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Cheng H, Zhang Z, Cheng Y, Guan J. Potential of hyperspectral imaging for nondestructive determination of α-farnesene and conjugated trienol content in 'Yali' pear. SPECTROCHIMICA ACTA. PART A, MOLECULAR AND BIOMOLECULAR SPECTROSCOPY 2024; 321:124688. [PMID: 38941754 DOI: 10.1016/j.saa.2024.124688] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/27/2023] [Revised: 04/22/2024] [Accepted: 06/18/2024] [Indexed: 06/30/2024]
Abstract
The sesquiterpene α-farnesene and its corresponding oxidation products, namely conjugated trienols (CTols) is well known to be correlated with the development of superficial scald, a typical physiological disorder after a long term of cold storage in pear fruit. In this work, hyperspectral imaging (HSI) technology was used for nondestructive predicting of α-farnesene and CTols [CT258, CT281 and CT(281-290)] content in 'Yali' pear. In order to obtain the best performance of calibration model and simplify the calibration model further, various preprocessing methods together with their combinations and different wavelength selection algorithms, including successive projections algorithm (SPA), competitive adaptive reweighted sampling (CARS) and uninformative variable elimination (UVE), were investigated and compared based on linear partial least square regression (PLSR) and nonlinear least square support vector machine (LS-SVM) models, respectively. In conclusion, compared to the PLSR models, the results of LS-SVM models based on original and preprocessing methods performed better for the prediction of α-farnesene and CTols, while the performance of LS-SVM models based on the selected characteristic wavelengths were worse. For α-farnesene, the best result was obtained by LS-SVM model based on MSC-FD pretreatment with the RPD value of 2.6, Rp = 0.925 and RMSEP = 4.387 nmol cm-2. And for CTols, CT281 performed better compared with CT258 and CT(281-290), achieving the result with RPD = 2.4, Rp = 0.913 and RMSEP = 2.734 nmol cm-2 based on LS-SVM model combined with SD pretreatment. The overall results illustrated HSI technology could be used for rapid and nondestructive prediction of α-farnesene and CTols in 'Yali' pear, which would be helpful for supporting postharvest decision systems.
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Affiliation(s)
- Hong Cheng
- Institute of Biotechnology and Food Science, Hebei Academy of Agricultural and Forestry Sciences, Shijiazhuang 050051, Hebei, China; Hebei Key Laboratory of Plant Genetic Engineering, Shijiazhuang 050051, Hebei, China
| | - Zishen Zhang
- Institute of Biotechnology and Food Science, Hebei Academy of Agricultural and Forestry Sciences, Shijiazhuang 050051, Hebei, China; Hebei Key Laboratory of Plant Genetic Engineering, Shijiazhuang 050051, Hebei, China; College of Horticulture, Xinjiang Agruicultural University, Urumqi 830000, Xinjiang, China
| | - Yudou Cheng
- Institute of Biotechnology and Food Science, Hebei Academy of Agricultural and Forestry Sciences, Shijiazhuang 050051, Hebei, China; Hebei Key Laboratory of Plant Genetic Engineering, Shijiazhuang 050051, Hebei, China
| | - Junfeng Guan
- Institute of Biotechnology and Food Science, Hebei Academy of Agricultural and Forestry Sciences, Shijiazhuang 050051, Hebei, China; Hebei Key Laboratory of Plant Genetic Engineering, Shijiazhuang 050051, Hebei, China.
