deviation spectrum is an unsupervised analysis method which demonstrates variability of only absorbance without considering N concentration. Standard deviations (SDs) of absorbance for the samples at each wavelength in group I, group II and combined data sets are described in Fig. 3. Peaks near 580 and 703 nm match with peaks in r spectra. The SD value at 580 and 703 nm of group II was higher than group I. In the near-infrared region, SD values of group II were less than group I. For wavelengths selection, SMLR performed well (Table 3). Due to good data structure in group II, only two wavelengths, 716 and 1910 nm, were selected from group II, which matched with high r wavelengths in r spectrum of group II. Wavelengths of 1467 and 1938 nm from group I, and 1910 nm from group II, were related to water bands. This indicated that water had a strong effect on chinese cabbage N prediction. In fact, the correlation coefficient between N concentrations and water content was 0.76 for group I and 0.74 for group II. It showed that with increase of N concentration, the water content would increase also. When the two groups were combined, a different set of wavelengths were selected because the combined data set introduced more variation to the data characteristics. Group I produced the best SEC and SEP values with 2.92 and 3.74 g kg 1, and r 2 for the validation data was 0.726. While group II yielded the best r 2 of 0.846 for the validation data set; the SEC and SEP values were 3.05 and 4.18 g kg 1. In most of the cases, better r 2 corresponded to lower SEP; however they are totally different statistical terms and not necessarily related to each other. In Table

3, better r 2 for group II had higher SEP than group I partly due to wider N range than group I. For the combined data set, since high r Fig. 5. B-coefficient determined from the calibration data set using PLS with five factors for group I. Fig. 4. N concentration prediction using SMLR for group II. This method generated r 2 = 0.846 and RMSD = 4.18 (g kg 1 ) for the validation data set. ranges were concentrated in the visible range, selected wavelengths were <850 nm, where chlorophyll had a strong effect. Wavelengths of 708 nm from group I, 716 nm from group II, and 710 nm from the combined data set correspond to a peak at 710 nm in the r spectra and 703 nm in the SD spectra, which was also identified by Min and Lee (2005) for citrus leaves. Wavelengths of 2135, 2229, and 2283 nm from group I may indicate an effect of protein since protein bands are located around 2054 and 2172 nm (Kokaly, 2001). Although wavelengths from group II and from the combined data set did not show any information indicating protein bands, protein may have potential effect for N prediction on chinese cabbage leaves. Other factors, such as water or cellular structure, may also obscure protein information. A regression line between actual N and predicted N for validation data set of group II with r 2 of 0.846 is represented in Fig. 4. PLS, a whole spectra analysis method, reduces data matrix dimensions first, and then identifies latent structure models in the data set. B-coefficient provides wavelength evaluation. Wavelengths with high absolute value of B-coefficient contribute more to prediction models. The B-coefficients for groups I and II and the combined data set are shown in Figs 5 to 7. The wavelength of 1937 nm in Fig. 5 using PLS with five factors had the highest absolute value of B-coefficient of 0.012 and had the largest contribution to the calibration model of group I. The wavelength of 1937 nm matched with the selected wavelength of 1938 nm from SMLR method for group I. B-coefficient graph of group II with only three factors had similar shape compared to r spectrum. Wavelengths of 530 to 650 nm, 705 nm, and 1913 to 1975 nm had high B-coefficients (Fig. 6), which were consistent with ranges having high correlation coefficient in r spectrum of group II. Wavelengths selected by SMLR for group II were also included in these regions. Wavelengths near 550, 710, and 1937 nm showed peaks in every B-coefficient spectrum. The B-coefficient graph of the combined data set with six factors had similar trends as the graph for group I. Wavelengths of 703 and 846 nm in Fig. 7 matched with wavelengths of 703 and 845 nm by SMLR for the combined data set. Wavelengths of 558, 703, 846, 1869, 1939, and 2135 nm in Fig. 7 were similar to wavelengths in Fig. 5. Table 4 shows PLS analysis results for the two chinese cabbage groups and the combined data set. The PLS analysis for group II had the smallest PRESS value of 3.50 g kg 1 with three factors (Table 4). Group I, using PLS with five factors, generated the best SEP value of 3.86 g kg 1 and an r 2 of 0.720 for the validation data set. The combined data set had an r 2 of 0.818 and RMSD of 3.95 g kg 1 for the validation data set with six factors. The relationships between actual N and N predicted by PLS for group II are shown in Fig. 8. Based on comparison of all four wavelength selection methods, a wavelength of 710 nm, which was correlated to chlorophyll content, was identified by all methods. It was the Fig. 6. B-coefficient determined from the calibration data set using PLS with three factors for group II. 165

