title: [Identification of Microalgae Species Using Visible/Near Infrared Transmission Spectroscopy].
abstract: 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.
authors.forename: Hong-yan
authors.initials: HY
authors.lastname: Zhu
authors.forename: Yong-ni
authors.initials: YN
authors.lastname: Shao
authors.forename: Lu-lu
authors.initials: LL
authors.lastname: Jiang
authors.forename: An-que
authors.initials: AQ
authors.lastname: Guo
authors.forename: Jian
authors.initials: J
authors.lastname: Pan
authors.forename: Yong
authors.initials: Y
authors.lastname: He
mesh_terms: D002338:Carotenoids / Q000032:analysis; D016825:Chlamydomonas reinhardtii; D002708:Chlorella; D002734:Chlorophyll / Q000032:analysis; D005336:Fiber Optic Technology; D000069550:Machine Learning; D058086:Microalgae / Q000145:classification*; D019265:Spectroscopy, Near-Infrared; D060388:Support Vector Machine
chemical_list: D002734:Chlorophyll; D002338:Carotenoids