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Mollazade K, Hashim N, Zude-Sasse M. Towards a Multispectral Imaging System for Spatial Mapping of Chemical Composition in Fresh-Cut Pineapple ( Ananas comosus). Foods 2023; 12:3243. [PMID: 37685176 PMCID: PMC10487212 DOI: 10.3390/foods12173243] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/12/2023] [Revised: 08/16/2023] [Accepted: 08/22/2023] [Indexed: 09/10/2023] Open
Abstract
With increasing public demand for ready-to-eat fresh-cut fruit, the postharvest industry requires the development and adaptation of monitoring technologies to provide customers with a product of consistent quality. The fresh-cut trade of pineapples (Ananas comosus) is on the rise, favored by the sensory quality of the product and mechanization of the cutting process. In this paper, a multispectral imaging-based approach is introduced to provide distribution maps of moisture content, soluble solids content, and carotenoids content in fresh-cut pineapple. A dataset containing hyperspectral images (380-1690 nm) and reference measurements in 10 regions of interest of 60 fruit (n = 600) was prepared. Ranking and uncorrelatedness (based on ReliefF algorithm) and subset selection (based on CfsSubset algorithm) approaches were applied to find the most informative wavelengths in which bandpass optical filters or light sources are commercially available. The correlation coefficient and error metrics obtained by cross-validated multilayer perceptron neural network models indicated that the superior selected wavelengths (495, 500, 505, 1215, 1240, and 1425 nm) resulted in prediction of moisture content with R = 0.56, MAPE = 1.92%, soluble solids content with R = 0.52, MAPE = 14.72%, and carotenoids content with R = 0.63, MAPE = 43.99%. Prediction of chemical composition in each pixel of the multispectral images using the calibration models yielded spatially distributed quantification of the fruit slice, spatially varying according to the maturation of single fruitlets in the whole pineapple. Calibration models provided reliable responses spatially throughout the surface of fresh-cut pineapple slices with a constant error. According to the approach to use commercially relevant wavelengths, calibration models could be applied in classifying fruit segments in the mechanized preparation of fresh-cut produce.
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Affiliation(s)
- Kaveh Mollazade
- Department of Biosystems Engineering, Faculty of Agriculture, University of Kurdistan, Sanandaj 6617715175, Iran;
- Department of Horticultural Engineering, Leibniz Institute for Agricultural Engineering and Bioeconomy (ATB), 14469 Potsdam, Germany
| | - Norhashila Hashim
- Department of Biological and Agricultural Engineering, Faculty of Engineering, Universiti Putra Malaysia, Serdang 43400, Selangor, Malaysia;
- SMART Farming Technology Research Centre, Faculty of Engineering, Universiti Putra Malaysia, Serdang 43400, Selangor, Malaysia
| | - Manuela Zude-Sasse
- Department of Horticultural Engineering, Leibniz Institute for Agricultural Engineering and Bioeconomy (ATB), 14469 Potsdam, Germany
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Ji J, He Y, Zhao K, Zhang M, Li M, Li H. Quality Information Detection of Agaricus bisporus Based on a Portable Spectrum Acquisition Device. Foods 2023; 12:2562. [PMID: 37444303 DOI: 10.3390/foods12132562] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/26/2023] [Revised: 06/22/2023] [Accepted: 06/29/2023] [Indexed: 07/15/2023] Open
Abstract
As one of the most popular edible fungi in the market, the quality of Agaricus bisporus will determine its sales volume. Therefore, to achieve rapid and nondestructive testing of the quality of Agaricus bisporus, this study first built a portable spectrum acquisition device for Agaricus bisporus. The Ocean Spectromeper was used to calibrate the spectral data of the device, and the linear regression analysis method was combined to analyze the two. The results showed that the Pearson correlation coefficient of significance between the two was 0.98. Then, the spectral data of Agaricus bisporus were collected, the spectral characteristic wavelength of Agaricus bisporus was extracted by the SPA and PCA algorithms, and the moisture content and whiteness prediction models based on a BP neural network and PLSR, respectively, were built. The parameters of the BP neural network model were optimized by SSA. The R2 values for the final moisture content and the predicted whiteness were 0.95 and 0.99, and the RMSE values were 5.04% and 0.60, respectively. The results show that the portable spectral acquisition and analysis device can be used for the accurate and rapid quality detection of Agaricus bisporus.
