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Chen R, Yan Q, Tuoheti T, Xu L, Gao Q, Zhang Y, Ren H, Zheng L, Wang F, Liu Y. A prediction model of rubber content in the dried root of Taraxacum kok-saghyz Rodin based on near-infrared spectroscopy. PLANT METHODS 2024; 20:77. [PMID: 38797847 PMCID: PMC11128126 DOI: 10.1186/s13007-024-01183-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/15/2023] [Accepted: 04/12/2024] [Indexed: 05/29/2024]
Abstract
BACKGROUND Taraxacum kok-saghyz Rodin (TKS) is a highly potential source of natural rubber (NR) due to its wide range of suitable planting areas, strong adaptability, and suitability for mechanized planting and harvesting. However, current methods for detecting NR content are relatively cumbersome, necessitating the development of a rapid detection model. This study used near-infrared spectroscopy technology to establish a rapid detection model for NR content in TKS root segments and powder samples. The K445 strain at different growth stages within a year and 129 TKS samples hybridized with dandelion were used to obtain their near-infrared spectral data. The rubber content in the root of the samples was detected using the alkaline boiling method. The Monte Carlo sampling method (MCS) was used to filter abnormal data from the root segments of TKS and powder samples, respectively. The SPXY algorithm was used to divide the training set and validation set in a 3:1 ratio. The original spectrum was preprocessed using moving window smoothing (MWS), standard normalized variate (SNV), multiplicative scatter correction (MSC), and first derivative (FD) algorithms. The competitive adaptive reweighted sampling (CARS) algorithm and the corresponding chemical characteristic bands of NR were used to screen the bands. Partial least squares (PLS), random forest (RF), Lightweight gradient augmentation machine (LightGBM), and convolutional neural network (CNN) algorithms were employed to establish a model using the optimal spectral processing method for three different bands: full band, CARS algorithm, and chemical characteristic bands corresponding to NR. The model with the best predictive performance for high rubber content intervals (rubber content > 15%) was identified. RESULT The results indicated that the optimal rubber content prediction models for TKS root segments and powder samples were MWS-FD CASR-RF and MWS-FD chemical characteristic band RF, respectively. Their respective R P 2 , RMSEP, and RPDP values were 0.951, 0.979, 1.814, 1.133, 4.498, and 6.845. In the high rubber content range, the model based on the LightGBM algorithm had the best prediction performance, with the RMSEP of the root segments and powder samples being 0.752 and 0.918, respectively. CONCLUSIONS This research indicates that dried TKS root powder samples are more appropriate for constructing a rubber content prediction model than segmented samples, and the predictive capability of root powder samples is superior to that of root segmented samples. Especially in the elevated rubber content range, the model formulated using the LightGBM algorithm has superior predictive performance, which could offer a theoretical basis for the rapid detection technology of TKS content in the future.
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Affiliation(s)
- Runfeng Chen
- Agricultural College, Xinjiang Agricultural University, Urumqi, 830052, People's Republic of China
- Institute of Crop Germplasm Resource, Xinjiang Academy of Agricultural Sciences, Urumqi, 830091, People's Republic of China
| | - Qingqing Yan
- Institute of Crop Germplasm Resource, Xinjiang Academy of Agricultural Sciences, Urumqi, 830091, People's Republic of China
- National Central Asian Characteristic Crop Germplasm Resources Medium-Term Gene Bank (Urumqi), Urumqi, 830091, People's Republic of China
| | - Tuhanguli Tuoheti
- Institute of Crop Germplasm Resource, Xinjiang Academy of Agricultural Sciences, Urumqi, 830091, People's Republic of China
- National Central Asian Characteristic Crop Germplasm Resources Medium-Term Gene Bank (Urumqi), Urumqi, 830091, People's Republic of China
| | - Lin Xu
- Institute of Crop Germplasm Resource, Xinjiang Academy of Agricultural Sciences, Urumqi, 830091, People's Republic of China.
- National Central Asian Characteristic Crop Germplasm Resources Medium-Term Gene Bank (Urumqi), Urumqi, 830091, People's Republic of China.
| | - Qiang Gao
- Institute of Crop Germplasm Resource, Xinjiang Academy of Agricultural Sciences, Urumqi, 830091, People's Republic of China.
