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Mohajan S, Huang Y, Beier NF, Dyck M, Hegmann F, Bais A, Hussein AE. Effect of laser wavelength on soil carbon measurements using laser-induced breakdown spectroscopy. OPTICS EXPRESS 2023; 31:32335-32349. [PMID: 37859039 DOI: 10.1364/oe.501741] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/27/2023] [Accepted: 09/01/2023] [Indexed: 10/21/2023]
Abstract
We investigate the effect of laser wavelength on laser-induced breakdown spectroscopy (LIBS) on the measurement of carbon in agricultural soils. Two laser wavelengths, 1064 nm and 532 nm, were used to determine soil carbon concentration. No chemical pretreatment, grinding, or pelletization was performed on soil samples to simulate in-field conditions. A multivariate calibration model with outlier filtering and optimized parameters in partial least squared regression (PLSR) was established and validated. The calibration model estimated carbon content in soils with an average prediction error of 4.7% at a laser wavelength of 1064 nm and 2.7% at 532 nm. The limit of detection (LOD) range for 532 nm was 0.34-0.5 w/w%, approximately half of the LOD range for 1064 nm laser wavelength. The improvement in prediction error and LOD of LIBS measurements is attributed to the increase in plasma density achieved at 532 nm.
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Soni S, Viljanen J, Uusitalo R, Veis P. Phosphorus quantification in soil using LIBS assisted by laser-induced fluorescence. Heliyon 2023; 9:e17523. [PMID: 37408919 PMCID: PMC10319222 DOI: 10.1016/j.heliyon.2023.e17523] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/02/2023] [Revised: 06/20/2023] [Accepted: 06/20/2023] [Indexed: 07/07/2023] Open
Abstract
Quantification and monitoring of phosphorus in soil plays a critical role in environmentally friendly agriculture, especially in mitigation of phosphorus leakages to water systems and subsequent risk for eutrophication. On the other hand, deficiency in phosphorus would lead to problems in development and growth of cultivated crops. Therefore, monitoring and quantification of phosphorus status in soil is essential. In this work, laser-induced breakdown spectroscopy assisted by laser-induced fluorescence (LIBS-LIF) is introduced for quantification of readily soluble phosphorus in soil and compared to the analytical performance of the conventional LIBS method. Mineral soils with variable phosphorus status were used for the analysis. The calibration curves are plotted to evaluate the detection limit of the soluble phosphorus. Compared results demonstrate improvement in detection limit from 3.74 mg/kg to 0.12 mg/kg for clay soil and from 10.94 mg/kg to 0.27 mg/kg for silt loam/loam soil in LIBS and LIBS-LIF measurements, respectively. For the LIBS-LIF measurement, detection limits are comparable with established chemical soil analyses. The proposed method would substantially reduce required sample preparation and laboratory work compared with conventional phosphorus quantification. In addition, as the calibration curves demonstrate that the calibration for soluble phosphorus holds within a soil type, LIBS-LIF has the potential to be used for high throughput soil analysis.
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Affiliation(s)
- Shweta Soni
- Photonics Laboratory, Physics Unit, Tampere University, FI-33101, Tampere, Finland
- Comenius University, FMPH, Mlynska dolina F2, 842 48, Bratislava, Slovakia
| | - Jan Viljanen
- Photonics Laboratory, Physics Unit, Tampere University, FI-33101, Tampere, Finland
| | - Risto Uusitalo
- Natural Resources Institute Finland, Tietotie 4, FI-31600, Jokioinen, Finland
| | - Pavel Veis
- Comenius University, FMPH, Mlynska dolina F2, 842 48, Bratislava, Slovakia
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Lednev VN, Sdvizhenskii PA, Dorohov AS, Gudkov SV, Pershin SM. Improving LIBS analysis of non-flat heterogeneous samples by signals mapping. APPLIED OPTICS 2023; 62:2030-2038. [PMID: 37133090 DOI: 10.1364/ao.473111] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/04/2023]
Abstract
Heterogeneous material analysis by the laser-induced breakdown spectroscopy (LIBS) technique is challenging in real practice due to requirements for representative sampling and non-flat surfaces of the samples. Methods complementary to LIBS (plasma imaging, plasma acoustics, sample surface color imaging) have been introduced to improve zinc (Zn) determination in soybean grist material by LIBS. The detailed statistical study revealed that atomic/ionic lines emission and other LIBS signals were distributed normally except for acoustics signals. The correlation between LIBS and complementary signals was rather poor due to the large variability of the particle properties of soybean grist material. Still, analyte line normalization on plasma background emission was rather simple and effective for Zn analysis but required a few hundred spot samplings for representative Zn quantification. Non-flat heterogeneous samples (soybean grist pellets) were analyzed by LIBS mapping but it was demonstrated that the choice of sampling area is crucial for reliably analyte determination.
