- Email: [email protected]
Rapid Determination of Nitrate in Chinese Cabbage Using Fourier Transforms Mid-infrared Spectroscopy
CHINESE JOURNAL OF ANALYTICAL CHEMISTRY Volume 41, Issue 8, August 2013 Online English edition of the Chinese language journal
Cite this article as: Chin J Anal Chem, 2013, 41(8), 1264–1268.
RESEARCH PAPER
Rapid Determination of Nitrate in Chinese Cabbage Using Fourier Transforms Mid-infrared Spectroscopy YANG Jia-Bao, DU Chang-Wen*, SHEN Ya-Zhen, ZHOU Jian-Min The State Key Laboratory of Soil and Sustainable Agriculture, Institute of Soil Science, Chinese Academy of Sciences, Nanjing 210008, China
Abstract:
The nitrate content in Chinese cabbage was rapidly predicted using the techniques of mid-infrared spectroscopy
(diffusion reflectance spectroscopy, photoacoustic spectroscopy and attenuated total reflectance spectroscopy). There existed interferences in the characteristic bands of nitrate (1200–1500 cm–1) for all the infrared spectra. The interferences for diffusion reflectance spectra were strongest, followed by photoacoustic spectra, and the weakest for the attenuated total reflectance spectra, which were also verified by the principal component regressions between infrared spectra and nitrate content in Chinese cabbage, and the coefficient (R2) were 0.4003, 0.4874 and 0.8741, respectively. Based on attenuated total reflectance spectra, the chemometrics method of partial least squares was involved to improve the prediction model of nitrate, the prediction accuracy was significantly decreased, the coefficient was 0.8851, and the RPD value was 3.19. Therefore, the technique of mid-infrared attenuated total reflectance spectroscopy was feasible for rapid monitoring of nitrate in Chinese cabbage. Key Words: Chinese cabbage; Attenuated total reflection (ATR); Infrared spectroscopy; Nitrate; Partial least squares; Chemometrics
1
Introduction
Vegetables can accumulate nitrate, and the most of nitrate in the human body came from vegetables[1]. Most vegetables, especially leaf vegetables, will like to absorb large amount of nutrients, especially nitrogen. In order to achieve a high yield, nitrogen fertilizers were usually excessively used, which resulted in luxury absorption of nitrogen and the heavy accumulation of nitrate in leaves[2]. Although nitrate does not direct harm for human health, it would be reduced to toxic nitrite after entering the body, and the nitrite could react with hemoglobin, which impacts the capacity of oxygen loading and then induce methemoglobinemia. In addition, nitrite can transfer into a strong carcinogen, i.e., nitrosamine in the gastral cavity[3]. Therefore, how to control nitrate content in vegetables received wide attention. Currently, excessive input of nitrogen fertilizer usually occurred in vegetable production in China, which resulted in a series of problems including low fertilizer utilization rate, high production cost, the decline in
qualities of agricultural products, and water pollution[4]. Nitrate content was one of an important quality index for vegetables, especially for leaf vegetables in which nitrate was easily accumulated. Generally, the vegetables with nitrate content below 432 mg kg–1 were allowed to be eaten raw; the vegetables with nitrate content between 432 mg kg–1 and 785 mg kg–1 were unsuitable to be eaten directly; the vegetables with nitrate content in the range of 785–1140 mg kg–1 were inedible after salting; the vegetables with nitrate content in the range of 1140–3100 mg kg–1 were inedible even after cooking[5]. The Chinese cabbage (Brassica chinensis L.) was a kind of leaf vegetables and was very popular among Chinese people. Its cultivated area accounted for 30%–40% of the area of multiple vegetable cropping in the large and medium-sized cities in the Middle-Lower Yangze River region of China, and the production of Chinese cabbage was also raised in the north of China. However, the problem of excessive nitrate content still existed[6]. Conventional methods of determining nitrate
Received 12 October 2012; accepted 28 February 2013 * Corresponding author. Email: [email protected] This work was supported by the National Natural Science Foundation of China (No. 41130749). Copyright © 2013, Changchun Institute of Applied Chemistry, Chinese Academy of Sciences. Published by Elsevier Limited. All rights reserved. DOI: 10.1016/S1872-2040(13)60675-1
YANG Jia-Bao et al. / Chinese Journal of Analytical Chemistry, 2013, 41(8): 1264–1268
content required sample pretreatment, which was time and cost consuming, and was unsuitable for fast measurement. Therefore, they couldn’t meet the requirement of quality monitoring and production management of leaf vegetables, while instrumental analysis indicated the potential for this purpose. Owing to the rapidity, low cost and simple sample preparation, infrared spectroscopy combined with chemometrics had been widely used in the quality monitoring of agricultural products, e.g., carbonhydrates, fats, proteins and celluloses[7–10]. Recently, Studies showed that infrared spectroscopy was suitable for nitrate determination, especially for samples with high level of nitrate, based on the vibration absorption of N–O in nitrate[11], which provided a new option to determine the nitrate in leaf vegetables. In this study, three types of infrared spectroscopy techniques, photoacoustic spectroscopy (FTIR-PAS), diffuse reflectance spectroscopy (DRIFT) and attenuated total reflection spectroscopy (FTIR-ATR) were applied in the fast prediction of nitrate, which provided a new fast method to evaluate nitrate content in the leaf vegetables.
