食品与发酵工业 >

2023 , Vol. 49 >Issue 20: 274 - 279

DOI: https://doi.org/10.13995/j.cnki.11-1802/ts.033555

分析与检测

中宁枸杞品种的近红外光谱快速鉴别

  • 龙若兰 ,
  • 李朵 ,
  • 李佩佩 ,
  • 胡娜 ,
  • 冯丹 ,
  • 孙菁
展开
  • 1(中国科学院西北高原生物研究所 青海省青藏高原特色生物资源研究重点实验室,青海 西宁,810008)
    2(中国科学院西北高原生物研究所 中国科学院藏药研究重点实验室,青海 西宁,810008)
    3(中国科学院大学 生命科学学院,北京,100049)
第一作者：硕士研究生(孙菁研究员为通信作者,E-mail:sunj@nwipb.cas.cn)

收稿日期: 2022-09-08

  修回日期: 2022-11-23

  网络出版日期: 2023-11-20

基金资助

国家自然科学基金项目(32270402);青海省科研基础条件平台项目(2020-ZJ-T05);青海省重点实验室建设专项(2022-ZJ-Y18);青海省创新平台建设专项(2020-0407-NCC-0001)

收起

Rapid variety discrimination of Lycium barbarum L. by near-infrared spectrum

  • LONG Ruolan ,
  • LI Duo ,
  • LI Peipei ,
  • HU Na ,
  • FENG Dan ,
  • SUN Jing
Expand
  • 1(Qinghai Provincial Key Laboratory of Qinghai-Tibet Plateau Biological Resources, Northwest Institute of Plateau Biology, Chinese Academy of Sciences, Xining 810008, China)
    2(Key Laboratory of Tibetan Medicine Research, Northwest Institute of Plateau Biology, Chinese Academy of Sciences, Xining 810008, China)
    3(College of Life Sciences, University of Chinese Academy of Sciences, Beijing 100049, China)

Received date: 2022-09-08

  Revised date: 2022-11-23

  Online published: 2023-11-20

Fold

摘要

利用2种不同采集方式(光纤、积分球)进行近红外光谱采集,优化并建立了12种不同枸杞品种的品种判别模型,实现了对中宁枸杞品种的快速准确判别。在获取得到12种中宁枸杞品种的近红外光谱的基础上,比较不同采集方式下的光谱差异。分别建立了距离匹配(distance match,DM)、判别分析(discriminant analysis,DA)、支持向量机(support vector machine,SVM)等3个不同的品种判别模型。结果表明,以积分球采集方式下的谱图质量较好,在此采集方式下所建的DM、DA、SVM品种判别模型的识别率、预测率和准确率均达到100.00%;而用光纤所采集的谱图只有DA模型的识别率、预测率和准确率达到100.00%。积分球采集方式下所建判别方法可快速、高效地实现中宁枸杞品种判别,为生产实践的应用提供技术支撑。

关键词： 近红外光谱; 光谱采集方式; 品种判别; 中宁枸杞品种

本文引用格式

龙若兰 , 李朵 , 李佩佩 , 胡娜 , 冯丹 , 孙菁 . 中宁枸杞品种的近红外光谱快速鉴别[J]. 食品与发酵工业, 2023 , 49(20) : 274 -279 . DOI: 10.13995/j.cnki.11-1802/ts.033555

Abstract

As a kind of Solanaceae plant, Lycium barbarum L. is not only a food material but also has medicinal functions of health preservation and anticancer, and it is also listed in the “Pharmacopoeia of the People′s Republic of China”. In this study, 12 different varieties of L. barbarum were considered as the research objects. The similarities and differences of the near-infrared spectrum (NIR) under different acquisition methods (optical fiber and integrating sphere) were compared, and the classification models were established by different methods, including distance match (DM), discriminant analysis (DA), and support vector machine (SVM) with TQ analyst and Python software. By optimizing the near-infrared spectrum collection method of the various discrimination model, the rapid discrimination of L. barbarum varieties was realized. Results showed that the integrating sphere outperformed optical fiber in both the spectral quality and effect of variety discrimination modeling. The recognition rate, prediction rate, and accuracy rate of DM, DA, and SVM variety discrimination models established by this collection method all reach 100.00%, which indicated that the established method was fast and efficient for the discrimination of L. barbarum. However, only the DA model achieved 100.00% recognition, prediction, and accuracy for the spectra collected with optical fibers. This study can provide technical support for the application in production practice.