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Han PC, Yang K, Jiao LZ, Li HC. Rapid quantitative analysis of potassium in soil based on direct-focused laser ablation-laser induced breakdown spectroscopy. Front Chem 2022; 10:967158. [PMID: 36118321 PMCID: PMC9474727 DOI: 10.3389/fchem.2022.967158] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/12/2022] [Accepted: 08/12/2022] [Indexed: 11/13/2022] Open
Abstract
A fast quantitative analysis method of soil potassium based on direct-focused laser ablation-laser induced breakdown spectroscopy (direct-focused LA-LIBS) was proposed and tested. A high single-pulse energy laser (200 mJ/pulse) beam was focused on the aerosols near the focus of the 10 kHz fiber laser to generate plasma spectra, and the analytical capability of the direct-focused LA-LIBS system was compared with traditional LIBS system using a high single-pulse energy laser (SP-LIBS). The result showed that for moist soil samples the data stability of the direct-focused LA-LIBS method was significantly improved and the R2 factor of the calibration curve improved from 0.64 to 0.93, the limit of detection improved from 159.2 μg/g to 140.9 μg/g. Three random soil samples from different areas of Beijing suburbs were analyzed by the direct-focused LA-LIBS method, and the results were consistent with AAS. The direct-focused LA-LIBS method proposed is different from the traditional double-pulse technology and laser ablation-assisted technology because it not only does not need carrier gas, but also can overcome the matrix differences better, especially the influence of moisture, which provides a new idea for the rapid detection of nutrient elements in field soils.
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Affiliation(s)
- Peng-Cheng Han
- Department of Chemistry and Chemical Engineering, University of Science and Technology Beijing, Beijing, China
- BGRIMM Technology Group, Beijing, China
| | - Kun Yang
- BGRIMM Technology Group, Beijing, China
| | - Lei-Zi Jiao
- Research Center of Intelligent Equipment, Beijing Academy of Agriculture and Forestry Sciences, Beijing, China
| | - Hua-Chang Li
- BGRIMM Technology Group, Beijing, China
- *Correspondence: Hua-Chang Li,
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Enhanced Laser-Induced Breakdown Spectroscopy for Heavy Metal Detection in Agriculture: A Review. SENSORS 2022; 22:s22155679. [PMID: 35957235 PMCID: PMC9370981 DOI: 10.3390/s22155679] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/30/2022] [Revised: 07/19/2022] [Accepted: 07/27/2022] [Indexed: 02/05/2023]
Abstract
Heavy metal pollution in agriculture is a significant problem that endangers human health. Laser-induced breakdown spectroscopy (LIBS) is an emerging technique for material and elemental analysis, especially heavy metals, based on atomic emission spectroscopy. The LIBS technique has been widely used for rapid detection of heavy metals with its advantages of convenient operation, simultaneous detection of multi-elements, wide range of elements, and no requirement for the state and quantity of samples. However, the development of LIBS is limited by its detection sensitivity and limit of detection (LOD). Therefore, in order to improve the detection sensitivity and LOD of LIBS, it is necessary to enhance the LIBS signal to achieve the purpose of detecting heavy metal elements in agriculture. This review mainly introduces the basic instruments and principles of LIBS and summarizes the methods of enhanced LIBS signal detection of heavy metal elements in agriculture over the past 10 years. The three main approaches to enhancing LIBS are sample pretreatment, adding laser pulses, and using auxiliary devices. An enhanced LIBS signal may improve the LOD of heavy metal elements in agriculture and the sensitivity and stability of the LIBS technique. The enhanced LIBS technique will have a broader prospect in agricultural heavy metal monitoring and can provide technical support for developing heavy metal detection instruments.
