LIU Zijian, GU Jiacheng, ZHOU Cong, et al. Identification of Geographical Origin for Hawthorn Based on Hyperspectral Imaging Technology[J]. Science and Technology of Food Industry, 2024, 45(10): 282−291. (in Chinese with English abstract). doi: 10.13386/j.issn1002-0306.2023090074.
Citation: LIU Zijian, GU Jiacheng, ZHOU Cong, et al. Identification of Geographical Origin for Hawthorn Based on Hyperspectral Imaging Technology[J]. Science and Technology of Food Industry, 2024, 45(10): 282−291. (in Chinese with English abstract). doi: 10.13386/j.issn1002-0306.2023090074.

Identification of Geographical Origin for Hawthorn Based on Hyperspectral Imaging Technology

More Information
  • Received Date: September 10, 2023
  • Available Online: March 12, 2024
  • The geographical origin was one of the important factors affecting the quality of hawthorn. To discriminate the geographical origin of hawthorn rapidly and nondestructively, hawthorns from five different provincial production areas were used as samples, and visible-shortwave infrared (410~2500 nm) band hyperspectral data were obtained for the pedicel face (G), side (C), and bottom (D) of each sample by using a near-infrared hyperspectral imaging system. Partial least squares discriminant analysis (PLS-DA), support vector machine (SVM), and random forests (RF) classification models were built by multivariate scattering correction (MSC), first derivative (D1), SG smoothing (Savitzky-Golay, SG), and standard normal transform (SNV) four preprocessing methods. The results showed that the D-D1-SVM model discriminated optimally, with 100% accuracy in both the training and prediction sets. To simplify the model, successive projections algorithm (SPA) and competitive adaptive reweighted sampling algorithm (CARS) were applied to select feature wavelength. The multivariate data analysis revealed that the D-SPA-SVM model had the best performance, with an accuracy of 95.2% and 93% for the training and prediction sets, respectively. This study could provide technical support for rapid and non-destructive identification of hawthorn origin.
  • [1]
    国家药典委员会. 中华人民共和国药典(2020年版)一部[M]. 北京:中国医药科技出版社, 2020:1902. [Chinese Pharmacopoeia Commission. Pharmacopoeia of the people's republic of China 2020[M]. Beijing:China Medical Science Press, 2020:1902.]

    Chinese Pharmacopoeia Commission. Pharmacopoeia of the people's republic of China 2020[M]. Beijing: China Medical Science Press, 2020: 1902.
    [2]
    WU J Q, PENG W, QIN R X, et al. Crataegus pinnatifida:Chemical constituents, pharmacology, and potential applications[J]. Molecules,2014,19(2):1685−1712. doi: 10.3390/molecules19021685
    [3]
    楼陆军, 罗洁霞, 高云. 山楂的化学成分和药理作用研究概述[J]. 中国药业,2014,23(3):92−94. [LOU L J, LUO J X, GAO Y. Overview of chemical compositions and pharmacological action of Crataegus pinnatifida Bunge[J]. China Pharmaceuticals,2014,23(3):92−94.]

    LOU L J, LUO J X, GAO Y. Overview of chemical compositions and pharmacological action of Crataegus pinnatifida Bunge[J]. China Pharmaceuticals, 2014, 23(3): 92−94.
    [4]
    杨晓宁, 孙欣光, 周丽娟, 等. 不同产地山楂及其炮制品质量研究[J]. 中国中医药信息杂志,2022,29(7):105−110. [YANG X N, SUN X G, ZHOU L J, et al. Quality study of Crataegus fructus from different producing areas and its processed products[J]. Chinese Journal of Information on Traditional Chinese Medicine,2022,29(7):105−110.]

    YANG X N, SUN X G, ZHOU L J, et al. Quality study of Crataegus fructus from different producing areas and its processed products[J]. Chinese Journal of Information on Traditional Chinese Medicine, 2022, 29(7): 105−110.
    [5]
    崔洁, 刘心悦, 常冠华, 等. 基于HPLC和AHP对不同产地山楂的比较研究[J]. 时珍国医国药,2021,32(9):2254−2257. [CUI J, LIU X Y, CHANG G H, et al. Comparative study on contents of four chemical components of dajinxing Crataegus pinnatifida Bge. var. major N. E. Br. fruits from different habitats[J]. Lishizhen Medicine and Materia Medica Research,2021,32(9):2254−2257.]