a diffuse reflectance accessory (Bruker, Germany) and a 45 °C ZnSe ATR accessory (Bruker, Germany), respectively. In conducting photoacoustic scan, fresh leaves were placed in the photoacoustic cell (height 5 mm, diameter 10 mm) and purging the cell with dry helium (10 mL min–1) for 10 s to ensure a CO2 and H2O free environment, and a carbon black was used as reference[12]. In recording diffuse reflectance spectra, fresh leaves were directly put into the sample chamber. In attenuated total reflection measuring mode, the fresh sample was ground into paste and then put into the ATR cell. All the scans above were conducted in the mid-infrared wavenumber range of 500–4000 cm–1 with a resolution of 4 cm–1 and a mirror velocity of 0.32 cm s–1. 32 successive scans were recorded, and the average for each sample was used in the following analysis. 2.4
Spectral preprocessing
Chinese cabbage, named Shanghaiqing, was planted in the green house in Institute of Soil Science Chinese Academy of Sciences, and 63 fresh samples were collected for FTIR-ATR spectra recording and 47 samples were analyzed by techniques of FTIR-PAS and DRIFT.
A Gaussian filter and the normalization were employed as the spectral pretreatment[13]. Then a comparison was made between different spectral measuring modes using principle component regression. Meanwhile, partial least squares were also applied to quantify nitrate level in Chinese cabbage[14]. Finally, the RPD, i.e., the ration of the standard deviation of reference values in calibration set to the prediction error in validation set[15], was used to evaluate the calibration models. The RPD could be expressed as following: RPD = SD/RMSEP (1) where, SD is the standard deviation of reference values in the calibration, and RMSEP is the prediction error in validation set.
2.2
3
2 2.1
Experimental Preparation of samples
Determination of nitrate
Salicylic acid digestion method was employed for the determination of nitrate. A proper amount of fresh leaves of Chinese cabbage were cut into small pieces and mixed, and then weighed 1.00 g fresh sample with three replicates. The samples and 10 mL of deioned water were put into graduated tubes, and then put into a water bath at 100 °C for 30 min. The liquid in the tube was filtered, and 0.1 mL of the filtered solution was drawn into a new tube, and 0.4 mL 5% salicylic acid-sulfuric acid was added. The mixed solution was stranded at room temperature for 20 min, and then 9.5 mL of 8% NaOH was added. Finally, the reaction solution cooled down to room temperature was used to determine nitrate by ultraviolet spectroscopy at 410 nm. 2.3
Infrared spectroscopy measurements
Photoacoustic spectra, diffuse reflectance spectra and attenuated total reflection spectra were recorded using a Fourier transform infrared spectroscopy (Nicolet 6700, USA) equipped with a photoacoustic cell (Model 300, MTEC, USA),
3.1
Results and discussions Infrared spectra of Chinese cabbage
Figure 1 shows the DRIFT spectra of Chinese cabbage. A wavenumber range around 3100–3800 cm–1 was the absorption of N–H and O–H stretching vibration. The peak at 2800–3000 cm–1 was mainly related to aliphatic C–H vibration. The peak around 1500–1700 cm–1 was assigned to stretching vibration of C=C, C=O and C=N stretch vibration as well as N–H deformation vibration. The peaks around 1000–1200 cm–1 corresponded to C–O and P–O stretching vibration. Besides, it was observed that the spectral profiles of different samples were fairly homogeneous. However, the absorption intensity and locations of certain peaks varied with different samples, which was mainly due to the variation of chemical components or structures in Chinese cabbage. Additionally, the absorption of N–O stretching vibration was in the range of 1200–1500 cm–1, which was obviously influenced by N–H and C–H vibrations. Figure 2 shows the FTIR-PAS spectra of Chinese cabbage. The main absorption peaks were basically the same with the
YANG Jia-Bao et al. / Chinese Journal of Analytical Chemistry, 2013, 41(8): 1264–1268
Fig.1 Mid-infrared diffusion spectra of Chinese cabbage (n = 16)