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Harefa E, Zhou W. Performing sequential forward selection and variational autoencoder techniques in soil classification based on laser-induced breakdown spectroscopy. ANALYTICAL METHODS : ADVANCING METHODS AND APPLICATIONS 2021; 13:4926-4933. [PMID: 34610059 DOI: 10.1039/d1ay01257f] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/13/2023]
Abstract
The feasibility and accuracy of several combination classification models, i.e., quadratic discriminant analysis (QDA), random forest (RF), Bernoulli naïve Bayes (BNB), and support vector machine (SVM) classification models combined with either sequential feature selection (SFS) or dimensionality reduction methods, for classifying soil with laser-induced breakdown spectroscopy (LIBS) had been explored in this study. Each algorithm combination was compared to assess their classification performance. After eliminating the irrelevant features of the data using sequential feature selection (SFS), the performances were all improved for the studied four classification models, and the best accuracy reached 97.88% by SFS-SVM. The dimensions of the data were then reduced using variational autoencoder (VAE), truncated singular value decomposition (TSVD), and isometric mapping (Isomap), respectively. The classification accuracy improved for all combination models with dimensionality reduction, and impressive accuracies of 98.12% from TSVD-SVM and 98.24% from VAE-SVM were obtained. These results demonstrate an effective way to reduce uncorrelated features, high dimensionality, and redundant information in the LIBS dataset. In addition, coupling classification models with feature selection and dimensionality reduction techniques could significantly optimize the classification performance of LIBS.
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Affiliation(s)
- Edward Harefa
- Key Laboratory of Optical Information Detection and Display Technology of Zhejiang, Zhejiang Normal University, Jinhua, 321004, China.
| | - Weidong Zhou
- Key Laboratory of Optical Information Detection and Display Technology of Zhejiang, Zhejiang Normal University, Jinhua, 321004, China.
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Favre A, Morel V, Bultel A, Godard G, Idlahcen S, Benyagoub A, Monnet I, Sémérok A, Dinescu M, Markelj S, Magaud P, Grisolia C. Double pulse laser-induced plasmas on W and Al by ps-LIBS: Focus on the plasma-second pulse interaction. FUSION ENGINEERING AND DESIGN 2021. [DOI: 10.1016/j.fusengdes.2021.112364] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
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Chen G, Yang G, Ling Z, Yang Y, Zhan Y, Jin X. The parameter optimization of lasers' energy ratio of the double-pulse laser induced breakdown spectrometry for heavy metal elements in the soil. ANALYTICAL METHODS : ADVANCING METHODS AND APPLICATIONS 2021; 13:1502-1510. [PMID: 33690762 DOI: 10.1039/d1ay00237f] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/12/2023]
Abstract
Laser-induced breakdown spectroscopy (LIBS) is a rapid, no-sample preparation, remote detection method that has been applied widely in the area of heavy metal detection in the soil. However, the promotion of LIBS is limited by its disadvantages, such as low precision analysis, a high detection limit, and so on. In recent years, many studies have been conducted to improve the LIBS spectral intensity. The double-pulse LIBS (DP-LIBS) is a representative technology in this area. Most of the research work focuses on the analytical methods of DP-LIBS, including the spatial configuration, the inter-pulse time, and the effect of signal enhancement of the DP-LIBS. However, there are few reports about the effect of the energy proportion of the two lasers and the contribution of different laser energies on the signal enhancement, and the inter-pulse time under the conditions of different laser energies. Moreover, DP-LIBS is mostly evaluated by the enhancement factor of the spectral signal, and there are few reports on the quantitative analysis of double-pulse LIBS. This study, which mainly detects Cu, Ni, and Pb in the soil, focuses on the contribution of the signal enhancement by adjusting the energy ratio of the two lasers and the best inter-pulse time under the conditions of different laser energies. Then, quantitative analysis of spectral signals obtained by single-pulse LIBS (SP-LIBS) and DP-LIBS are performed based on the random forest (RF) model. The results demonstrate that DP-LIBS shows better analytical performance than SP-LIBS, the coefficients of determination (R2) of the test have great improvement, the root-mean-squared error (RMSE) is much decreased and the relative error is much improved. Thus, this study shows that DP-LIBS is an effective method for the quantitative analysis of heavy metals in the soil.
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Affiliation(s)
- Guanyu Chen
- College of Instrumentation & Electrical Engineering, Jilin University, Changchun 130061, China.
