食品与发酵工业 >

2025 , Vol. 51 >Issue 24: 342 - 348

DOI: https://doi.org/10.13995/j.cnki.11-1802/ts.044080

分析与检测

基于近红外光谱技术建立刺糖中多糖含量的快速检测模型

  • 李进发 ,
  • 向阳 ,
  • 王宇 ,
  • 干振 ,
  • 陈思圆 ,
  • 常军民
展开
  • 1(新疆医科大学 药学院,新疆 乌鲁木齐,830017)
    2(新疆医科大学第一附属医院医学检验中心,新疆 乌鲁木齐,830054)
    3(新疆天然药物活性组分与释药技术重点实验室,新疆 乌鲁木齐,830017)
第一作者:硕士研究生(常军民教授为通信作者,E-mail:1617265908@qq.com)

收稿日期: 2025-07-30

  修回日期: 2025-08-08

  网络出版日期: 2026-01-12

基金资助

新疆维吾尔自治区自然科学基金重点项目(2022D01D37)

收起

Establishment of a rapid detection model for polysaccharide content in Alhagi sparsifolia honey based on near-infrared spectroscopy technology

  • LI Jinfa ,
  • XIANG Yang ,
  • WANG Yu ,
  • GAN Zhen ,
  • CHEN Siyuan ,
  • CHANG Junmin
Expand
  • 1(School of Pharmacy, Xinjiang Medical University, Urumqi 830017, China)
    2(The Center for Medical Laboratory, The First Affiliated Hospital of Xinjiang Medical University, Urumqi 830054, China)
    3(Xinjiang Key Laboratory of Natural Drug Active Components and Drug Release Technology, Urumqi 830017, China)

Received date: 2025-07-30

  Revised date: 2025-08-08

  Online published: 2026-01-12

Fold

摘要

该研究基于近红外光谱分析技术(near infrared spectroscopy,NIRS),结合偏最小二乘法(partial least squares,PLS),建立刺糖中多糖含量的快速检测定量模型。以100批次刺糖为研究对象,采用苯酚-硫酸法测定多糖含量作为参比值,并采集780~2 500 nm的近红外光谱。通过比较多种光谱预处理方法,卷积平滑法(savitzky-Golay smoothing, SG)、标准正态变量(standard normal variate,SNV)、多元散射校正(multiplicative scatter correction,MSC)、一阶导数(first derivative,FD)、二阶导数(second derivative,SD)及其组合;以决定系数(coefficient of determination,R2)、校正均方根偏差(root mean square error of calibration,RMSEC)、预测均方根偏差(root mean square error of prediction,RMSEP)为评价参数,确定FD+SNV为最佳预处理方式,建模特征波段为1 100~ 1 300 nm、1 350~1 500 nm、1 600~1 800 nm、2 000~2 300 nm,主因子数为5。所建模型性能优异(校正集:R2c=0.974 1,RMSEC=0.096 6;预测集:R2p=0.985 9,RMSEP=0.066 3),线性关系良好。进一步验证结果证实模型预测准确、稳定性良好,可用于刺糖中多糖的快速测定,为其质量评价和规模化开发利用提供技术支持。

关键词： 刺糖多糖; 近红外光谱技术; 含量测定; 偏最小二乘法

本文引用格式

李进发 , 向阳 , 王宇 , 干振 , 陈思圆 , 常军民 . 基于近红外光谱技术建立刺糖中多糖含量的快速检测模型[J]. 食品与发酵工业, 2025 , 51(24) : 342 -348 . DOI: 10.13995/j.cnki.11-1802/ts.044080

Abstract

In this study, a rapid quantitative model for the detection of polysaccharide content in Alhagi sparsifolia honey was established based on near infrared spectroscopy (NIRS) and partial least squares (PLS).Taking 100 batches of Alhagi sparsifolia honey as the research object, the polysaccharide content was determined by the phenol-sulfuric acid method as the reference value, and the near-infrared spectra in the range of 780-2 500 nm were collected.By comparing a variety of spectral preprocessing methods, savitzky-Golay smoothing (SG), standard normal variable (SNV), multiplicative scatter correction (MSC), first derivative (FD), and second derivative derivative, SD) and its combinations, using coefficient of determination (R2), root mean square error of calibration (RMSEC), and root mean square error of prediction (RMSEP) as the evaluation parameters, FD+SNV was determined to be the best preprocessing method, and the modeling characteristic band was 1 100-1 300 nm, 1 350-1 500 nm, 1 600-1 800 nm, 2 000-2 300 nm, and the number of principal factors was 5.The model had excellent performance (correction set:R2c=0.974 1, RMSEC=0.096 6;prediction set:R2p=0.985 9, RMSEP=0.066 3), and the linear relationship was good.Subsequent validation results affirm the predictive accuracy and operational stability of the model, establishing its practical utility for the rapid determination of polysaccharides in Alhagi sparsifolia honey and underpinning technical advancements in quality evaluation and large-scale resource development.