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Affiliation(s)
- Jiangtao Ji
- College of Agricultural Equipment Engineering, Henan University of Science and Technology, Luoyang 471003, China
- Collaborative Innovation Center of Machinery Equipment Advanced Manufacturing of Henan Province, Henan University of Science and Technology, Luoyang 471003, China
- Science & Technology Innovation Center for Completed Set Equipment, Longmen Laboratory, Luoyang 471023, China
| | - Yongkang He
- College of Agricultural Equipment Engineering, Henan University of Science and Technology, Luoyang 471003, China
| | - Kaixuan Zhao
- College of Agricultural Equipment Engineering, Henan University of Science and Technology, Luoyang 471003, China
- Collaborative Innovation Center of Machinery Equipment Advanced Manufacturing of Henan Province, Henan University of Science and Technology, Luoyang 471003, China
| | - Mengke Zhang
- College of Agricultural Equipment Engineering, Henan University of Science and Technology, Luoyang 471003, China
| | - Mengsong Li
- College of Agricultural Equipment Engineering, Henan University of Science and Technology, Luoyang 471003, China
| | - Hongzhen Li
- College of Agricultural Equipment Engineering, Henan University of Science and Technology, Luoyang 471003, China
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Nurkhoeriyati T, Arefi A, Kulig B, Sturm B, Hensel O. Non-destructive monitoring of quality attributes kinetics during the drying process: A case study of celeriac slices and the model generalisation in selected commodities. Food Chem 2023; 424:136379. [PMID: 37229901 DOI: 10.1016/j.foodchem.2023.136379] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/08/2022] [Revised: 05/10/2023] [Accepted: 05/12/2023] [Indexed: 05/27/2023]
Abstract
The potential of Visual-NIR hyperspectral imaging (VNIR-HSI, 425-1700 nm) to predict celeriac quality attributes during the drying process was investigated. The HSI-Gaussian Process Regression (GPR) fusion method excellently predicted moisture content (MC, R2 ≈ 1.00, RMSE = 0.77 gw 100 gs-1) and water activity (aw, R2 = 0.98, RMSE = 0.04). Moreover, the rehydration ratio (RR, R2 = 0.89, RMSE = 0.04) and colour indices (R2 = 0.80-0.93, RMSE = 0.17-1.45) were reasonably predicted. However, antioxidant activity (AA) and total phenolic compounds (TPC) were poorly predicted. These results are potentially due to MC variations dominating the NIR region, masking phenolic compounds. Finally, the celeriac-based-trained model was assessed by predicting the MC of apple, cocoyam, and carrot slices. The results were encouraging; however, a GPR model trained on the data of all four commodities was more robust (R2 ≈ 1.00, RMSE = 1-2 gw 100 gs-1).
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Affiliation(s)
- Tina Nurkhoeriyati
- Department of Agricultural and Biosystems Engineering, Faculty of Organic Agricultural Sciences, University of Kassel, 37213 Witzenhausen, Germany; Study Program of Food Technology, Faculty of Engineering, International University Liaison Indonesia (IULI), 15310 Tangerang, Indonesia.
| | - Arman Arefi
- Department of Agricultural and Biosystems Engineering, Faculty of Organic Agricultural Sciences, University of Kassel, 37213 Witzenhausen, Germany; Leibniz Institute for Agricultural Engineering and Bioeconomy (ATB), 14469 Potsdam, Germany.
| | - Boris Kulig
- Department of Agricultural and Biosystems Engineering, Faculty of Organic Agricultural Sciences, University of Kassel, 37213 Witzenhausen, Germany
| | - Barbara Sturm
- Leibniz Institute for Agricultural Engineering and Bioeconomy (ATB), 14469 Potsdam, Germany; Albrecht Daniel Thaer Institute for Agricultural and Horticultural Sciences, Humboldt Universität zu Berlin, 10115 Berlin, Germany
| | - Oliver Hensel
- Department of Agricultural and Biosystems Engineering, Faculty of Organic Agricultural Sciences, University of Kassel, 37213 Witzenhausen, Germany
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Ghanei Ghooshkhaneh N, Golzarian MR, Mollazade K. VIS-NIR spectroscopy for detection of citrus core rot caused by Alternaria alternata. Food Control 2022. [DOI: 10.1016/j.foodcont.2022.109320] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/14/2022]
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6
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Mohd Ali M, Hashim N. Non-destructive methods for detection of food quality. FUTURE FOODS 2022. [DOI: 10.1016/b978-0-323-91001-9.00003-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/01/2022] Open
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Nazir A, AlDhaheri M, Mudgil P, Marpu P, Kamal-Eldin A. Hyperspectral imaging based kinetic approach to assess quality deterioration in fresh mushrooms (Agaricus bisporus) during postharvest storage. Food Control 2022. [DOI: 10.1016/j.foodcont.2021.108298] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/04/2022]
