Chlorella is a unicellular spherical green microalga with alternate colors from blue green to yellowish or red due to different components of innate pigments. Light and salinity are two important environmental factors in Chlorella culture. Light conditions directly affect the growth and biochemical composition of microalgae, while salinity change could influence the pigment composition of Chlorella. Therefore, it has crucial research significance to monitor the response of Chlorella to salinity stress under different light conditions. Recently, Fluorescence Lifetime Imaging Microscopy (FLIM) technology has been widely applied into biological fields, providing fluorescence lifetime values for quantitative analysis. Here, FLIM method was used to observe the autofluorescence of a freshwater microalga, Chlorella sp.. Chlorella cells were treated with a series of salinity concentrations (control sample in normal culture medium, 3S sample with an additional 3× salinity, 7S sample with an additional 7× salinity, respectively) under light (12 h/12 h light/dark cycles) or dark (0 h/24 h light/dark cycles) treatments. After one day, images of the microalgae cells from each group were obtained with FLIM system, followed by an analysis with SPCImage software. The results showed that 3× salinity condition had little effect on Chlorella in both light/dark conditions, suggesting the adaptive capacity of Chlorella to seawater salinity. By contrast, the mean fluorescence lifetime values in 7S samples under light conditions were significantly decreased compared to that of the control. Interestingly, similar lifetime values were observed in 7S samples and the control samples under dark conditions, which indicated a potential high salinity resistance induced by different light/dark conditions. In conclusion, FLIM could work as a fast evaluation method of the physiological status of living Chlorella sp. under different culture conditions in a quantitative way.
Chlorella is a single-celled blue-green spherical microalga, whose color could change from green to red or yellowish due to the components of different types of innate pigments. Salinity change is one important environmental stressor that may influence the pigment composition of Chlorella. Therefore, it is necessary to monitor the salinity stress on Chlorella in a real-time mode. Recently, fluorescence lifetime imaging microscopy (FLIM) technology has been widely applied into biological fields, which could provide fluorescence lifetime values for quantitative analysis. Here, we used FLIM method to investigate a freshwater microalga, Chlorella sp. based on its autofluorescence. Chlorella cells were treated with a series of salinity concentrations (control sample in normal culture medium, 3S sample with an additional 3× salinity, 7S sample with an additional 7× salinity, respectively) for one day. Then images of the microalgae cells from each group were obtained with FLIM system and analyzed with SPCImage software, providing the fluorescence lifetime data. The results of fluorescence lifetime data showed that 3× salinity condition had little effect on Chlorella, which indicated that Chlorella had a strong adaptive capacity in environments close to seawater salinity. However, the significant left shift of lifetime distribution peak and decreased mean lifetime values were observed in 7S samples compared with the control. In conclusion, FLIM method has shown great potential as a fast identification method of living Chlorella sp. under high salinity conditions in a quantitative and non-invasive way.
There is a high demand of novel monitoring methods for apple content measurement, especially sugar content (SC). Two traditional methods are widely used: one obtains sugar degree of apple juicy; the other identifies SC from intact apples by using near-infrared reflectance and optical fiber sensing techniques. The former is destructive and cumbersome. The latter requires expensive spectrometer equipment. Recently, deep learning has played an important role in image recognition. Convolutional Neural Network (CNN) has stronger capabilities of feature extraction and model formulation. Here, we have applied CNN into evaluate apple SC. Firstly, images of apples with SC in the range between 10 to 15 were sampled to generate data sets, which were used for data augmentation to generate larger data sets. In image processing, semantic segmentation was used to separate the target apple image from ambient noise. In the following training process, the extracted data sets were input into CNN-based deep learning model to provide the apple SC prediction model and the accuracy of prediction yield. After that, the network structure and hyperparameters were optimized to a satisfactory level, ensuring this apple sugar degree prediction model to achieve an accuracy of about 90% on the test set of apple images. Moreover, this CNN-based apple SC model was deployed on the mobile phone for achieving high portability. In conclusion, the CNN-based prediction method of apple sugar content has the advantages of non-invasive property, low cost, fast speed, high accuracy and flexibility, indicating great potential in practical applications of fruit industry.
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