A Multi-Platform Optical Sensor for In Vivo and In Vitro Algae Classification

Ng, Chee Loon; Chen, Qingqing; Chua, Jia Jing; Hemond, Harold F.

doi:10.3390/s17040912

Cited by 8 publications

(8 citation statements)

References 18 publications

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“…The comparison results are shown in Table 7. In the table, the volume of our equipment is 27.1% smaller than Göröcs' team [42], and 64.7% smaller than Ng's team [43]. The weight is basically the same as [42] It is noteworthy that, our equipment includes all the modules which are needed to complete the whole cell culture and segmentation analysis work independently, including the microfluidic channel chip, the image acquisition module, the neural network acceleration circuit, voltage monitoring and MCU control module, and touch screen module.…”

Section: Cost and Equipment Sizementioning

confidence: 99%

“…The weight is basically the same as [42] It is noteworthy that, our equipment includes all the modules which are needed to complete the whole cell culture and segmentation analysis work independently, including the microfluidic channel chip, the image acquisition module, the neural network acceleration circuit, voltage monitoring and MCU control module, and touch screen module. But the weight and size of [42], [43] only includes the acquisition equipment, and an additional highperformance computer is needed for calculation, display, and control.…”

Section: Cost and Equipment Sizementioning

confidence: 99%

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An Intelligent Low-Power Low-Cost Mobile Lab-On-Chip Yeast Cell Culture Platform

Liao

Tian

et al. 2020

IEEE Access

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Cells are the fundamental unit of life activities, and the basis of studying life phenomena. It is very important to observe the growth state of yeast cells for exploring the law of life movement, diagnosis and treatment of diseases, drug screening and so on. This study proposes a kind of intelligent low-cost portable cell culture platform using the microfluidic channel and the special machine learning circuit. The platform can independently complete the whole work of living cell culture and monitoring. For realizing the reusable and low-power deep learning circuit, a complement optimization neural network algorithm for hardware optimization and corresponding multi-clock-domain reusable multi-level precision neural network accelerator circuit were proposed, which can reduce the circuit area and power of convolution operation in all precisions by average 18.11% and 23.5% respectively. Besides, a dynamic multi-level precision control method based on the battery level is proposed to dynamically adjust the precision of machine learning operation, in order to balance the working time and segmentation accuracy of the culture platform. In addition, a microcolumns-based three-port input microfluidic structure was designed for better yeast culture effect. The experiment showed that the culture platform can realize yeast cell culture and achieve almost the same segmentation accuracy as the large biological laboratory with low-power and low-cost. Compared with the previous work, the cost of mass production was reduced by 88.95%, and the equipment volume was 27.1% smaller. At the same time, it can achieve the best balance of working time and working accuracy under the condition of limited power of equipment according to the needs of users. INDEX TERMS Microfluidic channel, yeast culture, portable cell culture platform, intelligent, low-power, lab-on-chip.

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Section: Cost and Equipment Sizementioning

confidence: 99%

Section: Cost and Equipment Sizementioning

confidence: 99%

An Intelligent Low-Power Low-Cost Mobile Lab-On-Chip Yeast Cell Culture Platform

Liao

Tian

et al. 2020

IEEE Access

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“…Fluorescence spectroscopy has proven useful for continuous, in situ monitoring of algal pigments [23,24]. Furthermore, LED-based fluorescence sensors are often low-cost and small enough to be easily transported and deployed in the field.…”

Section: Introductionmentioning

confidence: 99%

Initial Deployment of a Mobile Sensing System for Water Quality in Urban Canals

Meyers

Zheng

Duarte

et al. 2022

Water

Self Cite

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Although water quality has extensively improved over the last decade, recreational uses of the canal network in Amsterdam are limited by variations in water quality associated with stormwater runoff and episodic harmful algal blooms. The current systems for monitoring water quality are based on a stationary network of sampling points, offline testing methods, and online measurements of conventional water quality parameters on board a boat that continuously navigates the urban canal network. Here we describe the development and deployment of online algal sensors on board the boat, including a prototype LED-induced fluorescence instrument for algal identification and quantification. We demonstrate that by using only a single patrol vessel, we are able to achieve enough sampling coverage to observe spatiotemporal heterogeneity of algal and chemical water quality within the canal network. The data provide encouraging evidence that opportunistic measurements from a small number of mobile platforms can enable high-resolution mapping and can be used to improve the monitoring of water quality across the city compared to the current network of fixed sampling locations. We also discuss the challenges of operating water quality sensors for long-term autonomous monitoring.

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“…and Cylindrospermum sp. cells by comparing the fluorescence spectra [13]. Microalgae have photosynthetic pigments providing different spectral signatures for different strains; thus, it is possible to build classes based on the presence of pigments.…”

Section: Introductionmentioning

confidence: 99%

Monoalgal and mixed algal cultures discrimination by using an artificial neural network

et al. 2019

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In this paper, a rapid methodology to elucidate microalgae species in suspensions has been developed and validated. To do this, microalgae spectral signatures from light absorption measurements of different algal species were analysed through an artificial neural network (ANN) in order to describe and classify them. Four important species were used: Nostoc sp., Scenedesmus almeriensis, Spirulina platensis and Chorella vulgaris. Absorbance from monoalgal and mixed algal cultures was the input data for training, testing and validating the ANN. The results show that the ANN was capable of distinguishing between monoalgal and mixed algal cultures, identifying the microalgae species in the monoalgal cultures and providing the approximate composition of mixed algal cultures. These results confirm that the application of spectral signatures with ANN is a suitable method for approximating the biological composition of microalgae cultures.

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A Multi-Platform Optical Sensor for In Vivo and In Vitro Algae Classification

Cited by 8 publications

References 18 publications

An Intelligent Low-Power Low-Cost Mobile Lab-On-Chip Yeast Cell Culture Platform

An Intelligent Low-Power Low-Cost Mobile Lab-On-Chip Yeast Cell Culture Platform

Initial Deployment of a Mobile Sensing System for Water Quality in Urban Canals

Monoalgal and mixed algal cultures discrimination by using an artificial neural network

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