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Chen T, Zhang T, Li H. Applications of laser-induced breakdown spectroscopy (LIBS) combined with machine learning in geochemical and environmental resources exploration. Trends Analyt Chem 2020. [DOI: 10.1016/j.trac.2020.116113] [Citation(s) in RCA: 28] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
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10
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Lu C, Lv G, Shi C, Qiu D, Jin F, Gu M, Sha W. Quantitative analysis of pH value in soil using laser-induced breakdown spectroscopy coupled with a multivariate regression method. APPLIED OPTICS 2020; 59:8582-8587. [PMID: 33104537 DOI: 10.1364/ao.401405] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/03/2020] [Accepted: 08/12/2020] [Indexed: 06/11/2023]
Abstract
The quantitative analyses of pH value in soil have been performed using laser-induced breakdown spectroscopy (LIBS) technology. The aim of this work was to obtain a reliable and accurate method for rapid detection of pH value in soil. Seventy-four samples were used as a calibration set, and 24 samples were used as a prediction set. To eliminate the matrix effect, the multivariate models of partial least-squares regression (PLSR) and least-squares support vector regression (LS-SVR) were used to construct the models. The intensities of nine emission lines of C, Ca, Na, O, H, Mg, Al, and Fe elements were used to fit the models. For the PLSR model, the correlation coefficient was 0.897 and 0.906 for the calibration and prediction set, respectively. Furthermore, the analysis accuracy was improved effectively by the LS-SVR method, and the correlation coefficients for calibration and prediction set were improved to 0.991 and 0.987. The prediction mean absolute error was pH 0.1 units, and the root mean square error of the prediction was only 0.079. The results indicated that the LIBS technique coupled with LS-SVR could be a reliable and accurate method for determining pH value in soil.
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Combining Laser-Induced Breakdown Spectroscopy (LIBS) and Visible Near-Infrared Spectroscopy (Vis-NIRS) for Soil Phosphorus Determination. SENSORS 2020; 20:s20185419. [PMID: 32967345 PMCID: PMC7571271 DOI: 10.3390/s20185419] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/21/2020] [Revised: 09/15/2020] [Accepted: 09/18/2020] [Indexed: 11/16/2022]
Abstract
Conventional wet chemical methods for the determination of soil phosphorus (P) pools, relevant for environmental and agronomic purposes, are labor-intensive. Therefore, alternative techniques are needed, and a combination of the spectroscopic techniques—in this case, laser-induced breakdown spectroscopy (LIBS)—and visible near-infrared spectroscopy (vis-NIRS) could be relevant. We aimed at exploring LIBS, vis-NIRS and their combination for soil P estimation. We analyzed 147 Danish agricultural soils with LIBS and vis-NIRS. As reference measurements, we analyzed water-extractable P (Pwater), Olsen P (Polsen), oxalate-extractable P (Pox) and total P (TP) by conventional wet chemical protocols, as proxies for respectively leachable, plant-available, adsorbed inorganic P, and TP in soil. Partial least squares regression (PLSR) models combined with interval partial least squares (iPLS) and competitive adaptive reweighted sampling (CARS) variable selection methods were tested, and the relevant wavelengths for soil P determination were identified. LIBS exhibited better results compared to vis-NIRS for all P models, except for Pwater, for which results were comparable. Model performance for both the LIBS and vis-NIRS techniques as well as the combined LIBS-vis-NIR approach was significantly improved when variable selection was applied. CARS performed better than iPLS in almost all cases. Combined LIBS and vis-NIRS models with variable selection showed the best results for all four P pools, except for Pox where the results were comparable to using the LIBS model with CARS. Merging LIBS and vis-NIRS with variable selection showed potential for improving soil P determinations, but larger and independent validation datasets should be tested in future studies.
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Erler A, Riebe D, Beitz T, Löhmannsröben HG, Gebbers R. Soil Nutrient Detection for Precision Agriculture Using Handheld Laser-Induced Breakdown Spectroscopy (LIBS) and Multivariate Regression Methods (PLSR, Lasso and GPR). SENSORS (BASEL, SWITZERLAND) 2020; 20:E418. [PMID: 31940811 PMCID: PMC7014682 DOI: 10.3390/s20020418] [Citation(s) in RCA: 34] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/20/2019] [Revised: 01/07/2020] [Accepted: 01/09/2020] [Indexed: 11/16/2022]
Abstract
Precision agriculture (PA) strongly relies on spatially differentiated sensor information. Handheld instruments based on laser-induced breakdown spectroscopy (LIBS) are a promising sensor technique for the in-field determination of various soil parameters. In this work, the potential of handheld LIBS for the determination of the total mass fractions of the major nutrients Ca, K, Mg, N, P and the trace nutrients Mn, Fe was evaluated. Additionally, other soil parameters, such as humus content, soil pH value and plant available P content, were determined. Since the quantification of nutrients by LIBS depends strongly on the soil matrix, various multivariate regression methods were used for calibration and prediction. These include partial least squares regression (PLSR), least absolute shrinkage and selection operator regression (Lasso), and Gaussian process regression (GPR). The best prediction results were obtained for Ca, K, Mg and Fe. The coefficients of determination obtained for other nutrients were smaller. This is due to much lower concentrations in the case of Mn, while the low number of lines and very weak intensities are the reason for the deviation of N and P. Soil parameters that are not directly related to one element, such as pH, could also be predicted. Lasso and GPR yielded slightly better results than PLSR. Additionally, several methods of data pretreatment were investigated.
