In situ and non-destructive detection of the lipid concentration of Scenedesmus obliquus using hyperspectral imaging technique

Liu, Yuanyuan; Chen, Kai; He, Yong

doi:10.1016/j.algal.2019.101680

Cited by 14 publications

(8 citation statements)

References 42 publications

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“…[28] [29] (r,g,b)/DWC: R 2 = 0.97-0.99 [28] Open container/biofilm, suspension [28] (r,b)/DWC: R 2 = 0.90-0.96 [29] Open container/suspension [29] Biomass Transmittance spectrum DWC/T 751 /T 676 : r = 0.51-0.93 [30] Microwells [30] Biomass Chlorophyll fluorometry DWC/Chl a fluorescence: r = 0.95 [35] Fiber probe in PBR bypass [35] [39] CC/T751/T676: r = 0.85-0.96 [30] Microwells [30] [30] [25] [56] [35] CC/HR,EC: R 2 = 0.99 [25] Open container [25] CC/OD560: R 2 = 0.992-0.999 [56] Flask bypass [56] CC/Chl a fluorescence: r = 0.92 [35] Fiber probe in PBR bypass [35] Viable Algal lipids/NMR signal: R 2 = 0.99 [52] Bleed [52] Lipids as %DW/QY(∆F /Fm ): r = −0.96 [46] In-situ fiber [46] Lipids predicted/observed: R 2 = 0.94 [31] Sampling [31] Fatty acids ISM/Image recognition DHA/cell diameter: R 2 = 0.98 (calibration) PBR in-situ probe [34] Protein Chlorophyll fluorometry Protein/Chl a fluorescence: r = 0.92 Fiber probe in PBR bypass [35] Carotenoids (C) VIS/NIR spectrum Predicted/observed C: r = 0.96 Fiber in sample [32] 2.1. Biomass Concentration Biomass concentration (BC, mostly expressed as dry weight concentration, DWC) is the basic biological variable in any microbial cultivation.…”

Section: Quantum Yield (Photosynthetic Efficiency) Contaminationmentioning

confidence: 99%

On-Line Monitoring of Biological Parameters in Microalgal Bioprocesses Using Optical Methods

et al. 2022

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Microalgae are promising sources of fuels and other chemicals. To operate microalgal cultivations efficiently, process control based on monitoring of process variables is needed. On-line sensing has important advantages over off-line and other analytical and sensing methods in minimizing the measurement delay. Consequently, on-line, in-situ sensors are preferred. In this respect, optical sensors occupy a central position since they are versatile and readily implemented in an on-line format. In biotechnological processes, measurements are performed in three phases (gaseous, liquid and solid (biomass)), and monitored process variables can be classified as physical, chemical and biological. On-line sensing technologies that rely on standard industrial sensors employed in chemical processes are already well-established for monitoring the physical and chemical environment of an algal cultivation. In contrast, on-line sensors for the process variables of the biological phase, whether biomass, intracellular or extracellular products, or the physiological state of living cells, are at an earlier developmental stage and are the focus of this review. On-line monitoring of biological process variables is much more difficult and sometimes impossible and must rely on indirect measurement and extensive data processing. In contrast to other recent reviews, this review concentrates on current methods and technologies for monitoring of biological parameters in microalgal cultivations that are suitable for the on-line and in-situ implementation. These parameters include cell concentration, chlorophyll content, irradiance, and lipid and pigment concentration and are measured using NMR, IR spectrophotometry, dielectric scattering, and multispectral methods. An important part of the review is the computer-aided monitoring of microalgal cultivations in the form of software sensors, the use of multi-parameter measurements in mathematical process models, fuzzy logic and artificial neural networks. In the future, software sensors will play an increasing role in the real-time estimation of biological variables because of their flexibility and extendibility.

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Section: Quantum Yield (Photosynthetic Efficiency) Contaminationmentioning

confidence: 99%

On-Line Monitoring of Biological Parameters in Microalgal Bioprocesses Using Optical Methods

et al. 2022

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“…Cultures were monitored in their exponential growth phase. Change in algae abundance between time points was typically minor for the naked eye; however, differences could be displayed based on the spectral data ( Figure 1), an advantage of an imaging system, as also demonstrated by Polerecky et al [12] and Li et al [14]. For processing, a rectangle ROI (Region of Interest) that excluded the edges of the sample well was chosen to make data processing as straightforward as possible.…”

