Two-Dimensional Visualization of Nitrogen Distribution in Leaves of

Article pubs.acs.org/JAFC

Two-Dimensional Visualization of Nitrogen Distribution in Leaves of Chinese Cabbage (Brassica rapa subsp. chinensis) by the Fourier Transform Infrared Photoacoustic Spectroscopy Technique Chunyang Li, Changwen Du,* Yin Zeng, Fei Ma, Yazhen Shen, Zhe Xing, and Jianmin Zhou State Key Laboratory of Soil and Sustainable Agriculture, Institute of Soil Science, Chinese Academy of Sciences, Nanjing, Jiangsu 210008, People’s Republic of China ABSTRACT: Understanding nitrogen (N) status in the leaves of Chinese cabbage (Brassica rapa subsp. chinensis) is of significance to both vegetable growth and quality control. Fourier transform infrared photoacoustic spectroscopy was used to perform rapid qualification of N distribution in leaves; a partial least squares algorithm was used to develop a model for prediction of the N content; and N distribution in individual leaves was mapped on the basis of interpolation analysis, which was found to be variable. A reasonable N input level (13 mmol L−1 N) showed the largest variance of the N content, benefiting N redistribution and use efficiency, but variance decreased at the old stage. Moreover, the pattern of N distribution within a leaf was irregular even among the replications performed for each treatment, and sunlight was found to be the dominant factor as a result of leaves receiving variable light intensities. KEYWORDS: Chinese cabbage, mid-infrared photoacoustic spectroscopy, leaf, nitrogen distribution, partial least squares

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INTRODUCTION

Although many studies have explored the vertical distribution of leaf N within the canopies of individual plants; however, N distribution in an individual leaf remains unclear for chemical analysis. Moreover, it is difficult to study N allocation in leaves, owing to the limitation of sample mass. Recently, infrared spectroscopic techniques have been increasingly used in qualitative and quantitative analyses of agricultural products, owing to their non-invasive nature, accurate prediction, and rapid detection.16−20 Among these methods, photoacoustic spectroscopy, which is based on the photoacoustic effect discovered by Alexander Graham Bell, has been extensively and successfully applied to studies involving soil analysis, plant physiology, and environmental monitoring.21−23 Photoacoustic spectroscopy can capture abundant information on leaves, without the interference of reflection and scatter from samples.24 Therefore, Fourier transform infrared photoacoustic spectroscopy (FTIR-PAS) would be a suitable alternative to qualify N distribution in individual leaves. Chinese cabbage is a popular vegetable grown and consumed in China, which is known to rapidly accumulate high levels of nitrate.25 Therefore, we qualitatively examined the N distribution in Chinese cabbage (Brassica rapa subsp. chinensis) leaves and characterized by FTIR-PAS spectra coupled with interval partial least squares (iPLS) regression modeling. We assumed that the N distribution is also heterogeneous in a single leaf as that at the canopy level and the heterogeneity might be affected by incident light as well as N input.

Nitrogen (N) distribution in an individual plant likely appears to be non-uniform as an adaptive response to environmental factors for optimization of metabolic activities, which involve maximizing photosynthesis and N utilization.1,2 Generally, N concentrations are higher at the top of the canopy.3,4 For instance, in the rose plant, a higher N allocation occurs at the uppermost leaves, whereas total N concentration at the bottom of the plant decreases by approximately 35%.5 Many studies have described the characteristics of N allocation and have investigated the impacts of some factors on N distribution at the canopy level.6 At the top of the canopy, the plant exhibits a high level of N content per unit leaf area, high leaf mass per area, and greater photosynthesis capacity because N distribution variance within the canopy is closely related to availability of sunlight.7 Foliar N content is known to be highly associated with photosynthesis in leaves during plant development.8 A previous study found a positive relationship between canopy N distribution and incident photosynthetic active radiation in a tropical fruit tree.9 Moreover, Kull and Niinemets reported, in three tree species, leaf N content per unit area decreased with a decline in fractional transmission of photon flux density.10 For dicotyledonous herbs, the leaves with higher irradiance have a higher N content per unit area.11 However, light availability is not the only factor essential for the determination of N distribution at the canopy level.12 The effect of nitrate supply on N distribution in leaves at the canopy level has also been previously investigated in a few species. For example, in Carex species, increased N additions resulted in a vertically steeper leaf N distribution,13 whereas in wheat, this result was achieved with reduced N application.4 On the other hand, in hybrid larch F1, N addition had no notable effect on the N allocation.14 During vegetative growth, foliar N distribution in wheat remained constant at a high supply of N but fluctuated between uniform and steep when N was supplied at low levels.15 © 2016 American Chemical Society

