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Abstract
Huanglongbing (HLB) is one of the most devastating bacterial diseases in citrus growth and there is no cure for it. The mastery of elemental migration and transformation patterns can effectively analyze the growth of crops. The law of element migration and transformation in citrus growth is not very clear. In order to obtain the law of element migration and transformation, healthy and HLB-asymptomatic navel oranges collected in the field were taken as research objects. Laser-induced breakdown spectroscopy (LIBS) is an atomic spectrometry technique for material component analysis. By analyzing the element composition of fruit flesh, peel and soil, it can know the specific process of nutrient exchange and energy exchange between plants and the external environment, as well as the rules of internal nutrient transportation, distribution and energy transformation. Through the study of elemental absorption, the growth of navel orange can be effectively monitored in real time. HLB has an inhibitory effect on the absorption of navel orange. In order to improve the detection efficiency, LIBS coupled with SVM algorithms was used to distinguish healthy navel oranges and HLB-asymptomatic navel oranges. The classification accuracy was 100%. Compared with the traditional detection method, the detection efficiency of LIBS technology is significantly better than the polymerase chain reaction method, which provides a new means for the diagnosis of HLB-asymptomatic citrus fruits.
© 2022 Optica Publishing Group under the terms of the Optica Open Access Publishing Agreement
1. Introduction
Navel oranges are rich in various nutrients such as potassium (K), calcium (Ca), magnesium (Mg), iron (Fe), manganese (Mn), copper (Cu), phosphorus (P) and other nutrients necessary for the human body. Navel oranges may be affected by various diseases and pests during the growth process. Among them, Citrus huanglongbing (HLB) is a devastating disease that is currently threatening citrus production worldwide [1,2]. It was reported that nearly 50 countries and regions in the main citrus producing areas, including Asia, Africa, and South America, were endangered by HLB.
HLB was mainly caused by a gram-negative bacteria Candidatus Liberibacter asiaticus (CLas) and two species of psyllids [3,]. Its symptoms were varied and include phloem degradation, nutrient deficiencies, changes in leaf and fruit color, and decreased fruit yield and quality, and ultimately tree death, however all of these symptoms typically appear in later stages several months after infection, making detection very difficult on affected but asymptomatic trees. The main reason for its rapid spread is the long incubation period, the lack of rapid early diagnosis methods, and effective controls.
Conventional analytical techniques, such as polymerase chain reaction (PCR) [4], Digbrain Network Authentication(DNA) [5], near infrared and thermal imaging techniques [6], etc [7]., have been applied for HLB diagnostics. Although these methods have obtained good detection limits, sensitivity, and stability. However, almost all these techniques produce large amounts of toxic waste, and require the consumption of chemical reagents for digesting samples, which are time-consuming, complex to operate, and may introduce pollutants into ecosystems. Therefore, it is still a challenging work to seek a rapid, eco-friendly and accurate detection method for HLB.
Laser-induced breakdown spectroscopy (LIBS) is a spectroscopic technique for material composition analysis. With the characteristics of minimal sample preparation, rapid analyzing speed and multi-elemental analysis, it is a useful tool for rapid, real-time and in-situ measurements for qualitative or quantitative analysis, and has been widely applied in industrial production [8,9], environmental monitoring [10,11], food safety monitoring [12,13], and other fields [14,15]. LIBS is playing an increasing potential in monitoring components of agricultural, environmental. For example, G. Bilge [16] et al. used LIBS technology combined with principal component analysis (PCA) algorithm to identify pork, beef and chicken powder samples with an average recognition rate of 83.37%. F. Yueh [17] et al. applied PCA, partial least squares discrimination analysis (PLS-DA), Hierarchical Cluster Analysis (HCA) and Back Propaganda-Artificial Neural Networks (BP-ANN) to conduct LIBS detection for tissues in different parts of frozen chicken, and the best average recognition rate was 93.18%. G. Kim [18] used LIBS technology to quantitatively analyze the major elements Mg, Ca, Na and K in standard samples of spinach and brown rice respectively. The calibration curve was established by mixing standard samples of spinach and brown rice of different qualities with Lactose Anhydrous. The detection limits of Mg, Ca, Na and K in spinach and brown rice were 29.63, 102.65, 36.36, 44.46 Mg /kg and 7.54, 1.76, 4.19, 6.70 Mg /kg respectively, with the relative standard deviation (RSD) between 0.74 and 5.90%.
