生物学杂志

生物学杂志 ›› 2026, Vol. 43 ›› Issue (5): 19-.doi: 10.3969/j.issn.2095-1736.2026.05.019

• 研究报告 • 上一篇    下一篇

红外光谱法预测灵芝属菌丝体中β-1，3-葡聚糖含量研究

陈红霞1， 施美伊2， 李　赫3， 肖世俊2， 韩雪容1,2   

  1. 1. 长春理工大学 生命科学技术学院， 长春 130022；
    2. 吉林农业大学 食药用菌教育部工程研究中心， 长春 130118；
    3. 内蒙古自治区兴安盟农牧科学研究所（内蒙古自治区生物育种技术创新中心）， 乌兰浩特 137400
  • 出版日期:2026-10-18 发布日期:2026-10-20
  • 通讯作者: 韩雪容，博士，教授，研究方向为菌物活性物质检测及生物材料，E-mail：hanxuerong@jlau.edu.cn
  • 作者简介:陈红霞，硕士研究生，研究方向为生物检测工程，E-mail：chx147852qwe@163.com
  • 基金资助:
    “十四五”国家重点研发计划子课题(2023YFF1000802-5)； 2024年度内蒙古自治区生物育种技术创新中心科技支撑项目(2024NSZC01-01)

#br# Study on the prediction of β-1,3-glucan content in mycelium of Ganoderma spp. based on infrared spectroscopy

CHEN Hongxia1, SHI Meiyi2, LI He3, XIAO Shijun2, HAN Xuerong1,2   

  1. 1. University of Life Science and Technology, Changchun University of Science and Technology, Changchun 130022,
    China; 2. Engineering Research Center of Edible and Medicinal Fungi, Ministry of Education, Jilin Agricultural
    University, Changchun 130118, China; 3. Hinggan League Institute of Agricultural and Husbandry Sciences (Inner
    Mongolia Innovation Center of Biological Breeding Technology), Ulanhot 137400, China
  • Online:2026-10-18 Published:2026-10-20

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摘要： 本文采用近红外光谱（NIRS）技术，采集160个灵芝属菌丝体样品的近红外光谱，并结合苯胺蓝荧光法测定对应样品的β-1，3-葡聚糖含量作为参考值，基于偏最小二乘回归（PLSR）算法构建灵芝属菌丝体β-1，3-葡聚糖含量的预测模型。经剔除异常样本后，共保留150个有效样本，利用竞争性自适应重加权采样（CARS）算法提取了13个特征波长，分别使用偏最小二乘回归、BP神经网络（BPNN）、极限学习机（ELM）、支持向量机（SVM）和随机森林（RF）等5种机器学习算法建模，最终确立PLSR算法模型精度最佳（R2=0.946, RMSEP=0.545, RPD=4.303）。使用模型对20株灵芝属菌丝体样品的β-1，3-葡聚糖含量进行预测与验证，结果准确性与鲁棒性良好(R2=0.8425, RMSEP=0.8025, RPD=2.4121>2.4)。研究的一套具备实际应用价值的β-1，3-葡聚糖含量测定方法可用于灵芝菌丝体样品中β-1，3-葡聚糖含量的快速无损定量检测。

关键词: 灵芝属菌丝体； β-1, 3-葡聚糖； 近红外光谱； 回归预测； PLSR

Abstract: This study employed near-infrared spectroscopy (NIRS) to acquire spectral data from 160Ganodermaspp. mycelium samples. The β-1,3-glucan content was measured using the aniline blue fluorescence method as the reference value. A predictive model for β-1,3-glucan content inGanodermaspp. mycelium was established based on partial least squares regression (PLSR). After excluding outliers, 150 valid samples were retained. Thirteen characteristic wavelengths were extracted using the competitive adaptive reweighted sampling (CARS) algorithm. Five machine learning algorithms were used for modeling,including partial least squares regression (PLSR), back propagation neural network (BPNN), extreme learning machine (ELM), support vector machine (SVM) and random forest (RF). The PLSR model achieved the highest accuracy (R2=0.946, RMSEP=0.545, RPD=4.303). Predictions and validation of β-1,3-glucan content in 20Ganodermaspp. mycelium samples using this model showed satisfactory accuracy and robustness (R2=0.8425, RMSEP=0.8025, RPD=2.4121, all exceeding 2.4). This study developed a practical β-1,3-glucan assay method, enabling rapid and non-destructive quantitative detection of β-1,3-glucan inGanodermaspp. mycelium samples.

Key words: Ganodermaspp. mycelium, β-1,3-glucan, near infrared spectral, regression prediction, PLSR

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