    CUI J, LIU X Y, CHANG G H, et al. Comparative study on contents of four chemical components of dajinxing Crataegus pinnatifida Bge. var. major N. E. Br. fruits from different habitats[J]. Lishizhen Medicine and Materia Medica Research, 2021, 32(9): 2254−2257.
    [6]
    王夏茵, 岳宝森, 张炜华, 等. 不同产地天麻HPLC指纹图谱、含量测定及化学模式识别研究[J]. 中南药学,2023,21(5):1358−1362. [WANG X Y, YUE B S, ZHANG W H, et al. HPLC fingerprint, content determination and pattern recognition of Gastrodia elata Bl. from different regions[J]. Central South Pharmacy,2023,21(5):1358−1362.]

    WANG X Y, YUE B S, ZHANG W H, et al. HPLC fingerprint, content determination and pattern recognition of Gastrodia elata Bl. from different regions[J]. Central South Pharmacy, 2023, 21(5): 1358−1362.
    [7]
    范胜莲, 何清春, 吴诗慧. 气相色谱-质谱联用技术对不同产地冬虫夏草的鉴别研究[J]. 四川中医,2021,39(5):48−51. [FAN S L, HE Q C, WU S H. Study on the identification of chinese caterpillar fungus in different producing areas by gas chromatography-mass spectrometry[J]. Journal of Sichuan of Traditional Chinese Medicine,2021,39(5):48−51.]

    FAN S L, HE Q C, WU S H. Study on the identification of chinese caterpillar fungus in different producing areas by gas chromatography-mass spectrometry[J]. Journal of Sichuan of Traditional Chinese Medicine, 2021, 39(5): 48−51.
    [8]
    贺光云, 侯雪, 闫志农, 等. 基于超高效液相色谱-四极杆串联飞行时间质谱的绿茶产地溯源研究[J]. 农产品质量与安全,2021,5(5):63−68. [HE G Y, HOU X, YAN Z N, et al. The geographical origin traceability of green tea by ultra high performance liquid chromatography-quadrupole time-offlight mass spectrometry[J]. Quality and Safety of Agro-Products,2021,5(5):63−68.]

    HE G Y, HOU X, YAN Z N, et al. The geographical origin traceability of green tea by ultra high performance liquid chromatography-quadrupole time-offlight mass spectrometry[J]. Quality and Safety of Agro-Products, 2021, 5(5): 63−68.
    [9]
    张海芳, 纳日, 韩育梅, 等. 光谱无损检测技术在农产品产地溯源中的研究进展[J]. 食品工业科技,2023,44(8):17−25. [ZHANG H F, NA R, HAN Y M, et al. Research progress of spectral nondestructive testing technology in traceability of agricultural products[J]. Science and Technology of Food Industry,2023,44(8):17−25.]

    ZHANG H F, NA R, HAN Y M, et al. Research progress of spectral nondestructive testing technology in traceability of agricultural products[J]. Science and Technology of Food Industry, 2023, 44(8): 17−25.
    [10]
    ZAREEF M, ARSLAN M, HASSAN M M, et al. Recent advances in assessing qualitative and quantitative aspects of cereals using nondestructive techniques:A review[J]. Trends in Food Science & Technology,2021,116:815−828.
    [11]
    张立欣, 张楠楠, 张晓. 基于机器学习算法对苹果产地的判别分析[J]. 激光与光电子学进展,2022,59(4):451−457. [ZHANG L X, ZHANG N N, ZHANG X. Discriminant analysis of apple origin based on machine learning algorithm[J]. Laser & Optoelectronics Progress,2022,59(4):451−457.]

    ZHANG L X, ZHANG N N, ZHANG X. Discriminant analysis of apple origin based on machine learning algorithm[J]. Laser & Optoelectronics Progress, 2022, 59(4): 451−457.
    [12]
    SUN Y, LI Y H, PAN L Q, et al. Authentication of the geographic origin of Yangshan region peaches based on hyperspectral imaging[J]. Postharvest Biology and Technology,2021,171:111320. doi: 10.1016/j.postharvbio.2020.111320
    [13]
    SMALLMAN L, ARTEMIOU A, MORGAN J. Sparse generalised principal component analysis[J]. Pattern Recognition,2018,83:443−455. doi: 10.1016/j.patcog.2018.06.014
    [14]
    LI J B, HUANG W Q, CHEN L P, et al. Variable selection in visible and near-infrared spectral analysis for noninvasive determination of soluble solids content of ‘Ya’ pear[J]. Food Analytical Methods,2014,7(9):1891−1902. doi: 10.1007/s12161-014-9832-8
    [15]
    第五鹏瑶, 卞希慧, 王姿方, 等. 光谱预处理方法选择研究[J]. 光谱学与光谱分析,2019,39(9):2800−2806. [DIWU P Y, BIAN X H, WANG Z F, et al. Study on the selection of spectral preprocessing methods[J]. Spectroscopy and Spectral Analysis,2019,39(9):2800−2806.]