Macronutrients and micronutrients play a decisive role in plant nutrition and can affect crop yields when not present in appropriate concentration levels. Nutrient profile deficiency can cause severe problems in plant growth and appearance. By analyzing the element composition of plants and soil, we can know the specific process of nutrient exchange and energy exchange between plants and the external environment, as well as the rules of internal nutrient transportation, distribution and energy transformation. Preliminary experiments on leaves and fruits of agricultural products were performed by our group in the previous work [19–21]and other groups [22]. For example, A. C. Ranulfi [22] et al. used LIBS combined with partial least squares regression to distinguishing the three categories (healthy, HLB-symptomatic, and HLB-asymptomatic) of leaves regression, the nutritional variation is sufficient to differentiate healthy leaves from diseased leaves with an average success rate of 73%. G.F. Rao [21] et al. identified for HLB-infected navel oranges based on LIBS combined with different chemometric methods. However, the migration and transformation patterns of elements during the growth of citrus are not clear, and mastering these patterns can effectively analyze the production of HLB diseases.
In this work, in order to understand which compounds are responsible for the spectral differences between fruit flesh, peel and soil elements from HLB-asymptomatic fruits were also analyzed. For verifying the potential of LIBS in classifying HLB-asymptomatic orange fruits, it was deficient in the field of the systematic and in-depth research. Considering the severity of citrus HLB, the mature HLB-asymptomatic orange fruits similar with healthy ones in color and shape will be analyzed by LIBS. Appropriate chemometric methods will be constructed to ensure the capability to improve discrimination accuracy between healthy and HLB asymptomatic fruits.
2. Experimental setup and sample preparation
2.1 Experimental setup
The schematic diagram of the experimental setup used in this work is shown in Fig. 1. A Q-switched Nd:YAG pulsed laser (Quantel Brilliant B, wavelength: 532 nm, pulse duration: 8 ns, repetition rates:10 Hz, focusability: M2 ≤ 2, beam diameter ∼6.5 mm) was used to ablate samples. The spot size of focused laser pulse ranges from 500 µm. The laser beam was reflected by a mirror, and focused at 1.25 mm below the sample surface by a UV-grade quartz lens with the focal length of 100 mm. The plasma emission was coupled into an echelle spectrometer (Andor Tech., Mechelle 5000, spectral range from 200 to 950 nm with a resolution of λ/Δλ = 5000, wavelength accuracy was ± 0.05 nm) attached with an intensified charge coupled device (ICCD 1) camera (Andor Tech., iStar DH-320 T). Meanwhile, the morphology of plasma with time evolution was collected by high-speed camera (Andor, DH-334 T, England) (ICCD 2). The acquisition and analysis of data were performed using a personal computer. To obtain a higher absolute emission intensity and signal-to-noise ratio (SNR), lens-to-sample distance (LTSD), laser pulse energy, and gate delay were investigated. The detailed work on optimization of spectral acquisition parameters has been optimized in our previous work [23].
The LIBS spectra were acquired under the following optimal conditions. The laser pulse energy was 40 mJ. The laser frequency was 10 Hz. The gate delay and gate width were 1.5 µs and 3 µs, respectively. To provide a fresh surface for each laser ablation, the target was mounted on a X-Y-Z motorized translation stage and moving at a speed of 4 mm/s during ablation. With the help of X-Y-Z motorized translation stage, LIBS spectra were acquired with 10 pairs of spectra for each sample, and each pair of spectra for each sample was obtained by cumulative averaging of 60 laser pulses.
2.2 Experimental sample
The Navel orange samples and soil samples were collected from adult citrus trees and identified by visual inspection as healthy, HLB-symptomatic, and HLB-asymptomatic. In order to reduce the influence of factors such as the growth process, the mature Gannan navel orange fruit samples used in this work were collected from the four orchards (see Fig. 2). Detail sample information is listed in Table 1. Ensure that all trees are planted at the same time in the orchard under study. Healthy and HLB-infected trees of 4 representative orchards in Nankang County, Xinfeng County, Anyuan County, and Xunwu County in Ganzhou are assessed through visual inspections by senior agronomists and gardeners.