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Affiliation(s)
- Alexander Erler
- Physical Chemistry, University of Potsdam, Karl-Liebknecht-Str. 24-25, 14476 Potsdam, Germany; (A.E.); (D.R.); (T.B.)
| | - Daniel Riebe
- Physical Chemistry, University of Potsdam, Karl-Liebknecht-Str. 24-25, 14476 Potsdam, Germany; (A.E.); (D.R.); (T.B.)
| | - Toralf Beitz
- Physical Chemistry, University of Potsdam, Karl-Liebknecht-Str. 24-25, 14476 Potsdam, Germany; (A.E.); (D.R.); (T.B.)
| | - Hans-Gerd Löhmannsröben
- Physical Chemistry, University of Potsdam, Karl-Liebknecht-Str. 24-25, 14476 Potsdam, Germany; (A.E.); (D.R.); (T.B.)
| | - Robin Gebbers
- Leibniz Institute for Agricultural Engineering and Bioeconomy (ATB), Max-Eyth-Allee 100, 14469 Potsdam, Germany;
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Ni F, Zhu X, Gu F, Hu Y. Nondestructive detection of apple crispness via optical fiber spectroscopy based on effective wavelengths. Food Sci Nutr 2019; 7:3654-3663. [PMID: 31763014 PMCID: PMC6848846 DOI: 10.1002/fsn3.1222] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/20/2019] [Revised: 08/16/2019] [Accepted: 08/23/2019] [Indexed: 11/25/2022] Open
Abstract
Crispness is regarded as a significant quality index for apples. Currently, destructive sensory evaluation is the accepted method used to detect apple crispness, making it essential to develop a method that can detect apple crispness in a nondestructive manner. In this study, spectroscopy was proposed as the nondestructive technique for detecting apples' crispness, ultimately obtaining a spectral reflectance curve between 450 nm and 1,000 nm. In order to simplify and improve modeling efficiency, successive projections algorithm (SPA) and x-loading weights (x-LW) methods were used to select the most effective wavelengths. Partial least squares (PLS) algorithm, radial basis neural networks (RBNN), and multilayer perceptron neural networks (MLPNN) methods were used to establish the models and to predict the crispness of "Fuji" and "Qinguan" apple varieties. Based on the full wavelength (FW), the prediction accuracy of the PLS model for "Fuji" and "Qinguan" apple varieties was 92.05% and 95.87%, respectively. The effective wavelengths selected via SPA for the "Fuji" apple variety were 450.41 nm, 476.80 nm, 677.75 nm, and 750.72 nm, and the effective wavelengths selected via x-LW for the "Qinguan" apple variety were 542.51 nm, 544.79 nm, 676.96 nm, and 718.29 nm. The prediction accuracy of the PLS model based on effective wavelengths for "Fuji" and "Qinguan" apple varieties reached 91.31% and 96.41%, respectively. Compared with the RBNN model, the MLPNN model achieved better prediction results for both "Fuji" and "Qinguan" apples, with the prediction accuracy reaching 97.8% and 99.9%, respectively. Based on the above findings, effective wavelength selection and MLPNN modeling were able to detect apple crispness with the highest accuracy. Overall, it can be concluded that the less effective wavelengths are conducive to developing an instrument for crispness detection.