absorption band was ranged from 1200 cm–1 to 1500 cm–1. The peak incorporated two peaks into a broad one. The left one was caused by N=O vibration and the right one was induced by N–O vibration. The latter was the main peak situated around 1300–1450 cm–1 which corresponded to the main absorption in the range of 1200–1500 cm–1, while the main peaks of DRIFT and FTIR-PAS spectra corresponded to the range of 1200–1400 cm–1 and 1350–1500 cm–1, respectively. Therefore, attenuated total reflection spectra could be expected to better quantify nitrate content in the leaves of Chinese cabbage. As shown in Fig.4, the peak intensity around 1200–1500 cm–1 was proportional to the nitrate concentration well. Thus, this peak could be used for the quantitative analysis of nitrate. 3.2
Fast determination of nitrate in Chinese cabbage
For the quantification of nitrate, principal component analysis (PCA) was used to extract principle components from raw spectra. The explained variance in each principle components scores were listed in Table 1. For FTIR-ATR spectra of potassium nitrate solutions with different concentrations, the first principle component (PCA1) Fig.2 Mid-infrared photoacoustic spectra of Chinese cabbage (n = 16)
DRIFT spectra. However, in the FTIR-PAS spectra, the peaks were more narrow, and the peaks around 1000–1700 cm–1 were more obviously separated rather than overlapped or close to shoulder peaks in the DRIFT spectra. Besides, in comparison with the DRIFT spectra, the absorption intensity of photoacoustic peaks around 1000–1200 cm–1 were much higher. Furthermore, in the absorption range of N–O vibration in the wavenumber range of 1200–1500 cm–1, there was significant differences between these two kind of spectra, specifically, the main peak of FTIR-PAS spectra was located about 1400 cm–1, while the main peak of DRIFT spectra was situated at 1300 cm–1, which were resulted from the vibrations of varied molecule. Figure 3 shows the FTIR-ATR spectra of Chinese cabbage. The spectra were strongly interfered by water absorption because the percentage of moisture content in fresh samples was more than 90%. The two strong bands around 3000–3800 cm–1 and 1500–1800 cm–1 were mainly caused by moisture. However, the wavenumber region of 1200–1500 cm–1 was less influenced. The range was mainly related to the anions, e.g., the nitrate ion and small organic molecules. As the accumulation of nitrate was carried out, the nitrate showed large contribution to the absorption peak due to the relatively low concentration of small organic molecules. The FTIR-ATR spectra of nitrate solution with different concentrations were shown in Fig.4. It could be seen that the characteristic
Fig.3 Mid-infrared attenuated total reflectance spectra of Chinese cabbage (n = 16)
Fig.4 Mid-infrared diffusion spectra of standard KNO3 solution
YANG Jia-Bao et al. / Chinese Journal of Analytical Chemistry, 2013, 41(8): 1264–1268
Table 1 Explained variance of each principal component of different infrared spectra in the region of 1200–1500 cm–1 (%) Infrared spectra
PCA 1
PCA 2
Total explained variance
FTIR-ATR spectra of KNO3 solution (n = 5)
96.24
3.74
99.98
DRIFT spectra of Chinese cabbage (n = 47)
44.81
27.33
72.14
FTIR-PAS spectra of Chinese cabbage (n = 47)
71.15
23.06
94.21
FTIR-ATR spectra of cabbage (n = 47)
89.30
8.16
97.46
explained 96.24% of total variance, and the second principle component (PCA2) explained 3.74%. Obviously, PCA1 was sufficient enough to quantify nitrate. As to the FTIR-ATR spectra of Chinese cabbage, the explained variances by the first two principal components declined in different extent. For FTIR-ATR spectra, the explained variance of PCA1 decreased to 89.30%, for FTIR-PAS spectra, it decreased to 71.15%; while for DRIFT spectra, it fell down to just 44.81%. The decline in explained variance by PCA1 indicated the increase of interference factors in the determination of nitrate. The principle component regression (PCR) was conducted by between PCA1 (independent variables) and nitrate concentration (dependent variables). The