Key words： Alhagi sparsifolia honey polysaccharides; near-infrared spectroscopy; content determination; partial least squares

参考文献

[1] 杨卫星. 新疆常见药用植物实用图谱[M]. 乌鲁木齐: 新疆科学技术出版社, 2006: 63.
YANG W X. Practical Atlas of Common Medicinal Plants in Xinjiang[M]. Urumqi: Xinjiang Science and Technology Press, 2006:63.
[2] 尹辉, 张波, 荆瑞雪, 等. 干旱区不同地理种群骆驼刺元素组成及表面结构特征的对比研究[J]. 生态学报, 2019, 39(18):6745-6752.
YIN H, ZHANG B, JING R X, et al. Elemental compositions and surface structures of Alhagi sparsifolia Shap. in different geographical populations in arid zone[J]. Acta Ecologica Sinica, 2019, 39(18):6745-6752.
[3] GOWTHAMI R, SHARMA N, PANDEY R, et al. Status and consolidated list of threatened medicinal plants of India[J]. Genetic Resources and Crop Evolution, 2021, 68(6):2235-2263.
[4] SHARAFATMANDRAD M, KHOSRAVI MASHIZI A. Ethnopharmacological study of native medicinal plants and the impact of pastoralism on their loss in arid to semiarid ecosystems of southEastern Iran[J]. Scientific Reports, 2020, 10:15526.
[5] UMAIR M, ALTAF M, BUSSMANN R W, et al. Ethnomedicinal uses of the local flora in Chenab riverine area, Punjab province Pakistan[J]. Journal of Ethnobiology and Ethnomedicine, 2019, 15(1):7.
[6] SHEWEITA S A, MASHALY S, NEWAIRY A A, et al. Changes in oxidative stress and antioxidant enzyme activities in streptozotocin-induced diabetes mellitus in rats: Role of Alhagi maurorum extracts[J]. Oxidative Medicine and Cellular Longevity, 2016, 2016:5264064.
[7] 高霜, 陈章浩, 李进发, 等. 基于肠道菌群探讨刺糖酸性多糖对UC小鼠的影响[J]. 食品与发酵工业, 2025, 51(7):74-81.
GAO S, CHEN Z H, LI J F, et al. Effects of Alhagi-honey acid polysaccharide on UC mice based on intestinal flora[J]. Food and Fermentation Industries, 2025, 51(7):74-81.
[8] 陈盈盈, 李杰, 宋建忠, 等. 刺糖多糖脱色脱蛋白工艺及抗氧化活性研究[J]. 化学试剂, 2023, 45(1):46-53.
CHEN Y Y, LI J, SONG J Z, et al. Study on decoloration and deproteinization technology and antioxidant activity of spinosad polysaccharide[J]. Chemical Reagents, 2023, 45(1):46-53.
[9] 李杰, 陈盈盈, 王昆, 等. 基于TLR4/MyD88信号通路探讨刺糖多糖对溃疡性结肠炎小鼠的作用及机制[J]. 中药新药与临床药理, 2022, 33(9):1143-1148.
LI J, CHEN Y Y, WANG K, et al. Discussion on the effect and mechanism of polysaccharide from Alhagi-honey on ulcerative colitis in mice based on TLR4/MyD88 signaling pathway[J]. Traditional Chinese Drug Research and Clinical Pharmacology, 2022, 33(9):1143-1148.
[10] LIU Y L, ZHOU M Y, YANG M, et al. Pulsatilla chinensis saponins ameliorate inflammation and DSS-induced ulcerative colitis in rats by regulating the composition and diversity of intestinal flora[J]. Frontiers in Cellular and Infection Microbiology, 2021, 11:728929.
[11] LIU S Q, SONG S L, ZHANG Y, et al. Delivery of penetration-enhancing antioxidant polyphenol nanoparticles with Codonopsis pilosula polysaccharide microneedles for synergistic treatment of psoriasis[J]. Carbohydrate Polymers, 2025, 363:123777.
[12] ZHANG T J, HUANG D J, LIU X Y, et al. Antioxidant activity and semi-solid emulsification of a polysaccharide from coffee cherry peel[J]. International Journal of Biological Macromolecules, 2023, 244:125207.
[13] ZHANG Y T, LI J, SUN J, et al. Rapid identification of Vicatia thibetica de boiss and quantitative analysis of the content of six chemical components based on near-infrared spectroscopy[J]. Molecules, 2025, 30(9):1867.
[14] HUA H M, YI Y, LI H L, et al. Monitoring the extraction process of acidic polysaccharides in Poria cocos by near infrared spectroscopy combined with chemometrics[J]. Infrared Physics & Technology, 2021, 113:103613.