Table 1. Navel orange collection information
2.3 Sample preparation process
Before LIBS testing, all the collect samples of fruit flesh, peel and soil were firstly dried in an oven at 80°C for 24 hours. The dried samples are pulverized in high aluminum ceramic lining ball mill. The samples were passed through a 200 mesh sieve. Then mixed it until they were homogeneous. The pellets were prepared as the following steps. Step 1: Weigh 3 g powder and 12 g boric acid powder, and place a silicone pads in the center of a cushion. Fill the gap with boric acid until it exceeds the top surface of the silicone pads. Step 2: Insert the punch into the sleeve and put the whole mold into the tablet press. Apply 10 MPa pressure to form a boric acid ring. Step 3: Remove the punch and the silicone pads, and clean the surface of the cushion with alcohol. Add 3 g powder into the boric acid ring and add boric acid until it covers the powder and the ring. Step 4: Insert the punch again, press and keep pressure at 25 MPa for 5 minutes and 30 mm in diameter before withdrawing the sample mold. The schematic diagram was shown in Fig. 3.
3. Results and discussion
3.1 Selection of characteristic spectral lines
LIBS spectra of plant samples are very rich in information. The obtained LIBS spectra contained more than 25,025 wavelength channels in a wavelength range starting at 200 nm in the ultraviolet (UV) and extending into the near-infrared (NIR) to 900 nm. Figure 4 shows the plasma emission spectra of navel orange pulp and peel from HLB-asymptomatic fruits. The selected HLB navel orange samples (No.1) and healthy navel orange samples (No.3) were from the same orchard in this work. In detail, most of the valuable and high-intensity spectral lines were located around the regions of 247∼255 nm, 370∼380 nm, 390∼405 nm, 515∼520 nm, and 740∼775 nm, respectively. Figure 4 showed the LIBS spectrum of flesh which clearly showed the presence of prominent lines of P, Cu, Fe, Ca, N, Mn, Cr, Mg, and K.
In order to study the migration and transformation laws of navel orange fruit flesh, and peel elements, the characteristic lines of the analytical elements in the sample are obtained by comparing the standard atomic spectrum database of the National Institute of Standards and Technology (NIST). The intensity of the characteristic spectrum peaks P I 253.56 nm, Cu I 327.40 nm, Fe I 373.71 nm, and Ca II 396.85 nm, Mn I 403.08 nm, Cr I 428.97 nm, Mg I 518.36 nm, KI 769.89 nm were selected as the analysis objects. The emission lines standing for different elements were accurately identified and tagged in corresponding positions. The spectral lines of analytical elements are listed in Table 2.
Table 2. Characteristic spectral lines and attributes for Navel orange samples.
3.2 Migration and transformation law of fruit flesh, peel and soil elements from fruits
By analyzing the element composition of plants and soil, we can know the specific process of nutrient exchange and energy exchange between plants and the external environment, as well as the rules of internal nutrient transportation, distribution and energy transformation. Figure 5 characteristic spectral lines of navel orange navel orange fruit flesh, peel and soil from HLB-asymptomatic fruits, (a) P I 253.56 nm, (b) Cu I 327.40 nm, (c) Fe I 373.71 nm, (d) Ca II 396.85 nm, (e) Mn I 403.08 nm, (f) Cr I 428.97 nm, (g) Mg I 518.36 nm, (h) K I 769.89 nm. The results of the navel orange collection site and the navel orange fruit flesh, peel and soil shows that the intensity of the analyte elements P, Cu, Fe, Mn, Cr, Mg, K characteristic lines in the navel orange collection site soil is significantly higher than that of the navel orange fruit flesh and peel. LIBS test were consistent with the law of plant growth.
Fig. 5. Characteristic spectral lines of navel orange navel orange fruit flesh, peel and soil from HLB-asymptomatic fruits, (a) P I 253.56 nm, (b) Cu I 327.40 nm, (c) Fe I 373.71 nm, (d) Ca II 396.85 nm, (e) Mn I 403.08 nm, (f) Cr I 428.97 nm, (g) Mg I 518.36 nm, (h) K I 769.89 nm.
The average differences in spectral intensities of characteristic elements between soil and fruit flesh and soil and peel are shown in Table 3. The absorption multiple shows that the higher the content of the analyzed elements in the navel orange planting soil, the more absorbed by the navel orange fruit. The results of the fruit flesh and peel analysis data show that the LIBS spectral intensities of the analyzed target elements Cu, Fe, Ca, Mn, Cr, and Mg in the navel orange peel are significantly higher than those in the pulp. From the relationship multiples of each element in navel orange pulp, peel and soil, it is feasible to study the early elementary absorption of HLB by LIBS technology on the element migration and transformation law of crops.
Table 3. Average difference in spectral intensity of characteristic elements.
