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Sun J, Yao K, Cheng J, Xu M, Zhou X. Nondestructive detection of saponin content in Panax notoginseng powder based on hyperspectral imaging. J Pharm Biomed Anal 2024; 242:116015. [PMID: 38364344 DOI: 10.1016/j.jpba.2024.116015] [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: 11/20/2023] [Revised: 01/16/2024] [Accepted: 02/03/2024] [Indexed: 02/18/2024]
Abstract
This study investigated the feasibility of using hyperspectral imaging (HSI) technique to detect the saponin content in Panax notoginseng (PN) powder. The reflectance hyperspectral images of PN powder samples were collected in the spectral range of 400.6-999.9 nm. Savitzky-golay (SG) smoothing combined with detrending correction was utilized to preprocess the original spectral data. Two model population analysis (MPA) based methods, namely bootstrapping soft shrinkage (BOSS) and iteratively retains informative variables (IRIV) were employed to extract feature wavelengths from the full spectra. A generalized normal distribution optimization based extreme learning machine (GNDO-ELM) model was proposed to establish calibration model between spectra and saponin content, and compared with existing methods (GA-ELM, PSO-ELM and SSA-ELM). The result showed that the IRIV-GNDO-ELM model gave the best performance, with coefficient of determination for prediction (R2P) of 0.953 and root mean square error for prediction (RMSEP) of 0.115%. Therefore, it is possible to determine the saponin content of PN powder by using HSI technique.
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Affiliation(s)
- Jun Sun
- School of Electrical and Information Engineering of Jiangsu University, Zhenjiang 212013, China.
| | - Kunshan Yao
- School of Electrical and Information Engineering of Changzhou Institute of Technology, Changzhou 213032, China.
| | - Jiehong Cheng
- School of Electrical and Information Engineering of Jiangsu University, Zhenjiang 212013, China
| | - Min Xu
- School of Electrical and Information Engineering of Jiangsu University, Zhenjiang 212013, China
| | - Xin Zhou
- School of Electrical and Information Engineering of Jiangsu University, Zhenjiang 212013, China
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Zhong H, Yadav V, Wen Z, Zhou X, Wang M, Han S, Pan M, Zhang C, Zhang F, Wu X. Comprehensive metabolomics-based analysis of sugar composition and content in berries of 18 grape varieties. FRONTIERS IN PLANT SCIENCE 2023; 14:1200071. [PMID: 37360706 PMCID: PMC10288860 DOI: 10.3389/fpls.2023.1200071] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 04/04/2023] [Accepted: 05/16/2023] [Indexed: 06/28/2023]
Abstract
Xinjiang is the largest grape-producing region in China and the main grape cultivation area in the world. The Eurasian grape resources grown in Xinjiang are very rich in diversity. The sugar composition and content are the main factors that determine the quality of berries. However, there are currently no systematic reports on the types and contents of sugars in grapes grown in Xinjiang region. In this research, we evaluated the appearance and fruit maturity indicators of 18 grape varieties during fruit ripening and determined their sugar content using GC-MS. All cultivars primarily contained glucose, D-fructose, and sucrose. The glucose content in varieties varied from 42.13% to 46.80% of the total sugar, whereas the fructose and sucrose contents varied from 42.68% to 50.95% and 6.17% to 12.69%, respectively. The content of trace sugar identified in grape varieties varied from 0.6 to 2.3 mg/g. The comprehensive assessment by principal component analysis revealed strong positive correlations between some sugar components. A comprehensive study on the content and types of sugar will provide the foundation to determine the quality of grape cultivars and effective ways to utilize resources to improve sugar content through breeding.
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Affiliation(s)
| | | | | | | | | | | | | | | | | | - Xinyu Wu
- *Correspondence: Fuchun Zhang, ; Xinyu Wu,
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Hu Y, Ma B, Wang H, Zhang Y, Li Y, Yu G. Detecting different pesticide residues on Hami melon surface using hyperspectral imaging combined with 1D-CNN and information fusion. FRONTIERS IN PLANT SCIENCE 2023; 14:1105601. [PMID: 37223822 PMCID: PMC10200917 DOI: 10.3389/fpls.2023.1105601] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/22/2022] [Accepted: 03/31/2023] [Indexed: 05/25/2023]
Abstract
Efficient, rapid, and non-destructive detection of pesticide residues in fruits and vegetables is essential for food safety. The visible/near infrared (VNIR) and short-wave infrared (SWIR) hyperspectral imaging (HSI) systems were used to detect different types of pesticide residues on the surface of Hami melon. Taking four pesticides commonly used in Hami melon as the object, the effectiveness of single-band spectral range and information fusion in the classification of different pesticides was compared. The results showed that the classification effect of pesticide residues was better by using the spectral range after information fusion. Then, a custom multi-branch one-dimensional convolutional neural network (1D-CNN) model with the attention mechanism was proposed and compared with the traditional machine learning classification model K-nearest neighbor (KNN) algorithm and random forest (RF). The traditional machine learning classification model accuracy of both models was over 80.00%. However, the classification results using the proposed 1D-CNN were more satisfactory. After the full spectrum data was fused, it was input into the 1D-CNN model, and its accuracy, precision, recall, and F1-score value were 94.00%, 94.06%, 94.00%, and 0.9396, respectively. This study showed that both VNIR and SWIR hyperspectral imaging combined with a classification model could non-destructively detect different pesticide residues on the surface of Hami melon. The classification result using the SWIR spectrum was better than that using the VNIR spectrum, and the classification result using the information fusion spectrum was better than that using SWIR. This study can provide a valuable reference for the non-destructive detection of pesticide residues on the surface of other large, thick-skinned fruits.
