Rapid identification of foodborne bacteria with hyperspectral microscopic imaging and artificial intelligence classification algorithms
An artificial intelligence (AI) assisted hyperspectral microscopic imaging (HMI) method was successfully developed to differentiate five common foodborne pathogens simultaneously. HMI is extremely powerful for characterizing living cells, with every pixel of the cell region containing abundant spect...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Kang, Rui [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2021transfer abstract |
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| Schlagwörter: |
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| Übergeordnetes Werk: |
Enthalten in: Defining Tumour Shape Irregularity for Preoperative Risk Stratification of Clinically Localised Renal Cell Carcinoma - Tanaka, Hajime ELSEVIER, 2022, Amsterdam [u.a.] |
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| Übergeordnetes Werk: |
volume:130 ; year:2021 ; pages:0 |
| Links: |
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| DOI / URN: |
10.1016/j.foodcont.2021.108379 |
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| 520 | |a An artificial intelligence (AI) assisted hyperspectral microscopic imaging (HMI) method was successfully developed to differentiate five common foodborne pathogens simultaneously. HMI is extremely powerful for characterizing living cells, with every pixel of the cell region containing abundant spectral information. Three regions of interest (ROIs), including the whole-cell ROI, boundary ROI (outer membrane of cells), and center ROI (inner area of cells) were investigated to assess their classification performance. An artificial recurrent neural network named the long-short term memory (LSTM) network was proposed and optimized to directly process the spectra acquired from different ROIs. Compared to principal component analysis (PCA) based classifiers such as latent discriminant analysis (PCA-LDA, 66.0%), the k-nearest neighbors (PCA-KNN, 74.0%), and the support vector machine (PCA-SVM, 85.0%), our AI-based classifier achieved the highest accuracy of 92.9% for the center ROI dataset. Furthermore, AI-assisted HMI is capable of predicting spectra instantly, making it an efficient tool for foodborne pathogen identification. | ||
| 520 | |a An artificial intelligence (AI) assisted hyperspectral microscopic imaging (HMI) method was successfully developed to differentiate five common foodborne pathogens simultaneously. HMI is extremely powerful for characterizing living cells, with every pixel of the cell region containing abundant spectral information. Three regions of interest (ROIs), including the whole-cell ROI, boundary ROI (outer membrane of cells), and center ROI (inner area of cells) were investigated to assess their classification performance. An artificial recurrent neural network named the long-short term memory (LSTM) network was proposed and optimized to directly process the spectra acquired from different ROIs. Compared to principal component analysis (PCA) based classifiers such as latent discriminant analysis (PCA-LDA, 66.0%), the k-nearest neighbors (PCA-KNN, 74.0%), and the support vector machine (PCA-SVM, 85.0%), our AI-based classifier achieved the highest accuracy of 92.9% for the center ROI dataset. Furthermore, AI-assisted HMI is capable of predicting spectra instantly, making it an efficient tool for foodborne pathogen identification. | ||
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10.1016/j.foodcont.2021.108379 doi /cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000001606.pica (DE-627)ELV054857678 (ELSEVIER)S0956-7135(21)00517-X DE-627 ger DE-627 rakwb eng 610 VZ Kang, Rui verfasserin aut Rapid identification of foodborne bacteria with hyperspectral microscopic imaging and artificial intelligence classification algorithms 2021transfer abstract nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier An artificial intelligence (AI) assisted hyperspectral microscopic imaging (HMI) method was successfully developed to differentiate five common foodborne pathogens simultaneously. HMI is extremely powerful for characterizing living cells, with every pixel of the cell region containing abundant spectral information. Three regions of interest (ROIs), including the whole-cell ROI, boundary ROI (outer membrane of cells), and center ROI (inner area of cells) were investigated to assess their classification performance. An artificial recurrent neural network named the long-short term memory (LSTM) network was proposed and optimized to directly process the spectra acquired from different ROIs. Compared to principal component analysis (PCA) based classifiers such as latent discriminant analysis (PCA-LDA, 66.0%), the k-nearest neighbors (PCA-KNN, 74.0%), and the support vector machine (PCA-SVM, 85.0%), our AI-based classifier achieved the highest accuracy of 92.9% for the center ROI dataset. Furthermore, AI-assisted HMI is capable of predicting spectra instantly, making it an efficient tool for foodborne pathogen identification. An artificial intelligence (AI) assisted hyperspectral microscopic imaging (HMI) method was successfully developed to differentiate five common foodborne pathogens simultaneously. HMI is extremely powerful for characterizing living cells, with every pixel of the cell region containing abundant spectral information. Three regions of interest (ROIs), including the whole-cell ROI, boundary ROI (outer membrane of cells), and center ROI (inner area of cells) were investigated to assess their classification performance. An artificial recurrent neural network named the long-short term memory (LSTM) network was proposed and optimized to directly process the spectra acquired from different ROIs. Compared to principal component analysis (PCA) based classifiers such as latent discriminant analysis (PCA-LDA, 66.0%), the k-nearest neighbors (PCA-KNN, 74.0%), and the support vector machine (PCA-SVM, 85.0%), our AI-based classifier achieved the highest accuracy of 92.9% for the center ROI dataset. Furthermore, AI-assisted HMI is capable of predicting spectra instantly, making it an efficient tool for foodborne pathogen identification. Artificial intelligence classifier Elsevier Foodborne pathogen Elsevier Hyperspectral microscopy Elsevier Rapid classification Elsevier Park, Bosoon oth Ouyang, Qin oth Ren, Ni oth Enthalten in Elsevier Science Tanaka, Hajime ELSEVIER Defining Tumour Shape Irregularity for Preoperative Risk Stratification of Clinically Localised Renal Cell Carcinoma 2022 Amsterdam [u.a.] (DE-627)ELV009139680 volume:130 year:2021 pages:0 https://doi.org/10.1016/j.foodcont.2021.108379 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OLC-PHA AR 130 2021 0 |