Received: Revised: Accepted: Published: 7696

July 20, 2016 September 29, 2016 October 3, 2016 October 3, 2016 DOI: 10.1021/acs.jafc.6b03234 J. Agric. Food Chem. 2016, 64, 7696−7701

Article

Journal of Agricultural and Food Chemistry Table 1. Composition of the Macroelement Content in Nutrient Solution for Three Different Nitrogen Treatments nitrate N treatment (mmol L−1)

Ca(NO3)2·4H2O (mmol L−1)

NaNO3 (mmol L−1)

CaCl2 (mmol L−1)

KNO3 (mmol L−1)

KH2PO4 (mmol L−1)

K2SO4 (mmol L−1)

MgSO4·7H2O (mmol L−1)

2 13 30

1 4 4

0 0 17

3 0 0

0 5 5

1 1 1

2.5 0 0

2 2 2

Figure 1. Mid-infrared photoacoustic spectra of leaves at both the young stage (n = 285) and the old stage (n = 245).

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fully exposed to light, which were treated for 5 days at three N levels. Samples from both sections were collected for N content analysis. FTIR-PAS Measurement. The spectra of all leaf discs were recorded using a Fourier transform infrared spectrometer (Nicolet 6700, Thermo Fisher Scientific, Waltham, MA, U.S.A.) coupled with a photoacoustic cell (model 300, MTEC, Ames, IA, U.S.A.). Samples were placed in the middle of the photoacoustic cell (height of 5 mm and diameter of 10 mm) and then purged with dry helium gas (5 mL min−1) for 20 s. The spectra scanning was conducted in the range of 500−4000 cm−1 with a resolution of 4 cm−1, and the mirror velocity was 0.32 cm s−1, with 32 successive scans; carbon black was used as the reference for spectral normalization. N Content Analysis. About 0.10 g samples of dried leaves were weighed and digested by concentrated sulfuric acid and hydrogen peroxide, and the total N content was determined using an automated discrete analyzer (Smartchem 200, Westco Scientific Instruments, Inc., Italy). Data Processing. The leaf spectra was filtered using the Savitzky− Golay function (25 points and first polynomial order filtering), and a PLS regression model was developed to correlate spectra and total N content in the Chinese cabbage leaves. The PLS algorithm extracts the new variables, called latent variables (LVs), that not only contain the maximal amount of information but are also relevant to chemical responses.26 The optimized wavelength variables through iPLS, an extension of PLS first proposed by Norgaard, were selected to develop the PLS model on spectral subdivisions with equal width and thereby focus on relevant, important regions instead of the full spectrum.27 The number of LVs was based on the lowest root-mean-square error of crossvalidation (RMSECV) by leave-five-out cross-validation. The model was evaluated in terms of the root-mean-square error of prediction (RMSEP), determination coefficient (R2), and residual predictive deviation (RPD).28 RPD is defined as the ratio of standard deviation (SD) and RMSEP. Good models should have a low RMSECV and