linear fittings were shown in Fig.5, in which an excellent relation coefficient of 0.9912 was observed. The result demonstrated the good capacity of FTIR-ATR spectra in quantifying nitrate in solution. As to nitrate in leaf, determination error should be larger due to the presence of interferences from different organic substances (Fig.6). The linear correlation between PCA1 and nitrate was obviously deteriorated with R2 of 0.4003 by DRIFT spectra, 0.4874 by FTIR-PAS spectra and 0.8741 by FTIR-ATR. Despite the significant relationship with three kinds of spectra, the FTIR-ATR spectra were less affected, which was verified by the characteristic absorption in Figs.1–4. The results indicated that PCR could be effectively used to measure nitrate in leaves (Fig.6). According to the variation of correlation between spectra and nitrate, the results suggested that it was not good for DRIFT spectra and FTIR-PAS spectra, but it was much better for FTIR-ATR spectra (R2 = 0.8741). Although a good correlation between
FTIR-ATR spectra and nitrate was obtained by PCR, the interferences were still evident and the prediction of nitrate content could be further improved by partial least squares regression (PLSR). Figure 7 showed the plotting of regression between FTIR-ATR spectra and nitrate concentration in Chinese cabbage by PLSR. The result presented a highly significant correlation with R2 value of 0.8851 and RPD value of 3.19. In soil spectral analysis, prediction models could be classified into six categories based on the RPD value[16,17]: the poor model with RPD lower than 1.0, the fair model with RPD between 1.0 and 1.4, an intermediate model with RPD in the range of 1.4 and 1.8, a good model with RPD from 1.8 to 2.0, a very good model with RPD between 2.0 and 2.5, and an excellent model with RPD more than 2.5. This classification was also suitable for plant spectral analysis. A high RPD 3.19 was obtained in our study, which indicated the resulting model could be used for the fast determination of nitrate in Chinese cabbage.
Fig.5 Linear regression between nitrate content and mid-infrared attenuated total reflectance spectra (n = 5)
Fig.6 Linear regression between PCA1 and nitrate content (a) Diffusion reflectance spectra; (b) photoacoustic spectra; (c) attenuated total reflectance spectra
YANG Jia-Bao et al. / Chinese Journal of Analytical Chemistry, 2013, 41(8): 1264–1268
[5]
Zhou Z Y, Hu C M, Wang M J, Wang J S. Techniques and Equipment for Environment Pollution Control, 1999, 7(5): 1–13
[6]
Hou X L. Journal of Nanjing Agricultural University, 2003, 26(4): 111–115
[7]
Tamas S, Jozsef L, Mihaly D, Sandor T. International Journal of Food Science and Technology, 2012, 47: 436–440
[8]
Brandao L F P, Braga J W B, Suarez P A Z. J. Chromatogr., 2012, 1225: 150–157
[9]
Vaknin Y, Ghanim M, Samra S, Dvash L, Hendelsman E, Eisikowitch D, Samocha Y. Industrial Crops and Products,
Fig.7
Linear regression between nitrate in Chinese cabbage and mid-infrared attenuated total reflectance spectra (n = 63, and 50 for calibration and 13 for validation; the optimized factor of partial least squares regression is 6, the calibration coefficient is 0.8851 and the RPD value is 3.19)
2012, 34: 1029–1034 [10] Movasaghi Z, Rehman S, Rehman I U. Applied Spectroscopy Reviews, 2008, 43: 134–179 [11] Du C, Zhou J, Linker R, Shaviv A. Applied Spectroscopy, 2009, 63: 1168–1173
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[13] Savitzky A, Golay M J E. Anal. Chem., 1964, 36: 1627–1639
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University, 2005, 25(4): 416–419 [4] Zhu J H, Li X L, Christie P, Li J L. Agriculture Ecosystems & Environment, 2005, 111: 70–80
Spectroscopy (Volume II). Chichester: Wiley & Sons, 2001 [14] Geladi1 P, Kowalski B R. Anal. Chim. Acta, 1986, 185: 1–17 Soil Science and Plant Analysis, 1999, 30: 999–1012 [16] Viscarra Rossel R A, Walvoort T D, McBratney A B, Janik L J, Skjemstad J O. Geoderma, 2006, 131: 59–75 [17] Du C, Zhou J. Chinese J. Anal. Chem., 2007, 35(1): 119–122
Cite this article as: Chin J Anal Chem, 2013, 41(8), 1264–1268.