Key words： near-infrared spectrum; spectrum acquisition mode; variety discrimination; Lycium barbarum L. varieties

参考文献

[1] 国家药典委员会. 中华人民共和国药典[M].北京:中国医药科技出版社, 2020:260.
Chinese Pharmacopoeia Commission.Pharmacopoeia of the People′s Republic of China[M].Beijing:China Medical Science Press, 2020:260.
[2] AI Y, SUN Y N, LIU L, et al.Determination of biogenic amines in different parts of Lycium barbarum L.by HPLC with precolumn dansylation[J].Molecules, 2021, 26(4):1046.
[3] LIN X E, SONG F Y, WU Y M, et al.Lycium barbarum polysaccharide attenuates Pseudomonas-aeruginosa pyocyanin-induced cellular injury in mice airway epithelial cells[J].Food & Nutrition Research,2022, 66:4585.
[4] 王磊, 覃鸿, 李静, 等.近红外高光谱图像的宁夏枸杞产地鉴别[J].光谱学与光谱分析, 2020, 40(4):1270-1275.
WANG L, QIN H, LI J, et al.Geographical origin identification of Lycium barbarum using near-infrared hyperspectral imaging[J].Spectroscopy and Spectral Analysis, 2020, 40(4):1270-1275.
[5] YANG L L, LIANG Q, ZHANG Y J, et al.Variation of phytochemical composition of Lycium chinense leaves as an endemic high-value healthy resource[J].Scientia Horticulturae, 2021, 281(8):109910.
[6] 葛邦国, 刘志勇, 朱风涛, 等.枸杞加工研究现状与前景展望[J].食品研究与开发, 2014, 35(4):93-97.
GE B G, LIU Z Y, ZHU F T, et al.Present situation and prospect of research on product development of Lycium barbarum L.[J].Food Research and Development, 2014, 35(4):93-97.
[7] 开建荣, 李彩虹, 赵丹青, 等.宁夏不同地区、不同品种枸杞中元素含量差异分析[J].食品与发酵工业, 2020, 46(7):257-264.
KAI J R, LI C H, ZHAO D Q, et al.Analysis of mineral elements in wolfberry (Lycium barbarum) from different regions and varieties[J].Food and Fermentation Industries, 2020, 46(7):257-264.
[8] 杨春霞, 李彩虹, 杨静, 等.宁夏产区不同品种枸杞中总酚含量分析[J].食品研究与开发, 2020, 41(8):183-187.
YANG C X, LI C H, YANG J, et al.Analysis of polyphenols content in different varieties of Lycium barbarum L.in Ningxia producing area[J].Food Research and Development, 2020, 41(8):183-187.
[9] 王益民, 张珂, 许飞华, 等.不同品种枸杞子营养成分分析及评价[J].食品科学, 2014, 35(1):34-38.
WANG Y M, ZHANG K, XU F H, et al.Chemical analysis and nutritional of different varieties of Goji berries (Lycium barbarum L.)[J].Food Science, 2014, 35(1):34-38.
[10] LIU W J, XIA M Q, BAI J, et al.Chemical characterization and 5α-reductase inhibitory activity of phenolic compounds in goji berries[J].Journal of Pharmaceutical and Biomedical Analysis, 2021, 201:114-119.
[11] 孙红亮. 11种枸杞子总黄酮和总硒含量分析[J].分子植物育种, 2022, 20(18):6177-6187.
SUN H L.Analysis of total flavonoids and total selenium content of Lycium barbarum L.of 11 cultivars[J].Molecular Plant Breeding, 2022, 20(18):6177-6187.
[12] 王莹, 时锐, 张南平, 等.不同产地和不同品种枸杞子中多糖成分测定[J].中国现代中药, 2022, 24(6):996-1002.
WANG Y, SHI R, ZHANG N P, et al.Determination of polysaccharides in wolfberry from different producing areas and species[J].Modern Chinese Medicine, 2022, 24(6):996-1002.
[13] 禄璐, 闫亚美, 米佳, 等.枸杞原浆品质分析与评价标准构建[J].食品工业科技, 2022, 43(21):271-281.
LU L, YA Y M, MI J, et al.Quality analysis and evaluation standard construction of fresh Goji (Lycium barbarum L.) pulp[J].Science and Technology of Food Industry, 2022, 43(21):271-281.
[14] 蒋兰, 杨毅, 江荣高.枸杞的药理作用及其加工现状[J].食品工业科技, 2018, 39(14):330-334.
JIANG L, YANG Y, JIANG R G, et al.Pharmacological action of Chinese wolfberry and its comprehensive processing utilization[J].Science and Technology of Food Industry, 2018, 39(14):330-334.
[15] 李曼祎. 枸杞果酒的研制[D].无锡:江南大学, 2021.
LI M Y.Research of wolfberry wine[D].Wuxi:Jiangnan University, 2021.
[16] 周琪乐, 龚凌慧, 纪凤娣, 等.红枸杞、黄枸杞和黑枸杞营养成分比较[J].中国酿造, 2021, 40(10):43-49.
ZHOU Q L, GONG L H, JI F D, et al.Comparison of nutritional constituents of red wolfberry, yellow wolfberry and black wolfberry[J].China Brewing, 2021, 40(10):43-49.