- National Central Asian Characteristic Crop Germplasm Resources Medium-Term Gene Bank (Urumqi), Urumqi, 830091, People's Republic of China.
| | - Yan Zhang
- Institute of Crop Germplasm Resource, Xinjiang Academy of Agricultural Sciences, Urumqi, 830091, People's Republic of China
- National Central Asian Characteristic Crop Germplasm Resources Medium-Term Gene Bank (Urumqi), Urumqi, 830091, People's Republic of China
| | - Hailong Ren
- Crops Research Institute, Guangdong Academy of Agricultural Sciences, Guangdong Provincial Key Laboratory of Crop Genetic Improvement, Guangzhou, 510308, People's Republic of China
| | - Lipeng Zheng
- Agricultural College, Xinjiang Agricultural University, Urumqi, 830052, People's Republic of China
- Institute of Crop Germplasm Resource, Xinjiang Academy of Agricultural Sciences, Urumqi, 830091, People's Republic of China
| | - Feng Wang
- Beijing Linglong Tyre Company Limited, Beijing, 101102, People's Republic of China
| | - Ya Liu
- Comprehensive Testing Ground, Xinjiang Academy of Agricultural Sciences, Urumqi, 830052, People's Republic of China
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Gallego B, García-Martínez MM, Latorre G, Carrión ME, Hurtado de Mendoza J, Carmona M, Zalacain A. New strategies to analyze argentatins A and B in guayule (Parthenium argentatum, A. Gray). Talanta 2023; 265:124856. [PMID: 37356192 DOI: 10.1016/j.talanta.2023.124856] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2023] [Revised: 05/31/2023] [Accepted: 06/20/2023] [Indexed: 06/27/2023]
Abstract
There is considerable interest in the exploitation of compounds belonging to the triterpenoid family from guayule (Parthenium argentatum, A. Gray), as they offer several beneficial effects to human health. The most abundant triterpenoids in guayule resin are the argentatins, which are currently analyzed by labor-intensive and time-consuming techniques. The purpose of the present study was to estimate argentatins and isoargentatins A and B in guayule using near-infrared spectroscopy (NIRS) and flow injection analysis (FIA). Results revealed that the best partial least squares regression model exhibited excellent correlation with the values estimated by NIRS calibration (r2c = 0.99-1.00) and cross-validation (r2cv = 0.94-0.99), and the residual predictive deviation was >3 in all cases. After optimization of the liquid chromatography-mass spectrometry and FIA parameters, the FIA mode could reliably collect data for argentatin A and B after applying a calculated coverage factor. In sum, NIRS and FIA appear to be a robust option for the estimation and routine analysis of argentatins in guayule stems and resin, respectively.
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Affiliation(s)
- Beatriz Gallego
- Instituto de Toxicología de La Defensa, Hospital Central de La Defensa Gómez Ulla, Gta. Ejército 1, 28047, Madrid, Spain.
| | - M Mercedes García-Martínez
- Instituto Técnico Agronómico Provincial de Albacete, ITAP. Parque Empresarial Campollano, 2(a) Avenida, 02007, Albacete, 61, Spain; Universidad de Castilla-La Mancha, E.T.S.I. Agronómica, de Montes y Biotecnología (ETSIAMB), Cátedra de Química Agrícola, Avda. de España S/n, Albacete, 02071, Spain.
| | - Guayente Latorre
- Universidad de Castilla-La Mancha, E.T.S.I. Agronómica, de Montes y Biotecnología (ETSIAMB), Cátedra de Química Agrícola, Avda. de España S/n, Albacete, 02071, Spain.
| | - M Engracia Carrión
- Universidad de Castilla-La Mancha, Institute for Regional Development (IDR), Food Quality Research Group, Campus Universitario S/n, Albacete, 02071, Spain.
| | | | - Manuel Carmona
- Universidad de Castilla-La Mancha, Institute for Regional Development (IDR), Food Quality Research Group, Campus Universitario S/n, Albacete, 02071, Spain.
| | - Amaya Zalacain
- Universidad de Castilla-La Mancha, E.T.S.I. Agronómica, de Montes y Biotecnología (ETSIAMB), Cátedra de Química Agrícola, Avda. de España S/n, Albacete, 02071, Spain.