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Affiliation(s)
- Fupeng Ni
- College of Mechanical and Electronic EngineeringNorthwest A&F UniversityYanglingChina
| | - Xiaowen Zhu
- College of Mechanical and Electronic EngineeringNorthwest A&F UniversityYanglingChina
| | - Fang Gu
- College of Mechanical and Electronic EngineeringNorthwest A&F UniversityYanglingChina
| | - Yaohua Hu
- College of Mechanical and Electronic EngineeringNorthwest A&F UniversityYanglingChina
- Key Laboratory of Agricultural Internet of ThingsMinistry of AgricultureYanglingChina
- Shaanxi Key Laboratory of Agricultural Information Perception and Intelligent ServiceYanglingChina
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Quantitative Analysis of Soil Total Nitrogen Using Hyperspectral Imaging Technology with Extreme Learning Machine. SENSORS 2019; 19:s19204355. [PMID: 31600914 PMCID: PMC6832974 DOI: 10.3390/s19204355] [Citation(s) in RCA: 24] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/29/2019] [Revised: 10/04/2019] [Accepted: 10/07/2019] [Indexed: 11/17/2022]
Abstract
Soil nutrient detection is important for precise fertilization. A total of 150 soil samples were picked from Lishui City. In this work, the total nitrogen (TN) content in soil samples was detected in the spectral range of 900-1700 nm using a hyperspectral imaging (HSI) system. Characteristic wavelengths were extracted using uninformative variable elimination (UVE) and the successive projections algorithm (SPA), separately. Partial least squares (PLS) and extreme learning machine (ELM) were used to establish the calibration models with full spectra and characteristic wavelengths, respectively. The results indicated that the prediction effect of the nonlinear ELM model was superior to the linear PLS model. In addition, the models using the characteristic wavelengths could also achieve good results, and the UVE-ELM model performed better, having a correlation coefficient of prediction (rp), root-mean-square error of prediction (RMSEP), and residual prediction deviation (RPD) of 0.9408, 0.0075, and 2.97, respectively. The UVE-ELM model was then used to estimate the TN content in the soil sample and obtain a distribution map. The research results indicate that HSI can be used for the detection and visualization of the distribution of TN content in soil, providing a basis for future large-scale monitoring of soil nutrient distribution and rational fertilization.
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Zhang H, Wang S, Li D, Zhang Y, Hu J, Wang L. Edible Gelatin Diagnosis Using Laser-Induced Breakdown Spectroscopy and Partial Least Square Assisted Support Vector Machine. SENSORS (BASEL, SWITZERLAND) 2019; 19:s19194225. [PMID: 31569410 PMCID: PMC6806298 DOI: 10.3390/s19194225] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 09/05/2019] [Revised: 09/25/2019] [Accepted: 09/27/2019] [Indexed: 06/10/2023]
Abstract
Edible gelatin has been widely used as a food additive in the food industry, and illegal adulteration with industrial gelatin will cause serious harm to human health. The present work used laser-induced breakdown spectroscopy (LIBS) coupled with the partial least square-support vector machine (PLS-SVM) method for the fast and accurate estimation of edible gelatin adulteration. Gelatin samples with 11 different adulteration ratios were prepared by mixing pure edible gelatin with industrial gelatin, and the LIBS spectra were recorded to analyze their elemental composition differences. The PLS, SVM, and PLS-SVM models were separately built for the prediction of gelatin adulteration ratios, and the hybrid PLS-SVM model yielded a better performance than only the PLS and SVM models. Besides, four different variable selection methods, including competitive adaptive reweighted sampling (CARS), Monte Carlo uninformative variable elimination (MC-UVE), random frog (RF), and principal component analysis (PCA), were adopted to combine with the SVM model for comparative study; the results further demonstrated that the PLS-SVM model was superior to the other SVM models. This study reveals that the hybrid PLS-SVM model, with the advantages of low computational time and high prediction accuracy, can be employed as a preferred method for the accurate estimation of edible gelatin adulteration.
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Affiliation(s)
- Hao Zhang
- College of Mechanical and Electrical Engineering, Henan Agricultural University, Zhengzhou 450002, China.
- Henan International Joint Laboratory of Laser Technology in Agriculture Sciences, Zhengzhou 450002, China.
| | - Shun Wang
- College of Science, Henan Agricultural University, Zhengzhou 450002, China.
| | - Dongxian Li
- College of Mechanical and Electrical Engineering, Henan Agricultural University, Zhengzhou 450002, China.
- Henan International Joint Laboratory of Laser Technology in Agriculture Sciences, Zhengzhou 450002, China.
| | - Yanyan Zhang
- College of Mechanical and Electrical Engineering, Henan Agricultural University, Zhengzhou 450002, China.
- Henan International Joint Laboratory of Laser Technology in Agriculture Sciences, Zhengzhou 450002, China.
| | - Jiandong Hu
- College of Mechanical and Electrical Engineering, Henan Agricultural University, Zhengzhou 450002, China.
- Henan International Joint Laboratory of Laser Technology in Agriculture Sciences, Zhengzhou 450002, China.
| | - Ling Wang
- College of Mechanical and Electrical Engineering, Henan Agricultural University, Zhengzhou 450002, China.