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8
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Classification of Granite Soils and Prediction of Soil Water Content Using Hyperspectral Visible and Near-Infrared Imaging. SENSORS 2020; 20:s20061611. [PMID: 32183206 PMCID: PMC7146463 DOI: 10.3390/s20061611] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/29/2020] [Revised: 03/10/2020] [Accepted: 03/10/2020] [Indexed: 11/17/2022]
Abstract
Soil water content is one of the most important physical indicators of landslide hazards. Therefore, quickly and non-destructively classifying soils and determining or predicting water content are essential tasks for the detection of landslide hazards. We investigated hyperspectral information in the visible and near-infrared regions (400–1000 nm) of 162 granite soil samples collected from Seoul (Republic of Korea). First, effective wavelengths were extracted from pre-processed spectral data using the successive projection algorithm to develop a classification model. A gray-level co-occurrence matrix was employed to extract textural variables, and a support vector machine was used to establish calibration models and the prediction model. The results show that an optimal correct classification rate of 89.8% could be achieved by combining data sets of effective wavelengths and texture features for modeling. Using the developed classification model, an artificial neural network (ANN) model for the prediction of soil water content was constructed. The input parameter was composed of Munsell soil color, area of reflectance (near-infrared), and dry unit weight. The accuracy in water content prediction of the developed ANN model was verified by a coefficient of determination and mean absolute percentage error of 0.91 and 10.1%, respectively.
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Qi X, Jiang J, Cui X, Yuan D. Moldy Peanut Kernel Identification Using Wavelet Spectral Features Extracted from Hyperspectral Images. FOOD ANAL METHOD 2019. [DOI: 10.1007/s12161-019-01670-w] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
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10
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Wang Q, Liu Y, Gao X, Xie A, Yu H. Potential of hyperspectral imaging for nondestructive determination of chlorogenic acid content in Flos Lonicerae. JOURNAL OF FOOD MEASUREMENT AND CHARACTERIZATION 2019. [DOI: 10.1007/s11694-019-00180-x] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/31/2022]
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Non-destructive detection of Flos Lonicerae treated by sulfur fumigation based on hyperspectral imaging. JOURNAL OF FOOD MEASUREMENT AND CHARACTERIZATION 2018. [DOI: 10.1007/s11694-018-9896-z] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/28/2022]
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12
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Evaluation of Near-Infrared Hyperspectral Imaging for Detection of Peanut and Walnut Powders in Whole Wheat Flour. APPLIED SCIENCES-BASEL 2018. [DOI: 10.3390/app8071076] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/15/2022]
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13
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Sun J, Tang K, Wu X, Dai C, Chen Y, Shen J. Nondestructive identification of green tea varieties based on hyperspectral imaging technology. J FOOD PROCESS ENG 2018. [DOI: 10.1111/jfpe.12800] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Affiliation(s)
- Jun Sun
- School of Electrical and Information Engineering; Jiangsu University; Zhenjiang, 212013 China
| | - Kai Tang
- School of Electrical and Information Engineering; Jiangsu University; Zhenjiang, 212013 China
| | - Xiaohong Wu
- School of Electrical and Information Engineering; Jiangsu University; Zhenjiang, 212013 China
| | - Chunxia Dai
- School of Electrical and Information Engineering; Jiangsu University; Zhenjiang, 212013 China
| | - Yong Chen
- School of Electrical and Information Engineering; Jiangsu University; Zhenjiang, 212013 China
| | - Jifeng Shen
- School of Electrical and Information Engineering; Jiangsu University; Zhenjiang, 212013 China
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Nondestructive Freshness Discriminating of Shrimp Using Visible/Near-Infrared Hyperspectral Imaging Technique and Deep Learning Algorithm. FOOD ANAL METHOD 2017. [DOI: 10.1007/s12161-017-1050-8] [Citation(s) in RCA: 21] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/18/2022]
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15