[17] TANG N Q, SUN J, YAO K S, et al.Identification of Lycium barbarum varieties based on hyperspectral imaging technique and competitive adaptive reweighted sampling-whale optimization algorithm-support vector machine[J].Journal of Food Process Engineering, 2021, 44(1):e13603.
[18] 许春瑾, 张睿, 于修烛, 等.基于近红外光谱的中宁枸杞子判别分析[J].食品科学, 2014, 35(2):164-167.
XU C J, ZHANG R, YU X Z, et al.Discrimination of Chinese wolfberry from Zhongning based on near infrared spectroscopy[J].Food Science, 2014, 35(2):164-167.
[19] 许生陆, 高媛, 胡国梁, 等.基于有效波长选择的近红外光谱枸杞总糖含量快速检测[J].食品科学, 2016, 37(12):105-109.
XU S L, GAO Y, HU G L, et al.Rapid determination of total sugar content of Goji berries (Lycium barbarum) by near infrared spectroscopy with effective wavenumber selection[J].Food Science, 2016, 37(12):105-109.
[20] 李曼祎, 沈天辰, 刘春凤, 等.不同产地枸杞品质差异研究[J].食品与发酵工业, 2021, 47(24):56-63.
LI M Y, SHEN T C, LIU C F, et al.Research on the quality of wolfberry from different production areas[J].Food and Fermentation Industries, 2021, 47(24):56-63.
[21] 季晓菲, 游明鸿, 白史且, 等.虉草粗蛋白近红外定量分析模型的建立[J].光谱学与光谱分析, 2019, 39(6):1731-1735.
LI X F, YOU M H, BAI S Q, et al.Establishment of quantitative model for analyzing crude protein in Lycium barbarum L.by near infrared spectroscopy (NIR)[J].Spectroscopy and Spectral Analysis, 2019, 39(6):1731-1735.
[22] 严衍禄. 近红外光谱分析基础与应用[M].北京:中国轻工业出版社, 2005:31.
YAN Y L.Fundamentals and Applications of Near-Infrared Spectroscopy[M].Beijing:China Light Industry Press, 2005:31.
[23] 吴喆, 张霁, 金航, 等.红外光谱结合化学计量学对不同采收期滇重楼的定性定量分析[J].光谱学与光谱分析, 2017, 37(6):1754-1758.
WU Z, ZHANG J, JIN H, et al.Qualitative and quantitative analysis of Paris polyphylla var.yuannanensisin different harvest times with infrared spectroscopy combined with chemometrics[J].Spectroscopy and Spectral Analysis, 2017, 37(6):1754-1758.
[24] YAN H, LI P H, ZHOU G S, et al.Rapid and practical qualitative and quantitative evaluation of non-fumigated ginger and sulfur-fumigated ginger via Fourier-transform infrared spectroscopy and chemometric methods[J].Food Chemistry, 2021, 341(1):128241.
[25] 丁世飞, 齐丙娟, 谭红艳.支持向量机理论与算法研究综述[J].电子科技大学学报, 2011, 40(1):1-10.
DING S F, QI B J, TAN H Y.An overview on theory and algorithm of support vector machines[J].Journal of University of Electronic Science and Technology of China, 2011, 40(1):1-10.
[26] SAMPAIO P S, CASTANHO A, ALMEIDA A S, et al.Identification of rice flour types with near-infrared spectroscopy associated with PLS-DA and SVM methods[J].European Food Research and Technology, 2020, 246(3):527-537.
[27] LI Y, ZHANG J Y, WANG Y Z.FT-MIR and NIR spectral data fusion:A synergetic strategy for the geographical traceability of Panax notoginseng[J].Analytical and Bioanalytical Chemistry, 2018, 410(1):91-103.
[28] Yao S, LI T, LI J Q, et al.Geographic identification of Boletus mushrooms by data fusion of FT-IR and UV spectroscopies combined with multivariate statistical analysis[J].Spectrochimica Acta Part A:Molecular and Biomolecular Spectroscopy, 2018, 198:257-263.
[29] 耿姝. 适宜中药材体系的近红外分析方法影响因素研究[D].杭州:浙江大学, 2016.
GENG S.Studies on influential factors of near-infrared spectroscopy for Chinese medicinal materials system[D].Hangzhou:Zhejiang University, 2016.
[30] CHEN W, FENG Y Z, JIA G F, et al.Application of artificial fish swarm algorithm for synchronous selection of wavelengths and spectral pretreatment methods in spectrometric analysis of beef adulteration[J].Food Analytical Methods, 2018, 11(8):2229-2236.
[31] AN C Q, YAN X, LU C, et al.Effect of spectral pretreatment on qualitative identification of adulterated bovine colostrum by near-infrared spectroscopy[J].Infrared Physics & Technology, 2021, 118:103869.
Options
文章导航

/

〈 〉

技术支持：北京玛格泰克科技发展有限公司