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Fast and Simultaneous Determination of Soil Properties Using Laser-Induced Breakdown Spectroscopy (LIBS): A Case Study of Typical Farmland Soils in China. SOIL SYSTEMS 2019. [DOI: 10.3390/soilsystems3040066] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/20/2022]
Abstract
Accurate management of soil nutrients and fast and simultaneous acquisition of soil properties are crucial in the development of sustainable agriculture. However, the conventional methods of soil analysis are generally labor-intensive, environmentally unfriendly, as well as time- and cost-consuming. Laser-induced breakdown spectroscopy (LIBS) is a “superstar” technique that has yielded outstanding results in the elemental analysis of a wide range of materials. However, its application for analysis of farmland soil faces the challenges of matrix effects, lack of large-scale soil samples with distinct origin and nature, and problems with simultaneous determination of multiple soil properties. Therefore, LIBS technique, in combination with partial least squares regression (PLSR), was applied to simultaneously determinate soil pH, cation exchange capacity (CEC), soil organic matter (SOM), total nitrogen (TN), total phosphorus (TP), total potassium (TK), available phosphorus (AP), and available potassium (AK) in 200 soils from different farmlands in China. The prediction performances of full spectra and characteristic lines were evaluated and compared. Based on full spectra, the estimates of pH, CEC, SOM, TN, and TK achieved excellent prediction abilities with the residual prediction deviation (RPDV) values > 2.0 and the estimate of TP featured good performance with RPDV value of 1.993. However, using characteristic lines only improved the predicted accuracy of SOM, but reduced the prediction accuracies of TN, TP, and TK. In addition, soil AP and AK were predicted poorly with RPDV values of < 1.4 based on both full spectra and characteristic lines. The weak correlations between conventionally analyzed soil AP and AK and soil LIBS spectra are responsible for the poor prediction abilities of AP and AK contents. Findings from this study demonstrated that the LIBS technique combined with multivariate methods is a promising alternative for fast and simultaneous detection of some properties (i.e., pH and CEC) and nutrient contents (i.e., SOM, TN, TP, and TK) in farmland soils because of the extraordinary prediction performances achieved for these attributes.
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Lu C, Wang M, Wang L, Hu H, Wang R. Univariate and multivariate analyses of strontium and vanadium in soil by laser-induced breakdown spectroscopy. APPLIED OPTICS 2019; 58:7510-7516. [PMID: 31674402 DOI: 10.1364/ao.58.007510] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/30/2019] [Accepted: 08/29/2019] [Indexed: 06/10/2023]
Abstract
Univariate and multivariate analyses of strontium (Sr) and vanadium (V) elements in soil have been performed using laser-induced breakdown spectroscopy technology. Thirty-three samples were used as a calibration set, and 11 samples were used as a prediction set. The results demonstrated that the correlation coefficients of the calibration curves method were poor due to the matrix effect. Then, the multivariate models of partial least-squares regression and least squares support vector regression (LS-SVR) were used to construct models. The analysis accuracy was improved effectively by the LS-SVR method, and the correlation coefficient is 0.999 for Sr and 0.983 for V. The average relative errors for the prediction set are lower than 7.45% and 2.88% for Sr and V, respectively. The results indicated that the LIBS technique coupled with LS-SVR could be a reliable and accurate method in the quantitative determination of elemental Sr and V in complex matrices like soil.
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Sha W, Li J, Xiao W, Ling P, Lu C. Quantitative Analysis of Elements in Fertilizer Using Laser-Induced Breakdown Spectroscopy Coupled with Support Vector Regression Model. SENSORS 2019; 19:s19153277. [PMID: 31349648 PMCID: PMC6696108 DOI: 10.3390/s19153277] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/21/2019] [Revised: 07/18/2019] [Accepted: 07/22/2019] [Indexed: 11/16/2022]
Abstract
The rapid detection of the elements nitrogen (N), phosphorus (P), and potassium (K) is beneficial to the control of the compound fertilizer production process, and it is of great significance in the fertilizer industry. The aim of this work was to compare the detection ability of laser-induced breakdown spectroscopy (LIBS) coupled with support vector regression (SVR) and obtain an accurate and reliable method for the rapid detection of all three elements. A total of 58 fertilizer samples were provided by Anhui Huilong Group. The collection of samples was divided into a calibration set (43 samples) and a prediction set (15 samples) by the Kennard–Stone (KS) method. Four different parameter optimization methods were used to construct the SVR calibration models by element concentration and the intensity of characteristic line variables, namely the traditional grid search method (GSM), genetic algorithm (GA), particle swarm optimization (PSO), and least squares (LS). The training time, determination coefficient, and the root-mean-square error for all parameter optimization methods were analyzed. The results indicated that the LIBS technique coupled with the least squares–support vector regression (LS-SVR) method could be a reliable and accurate method in the quantitative determination of N, P, and K elements in complex matrix like compound fertilizers.