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Okyere FG, Cudjoe D, Sadeghi-Tehran P, Virlet N, Riche AB, Castle M, Greche L, Simms D, Mhada M, Mohareb F, Hawkesford MJ. Modeling the spatial-spectral characteristics of plants for nutrient status identification using hyperspectral data and deep learning methods. FRONTIERS IN PLANT SCIENCE 2023; 14:1209500. [PMID: 37908836 PMCID: PMC10613979 DOI: 10.3389/fpls.2023.1209500] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 04/20/2023] [Accepted: 09/05/2023] [Indexed: 11/02/2023]
Abstract
Sustainable fertilizer management in precision agriculture is essential for both economic and environmental reasons. To effectively manage fertilizer input, various methods are employed to monitor and track plant nutrient status. One such method is hyperspectral imaging, which has been on the rise in recent times. It is a remote sensing tool used to monitor plant physiological changes in response to environmental conditions and nutrient availability. However, conventional hyperspectral processing mainly focuses on either the spectral or spatial information of plants. This study aims to develop a hybrid convolution neural network (CNN) capable of simultaneously extracting spatial and spectral information from quinoa and cowpea plants to identify their nutrient status at different growth stages. To achieve this, a nutrient experiment with four treatments (high and low levels of nitrogen and phosphorus) was conducted in a glasshouse. A hybrid CNN model comprising a 3D CNN (extracts joint spectral-spatial information) and a 2D CNN (for abstract spatial information extraction) was proposed. Three pre-processing techniques, including second-order derivative, standard normal variate, and linear discriminant analysis, were applied to selected regions of interest within the plant spectral hypercube. Together with the raw data, these datasets were used as inputs to train the proposed model. This was done to assess the impact of different pre-processing techniques on hyperspectral-based nutrient phenotyping. The performance of the proposed model was compared with a 3D CNN, a 2D CNN, and a Hybrid Spectral Network (HybridSN) model. Effective wavebands were selected from the best-performing dataset using a greedy stepwise-based correlation feature selection (CFS) technique. The selected wavebands were then used to retrain the models to identify the nutrient status at five selected plant growth stages. From the results, the proposed hybrid model achieved a classification accuracy of over 94% on the test dataset, demonstrating its potential for identifying nitrogen and phosphorus status in cowpea and quinoa at different growth stages.
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Affiliation(s)
- Frank Gyan Okyere
- Sustainable Soils and Crops, Rothamsted Research, Harpenden, United Kingdom
- School of Water, Energy and Environment, Cranfield University, Cranfield, United Kingdom
| | - Daniel Cudjoe
- Sustainable Soils and Crops, Rothamsted Research, Harpenden, United Kingdom
- School of Water, Energy and Environment, Cranfield University, Cranfield, United Kingdom
| | | | - Nicolas Virlet
- Sustainable Soils and Crops, Rothamsted Research, Harpenden, United Kingdom
| | - Andrew B. Riche
- Sustainable Soils and Crops, Rothamsted Research, Harpenden, United Kingdom
| | - March Castle
- Sustainable Soils and Crops, Rothamsted Research, Harpenden, United Kingdom
| | - Latifa Greche
- Sustainable Soils and Crops, Rothamsted Research, Harpenden, United Kingdom
| | - Daniel Simms
- School of Water, Energy and Environment, Cranfield University, Cranfield, United Kingdom
| | - Manal Mhada
- AgroBioSciences Department, University of Mohammed VI Polytechnic, Ben Guerir, Morocco
| | - Fady Mohareb
- School of Water, Energy and Environment, Cranfield University, Cranfield, United Kingdom
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Wang F, Wang C, Song S. Rapid and Low-Cost Detection of Millet Quality by Miniature Near-Infrared Spectroscopy and Iteratively Retaining Informative Variables. Foods 2022; 11:foods11131841. [PMID: 35804657 PMCID: PMC9265786 DOI: 10.3390/foods11131841] [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: 05/04/2022] [Revised: 06/10/2022] [Accepted: 06/20/2022] [Indexed: 02/01/2023] Open
Abstract
Traditional chemical methods for testing the fat content of millet, a widely consumed grain, are time-consuming and costly. In this study, we developed a low-cost and rapid method for fat detection and quantification in millet. A miniature NIR spectrometer connected to a smartphone was used to collect spectral data from millet samples of different origins. The standard normal variate (SNV) and first derivative (1D) methods were used to preprocess spectral signals. Variable selection methods, including bootstrapping soft shrinkage (BOSS), the variable iterative space shrinkage approach (VISSA), iteratively retaining informative variables (IRIV), iteratively variable subset optimization (IVSO), and competitive adaptive reweighted sampling (CARS), were used to select characteristic wavelengths. The partial least squares regression (PLSR) algorithm was employed to develop the regression models aimed at predicting the fat content in millet. The results showed that the proposed 1D-IRIV-PLSR model achieved optimal accuracy for fat detection, with a correlation coefficient for prediction (Rp) of 0.953, a root mean square error for prediction (RMSEP) of 0.301 g/100 g, and a residual predictive deviation (RPD) of 3.225, by using only 18 characteristic wavelengths. This result highlights the feasibility of using this low-cost and high-portability assessment tool for millet quality testing, which provides an optional solution for in situ inspection of millet quality in different scenarios, such as production lines or sales stores.