RESEARCH PAPER
Rapid Determination of Nitrate in Chinese Cabbage Using Fourier Transforms Mid-infrared Spectroscopy YANG Jia-Bao, DU Chang-Wen*, SHEN Ya-Zhen, ZHOU Jian-Min The State Key Laboratory of Soil and Sustainable Agriculture, Institute of Soil Science, Chinese Academy of Sciences, Nanjing 210008, China
Abstract:
The nitrate content in Chinese cabbage was rapidly predicted using the techniques of mid-infrared spectroscopy
(diffusion reflectance spectroscopy, photoacoustic spectroscopy and attenuated total reflectance spectroscopy). There existed interferences in the characteristic bands of nitrate (1200–1500 cm–1) for all the infrared spectra. The interferences for diffusion reflectance spectra were strongest, followed by photoacoustic spectra, and the weakest for the attenuated total reflectance spectra, which were also verified by the principal component regressions between infrared spectra and nitrate content in Chinese cabbage, and the coefficient (R2) were 0.4003, 0.4874 and 0.8741, respectively. Based on attenuated total reflectance spectra, the chemometrics method of partial least squares was involved to improve the prediction model of nitrate, the prediction accuracy was significantly decreased, the coefficient was 0.8851, and the RPD value was 3.19. Therefore, the technique of mid-infrared attenuated total reflectance spectroscopy was feasible for rapid monitoring of nitrate in Chinese cabbage. Key Words: Chinese cabbage; Attenuated total reflection (ATR); Infrared spectroscopy; Nitrate; Partial least squares; Chemometrics
1
Introduction
Vegetables can accumulate nitrate, and the most of nitrate in the human body came from vegetables[1]. Most vegetables, especially leaf vegetables, will like to absorb large amount of nutrients, especially nitrogen. In order to achieve a high yield, nitrogen fertilizers were usually excessively used, which resulted in luxury absorption of nitrogen and the heavy accumulation of nitrate in leaves[2]. Although nitrate does not direct harm for human health, it would be reduced to toxic nitrite after entering the body, and the nitrite could react with hemoglobin, which impacts the capacity of oxygen loading and then induce methemoglobinemia. In addition, nitrite can transfer into a strong carcinogen, i.e., nitrosamine in the gastral cavity[3]. Therefore, how to control nitrate content in vegetables received wide attention. Currently, excessive input of nitrogen fertilizer usually occurred in vegetable production in China, which resulted in a series of problems including low fertilizer utilization rate, high production cost, the decline in
qualities of agricultural products, and water pollution[4]. Nitrate content was one of an important quality index for vegetables, especially for leaf vegetables in which nitrate was easily accumulated. Generally, the vegetables with nitrate content below 432 mg kg–1 were allowed to be eaten raw; the vegetables with nitrate content between 432 mg kg–1 and 785 mg kg–1 were unsuitable to be eaten directly; the vegetables with nitrate content in the range of 785–1140 mg kg–1 were inedible after salting; the vegetables with nitrate content in the range of 1140–3100 mg kg–1 were inedible even after cooking[5]. The Chinese cabbage (Brassica chinensis L.) was a kind of leaf vegetables and was very popular among Chinese people. Its cultivated area accounted for 30%–40% of the area of multiple vegetable cropping in the large and medium-sized cities in the Middle-Lower Yangze River region of China, and the production of Chinese cabbage was also raised in the north of China. However, the problem of excessive nitrate content still existed[6]. Conventional methods of determining nitrate