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Jia S, Li H, Wang Y, Tong R, Li Q. Hyperspectral Imaging Analysis for the Classification of Soil Types and the Determination of Soil Total Nitrogen. SENSORS 2017; 17:s17102252. [PMID: 28974005 PMCID: PMC5677396 DOI: 10.3390/s17102252] [Citation(s) in RCA: 28] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/04/2017] [Revised: 09/27/2017] [Accepted: 09/28/2017] [Indexed: 11/16/2022]
Abstract
Soil is an important environment for crop growth. Quick and accurately access to soil nutrient content information is a prerequisite for scientific fertilization. In this work, hyperspectral imaging (HSI) technology was applied for the classification of soil types and the measurement of soil total nitrogen (TN) content. A total of 183 soil samples collected from Shangyu City (People's Republic of China), were scanned by a near-infrared hyperspectral imaging system with a wavelength range of 874-1734 nm. The soil samples belonged to three major soil types typical of this area, including paddy soil, red soil and seashore saline soil. The successive projections algorithm (SPA) method was utilized to select effective wavelengths from the full spectrum. Pattern texture features (energy, contrast, homogeneity and entropy) were extracted from the gray-scale images at the effective wavelengths. The support vector machines (SVM) and partial least squares regression (PLSR) methods were used to establish classification and prediction models, respectively. The results showed that by using the combined data sets of effective wavelengths and texture features for modelling an optimal correct classification rate of 91.8%. could be achieved. The soil samples were first classified, then the local models were established for soil TN according to soil types, which achieved better prediction results than the general models. The overall results indicated that hyperspectral imaging technology could be used for soil type classification and soil TN determination, and data fusion combining spectral and image texture information showed advantages for the classification of soil types.
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Affiliation(s)
- Shengyao Jia
- College of Mechanical and Electrical Engineering, China Jiliang University, Hangzhou 310018, China.
| | - Hongyang Li
- College of Mechanical and Electrical Engineering, China Jiliang University, Hangzhou 310018, China.
- College of Computer Science and Technology, Zhejiang University of Technology, Hangzhou 310014, China.
| | - Yanjie Wang
- College of Mechanical and Electrical Engineering, China Jiliang University, Hangzhou 310018, China.
| | - Renyuan Tong
- College of Mechanical and Electrical Engineering, China Jiliang University, Hangzhou 310018, China.
| | - Qing Li
- College of Mechanical and Electrical Engineering, China Jiliang University, Hangzhou 310018, China.
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Determination and Visualization of Peimine and Peiminine Content in Fritillaria thunbergii Bulbi Treated by Sulfur Fumigation Using Hyperspectral Imaging with Chemometrics. Molecules 2017; 22:molecules22091402. [PMID: 28832506 PMCID: PMC6151643 DOI: 10.3390/molecules22091402] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/24/2017] [Revised: 08/21/2017] [Accepted: 08/21/2017] [Indexed: 12/11/2022] Open
Abstract
Rapid, non-destructive, and accurate quantitative determination of the effective components in traditional Chinese medicine (TCM) is required by industries, planters, and regulators. In this study, near-infrared hyperspectral imaging was applied for determining the peimine and peiminine content in Fritillaria thunbergii bulbi under sulfur fumigation. Spectral data were extracted from the hyperspectral images. High-performance liquid chromatography (HPLC) was conducted to determine the reference peimine and peiminine content. The successive projection algorithm (SPA), weighted regression coefficient (Bw), competitive adaptive reweighted sampling (CARS), and random frog (RF) were used to select optimal wavelengths, while the partial least squares (PLS), least-square support vector machine (LS–SVM) and extreme learning machine (ELM) were used to build regression models. Regression models using the full spectra and optimal wavelengths obtained satisfactory results with the correlation coefficient of calibration (rc), cross-validation (rcv) and prediction (rp) of most models being over 0.8. Prediction maps of peimine and peiminine content in Fritillaria thunbergii bulbi were formed by applying regression models to the hyperspectral images. The overall results indicated that hyperspectral imaging combined with regression models and optimal wavelength selection methods were effective in determining peimine and peiminine content in Fritillaria thunbergii bulbi, which will help in the development of an online detection system for real-world quality control of Fritillaria thunbergii bulbi under sulfur fumigation.
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