[15] 吕悦, 吴杭莎, 韦飞扬, 等. 黄精多指标成分近红外光谱快速定量分析模型建立[J]. 中成药, 2022, 44(9):2878-2884.
LYU Y, WU H S, WEI F Y, et al. Model establishment for quantitative analysis of multiple bioactive constituents of Polygonati Rhizoma by near-infrared spectrometry[J]. Chinese Traditional Patent Medicine, 2022, 44(9):2878-2884.
[16] 李珊珊, 张付杰, 李丽霞, 等. 基于近红外光谱技术结合ARO-LSSVR的天麻中有效成分含量快速检测[J]. 食品科学, 2024, 45(4):207-213.
LI S S, ZHANG F J, LI L X, et al. Rapid determination of active ingredient contents in rhizoma gastrodiae using near-infrared spectroscopy combined with artificial rabbits optimization-least square support vector regression[J]. Food Science, 2024, 45(4):207-213.
[17] ZHONG L J, FAN Y J, WU Y L, et al. Data fusion strategy for rapid prediction of glyceryl trioleate and polysaccharide content in Ganoderma lucidum spore powder based on near-infrared spectroscopy and hyperspectral imaging[J]. Journal of Food Composition and Analysis, 2025, 141:107403.
[18] WANG Y, TIAN Z P, XIE J J, et al. Rapid determination of polysaccharides in Cistanche tubulosa using near-infrared spectroscopy combined with machine learning[J]. Journal of AOAC International, 2023, 106(4):1118-1125.
[19] 薛忠, 刘德玄, 曹春琪, 等. 基于不确定曲线策略的近红外定量分析丹参酮提取物中丹参酮ⅡA含量[J]. 药物分析杂志, 2023, 43(12):2147-2153.
XUE Z, LIU D X, CAO C Q, et al. Determination of tanshinone ⅡA content in tanshinone extract by near infrared spectroscopy based on uncertainty profile strategy[J]. Chinese Journal of Pharmaceutical Analysis, 2023, 43(12):2147-2153.
[20] 李琳, 孙慧慧, 曹荣, 等. 基于近红外光谱技术的南极磷虾品质快速评定[J/OL]. 食品工业科技, 2025:1-12. (2025-04-27). https://link.cnki.net/doi/10.13386/j.issn1002-0306.2024120219.
LI L, SUN H H, CAO R, et al. Rapid quality assessment of Antarctic krill based on near-infrared spectroscopy[J/OL]. Science and Technology of Food Industry, 2025:1-12. (2025-04-27). https://link.cnki.net/doi/10.13386/j.issn1002-0306.2024120219.
[21] 黄若冰, 刘易佳, 郭亚勤. 基于近红外光谱技术的电子烟油中多种添加剂含量的快速定量检测研究[J]. 分析测试学报, 2025, 44(6):1196-1201.
HUANG R B, LIU Y J, GUO Y Q. Rapid quantitative detection of multiple additives in E-liquids based on near-infrared spectroscopy technology[J]. Journal of Instrumental Analysis, 2025, 44(6):1196-1201.
[22] ESPAÑA-FARIÑAS M P, CAZÓN P, URQUIJO-ZAMORA L, et al. Application of near infrared spectroscopy combined with chemometrics to authenticate local cultivar flour content in the production of protected geographical indication “Galician bread”[J]. Food Control, 2025, 176:111409.
[23] 占可, 陈季旺, 徐言, 等. 基于近红外光谱特征的冷冻小龙虾鲜度快速检测方法[J]. 食品科学, 2024, 45(2):299-307.
ZHAN K, CHEN J W, XU Y, et al. A rapid detection method for freshness of frozen crayfish based on near-infrared spectroscopy[J]. Food Science, 2024, 45(2):299-307.
[24] IZIDORO M, PEDROSA V M D, SPRICIGO P C, et al. Feasibility of near-infrared spectroscopy as a tool to estimate carotenoid content in ‘IAC Rurik' specialty potato cultivar[J]. Journal of Food Composition and Analysis, 2025, 139:107130.
[25] 杨唯瀚, 郝经文, 黄和平, 等. 近红外漫反射光谱法快速测定蕨菜多糖含量的研究[J]. 中国现代应用药学, 2023, 40(5):597-602.
YANG W H, HAO J W, HUANG H P, et al. Rapid determination of polysaccharide in Pteridium aquilinum by near infrared diffuse reflectance spectroscopy[J]. Chinese Journal of Modern Applied Pharmacy, 2023, 40(5):597-602.
[26] XIAO Y X, LI C, JIN C, et al. Detection of soluble solid content in citrus fruit using near-infrared spectroscopy with machine learning regression: An exploration of the influence of sampling positions[J]. Journal of Food Composition and Analysis, 2025, 142:107554.
Options
文章导航

/

〈 〉

技术支持：北京玛格泰克科技发展有限公司