Received 12 October 2012; accepted 28 February 2013 * Corresponding author. Email: [email protected] This work was supported by the National Natural Science Foundation of China (No. 41130749). Copyright © 2013, Changchun Institute of Applied Chemistry, Chinese Academy of Sciences. Published by Elsevier Limited. All rights reserved. DOI: 10.1016/S1872-2040(13)60675-1
YANG Jia-Bao et al. / Chinese Journal of Analytical Chemistry, 2013, 41(8): 1264–1268
content required sample pretreatment, which was time and cost consuming, and was unsuitable for fast measurement. Therefore, they couldn’t meet the requirement of quality monitoring and production management of leaf vegetables, while instrumental analysis indicated the potential for this purpose. Owing to the rapidity, low cost and simple sample preparation, infrared spectroscopy combined with chemometrics had been widely used in the quality monitoring of agricultural products, e.g., carbonhydrates, fats, proteins and celluloses[7–10]. Recently, Studies showed that infrared spectroscopy was suitable for nitrate determination, especially for samples with high level of nitrate, based on the vibration absorption of N–O in nitrate[11], which provided a new option to determine the nitrate in leaf vegetables. In this study, three types of infrared spectroscopy techniques, photoacoustic spectroscopy (FTIR-PAS), diffuse reflectance spectroscopy (DRIFT) and attenuated total reflection spectroscopy (FTIR-ATR) were applied in the fast prediction of nitrate, which provided a new fast method to evaluate nitrate content in the leaf vegetables.
a diffuse reflectance accessory (Bruker, Germany) and a 45 °C ZnSe ATR accessory (Bruker, Germany), respectively. In conducting photoacoustic scan, fresh leaves were placed in the photoacoustic cell (height 5 mm, diameter 10 mm) and purging the cell with dry helium (10 mL min–1) for 10 s to ensure a CO2 and H2O free environment, and a carbon black was used as reference[12]. In recording diffuse reflectance spectra, fresh leaves were directly put into the sample chamber. In attenuated total reflection measuring mode, the fresh sample was ground into paste and then put into the ATR cell. All the scans above were conducted in the mid-infrared wavenumber range of 500–4000 cm–1 with a resolution of 4 cm–1 and a mirror velocity of 0.32 cm s–1. 32 successive scans were recorded, and the average for each sample was used in the following analysis. 2.4
Spectral preprocessing
Chinese cabbage, named Shanghaiqing, was planted in the green house in Institute of Soil Science Chinese Academy of Sciences, and 63 fresh samples were collected for FTIR-ATR spectra recording and 47 samples were analyzed by techniques of FTIR-PAS and DRIFT.
A Gaussian filter and the normalization were employed as the spectral pretreatment[13]. Then a comparison was made between different spectral measuring modes using principle component regression. Meanwhile, partial least squares were also applied to quantify nitrate level in Chinese cabbage[14]. Finally, the RPD, i.e., the ration of the standard deviation of reference values in calibration set to the prediction error in validation set[15], was used to evaluate the calibration models. The RPD could be expressed as following: RPD = SD/RMSEP (1) where, SD is the standard deviation of reference values in the calibration, and RMSEP is the prediction error in validation set.