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Affiliation(s)
- Wen Sha
- Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education, School of Electric Engineering and Automation, Anhui University, Hefei 230061, China
| | - Jiangtao Li
- Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education, School of Electric Engineering and Automation, Anhui University, Hefei 230061, China
| | - Wubing Xiao
- Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education, School of Electric Engineering and Automation, Anhui University, Hefei 230061, China
| | - Pengpeng Ling
- Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education, School of Electric Engineering and Automation, Anhui University, Hefei 230061, China
| | - Cuiping Lu
- Laboratory of Intelligent Decision, Institute of Intelligent Machines, Chinese Academy of Sciences, Hefei 230031, China.
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Nicolodelli G, Cabral J, Menegatti CR, Marangoni B, Senesi GS. Recent advances and future trends in LIBS applications to agricultural materials and their food derivatives: An overview of developments in the last decade (2010–2019). Part I. Soils and fertilizers. Trends Analyt Chem 2019. [DOI: 10.1016/j.trac.2019.03.032] [Citation(s) in RCA: 41] [Impact Index Per Article: 8.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/27/2022]
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Shen T, Li W, Zhang X, Kong W, Liu F, Wang W, Peng J. High-Sensitivity Determination of Nutrient Elements in Panax notoginseng by Laser-induced Breakdown Spectroscopy and Chemometric Methods. Molecules 2019; 24:molecules24081525. [PMID: 31003405 PMCID: PMC6515346 DOI: 10.3390/molecules24081525] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/20/2019] [Revised: 04/12/2019] [Accepted: 04/16/2019] [Indexed: 12/25/2022] Open
Abstract
High-accuracy and fast detection of nutritive elements in traditional Chinese medicine Panax notoginseng (PN) is beneficial for providing useful assessment of the healthy alimentation and pharmaceutical value of PN herbs. Laser-induced breakdown spectroscopy (LIBS) was applied for high-accuracy and fast quantitative detection of six nutritive elements in PN samples from eight producing areas. More than 20,000 LIBS spectral variables were obtained to show elemental differences in PN samples. Univariate and multivariate calibrations were used to analyze the quantitative relationship between spectral variables and elements. Multivariate calibration based on full spectra and selected variables by the least absolute shrinkage and selection operator (Lasso) weights was used to compare the prediction ability of the partial least-squares regression (PLS), least-squares support vector machines (LS-SVM), and Lasso models. More than 90 emission lines for elements in PN were found and located. Univariate analysis was negatively interfered by matrix effects. For potassium, calcium, magnesium, zinc, and boron, LS-SVM models based on the selected variables obtained the best prediction performance with Rp values of 0.9546, 0.9176, 0.9412, 0.9665, and 0.9569 and root mean squared error of prediction (RMSEP) of 0.7704 mg/g, 0.0712 mg/g, 0.1000 mg/g, 0.0012 mg/g, and 0.0008 mg/g, respectively. For iron, the Lasso model based on full spectra obtained the best result with an Rp value of 0.9348 and RMSEP of 0.0726 mg/g. The results indicated that the LIBS technique coupled with proper multivariate chemometrics could be an accurate and fast method in the determination of PN nutritive elements for traditional Chinese medicine management and pharmaceutical analysis.
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Affiliation(s)
- Tingting Shen
- College of Biosystems Engineering and Food Science, Zhejiang University, 866 Yuhangtang Road, Hangzhou 310058, China.
| | - Weijiao Li
- Chinese Materia Medica, Yunnan University of Chinese Medicine, Kunming 650500, China.
| | - Xi Zhang
- Chinese Materia Medica, Yunnan University of Chinese Medicine, Kunming 650500, China.
| | - Wenwen Kong
- School of Information Engineering, Zhejiang A & F University, Hangzhou 311300, China.
| | - Fei Liu
- College of Biosystems Engineering and Food Science, Zhejiang University, 866 Yuhangtang Road, Hangzhou 310058, China.
- Key Laboratory of Spectroscopy Sensing, Ministry of Agriculture and Rural Affairs, Hangzhou 310058, China.
| | - Wei Wang
- College of Biosystems Engineering and Food Science, Zhejiang University, 866 Yuhangtang Road, Hangzhou 310058, China.
| | - Jiyu Peng
- College of Biosystems Engineering and Food Science, Zhejiang University, 866 Yuhangtang Road, Hangzhou 310058, China.
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