Section: Cell Abundance and Near-infrared (Nir)/red Ratiomentioning

confidence: 99%

“…Mehrubeoglu et al [ 13 ] imaged microalgae cultures in pairwise mixtures to assess their proportions using constrained linear spectral unmixing. Li et al [ 14 ] tested different data processing methods and models to resolve lipid concentration of Scenedesmus obliquus from near-infrared transmission spectral images. Although spectral cameras are increasingly used, their usability might still be restrained by their high price and the complexity of data processing [ 3 ].…”

Section: Introductionmentioning

confidence: 99%

Rapid Quantification of Microalgae Growth with Hyperspectral Camera and Vegetation Indices

Salmi¹,

Eskelinen²,

Leppänen

et al. 2021

Plants

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Spectral cameras are traditionally used in remote sensing of microalgae, but increasingly also in laboratory-scale applications, to study and monitor algae biomass in cultures. Practical and cost-efficient protocols for collecting and analyzing hyperspectral data are currently needed. The purpose of this study was to test a commercial, easy-to-use hyperspectral camera to monitor the growth of different algae strains in liquid samples. Indices calculated from wavebands from transmission imaging were compared against algae abundance and wet biomass obtained from an electronic cell counter, chlorophyll a concentration, and chlorophyll fluorescence. A ratio of selected wavebands containing near-infrared and red turned out to be a powerful index because it was simple to calculate and interpret, yet it yielded strong correlations to abundances strain-specifically (0.85 < r < 0.96, p < 0.001). When all the indices formulated as A/B, A/(A + B) or (A − B)/(A + B), where A and B were wavebands of the spectral camera, were scrutinized, good correlations were found amongst them for biomass of each strain (0.66 < r < 0.98, p < 0.001). Comparison of near-infrared/red index to chlorophyll a concentration demonstrated that small-celled strains had higher chlorophyll absorbance compared to strains with larger cells. The comparison of spectral imaging to chlorophyll fluorescence was done for one strain of green algae and yielded strong correlations (near-infrared/red, r = 0.97, p < 0.001). Consequently, we described a simple imaging setup and information extraction based on vegetation indices that could be used to monitor algae cultures.

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“…Soil quality can be detected by measuring the carbon level [47]. Non-destructive techniques in the agricultural sector are becoming widespread every day due to technological developments in imaging [36,41,42,45,46]. Diseases can be determined and healed in advance [40,61].…”

Section: Hyperspectral Imaging Applicationsmentioning

confidence: 99%

Deep Learning Applications for Hyperspectral Imaging: A Systematic Review

Özdemir¹,

Polat²

2020

JIEC

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Since the acquisition of digital images, scientific studies on these images have been making significant progress. The sizes and quality of the images obtained have increased greatly from past to present. However, when the information contained in these images remains on the visible band (RGB band), the results that can be obtained are limited. For this reason, the need to acquire images with more broadband information has emerged. Hyperspectral Imaging (HSI) method has been developed to meet this need. A hyperspectral image consists of reflections in hundreds of different bands of the electromagnetic spectrum. Each object exhibits a unique reflection characteristic. Due to this characteristic, objects can be separated from each other using hyperspectral imaging. Hyperspectral cameras are used to obtain this image. The information it contains is much more than an RGB image, so deeper results can be achieved than the human eye can see. In this respect, it has great importance. Artificial intelligence technologies are used extensively in image processing as well as in many other fields. As a result, classification studies are carried out on hyperspectral images with machine learning methods. Machine learning methods can be considered as the most general terms of supervised machine learning, unsupervised machine learning, and reinforced machine learning. Supervised machine learning methods mainly: Support Vector Machines(SVM), k-Nearest Neighborhood(k-NN), Decision Trees, Random Forest, Linear Regression and Neural Networks(NN). Neural networks are also used as an unsupervised learning method. However, Deep Learning, a specialized method of artificial neural networks, is highly preferred due to its unique structure. Artificial intelligence methods have been widely used in recent years, especially for the classification of hyperspectral images containing complex information. Considering the studies, it is seen that especially deep learning is used intensively. At this point, studies have revealed different types of models. The number of models and their successes are increasing day by day.

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In situ and non-destructive detection of the lipid concentration of Scenedesmus obliquus using hyperspectral imaging technique

Cited by 14 publications

References 42 publications

On-Line Monitoring of Biological Parameters in Microalgal Bioprocesses Using Optical Methods

On-Line Monitoring of Biological Parameters in Microalgal Bioprocesses Using Optical Methods

Rapid Quantification of Microalgae Growth with Hyperspectral Camera and Vegetation Indices

Deep Learning Applications for Hyperspectral Imaging: A Systematic Review

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