The Identification of Spherical Engineered Microplastics and Microalgae by Micro-hyperspectral Imaging

Huang, Hui; Sun, Zehao; Zhang, Zhao; Chen, Xiaojie; Di, Yanan; Zhu, Fengle; Zhang, Xiaochao; Zhan, Shuyue

doi:10.1007/s00128-021-03131-9

Cited by 13 publications

(3 citation statements)

References 34 publications

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“…The accuracy of the method reached 97.22% in the experiment. Hui Huang et al employed multiple machine learning algorithms to classify microalgae and microplastics in seawater (Huang et al, 2021). The data processed by the various algorithms were the image data acquired by spectral microscopy.…”

Section: Microalgae Detection and Classification With Machine Learningmentioning

confidence: 99%

Machine learning for microalgae detection and utilization

Ning

Zhou

2022

Front. Mar. Sci.

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Microalgae are essential parts of marine ecology, and they play a key role in species balance. Microalgae also have significant economic value. However, microalgae are too tiny, and there are many different kinds of microalgae in a single drop of seawater. It is challenging to identify microalgae species and monitor microalgae changes. Machine learning techniques have achieved massive success in object recognition and classification, and have attracted a wide range of attention. Many researchers have introduced machine learning algorithms into microalgae applications, and similarly significant effects are gained. The paper summarizes recent advances based on various machine learning algorithms in microalgae applications, such as microalgae classification, bioenergy generation from microalgae, environment purification with microalgae, and microalgae growth monitor. Finally, we prospect development of machine learning algorithms in microalgae treatment in the future.

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Section: Microalgae Detection and Classification With Machine Learningmentioning

confidence: 99%

Machine learning for microalgae detection and utilization

Ning

Zhou

2022

Front. Mar. Sci.

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“…Micro-hyperspectral imaging (MHSI) combines micro-optical imaging technology and spectral analysis technology. It has been widely used in medical diagnosis (Hu et al, 2019;Hu et al, 2020;Lv et al, 2021), food safety detection (Zhu et al, 2020;Huang et al, 2021;Jiao et al, 2021), and other fields. Based on the transmittance MHSI technique, Xu et al (2020) used the support vector machine (SVM) model to distinguish two microalgae with an accuracy and specificity of 94.4% and 97.2%, respectively.…”

Section: Introductionmentioning

confidence: 99%

Nondestructive detection of Pleurotus geesteranus strain degradation based on micro-hyperspectral imaging and machine learning

Wei,

Liu,

Xie

et al. 2023

Front. Plant Sci.

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In the production of edible fungi, the use of degraded strains in cultivation incurs significant economic losses. Based on micro-hyperspectral imaging and machine learning, this study proposes an early, nondestructive method for detecting different degradation degrees of Pleurotus geesteranus strains. In this study, an undegraded strain and three different degradation-level strains were used. During the mycelium growth, 600 micro-hyperspectral images were obtained. Based on the average transmittance spectra of the region of interest (ROI) in the range of 400-1000 nm and images at feature bands, feature spectra and images were extracted using the successive projections algorithm (SPA) and the deep residual network (ResNet50), respectively. Different feature input combinations were utilized to establish support vector machine (SVM) classification models. Based on the results, the spectra-input-based model performed better than the image-input-based model, and feature extraction improved the classification results for both models. The feature-fusion-based SPA+ResNet50-SVM model was the best; the accuracy rate of the test set was up to 90.8%, which was better than the accuracy rates of SPA-SVM (83.3%) and ResNet50-SVM (80.8%). This study proposes a nondestructive method to detect the degradation of Pleurotus geesteranus strains, which could further inspire new methods for the phenotypic identification of edible fungi.

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“…VIS/NIR transmittance spectroscopy is often used as a non-destructive testing method to evaluate food quality and detect internal damage in fruits ( Dong et al., 2019 ; Huang et al., 2021 ). Compared to the reflectance spectra, transmittance spectra reflect deeper regions of the fruit ( McGlone and Martinsen, 2004 ).…”

Section: Introductionmentioning

confidence: 99%

Detection of seed purity of hybrid wheat using reflectance and transmittance hyperspectral imaging technology

Zhang

Hou

Luo³

et al. 2022

Front. Plant Sci.

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Chemical hybridization and genic male sterility systems are two main methods of hybrid wheat production; however, complete sterility of female wheat plants cannot be guaranteed owing to the influence of the growth stage and weather. Consequently, hybrid wheat seeds are inevitably mixed with few parent seeds, especially female seeds. Therefore, seed purity is a key factor in the popularization of hybrid wheat. However, traditional seed purity detection and variety identification methods are time-consuming, laborious, and destructive. Therefore, to establish a non-destructive classification method for hybrid and female parent seeds, three hybrid wheat varieties (Jingmai 9, Jingmai 11, and Jingmai 183) and their parent seeds were sampled. The transmittance and reflectance spectra of all seeds were collected via hyperspectral imaging technology, and a classification model was established using partial least squares-discriminant analysis (PLS-DA) combined with various preprocessing methods. The transmittance spectrum significantly improved the classification of hybrids and female parents compared to that obtained using reflectance spectrum. Specifically, using transmittance spectrum combined with a characteristic wavelength-screening algorithm, the Detrend-CARS-PLS-DA model was established, and the accuracy rates in the testing sets of Jingmai 9, Jingmai 11, and Jingmai 183 were 95.69%, 98.25%, and 97.25%, respectively. In conclusion, transmittance hyperspectral imaging combined with a machine learning algorithm can effectively distinguish female parent seeds from hybrid seeds. These results provide a reference for rapid seed purity detection in the hybrid production process. Owing to the non-destructive and rapid nature of hyperspectral imaging, the detection of hybrid wheat seed purity can be improved by online sorting in the future.

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The Identification of Spherical Engineered Microplastics and Microalgae by Micro-hyperspectral Imaging

Cited by 13 publications

References 34 publications

Machine learning for microalgae detection and utilization

Machine learning for microalgae detection and utilization

Nondestructive detection of Pleurotus geesteranus strain degradation based on micro-hyperspectral imaging and machine learning

Detection of seed purity of hybrid wheat using reflectance and transmittance hyperspectral imaging technology

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