    DIWU P Y, BIAN X H, WANG Z F, et al. Study on the selection of spectral preprocessing methods[J]. Spectroscopy and Spectral Analysis, 2019, 39(9): 2800−2806.
    [16]
    吴静珠, 李晓琪, 林珑, 等. 基于AlexNet卷积神经网络的大米产地高光谱快速判别[J]. 中国食品学报,2022,22(1):282−288. [WU J Z, LI X Q, LIN L, et al. Fast hyperspectral discrimination of rice origin based on AlexNet convolutional neural network[J]. Journal of Chinese Institute of Food Science and Technology,2022,22(1):282−288.]

    WU J Z, LI X Q, LIN L, et al. Fast hyperspectral discrimination of rice origin based on AlexNet convolutional neural network[J]. Journal of Chinese Institute of Food Science and Technology, 2022, 22(1): 282−288.
    [17]
    LI X L, WEI Y Z, XU J, et al. SSC and pH for sweet assessment and maturity classification of harvested cherry fruit based on NIR hyperspectral imaging technology[J]. Postharvest Biology and Technology,2018,143:112−118. doi: 10.1016/j.postharvbio.2018.05.003
    [18]
    陈书媛, 张友超, 杨杰, 等. 基于高光谱成像技术的白茶储藏年份判别[J]. 食品工业科技,2021,42(18):276−283. [CHEN S Y, ZHANG Y C, YANG J, et al. Discrimination of storage time of white tea using hyperspectral imaging[J]. Science and Technology of Food Industry,2021,42(18):276−283.]

    CHEN S Y, ZHANG Y C, YANG J, et al. Discrimination of storage time of white tea using hyperspectral imaging[J]. Science and Technology of Food Industry, 2021, 42(18): 276−283.
    [19]
    朱潘雨, 方雯昕, 黄敏, 等. 基于高光谱图像的小麦种子纯度/含水量检测系统[J]. 计算机测量与控制,2023,31(4):76−82. [ZHU P Y, FANG W X, HUANG M, et al. Wheat seed purity or moisture content detection system based on hyperspectral images[J]. Computer Measurement & Control,2023,31(4):76−82.]

    ZHU P Y, FANG W X, HUANG M, et al. Wheat seed purity or moisture content detection system based on hyperspectral images[J]. Computer Measurement & Control, 2023, 31(4): 76−82.
    [20]
    ZHOU J, HUANG S, WANG M Z, et al. Performance evaluation of hybrid GA–SVM and GWO–SVM models to predict earthquake-induced liquefaction potential of soil:A multi-dataset investigation[J]. Engineering with Computers,2021,38:4197−4215.
    [21]
    MATLAB中文论坛. MATLAB神经网络30个案例分析[M]. 北京:北京航空航天大学出版社, 2010:289. [MATLAB Chinese Forum. Matlab neural network analysis of 30 cases[M]. Beijing:Beihang University Press, 2010:289.]

    MATLAB Chinese Forum. Matlab neural network analysis of 30 cases[M]. Beijing: Beihang University Press, 2010: 289.
    [22]
    周元哲. 机器学习入门[M]. 北京:清华大学出版社, 2022:268. [ZHOU Y Z. Introduction to machine learning[M]. Beijing:Tsinghua University Press, 2022:268.]

    ZHOU Y Z. Introduction to machine learning[M]. Beijing: Tsinghua University Press, 2022: 268.
    [23]
    PAUL A, MUKHERJEE D P, DAS P, et al. Improved random forest for classification[J]. IEEE Transactions on Image Processing,2018,27(8):4012−4024. doi: 10.1109/TIP.2018.2834830
    [24]
    ZHANG J, HUANG W J, ZHOU Q F. Reflectance variation within the in-chlorophyll centre waveband for robust retrieval of leaf chlorophyll content[J]. PLoS One,2014,9(11):e110812. doi: 10.1371/journal.pone.0110812
    [25]
    YU K Q, ZHAO Y R, LIU Z Y, et al. Application of visible and near-infrared hyperspectral imaging for detection of defective features in loquat[J]. Food and Bioprocess Technology,2014,7(11):3077−3087. doi: 10.1007/s11947-014-1357-z
    [26]
    JIANG S Q, HE H J, MA H J, et al. Quick assessment of chicken spoilage based on hyperspectral NIR spectra combined with partial least squares regression[J]. International Journal of Agricultural and Biological Engineering,2021,14:243−250. doi: 10.25165/j.ijabe.20211401.5726
    [27]
    YANG L, GAO H Q, MENG L W, et al. Nondestructive measurement of pectin polysaccharides using hyperspectral imaging in mulberry fruit[J]. Food Chemistry,2021,334:127614. doi: 10.1016/j.foodchem.2020.127614
    [28]
    张悦, 王游游, 张婷, 等. 高光谱结合图分割算法快速鉴别不同尺度产地陈皮[J]. 化学试剂,2023,45(1):136−143. [ZHANG Y, WANG Y Y, ZHANG T, et al. Identification of Citri reticulatae pericarpium form different scales geographical origin by hyperspectral imaging combined with image segmentation algorithm[J]. Chemical Reagents,2023,45(1):136−143.]