2.2
3
2 2.1
Experimental Preparation of samples
Determination of nitrate
Salicylic acid digestion method was employed for the determination of nitrate. A proper amount of fresh leaves of Chinese cabbage were cut into small pieces and mixed, and then weighed 1.00 g fresh sample with three replicates. The samples and 10 mL of deioned water were put into graduated tubes, and then put into a water bath at 100 °C for 30 min. The liquid in the tube was filtered, and 0.1 mL of the filtered solution was drawn into a new tube, and 0.4 mL 5% salicylic acid-sulfuric acid was added. The mixed solution was stranded at room temperature for 20 min, and then 9.5 mL of 8% NaOH was added. Finally, the reaction solution cooled down to room temperature was used to determine nitrate by ultraviolet spectroscopy at 410 nm. 2.3
Infrared spectroscopy measurements
Photoacoustic spectra, diffuse reflectance spectra and attenuated total reflection spectra were recorded using a Fourier transform infrared spectroscopy (Nicolet 6700, USA) equipped with a photoacoustic cell (Model 300, MTEC, USA),
3.1
Results and discussions Infrared spectra of Chinese cabbage
Figure 1 shows the DRIFT spectra of Chinese cabbage. A wavenumber range around 3100–3800 cm–1 was the absorption of N–H and O–H stretching vibration. The peak at 2800–3000 cm–1 was mainly related to aliphatic C–H vibration. The peak around 1500–1700 cm–1 was assigned to stretching vibration of C=C, C=O and C=N stretch vibration as well as N–H deformation vibration. The peaks around 1000–1200 cm–1 corresponded to C–O and P–O stretching vibration. Besides, it was observed that the spectral profiles of different samples were fairly homogeneous. However, the absorption intensity and locations of certain peaks varied with different samples, which was mainly due to the variation of chemical components or structures in Chinese cabbage. Additionally, the absorption of N–O stretching vibration was in the range of 1200–1500 cm–1, which was obviously influenced by N–H and C–H vibrations. Figure 2 shows the FTIR-PAS spectra of Chinese cabbage. The main absorption peaks were basically the same with the
YANG Jia-Bao et al. / Chinese Journal of Analytical Chemistry, 2013, 41(8): 1264–1268
Fig.1 Mid-infrared diffusion spectra of Chinese cabbage (n = 16)
absorption band was ranged from 1200 cm–1 to 1500 cm–1. The peak incorporated two peaks into a broad one. The left one was caused by N=O vibration and the right one was induced by N–O vibration. The latter was the main peak situated around 1300–1450 cm–1 which corresponded to the main absorption in the range of 1200–1500 cm–1, while the main peaks of DRIFT and FTIR-PAS spectra corresponded to the range of 1200–1400 cm–1 and 1350–1500 cm–1, respectively. Therefore, attenuated total reflection spectra could be expected to better quantify nitrate content in the leaves of Chinese cabbage. As shown in Fig.4, the peak intensity around 1200–1500 cm–1 was proportional to the nitrate concentration well. Thus, this peak could be used for the quantitative analysis of nitrate. 3.2
Fast determination of nitrate in Chinese cabbage
For the quantification of nitrate, principal component analysis (PCA) was used to extract principle components from raw spectra. The explained variance in each principle components scores were listed in Table 1. For FTIR-ATR spectra of potassium nitrate solutions with different concentrations, the first principle component (PCA1) Fig.2 Mid-infrared photoacoustic spectra of Chinese cabbage (n = 16)
DRIFT spectra. However, in the FTIR-PAS spectra, the peaks were more narrow, and the peaks around 1000–1700 cm–1 were more obviously separated rather than overlapped or close to shoulder peaks in the DRIFT spectra. Besides, in comparison with the DRIFT spectra, the absorption intensity of photoacoustic peaks around 1000–1200 cm–1 were much higher. Furthermore, in the absorption range of N–O vibration in the wavenumber range of 1200–1500 cm–1, there was significant differences between these two kind of spectra, specifically, the main peak of FTIR-PAS spectra was located about 1400 cm–1, while the main peak of DRIFT spectra was situated at 1300 cm–1, which were resulted from the vibrations of varied molecule. Figure 3 shows the FTIR-ATR spectra of Chinese cabbage. The spectra were strongly interfered by water absorption because the percentage of moisture content in fresh samples was more than 90%. The two strong bands around 3000–3800 cm–1 and 1500–1800 cm–1 were mainly caused by moisture. However, the wavenumber region of 1200–1500 cm–1 was less influenced. The range was mainly related to the anions, e.g., the nitrate ion and small organic molecules. As the accumulation of nitrate was carried out, the nitrate showed large contribution to the absorption peak due to the relatively low concentration of small organic molecules. The FTIR-ATR spectra of nitrate solution with different concentrations were shown in Fig.4. It could be seen that the characteristic