3.3 Analyze images of plasma time evolution
Figure 6 was shown the time evolution of plasma image at 0.5 ∼ 5.0 µs delay between laser emitting and spectral collection. It can be seen that the plasma shape of healthy and HLB-asymptomatic fruits showed a similar evolution trend with time, that is, first increasing, then decreasing, and finally slowly shrinking until disappearing. The plasma appeared at about 1.0 µs after the collection delay, and expanded to the maximum at about 1.5 µs, then began to collapse until it disappeared. The selected HLB navel orange samples (No.1) and healthy navel orange samples(No.3) were from the same orchard.
The results showed that in the early stage of laser ablation, the plasma emission intensity of HLB fruit was higher than that of healthy fruit. At the same time, plasma lived longer and began to collapse later than healthy samples. When the laser strikes the surface of the HLB fruit to generate plasma, the energy is absorbed by water and organic molecules in the form of heat conduction, and there is less energy exchange. The time evolution analysis of LIBS spectra and plasma morphology showed that the surface spectra and plasma changes over time were different between healthy and HLB fruits. It demonstrates the possibility of identifying citrus HLB by LIBS. The detailed work can see in our previous work [24].
3.4 Automatic recognition model
In this work, continuous wavelet transform (CWT) is used to the automatic recognition of LIBS spectral peak, and then support vector machine (SVM) was used to identify healthy and HLB-asymptomatic navel oranges. The detail model description can be seen in our former work [23,25]. The Mexican hat wavelet function is used to decompose the LIBS spectral signal respectively. The corresponding decomposition layer number is 6, and the displacement step is 1. The ridge line is obtained through the local maximum value. The corresponding position of the spectral peak is obtained, and removes repetitive spectral peaks positions. Compared with the pre-set SNR = 30 and the SBR = 10 threshold, the spectral peaks below the threshold are eliminated. 71 spectral peaks were obtained. With the resolution of the spectrometer is 0.05 nm, multiple spectral peak may be marked in the 0.1nm spectral range. Then, the spectral peaks overlapped in the range of 0.1 nm were removed. Eventually, gets 63 spectral peaks. Then, the spectral intensities of each spectrum were normalized by C I 247.86 nm.
The half of HLB-asymptomatic and healthy spectra were selected as training set. The other half were used to evaluate the performance of the SVM model and to obtain identification accuracy. The former 1st∼40th spectra were used to train the SVM model. The other 40 spectra were used to evaluate the performance of the SVM model and to obtain classification accuracy. As saw in the Table 4, the average classification accuracy of test was 100%. The penalty parameter “c” of the error term and kernel parameter “g” of RBF were optimized by genetic algorithms combined with cross validation method and fixed. The values of c and g were 6.9644 and 0.25. Based on the LIBS technology can be used to identify novel orange HLB disease.
Table 4. Identification accuracy of HLB-asymptomatic fruits using the SVM model.
4. Conclusion
In summary, this work aims to evaluate the feasibility of LIBS detection of the HLB-asymptomatic citrus fruits. From the relationship multiples of each element in navel orange pulp, peel and soil, it is feasible to study the early elementary absorption of HLB by LIBS technology on the element migration and transformation law of crops. Form time evolution of plasma image, in the early stage of laser ablation, the plasma emission intensity of HLB fruit was higher than that of healthy fruit. The LIBS spectra of two groups of fruits including healthy and HLB-asymptomatic navel oranges could be obviously distinguished according to compare the LIBS characteristic lines. The results showed that LIBS combined with chemometric methods should be a promising tool to rapidly distinguish healthy and HLB-asymptomatic navel oranges even though they have similar elemental compositions.
Funding
Opto-electronic Information Technology Laboratory, CCIT (KYPT202101Z, KYPT202102Z); 2020 Jiangsu PhD Holder Plan; the University-level scientific research projects of Changzhou Institute of Technology (CXKZ201910Q); Natural Science Foundation of Jiangsu Province youth project (BK20200190); Natural Science Foundation of Higher Education Institutions of Jiangsu Province (20KJB140013); Changzhou Sci & Tech Program (CJ20200016).
Acknowledgments
This work was supported by the Wuhan National Laboratory for Optoelectronics (WNLO), Huazhong University of Science and Technology (HUST).
Disclosures
The authors declare that there are no conflicts of interest related to this article.
Data availability
Data underlying the results presented in this paper are not publicly available at this time but may be obtained from the authors upon reasonable request.
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