    ZHANG Y, WANG Y Y, ZHANG T, et al. Identification of Citri reticulatae pericarpium form different scales geographical origin by hyperspectral imaging combined with image segmentation algorithm[J]. Chemical Reagents, 2023, 45(1): 136−143.
    [29]
    MANSURI S M, CHAKRABORTY S K, MAHANTI N K, et al. Effect of germ orientation during Vis-NIR hyperspectral imaging for the detection of fungal contamination in maize kernel using PLS-DA, ANN and 1D-CNN modelling[J]. Food Control,2022,139:109077. doi: 10.1016/j.foodcont.2022.109077
    [30]
    LI X, FENG F, GAO R Z, et al. Application of near infrared reflectance (NIR) spectroscopy to identify potential PSE meat[J]. Journal of the Science of Food and Agriculture,2016,96(9):3148−3156. doi: 10.1002/jsfa.7493
    [31]
    MA C Y, REN Z S, ZHANG Z H, et al. Development of simplified models for nondestructive testing of rice (with husk) protein content using hyperspectral imaging technology[J]. Vibrational Spectroscopy,2021,114:103230. doi: 10.1016/j.vibspec.2021.103230
    [32]
    李涛, 钟玉琴, 曲明亮. 高光谱成像技术鉴别红景天的品种[J]. 华西药学杂志,2021,36(5):526−530. [LI T, ZHONG Y Q, QU M L. Identification of Rhodiola varieties by hyperspectral imaging technology[J]. West China Journal of Pharmaceutical Sciences,2021,36(5):526−530.]

    LI T, ZHONG Y Q, QU M L. Identification of Rhodiola varieties by hyperspectral imaging technology[J]. West China Journal of Pharmaceutical Sciences, 2021, 36(5): 526−530.
    [33]
    宁素云, 孟美黛, 王鹏, 等. 基于UHPLC-Q ExactiveOrbitrap-MS法的山楂不同部位化学成分分析[J]. 广州化工,2021,49(13):97−101. [NING S Y, MENG M D, WANG P, et al. Analysis of chemical constituents in different parts of hawthorn by UHPLC-Q ExactiveOrbitrap-MS[J]. Guangzhou Chemical Industry,2021,49(13):97−101.]

    NING S Y, MENG M D, WANG P, et al. Analysis of chemical constituents in different parts of hawthorn by UHPLC-Q ExactiveOrbitrap-MS[J]. Guangzhou Chemical Industry, 2021, 49(13): 97−101.
  • Other Related Supplements

  • Cited by

    Periodical cited type(5)

    1. 梅天娇,司家勇,张治中,刘佳妮,黄博荣,仪锦文. 基于油茶茶枯的生物质碳点制备及对Fe~(3+)检测研究. 化学世界. 2025(01): 25-32 .
    2. 何芳,张颖,张运良,孙双姣. 电化学法制备碳点荧光探针测定氯霉素含量的研究. 邵阳学院学报(自然科学版). 2024(01): 57-65 .
    3. 刘凯. 基于荧光探针技术的畜产品兽药残留检测方法. 饲料博览. 2024(01): 35-39 .
    4. 刘梅,米琳静,张雅欣,周怡伽,唐青愉,王艳虹,陈红,廉向金,付春梅. 荧光氮掺杂碳点构建鸡肉中氟喹诺酮类药物的高通量检测方法. 中国测试. 2024(11): 73-81 .
    5. 王小燕,刘峥,郭容婷,丁智远,吕奕菊,孔翔飞. 荧光可视化技术在食品分析中的应用进展. 理化检验-化学分册. 2023(11): 1357-1364 .

    Other cited types(0)

Catalog

    Article Metrics

    Article views (113) PDF downloads (20) Cited by(5)

    /

    DownLoad:  Full-Size Img  PowerPoint
    Return
    Return