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Affiliation(s)
- Yating Hu
- College of Mechanical and Electrical Engineering, Shihezi University, Shihezi, China
| | - Benxue Ma
- College of Mechanical and Electrical Engineering, Shihezi University, Shihezi, China
- Key Laboratory of Northwest Agricultural Equipment, Ministry of Agriculture and Rural Affairs, Shihezi, China
- Engineering Research Center for Production Mechanization of Oasis Characteristic Cash Crop, Ministry of Education, Shihezi, China
| | - Huting Wang
- College of Mechanical and Electrical Engineering, Shihezi University, Shihezi, China
- Key Laboratory of Northwest Agricultural Equipment, Ministry of Agriculture and Rural Affairs, Shihezi, China
- Engineering Research Center for Production Mechanization of Oasis Characteristic Cash Crop, Ministry of Education, Shihezi, China
| | - Yuanjia Zhang
- College of Mechanical and Electrical Engineering, Shihezi University, Shihezi, China
| | - Yujie Li
- College of Mechanical and Electrical Engineering, Shihezi University, Shihezi, China
| | - Guowei Yu
- College of Mechanical and Electrical Engineering, Shihezi University, Shihezi, China
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Marín-San Román S, Fernández-Novales J, Cebrián-Tarancón C, Sánchez-Gómez R, Diago MP, Garde-Cerdán T. Monitorization of Varietal Aroma Composition Dynamics during Ripening in Intact Vitis vinifera L. Tempranillo Blanco Berries by Hyperspectral Imaging. JOURNAL OF AGRICULTURAL AND FOOD CHEMISTRY 2023; 71:2616-2627. [PMID: 36700632 PMCID: PMC9912339 DOI: 10.1021/acs.jafc.2c07425] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/27/2022] [Revised: 01/13/2023] [Accepted: 01/13/2023] [Indexed: 06/17/2023]
Abstract
The measurement of aromatic maturity during grape ripening provides very important information for determining the harvest date, particularly in white cultivars. However, there are currently no tools that allow this measurement to be carried out in a noninvasive and rapid way. For this reason, in the present work, we have studied the use of hyperspectral imaging (HSI)) to estimate the aromatic composition of Vitis vinifera L. Tempranillo Blanco berries during ripening. A total of 236 spectra in the VIS+short wave near-infrared (VIS+SW-NIR) range (400-1000 nm) of intact berries were acquired contactless under laboratory conditions. As gold standard values, a total of 20 volatile compounds were quantified by gas chromatography-mass spectrometry (GC-MS), and the concentration of total soluble solids (TSS) was measured by refractometry. Calibration, cross-validation, and prediction models were built using partial least squares (PLS). Values of RCV2 ≥ 0.70 were obtained for α-terpineol, p-cymene, β-damascenone, β-ionone, benzaldehyde, benzyl alcohol, hexanal, citral, linalool, 2-phenylethanol, octanoic acid, nonanoic acid, 2-hexenal, 2-hexen-1-ol, (Z)-3-hexen-1-ol, total C13 norisoprenoids, total C6 compounds, total positive compounds (i.e., the sum of all families except C6 compounds), total benzenoids, and total soluble solids (TSS). Therefore, it can be affirmed that HSI in the VIS + SW-NIR range could be a good tool to estimate the aromatic composition of Tempranillo Blanco grape berries in a contactless, fast, and nondestructive way.