MATERIALS AND METHODS

Water Culture Experiment. Chinese cabbage (B. rapa subsp. chinensis) seeds were germinated by soaking them in deionized water and placing onto filter papers at 25 °C for 12 h. Seedlings with two leaves were individually transplanted into plastic pots (32 × 24 × 11 cm) supplied with 5 L nutrient solution described by Hoagland−Arnon, formulated with some modifications (see Table 1). Three N levels were tested, i.e., N1 (2 mmol L−1 NO3−-N), N2 (13 mmol L−1 NO3−-N), and N3 (30 mmol L−1 NO3−-N), and three replicates were performed. The nutrient solution was renewed every 4 days, and pH was adjusted to approximately 6.50 by adding 0.01 mol L−1 KOH. An oxygen pump (ACO-002, China) was used to daily charge enough oxygen (40 L min−1) at 0900−1100 and 1400−1600. Leaf Samples. Leaf samples were harvested from Chinese cabbages (B. rapa subsp. chinensis) to collect fresh leaf discs (diameter of 10 mm) at the middle positions of individual leaves within three N treatments, and samples (n = 38) were used to measure photoacoustic spectra and N content analysis for calibration. Fresh leaf samples within three N levels were harvested after transplantation in the fifth week (young stage) and the seventh week (old stage), respectively. Around 25 leaf discs (diameter of 10 mm) were prepared from different sites of each individual leaf at similar intervals. The locations of three points (selected at the base, apex, and margin) in a single leaf were determined prior in the same grid paper. According to images and three known points of leaves, the position (center) of each leaf disc and the edge of a single leaf were obtained in ScanIt. A total of 285 and 245 leaf discs collected at young and old stages, respectively, were used to measure photoacoustic spectra for mapping N distribution. The N contents of these leaf discs could be obtained by the quantitative model. Incident Light Experiment. Chinese cabbage leaves with half area along the midvein were shielded by letter paper, and the other half was 7697

DOI: 10.1021/acs.jafc.6b03234 J. Agric. Food Chem. 2016, 64, 7696−7701

Article

Journal of Agricultural and Food Chemistry RMSEP as well as a high R2 and RPD. The N distribution was mapped in individual leaves by an interpolation procedure (scatteredInterpolant function in MATLAB) according to the locations and N contents of these leaf discs. MATLAB R2013a (MathWorks, Natick, MA, U.S.A.) was used for spectral analysis, and SPSS Statistics 18.0 was used for statistical analysis.

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RESULTS AND DISCUSSION Spectral Characterization. The FTIR-PAS spectra of Chinese cabbage leaves at both young and old stages are shown in Figure 1. Very large variances in spectral appearance were observed as a result of the nutrition status and growth stage. In total, the bands in 3100−3800 cm−1 largely reflected N−H and O−H stretching vibrations.29 The band occurring at 1600− 1700 cm−1 was known as amide I, which corresponded to CO stretching vibration, and a combination of N−H deformation vibration and C−N stretching vibration was represented by amide II, occurring at approximately 1500−1600 cm−1.29 The absorption around 1200−1500 cm−1 was ascribed to C−H and N−H deformation vibrations as well as N−O and NO stretching vibrations.30 Owing to the involvement of these significant peaks related with N, mid-infrared photoacoustic spectra could be used to acquire information about the N content and distribution in leaves. However, the N content from the spectra was hard to directly evaluate, and a multivariate calibration method should be used for this evaluation. iPLS Model. The iPLS model was constructed to predict the N content. Figure 2A shows the method of interval selection by iPLS to calculate the total N content, with RMSECV values for 20 interval models (bars) and for the full-spectrum model (dashed line). The numbers at the bottom of the bars indicate the number of latent variables for each interval model. As seen, the 15th interval should be selected to achieve the quantification of total N using the lowest possible RMSECV value. In addition, the range of the selected interval includes the amide II peak, associated with the N−H deformation and C−N stretching vibrations, suggesting that the extent of N evaluation is sufficient for analysis. Eventually, the 15th interval with seven latent variables was used to develop the quantitative model correlating spectra and total N content. The scattered plot of measured versus predicted values of the N content is shown in Figure 2B. R2 of the model was 0.85, with a RMSECV of 6.68 mg g−1 and a RMSEP of 2.70 mg g−1. Model performance was also evaluated via the RPD value; a RPD value more than 2.5 suggests that models could have excellent prediction ability.31 In this study, the model developed by the PLS algorithm had a RPD value of 2.66, suggesting that the N content of each leaf disc could be accurately predicted and, thereof, the N distribution in a single leaf could be determined. Effect of N Input on N Distribution in Individual Leaves. The N distributions in the individual leaves at young and old stages are shown in Figures 3 and 4, respectively. The N content was higher in some locations than at others, and the abrupt shifts in color over the surface of the whole leaf visually demonstrated the considerable variance of the N content in the leaf. The coefficient of variation (CV) was used to express the variance of the N content in a single leaf, and we determined that the influence of N treatments on the variance was variable (Table 2). At the young stage, variance was the strongest (19.72%) in N2 treatment (13 mmol L−1 nitrate N) and was the weakest (15.13%) in N3 treatment (30 mmol L−1 nitrate N); at the old stage, variance was the highest (15.84%) in N1 treatment (2