Fig.3 Mid-infrared attenuated total reflectance spectra of Chinese cabbage (n = 16)
Fig.4 Mid-infrared diffusion spectra of standard KNO3 solution
YANG Jia-Bao et al. / Chinese Journal of Analytical Chemistry, 2013, 41(8): 1264–1268
Table 1 Explained variance of each principal component of different infrared spectra in the region of 1200–1500 cm–1 (%) Infrared spectra
PCA 1
PCA 2
Total explained variance
FTIR-ATR spectra of KNO3 solution (n = 5)
96.24
3.74
99.98
DRIFT spectra of Chinese cabbage (n = 47)
44.81
27.33
72.14
FTIR-PAS spectra of Chinese cabbage (n = 47)
71.15
23.06
94.21
FTIR-ATR spectra of cabbage (n = 47)
89.30
8.16
97.46
explained 96.24% of total variance, and the second principle component (PCA2) explained 3.74%. Obviously, PCA1 was sufficient enough to quantify nitrate. As to the FTIR-ATR spectra of Chinese cabbage, the explained variances by the first two principal components declined in different extent. For FTIR-ATR spectra, the explained variance of PCA1 decreased to 89.30%, for FTIR-PAS spectra, it decreased to 71.15%; while for DRIFT spectra, it fell down to just 44.81%. The decline in explained variance by PCA1 indicated the increase of interference factors in the determination of nitrate. The principle component regression (PCR) was conducted by between PCA1 (independent variables) and nitrate concentration (dependent variables). The linear fittings were shown in Fig.5, in which an excellent relation coefficient of 0.9912 was observed. The result demonstrated the good capacity of FTIR-ATR spectra in quantifying nitrate in solution. As to nitrate in leaf, determination error should be larger due to the presence of interferences from different organic substances (Fig.6). The linear correlation between PCA1 and nitrate was obviously deteriorated with R2 of 0.4003 by DRIFT spectra, 0.4874 by FTIR-PAS spectra and 0.8741 by FTIR-ATR. Despite the significant relationship with three kinds of spectra, the FTIR-ATR spectra were less affected, which was verified by the characteristic absorption in Figs.1–4. The results indicated that PCR could be effectively used to measure nitrate in leaves (Fig.6). According to the variation of correlation between spectra and nitrate, the results suggested that it was not good for DRIFT spectra and FTIR-PAS spectra, but it was much better for FTIR-ATR spectra (R2 = 0.8741). Although a good correlation between
FTIR-ATR spectra and nitrate was obtained by PCR, the interferences were still evident and the prediction of nitrate content could be further improved by partial least squares regression (PLSR). Figure 7 showed the plotting of regression between FTIR-ATR spectra and nitrate concentration in Chinese cabbage by PLSR. The result presented a highly significant correlation with R2 value of 0.8851 and RPD value of 3.19. In soil spectral analysis, prediction models could be classified into six categories based on the RPD value[16,17]: the poor model with RPD lower than 1.0, the fair model with RPD between 1.0 and 1.4, an intermediate model with RPD in the range of 1.4 and 1.8, a good model with RPD from 1.8 to 2.0, a very good model with RPD between 2.0 and 2.5, and an excellent model with RPD more than 2.5. This classification was also suitable for plant spectral analysis. A high RPD 3.19 was obtained in our study, which indicated the resulting model could be used for the fast determination of nitrate in Chinese cabbage.
Fig.5 Linear regression between nitrate content and mid-infrared attenuated total reflectance spectra (n = 5)
Fig.6 Linear regression between PCA1 and nitrate content (a) Diffusion reflectance spectra; (b) photoacoustic spectra; (c) attenuated total reflectance spectra
YANG Jia-Bao et al. / Chinese Journal of Analytical Chemistry, 2013, 41(8): 1264–1268
[5]
Zhou Z Y, Hu C M, Wang M J, Wang J S. Techniques and Equipment for Environment Pollution Control, 1999, 7(5): 1–13
[6]
Hou X L. Journal of Nanjing Agricultural University, 2003, 26(4): 111–115
[7]
Tamas S, Jozsef L, Mihaly D, Sandor T. International Journal of Food Science and Technology, 2012, 47: 436–440
[8]
Brandao L F P, Braga J W B, Suarez P A Z. J. Chromatogr., 2012, 1225: 150–157
[9]
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Fig.7
Linear regression between nitrate in Chinese cabbage and mid-infrared attenuated total reflectance spectra (n = 63, and 50 for calibration and 13 for validation; the optimized factor of partial least squares regression is 6, the calibration coefficient is 0.8851 and the RPD value is 3.19)
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