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Affiliation(s)
- Fuxiang Wang
- School of Mechanical and Electrical Engineering, Inner Mongolia Agricultural University, Hohhot 010000, China;
| | - Chunguang Wang
- School of Mechanical and Electrical Engineering, Inner Mongolia Agricultural University, Hohhot 010000, China;
- Correspondence: ; Tel.: +86-0471-4304788
| | - Shiyong Song
- Mongolia Lvtao Detection Technology Company Limited, Hohhot 010000, China;
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Tea Analyzer: A low-cost and portable tool for quality quantification of postharvest fresh tea leaves. Lebensm Wiss Technol 2022. [DOI: 10.1016/j.lwt.2022.113248] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
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Fast Determination of the Rubber Content in Taraxacum kok-saghyz Fresh Biomass Using Portable Near-Infrared Spectroscopy and Pyrolysis–Gas Chromatography. JOURNAL OF ANALYSIS AND TESTING 2022. [DOI: 10.1007/s41664-022-00217-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/27/2022]
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Grabska J, Beć KB, Mayr S, Huck CW. Theoretical Simulation of Near-Infrared Spectrum of Piperine: Insight into Band Origins and the Features of Regression Models. APPLIED SPECTROSCOPY 2021; 75:1022-1032. [PMID: 34236925 PMCID: PMC8320572 DOI: 10.1177/00037028211027951] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/22/2021] [Accepted: 06/05/2021] [Indexed: 06/13/2023]
Abstract
We investigated the near-infrared spectrum of piperine using quantum mechanical calculations. We evaluated two efficient approaches, DVPT2//PM6 and DVPT2//ONIOM [PM6:B3LYP/6-311++G(2df, 2pd)] that yielded a simulated spectrum with varying accuracy versus computing time factor. We performed vibrational assignments and unveiled complex nature of the near-infrared spectrum of piperine, resulting from a high level of band convolution. The most meaningful contribution to the near-infrared absorption of piperine results from binary combination bands. With the available detailed near-infrared assignment of piperine, we interpreted the properties of partial least square regression models constructed in our earlier study to describe the piperine content in black pepper samples. Two models were compared with spectral data sets obtained with a benchtop and a miniaturized spectrometer. The two spectrometers implement distinct technology which leads to a profound instrumental difference and discrepancy in the predictive performance when analyzing piperine content. We concluded that the sensitivity of the two instruments to certain types of piperine vibrations is different and that the benchtop spectrometer unveiled higher selectivity. Such difference in obtaining chemical information from a sample can be one of the reasons why the benchtop spectrometer performs better in analyzing the piperine content of black pepper. This evidenced direct correspondence between the features critical for applied near-infrared spectroscopic routine and the underlying vibrational properties of the analyzed constituent in a complex sample.
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Affiliation(s)
| | - Krzysztof B. Beć
- Krzysztof B. Beć, University of Innsbruck, Innrain 80-82, Innsbruck 6020, Austria.