Figure 2. Nitrogen prediction by the iPLS model (n = 38): (A) selection of the optimal sub-intervals by iPLS and (B) plot of the predicted versus reference values for the nitrogen content.

mmol L−1 nitrate N) and the lowest (12.68%) in N3 treatment. N1 and N3 obviously inhibited growth, as demonstrated by the less fresh biomass, whereas N2 maintained greater fresh biomass, suggesting that N2 provided suitable N input and that the nitrate N supply of N1 was slightly deficient, while that of N3 treatment was excessive. Furthermore, a significant difference in the total N content was observed between N2 and N3, implying that excessive N input did not sustain an increase in the N content.32 Significant differences in the variance of the N content were also observed between N2 and N3. Consequently, excessive N input adversely affected N metabolism, which is indicated by the variance of the N content, i.e., the redistribution of N in the leaf. Suitable N application could enhance N transportation in the leaf.32−34 Thus, the variance of the N content in a single leaf could be increased owing to the metabolism requirements resulting from a reasonable increase in N supply, but this variance reduced with excessive N application maybe because of damage to root or cell metabolism. The variance at the old stage was slightly less than that at the young stage (Table 2). Fresh biomass increased dramatically from approximate 20 g/plant at the young phase to 50−80 g/ plant at the old stage; the N content was shown to differ 7698

DOI: 10.1021/acs.jafc.6b03234 J. Agric. Food Chem. 2016, 64, 7696−7701

Article

Journal of Agricultural and Food Chemistry

Figure 3. Mapping of the nitrogen distribution in individual leaves at the young stage, with treatments of (A) 2 mmol L−1 nitrate N, (B) 13 mmol L−1 nitrate N, and (C) 30 mmol L−1 nitrate N.

Figure 4. Mapping of the nitrogen distribution in individual leaves at the old stage, with treatments of (A) 2 mmol L−1 nitrate N, (B) 13 mmol L−1 nitrate N, and (C) 30 mmol L−1 nitrate N.

chloroplast structure and photosynthetic activity changed remarkably over the growth of the plant; metabolic activity was high at earlier growth stages, reached a maximum, and decreased as senescence began.35 For instance, foliar N and photosynthetic

obviously between two stages; and a decreased trend in the variance of N distribution was observed. An older leaf with reduced activity of N metabolism, especially the transportation or redistribution of N, exhibited lower variance. Generally, the 7699

DOI: 10.1021/acs.jafc.6b03234 J. Agric. Food Chem. 2016, 64, 7696−7701

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Journal of Agricultural and Food Chemistry Table 2. Fresh Biomass, Nitrogen Content, and Variancea nitrate N treatment (mmol L−1)

fresh biomass (g/plant)

nitrogen content (mg g−1)

variance (%)

2 13 30 2 13 30

22.23 ± 3.42 c 24.24 ± 5.47 c 18.81 ± 1.70 c 53.87 ± 2.92 b 79.94 ± 5.90 a 51.72 ± 3.25 b

45.40 ± 3.63 ab 52.06 ± 5.08 a 41.88 ± 1.11 b 40.73 ± 1.89 b 40.31 ± 3.11 b 40.38 ± 1.86 b

16.91 ± 1.07 ab 19.72 ± 1.63 a 15.13 ± 2.14 b 15.84 ± 1.63 ab 13.45 ± 3.88 b 12.68 ± 2.88 b

young stage

old stage

a

Significant differences are shown by lowercase letters (p < 0.05).