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Affiliation(s)
- Sandra Marín-San Román
- Grupo
VIENAP, Instituto de Ciencias de la Vid
y del Vino (CSIC, Universidad de La Rioja, Gobierno de La Rioja), Ctra. de Burgos, Km. 6, 26007 Logroño, Spain
| | - Juan Fernández-Novales
- Grupo
TELEVITIS, Instituto de Ciencias de la Vid
y del Vino (Universidad de La Rioja, CSIC, Gobierno de La Rioja), Ctra. de Burgos, Km. 6, 26007 Logroño, Spain
| | - Cristina Cebrián-Tarancón
- Cátedra
de Química Agrícola, E.T.S.I. Agrónomos y Montes,
Departamento de Ciencia y Tecnología Agroforestal y Genética, Universidad de Castilla-La Mancha, Avda. de España, s/n, 02071 Albacete, Spain
| | - Rosario Sánchez-Gómez
- Cátedra
de Química Agrícola, E.T.S.I. Agrónomos y Montes,
Departamento de Ciencia y Tecnología Agroforestal y Genética, Universidad de Castilla-La Mancha, Avda. de España, s/n, 02071 Albacete, Spain
| | - Maria Paz Diago
- Grupo
TELEVITIS, Instituto de Ciencias de la Vid
y del Vino (Universidad de La Rioja, CSIC, Gobierno de La Rioja), Ctra. de Burgos, Km. 6, 26007 Logroño, Spain
| | - Teresa Garde-Cerdán
- Grupo
VIENAP, Instituto de Ciencias de la Vid
y del Vino (CSIC, Universidad de La Rioja, Gobierno de La Rioja), Ctra. de Burgos, Km. 6, 26007 Logroño, Spain
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The effects of different concentration methods on the chemical composition, functional and sensory attributes of molasses produced from grape (Vitis vinifera L.) juice. JOURNAL OF FOOD MEASUREMENT AND CHARACTERIZATION 2023. [DOI: 10.1007/s11694-023-01808-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/15/2023]
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Ye W, Xu W, Yan T, Yan J, Gao P, Zhang C. Application of Near-Infrared Spectroscopy and Hyperspectral Imaging Combined with Machine Learning Algorithms for Quality Inspection of Grape: A Review. Foods 2022; 12:foods12010132. [PMID: 36613348 PMCID: PMC9818947 DOI: 10.3390/foods12010132] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/07/2022] [Revised: 12/06/2022] [Accepted: 12/22/2022] [Indexed: 12/29/2022] Open
Abstract
Grape is a fruit rich in various vitamins, and grape quality is increasingly highly concerned with by consumers. Traditional quality inspection methods are time-consuming, laborious and destructive. Near-infrared spectroscopy (NIRS) and hyperspectral imaging (HSI) are rapid, non-destructive and accurate techniques for quality inspection and safety assessment of agricultural products, which have great potential in recent years. The review summarized the applications and achievements of NIRS and HSI for the quality inspection of grapes for the last ten years. The review introduces basic principles, signal mode, data acquisition, analysis and processing of NIRS and HSI data. Qualitative and quantitative analysis were involved and compared, respectively, based on spectral features, image features and fusion data. The advantages, disadvantages and development trends of NIRS and HSI techniques in grape quality and safety inspection are summarized and discussed. The successful application of NIRS and HSI in grape quality inspection shows that many fruit inspection tasks could be assisted with NIRS and HSI.
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Affiliation(s)
- Weixin Ye
- College of Information Science and Technology, Shihezi University, Shihezi 832003, China
| | - Wei Xu
- College of Agriculture, Shihezi University, Shihezi 832003, China
| | - Tianying Yan
- College of Information Science and Technology, Shihezi University, Shihezi 832003, China
| | - Jingkun Yan
- College of Information Science and Technology, Shihezi University, Shihezi 832003, China
| | - Pan Gao
- College of Information Science and Technology, Shihezi University, Shihezi 832003, China
- Correspondence: (P.G.); (C.Z.)
| | - Chu Zhang
- School of Information Engineering, Huzhou University, Huzhou 313000, China
- Correspondence: (P.G.); (C.Z.)
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Xu M, Sun J, Cheng J, Yao K, Wu X, Zhou X. Non‐destructive prediction of total soluble solids and titratable acidity in Kyoho grape using hyperspectral imaging and deep learning algorithm. Int J Food Sci Technol 2022. [DOI: 10.1111/ijfs.16173] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022]
Affiliation(s)
- Min Xu
- School of Electrical and Information Engineering Jiangsu University Zhenjiang 212013 Jiangsu China
- School of Electronic Engineering, Changzhou College of Information Technology Changzhou 213164 Jiangsu China
| | - Jun Sun
- School of Electrical and Information Engineering Jiangsu University Zhenjiang 212013 Jiangsu China
| | - Jiehong Cheng
- School of Electrical and Information Engineering Jiangsu University Zhenjiang 212013 Jiangsu China
| | - Kunshan Yao
- School of Electrical and Information Engineering Jiangsu University Zhenjiang 212013 Jiangsu China
| | - Xiaohong Wu
- School of Electrical and Information Engineering Jiangsu University Zhenjiang 212013 Jiangsu China
| | - Xin Zhou
- School of Electrical and Information Engineering Jiangsu University Zhenjiang 212013 Jiangsu China
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MA X, LUO H, ZHANG F, GAO F. Study on the influence of region of interest on the detection of total sugar content in apple using hyperspectral imaging technology. FOOD SCIENCE AND TECHNOLOGY 2022. [DOI: 10.1590/fst.87922] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/22/2022]
Affiliation(s)
- Xueting MA
- Tarim University, China; Tarim University, China
| | - Huaping LUO
- Tarim University, China; Tarim University, China
| | - Fei ZHANG
- Tarim University, China; Tarim University, China
| | - Feng GAO
- Tarim University, China; Tarim University, China
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