Literature Cited Fig. 7. B-coefficient determined from the calibration data set using PLS with six factors for the combined data set. Table 3. SMLR analysis result for two chinese cabbage experimental groups and combined data sets. Selected wavelengths SEC SEP RMSD (g kg 1 ) r 2 Group (nm) (g kg 1 ) (g kg 1 ) Calibration Validation Calibration Validation Group I 708, 1467, 1938, 2.92 3.74 2.75 3.71 0.864 0.726 2135, 2283, 2229 Group II 716, 1910 3.05 4.18 2.97 4.18 0.911 0.846 Combined 405, 432, 454, 560, 3.56 4.42 3.44 4.40 0.854 0.776 703, 710, 845 Table 4. PLS regression analysis results for two chinese cabbage experiment groups and combined data sets. SEC SEP RMSD (g kg 1 ) r 2 Group Factor (g kg 1 ) (g kg 1 ) Calibration Validation Calibration Validation Group I 5 3.91 3.86 3.71 3.84 0.751 0.720 Group II 3 3.15 4.29 3.04 4.31 0.907 0.840 Combined 6 3.40 4.00 3.30 3.95 0.866 0.818 most robust one for N prediction. Wavelengths of 2135 nm by SMLR from group I and 2136 nm by PLS from group II and combined data set were related to protein. It showed that protein may have a potential contribution for N 166 prediction. Water contents are strongly related to N concentrations (r = 0.76). Wavelengths of 1910, 1938, and 1467 nm selected by SMLR, wavelengths of 1937 nm and 1913 to 1975 nm by PLS from both groups showed that water had a strong effect. Wavelengths near 550 nm, identified by r spectra, SD spectra and B-coefficient spectra, and wavelengths around 840 and 2136 nm, identified by the B-coefficient spectra and SMLR analysis, also contributed to N detection. For calibration model development, PLS and SMLR showed very similar performance. A strong relationship between reflectance and N content was identified in chinese cabbage leaves (r 2 = 0.846 for group II by SMLR). Fig. 8. Nitrogen concentration prediction using PLS for group II. This method generated r 2 = 0.840 and RMSD = 4.31 (g kg 1 ) for the validation data set. AOAC. 1995. Official methods of analysis of AOAC international. 16th ed. AOAC 990.03. Protein in animal feed. AOAC Intl., Arlington, Va. Bell, G.E., B.M. Howell, G. V. Johnson, W.R. Raun, J.B. Solie, and M.L. Stone. 2004. Optical sensing of turfgrass chlorophyll content and tissue nitrogen. HortScience 39(5):1130 1132. Card, D.H., D.L. Peterson, P.A. Matson, and J.D. Aber. 1988. Prediction of leaf chemistry by the use of visible and near infrared reflectance spectroscopy. Remote Sens. Environ. 26:123 147. Carter, G.A. and A.K. Knapp. 2001. Leaf optical properties in higher plants: Linking spectral characteristics to stress and chlorophyll concentration. Amer. J. Bot. 88(4):677 684. Curcio, J.A. and C.C. Petty. 1951. The near infrared absorption spectrum of liquid water. J. Optical Soc. Amer. 41:302 304. Esbensen, K.H. 2002. Multivariate data analysis in practice. 5th ed. CAMO Technologies, Woodbridge, N.J. Farabee, M.J. 2001. Photosynthesis. Estrella Mountain Community College, Avondale, Ariz. 1 Dec. 2004. http://www.emc.maricopa.edu/faculty/farabee/ BIOBK/BIOBK/BioBookTOC.html. Helland, I.S. 2001. Some theoretical aspects of partial least squares regression. Chemometrics Intelligent Lab. Syst. 58(2):97 107. Katayama, K., K. Komaki, and S. Tamiya. 1996. Prediction of starch, moisture, and sugar in sweet potato by near infrared transmittance. HortScience 31(6):1003 1006. Katz, J.J., R.C. Dougherty, and L.J. Boucher. 1966. Infrared and nuclear magnetic resonance spectroscopy of chlorophyll, p. 186 249. In: L.P. Vernon and G.R. Seely (eds.). The chlorophylls. Academic Press, New York. Kim, K.Y. and J.W. Lee. 2000. Current status and task for production of vegetable plug seedlings in Korea, p. 1 26. Proc. Sem. Problems and Counter plan of Vegetable Seedling Production. Chungcheongnam-do Agr. Res. Ext. Serv., Yesan, 14 15 Apr. Kokaly, R.F. 2001. Investigating a physical basis for spectroscopic estimates of leaf nitrogen concentration. Remote Sens. Environ. 75:153 161. Lee, J.W., B.Y. Lee, K.Y. Kim, and J.K. Son. 2000. Growth of vegetable seedlings in decomposed expanded rice hull-based substrates. J. Kor. Soc. Hort. Sci. 41(3):249 253. Lee, W.S., S.W. Searcy, and T. Kataoka. 1999. Assessing nitrogen stress in corn varieties of varying color. Amer. Soc. Agr. Eng. Paper 993034. Lingjaerde, O.L. and N. Christophersen. 2000. Shrinkage structure of partial least squares. Scand. J. Stat. 27(3):459 473. Miller, G. and A. Thomas. 2003. Using near infrared reflectance spectroscopy to evaluate phosphorus, potassium, calcium, and magnesium concentrations in bermudagrass. HortScience 38(6):1247 1250. Min, M. and W.S. Lee. 2005. Determination of significant wavelengths and prediction of nitrogen content for citrus. Trans. Amer. Soc. Agr. Eng. 48(2):455 461. Peet, M. 2004. Cabbage, Broccoli, and Other Cole Crops. NCSU. 7 Nov. 2004. http://www.cals.ncsu.edu/sustainable/peet/profiles/ppcabage.html. SAS Institute, Inc. 1990. SAS/STAT user s guide. version 6. 4th ed. vols. 1 2. SAS Inst., Cary, N.C. Sundberg, R. 1999. Multivariate calibration: Direct and indirect regression methodology. Scand. J. Stat. 26(2):161 207. Thomas, J.R. and G.F. Oerther. 1972. Estimating nitrogen content of sweet pepper leaves by reflectance measurements. Agron. J. 64:11 13.