activity apparently declined in aged annual plants at Illinois,36 and the CO2 exchange rate, which is an index reflecting photosynthesis activity, decreased with aging in corn.37 Therefore, at the old stage, the variance decreased overall and the differences among the N treatments reduced. In both different N treatments and different growth stages as well as replications of each treatment, N distributions were found to be completely random. The N content was neither symmetrical with respect to the midvein nor was there a gradient trend along a certain direction, and the N distribution showed an irregular pattern in the leaf (Figures 3 and 4). This implied that the N content in a specific location in the leaf might be affected by factors other than N input and growth stage, such as incident light, the intensity of which varies over the course of a day, and the location of leaves that might interfere with each other by shading. Foliar samples were taken from the peripheral leaves of individual plants, which could be easily overlapped or shaded by young leaves.36 Chen et al. observed that nitrate reductase activity and net photosynthetic rate in Chinese cabbages significantly increased but nitrate content reduced with an increase in light intensity.38 Zhang suggested that, at the low level of light, weight, photosynthetic rate, and total N accumulation in Chinese cabbages could be remarkably restricted.39 Therefore, it was important to determine the effect of incident light on the N content in leaves. Effect of Light on N Distribution in Individual Leaves. The differences in N allocation observed in this study might be ascribed to the areas across a leaf receiving incident light at varying intensities. Differing light intensities led to significantly different N content in the same leaf, and a remarkable decrease in N content was observed in the shaded section (Table 3). Therefore, it demonstrated that light availability also affected N redistribution in individual leaves.

optimization of metabolism in response to environmental stresses, including light, high temperature, and nutrient deficiency.40 For example, chloroplast movement is vulnerable to environmental factors, mechanical stress, and particularly light, which causes adjustment in chloroplast position to optimize photosynthesis.40,41 Chloroplast is the site of photosynthesis and N metabolism for the higher plants. The relocation of the chloroplast for optimal metabolism in an isolated leaf, as a result of receiving light at varying intensities, was inferred as another reason for the observed variance in the N content. Thus, sunlight was the predominant factor leading to the variance of the N content. In conclusion, mid-infrared photoacoustic spectroscopy was used to characterize and visualize the distribution of the N content in individual leaves via interpolation mapping, and the variance of the N content in the leaf was observed and verified. Variance was slightly affected by the nitrate level and growth stage: a suitable supply of N could increase variance, which benefited N redistribution and use efficiency, and variance decreased with vegetable growth. The N content in a specific location in the leaf was random, even among replications within a treatment; thus, this distribution pattern was not directly related to the N input level and growth stage. However, it was closely linked to the presence of incident light, and changes in incident light during growth and the shading of leaves might have resulted in the observed random pattern of N distribution in individual leaves.

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Corresponding Author

*Telephone: +86-25-86881565. Fax: +86-25-86881000. E-mail: [email protected]. Funding

The authors gratefully acknowledge the financial support from the National Key Basic Research Program of China (2015CB150403) and the National Scientific Foundation of China (41130749).

Table 3. Effect of Light on the Nitrogen Content across the Same Leafa nitrate N treatment (mmol L−1) with incident light (mg g−1) without incident light (mg g−1) a

2

13

30

61.11 ± 4.23 a

76.88 ± 1.42 a

64.20 ± 6.56 a

54.37 ± 2.08 a

62.19 ± 0.62 b

48.51 ± 6.82 b

AUTHOR INFORMATION

Notes

The authors declare no competing financial interest.

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ABBREVIATIONS USED FTIR-PAS, Fourier transform infrared photoacoustic spectroscopy; PLS, partial least squares; LV, latent variable; RMSECV, root-mean-square error of cross-validation; R2, determination coefficient; RMSEP, root-mean-square error of prediction; RPD, residual predictive deviation; CV, coefficient of variation

Significant differences are shown by lowercase letters (p < 0.05).

Exposure to differing light intensities could result in variations of photosynthetic capacity that, in turn, induce differences in the N content across the whole area of a leaf, because N assimilation requires substances as well as the energy obtained from photosynthesis.8 On the other hand, Wada and Suetsugu suggested that, despite the fact that plants are immobile, they have developed mechanisms at the organelle level that allow organelles to move actively and attain optimal positions for the

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