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Wang Y, Liu Y, Chen Y, Cui Q, Li L, Ning J, Zhang Z. Spatial distribution of total polyphenols in multi-type of tea using near-infrared hyperspectral imaging. Lebensm Wiss Technol 2021. [DOI: 10.1016/j.lwt.2021.111737] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/15/2022]
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Zhang ZY, Wang YJ, Yan H, Chang XW, Zhou GS, Zhu L, Liu P, Guo S, Dong TTX, Duan JA. Rapid Geographical Origin Identification and Quality Assessment of Angelicae Sinensis Radix by FT-NIR Spectroscopy. JOURNAL OF ANALYTICAL METHODS IN CHEMISTRY 2021; 2021:8875876. [PMID: 33505766 PMCID: PMC7815386 DOI: 10.1155/2021/8875876] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 09/26/2020] [Revised: 12/16/2020] [Accepted: 12/30/2020] [Indexed: 06/12/2023]
Abstract
Angelicae Sinensis Radix is a widely used traditional Chinese medicine and spice in China. The purpose of this study was to develop a methodology for geographical classification of Angelicae Sinensis Radix and determine the contents of ferulic acid and Z-ligustilide in the samples using near-infrared spectroscopy. A qualitative model was established to identify the geographical origin of Angelicae Sinensis Radix using Fourier transform near-infrared (FT-NIR) spectroscopy. Support vector machine (SVM) algorithms were used for the establishment of a qualitative model. The optimum SVM model had a recognition rate of 100% for the calibration set and 83.72% for the prediction set. In addition, a quantitative model was established to predict the content of ferulic acid and Z-ligustilide using FT-NIR. Partial least squares regression (PLSR) algorithms were used for the establishment of a quantitative model. Synergy interval-PLS (Si-PLS) was used to screen the characteristic spectral interval to obtain the best PLSR model. The coefficient of determination for calibration (R2C) for the best PLSR models established with the optimal spectral preprocessing method and selected important spectral regions for the quantitative determination of ferulic acid and Z-ligustilide was 0.9659 and 0.9611, respectively, while the coefficient of determination for prediction (R2P) was 0.9118 and 0.9206, respectively. The values of the ratio of prediction to deviation (RPD) of the two final optimized PLSR models were greater than 2. The results suggested that NIR spectroscopy combined with SVM and PLSR algorithms could be exploited in the discrimination of Angelicae Sinensis Radix from different geographical locations for quality assurance and monitoring. This study might serve as a reference for quality evaluation of agricultural, pharmaceutical, and food products.
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Affiliation(s)
- Zhen-yu Zhang
- National and Local Collaborative Engineering Center of Chinese Medicinal Resources Industrialization and Formulae Innovative Medicine, and Jiangsu Collaborative Innovation Center of Chinese Medicinal Resources Industrialization, Nanjing University of Chinese Medicine, Nanjing 210023, China
| | - Ying-jun Wang
- National and Local Collaborative Engineering Center of Chinese Medicinal Resources Industrialization and Formulae Innovative Medicine, and Jiangsu Collaborative Innovation Center of Chinese Medicinal Resources Industrialization, Nanjing University of Chinese Medicine, Nanjing 210023, China
| | - Hui Yan
- National and Local Collaborative Engineering Center of Chinese Medicinal Resources Industrialization and Formulae Innovative Medicine, and Jiangsu Collaborative Innovation Center of Chinese Medicinal Resources Industrialization, Nanjing University of Chinese Medicine, Nanjing 210023, China
| | - Xiang-wei Chang
- School of Pharmacy, Anhui University of Chinese Medicine, Hefei 230012, China
| | - Gui-sheng Zhou
- National and Local Collaborative Engineering Center of Chinese Medicinal Resources Industrialization and Formulae Innovative Medicine, and Jiangsu Collaborative Innovation Center of Chinese Medicinal Resources Industrialization, Nanjing University of Chinese Medicine, Nanjing 210023, China
| | - Lei Zhu
- National and Local Collaborative Engineering Center of Chinese Medicinal Resources Industrialization and Formulae Innovative Medicine, and Jiangsu Collaborative Innovation Center of Chinese Medicinal Resources Industrialization, Nanjing University of Chinese Medicine, Nanjing 210023, China
| | - Pei Liu
- National and Local Collaborative Engineering Center of Chinese Medicinal Resources Industrialization and Formulae Innovative Medicine, and Jiangsu Collaborative Innovation Center of Chinese Medicinal Resources Industrialization, Nanjing University of Chinese Medicine, Nanjing 210023, China
| | - Sheng Guo
- National and Local Collaborative Engineering Center of Chinese Medicinal Resources Industrialization and Formulae Innovative Medicine, and Jiangsu Collaborative Innovation Center of Chinese Medicinal Resources Industrialization, Nanjing University of Chinese Medicine, Nanjing 210023, China
| | - Tina T. X. Dong
- Division of Life Science and Centre for Chinese Medicine, The Hong Kong University of Science and Technology, Hong Kong, China
| | - Jin-ao Duan
- National and Local Collaborative Engineering Center of Chinese Medicinal Resources Industrialization and Formulae Innovative Medicine, and Jiangsu Collaborative Innovation Center of Chinese Medicinal Resources Industrialization, Nanjing University of Chinese Medicine, Nanjing 210023, China
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