基于快照式多光谱特征波长的小球藻叶黄素产量快速测定

doi:10.3964/j.issn.1000-0593(2024)08-2216-08

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摘要：叶黄素是天然的抗氧化剂，对人体健康有多种益处，异养小球藻具有叶黄素纯度和产量均较高的优势，而小球藻叶黄素产量主要取决于生物质产量和叶黄素含量两个因素。传统的光密度法测生物质产量和高效液相色谱法测叶黄素含量存在操作复杂、时效性低等不足。为了快速、无损测定小球藻生长过程中叶黄素含量变化，搭建可见-近红外双模式快照式多光谱成像检测系统，根据光谱响应区域，分别利用可见光相机获取叶黄素光谱信息，近红外相机获取生物质光谱信息，构建含有生物质量和叶黄素含量信息的可见-近红外双模式多光谱数据集。针对系统所使用的快照式多光谱相机光谱范围宽、波长数量少的特征波长选取问题，提出一种结合序列浮动前向选择的改进型连续投影算法（mSPA）；将mSPA与常规的连续投影算法、遗传算法及随机蛙跳三种波长选择算法作对比分析后，构建了基于特征波长的多元线性回归和极限学习机模型；最后，利用生物质产量和叶黄素含量的最佳预测模型生成小球藻叶黄素产量的可视化分布图。结果表明，在利用近红外、可见光相机分别检测小球藻生物质、叶黄素量时，mSPA得到的特征波长数均较少，并具有最高的预测精度。生物质量与叶黄素含量的最佳模型均为mSPA筛选特征波长后建立的极限学习机模型，对应的预测集决定系数分别为0.947和0.907，预测集均方根误差分别为0.698 g·L^-1和0.077 mg·g^-1，剩余预测偏差分别为3.535和3.338，模型的预测能力较好。可视化分布实现了直观监测小球藻叶黄素产量的变化，有助于后续实际生产中在线检测叶黄素产量。mSPA在快照式多光谱检测小球藻生物质含量及叶黄素含量中，通过对排序波长逐个评估以选择出最佳特征波长组合，有效地避免了特征波长的错选、漏选，提高了模型的预测精度，为快照式多光谱成像技术应用提供新的波长选择思路。

关键词：小球藻；叶黄素产量；快照式多光谱；特征波长

Abstract：Lutein is a natural antioxidant that has numerous benefits for human health. Heterotrophic Chlorella sorokiniana has the advantage of high purity and production of lutein. In contrast, the production of lutein in Chlorella sorokiniana mainly depends on two factors: biomass productivity and lutein content. However, conventional approaches such as the optical density method for measuring biomass productivity and high-performance liquid chromatography for measuring lutein content suffer from drawbacks, including complex procedures and limited timeliness. A visible near-infrared dual-mode snapshot multispectral imaging detection system was constructed to rapidly and non-destructively determine the variations in lutein production during the growth process of Chlorella sorokiniana. Based on the spectral response range, the visible camera was used to obtain the spectral information image of lutein content, and the near-infrared camera was used to obtain the spectral information image of biomass productivity to build a visible near-infrared dual mode multispectral dataset containing biomass productivity and lutein content information. To address the issue of wide spectral range and limited wavelengths in the snapshot multispectral camera used in the system, a novel approach combining sequential floating forward selection with a modified successive projections algorithm (mSPA) was proposed. A comparative study was conducted, evaluating mSPA against successive projections algorithm, genetic algorithm, and random frog algorithm for wavelength selection. Multiple linear regression and extreme learning machine models were constructed based on the selected feature wavelengths. Finally, the optimal predictive models for biomass productivity and lutein content were used to generate a visualization distribution map of lutein production in Chlorella sorokiniana. The results indicated that when using near-infrared and visible cameras for biomass productivity and lutein detection in Chlorella sorokiniana, the mSPA algorithm consistently yielded fewer feature wavelengths for both biomass productivity and lutein and achieved the highest prediction accuracy. The optimal models of biomass productivity and lutein content were established using the mSPA-selected feature wavelengths in combination with an extreme learning machine. The corresponding coefficients of determination for the prediction sets were 0.947 for biomass productivity and 0.907 for lutein, with root mean square errors of 0.698 g·L^-1 and 0.077 mg·g^-1 and residual prediction deviations of 3.535 and 3.338, respectively. The models demonstrated good predictive capabilities. The visualization distribution successfully achieved intuitive monitoring of lutein production variations in Chlorella sorokiniana, which is beneficial for online detection of lutein content in practical production scenarios. The mSPA algorithm, employed in the snapshot multispectral detection of biomass productivity and lutein content in Chlorella sorokiniana, effectively avoided the incorrect selection and omission of feature wavelengths by evaluating each sorted wavelength individually, thereby improving the prediction accuracy of the models. This approach provides a new wavelength selection strategy for applying snapshot multispectral imaging technology.

Key words：Chlorella sorokiniana; Lutein production; Snapshot multispectral; Feature wavelengths

收稿日期: 2023-07-03 修订日期: 2023-10-10

中图分类号:

O657.3

基金资助: 国家自然科学基金项目（62105068）资助

通讯作者: 黄峰 E-mail: huangf@fzu.edu.cn

作者简介: 沈英，女，1982年生，福州大学机械工程及自动化学院教授 e-mail: yshen@fzu.edu.cn

引用本文:

沈英，占秀兴，黄春红，谢友坪，郭翠霞，黄峰. 基于快照式多光谱特征波长的小球藻叶黄素产量快速测定[J]. 光谱学与光谱分析, 2024, 44(08): 2216-2223.
SHEN Ying, ZHAN Xiu-xing, HUANG Chun-hong, XIE You-ping, GUO Cui-xia, HUANG Feng. Rapid Determination of Chlorella Sorokiniana Lutein Production Based on Snapshot Multispectral Feature Wavelengths. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(08): 2216-2223.

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