对藻类的识别分类及其生化分析已成为海洋生物学的研究热点之一。 以普通小球藻、 蛋白核小球藻、 微绿球藻、 莱茵衣藻为样品, 通过便携式USB4000微型光纤光谱仪、 Y形光纤和探针, 卤素光源构建的光谱采集系统对不同浓度梯度的120个微藻样本进行浸入式可见/近红外透射光谱的原位采集, 比较去基线、 卷积平滑等光谱预处理方法的效果, 并基于连续投影算法(SPA)筛选特征波长, 通过偏最小二乘法(PLS)、 最小二乘支持向量机(LS-SVM)和极限学习机(ELM)进行建模, 探讨采用透射光谱原位快速鉴别四种不同藻种的可行性。 结果表明: 卷积平滑的处理效果较为理想, 有效波长可用于代替原始光谱建立微藻种类判别分析模型。 SPA-LV-SVM和SPA-ELM的预测效果显著高于SPA-PLS, 三者的平均预测正确率分别是80%, 85%, 65%。 浸入式可见/近红外光谱技术和便携式光纤探针结合的藻种鉴别方法, 有效实现了对四种微藻的鉴别, 为藻种鉴别和藻种分类研究领域提供了一种新思路。

At present, the identification and classification of the microalgae and its biochemical analysis have become one of the hot spots on marine biology research. Four microalgae species, including Chlorella vulgaris, Chlorella pyrenoidosa, Nannochloropsis oculata, Chlamydomonas reinhardtii, were chosen as the experimental materials. Using an established spectral acquisition system,which consists of a portable USB 4000 spectrometer having transmitting and receiving fiber bundles connected by a fiber optic probe, a halogen light source, and a computer, the Vis/NIR transmission spectral data of 120 different samples of the microalgae with different concentration gradients were collected, and the spectral curves of fourmicroalgae species were pre-processed by different pre-treatment methods (baseline filtering, convolution smoothing, etc.). Based on the pre-treated effects, SPA was applied to select effective wavelengths (EWs), and the selected EWs were introduced as inputs to develop and compare PLS, Least Square Support Vector Machines (LS-SVM), Extreme Learning Machine (ELM)models, so as to explore the feasibility of using Vis/NIR transmission spectroscopy technology for the rapid identification of four microalgae species in situ. The results showed that: the effect of Savitzky-Golay smoothing was much better than the other pre-treatment methods. Six EWs selected in the spectraby SPA were possibly relevant to the content of carotenoids, chlorophyll in the microalgae. Moreover, the SPA-PLS model obtained better performance than the Full-Spectral-PLS model. The average prediction accuracy of three methods including SPA-LV-SVM, SPA-ELM, and SPA-PLS were 80%, 85% and 65%. The established method in this study may identify four microalgae species effectively, which provides a new way for the identification and classification of the microalgae species. The methodology using Vis/NIR spectroscopy with a portable optic probe would be applicable to a diverse range of microalgae species and proves to be a rapid, real-time, non-destructive, precise method for the physiological and biochemical detection for microalgae.