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10.1016/j.foodcont.2021.108379 doi /cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000001606.pica (DE-627)ELV054857678 (ELSEVIER)S0956-7135(21)00517-X DE-627 ger DE-627 rakwb eng 610 VZ Kang, Rui verfasserin aut Rapid identification of foodborne bacteria with hyperspectral microscopic imaging and artificial intelligence classification algorithms 2021transfer abstract nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier An artificial intelligence (AI) assisted hyperspectral microscopic imaging (HMI) method was successfully developed to differentiate five common foodborne pathogens simultaneously. HMI is extremely powerful for characterizing living cells, with every pixel of the cell region containing abundant spectral information. Three regions of interest (ROIs), including the whole-cell ROI, boundary ROI (outer membrane of cells), and center ROI (inner area of cells) were investigated to assess their classification performance. An artificial recurrent neural network named the long-short term memory (LSTM) network was proposed and optimized to directly process the spectra acquired from different ROIs. Compared to principal component analysis (PCA) based classifiers such as latent discriminant analysis (PCA-LDA, 66.0%), the k-nearest neighbors (PCA-KNN, 74.0%), and the support vector machine (PCA-SVM, 85.0%), our AI-based classifier achieved the highest accuracy of 92.9% for the center ROI dataset. Furthermore, AI-assisted HMI is capable of predicting spectra instantly, making it an efficient tool for foodborne pathogen identification. An artificial intelligence (AI) assisted hyperspectral microscopic imaging (HMI) method was successfully developed to differentiate five common foodborne pathogens simultaneously. HMI is extremely powerful for characterizing living cells, with every pixel of the cell region containing abundant spectral information. Three regions of interest (ROIs), including the whole-cell ROI, boundary ROI (outer membrane of cells), and center ROI (inner area of cells) were investigated to assess their classification performance. An artificial recurrent neural network named the long-short term memory (LSTM) network was proposed and optimized to directly process the spectra acquired from different ROIs. Compared to principal component analysis (PCA) based classifiers such as latent discriminant analysis (PCA-LDA, 66.0%), the k-nearest neighbors (PCA-KNN, 74.0%), and the support vector machine (PCA-SVM, 85.0%), our AI-based classifier achieved the highest accuracy of 92.9% for the center ROI dataset. Furthermore, AI-assisted HMI is capable of predicting spectra instantly, making it an efficient tool for foodborne pathogen identification. Artificial intelligence classifier Elsevier Foodborne pathogen Elsevier Hyperspectral microscopy Elsevier Rapid classification Elsevier Park, Bosoon oth Ouyang, Qin oth Ren, Ni oth Enthalten in Elsevier Science Tanaka, Hajime ELSEVIER Defining Tumour Shape Irregularity for Preoperative Risk Stratification of Clinically Localised Renal Cell Carcinoma 2022 Amsterdam [u.a.] (DE-627)ELV009139680 volume:130 year:2021 pages:0 https://doi.org/10.1016/j.foodcont.2021.108379 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OLC-PHA AR 130 2021 0 |
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10.1016/j.foodcont.2021.108379 doi /cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000001606.pica (DE-627)ELV054857678 (ELSEVIER)S0956-7135(21)00517-X DE-627 ger DE-627 rakwb eng 610 VZ Kang, Rui verfasserin aut Rapid identification of foodborne bacteria with hyperspectral microscopic imaging and artificial intelligence classification algorithms 2021transfer abstract nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier An artificial intelligence (AI) assisted hyperspectral microscopic imaging (HMI) method was successfully developed to differentiate five common foodborne pathogens simultaneously. HMI is extremely powerful for characterizing living cells, with every pixel of the cell region containing abundant spectral information. Three regions of interest (ROIs), including the whole-cell ROI, boundary ROI (outer membrane of cells), and center ROI (inner area of cells) were investigated to assess their classification performance. An artificial recurrent neural network named the long-short term memory (LSTM) network was proposed and optimized to directly process the spectra acquired from different ROIs. Compared to principal component analysis (PCA) based classifiers such as latent discriminant analysis (PCA-LDA, 66.0%), the k-nearest neighbors (PCA-KNN, 74.0%), and the support vector machine (PCA-SVM, 85.0%), our AI-based classifier achieved the highest accuracy of 92.9% for the center ROI dataset. Furthermore, AI-assisted HMI is capable of predicting spectra instantly, making it an efficient tool for foodborne pathogen identification. An artificial intelligence (AI) assisted hyperspectral microscopic imaging (HMI) method was successfully developed to differentiate five common foodborne pathogens simultaneously. HMI is extremely powerful for characterizing living cells, with every pixel of the cell region containing abundant spectral information. Three regions of interest (ROIs), including the whole-cell ROI, boundary ROI (outer membrane of cells), and center ROI (inner area of cells) were investigated to assess their classification performance. An artificial recurrent neural network named the long-short term memory (LSTM) network was proposed and optimized to directly process the spectra acquired from different ROIs. Compared to principal component analysis (PCA) based classifiers such as latent discriminant analysis (PCA-LDA, 66.0%), the k-nearest neighbors (PCA-KNN, 74.0%), and the support vector machine (PCA-SVM, 85.0%), our AI-based classifier achieved the highest accuracy of 92.9% for the center ROI dataset. Furthermore, AI-assisted HMI is capable of predicting spectra instantly, making it an efficient tool for foodborne pathogen identification. Artificial intelligence classifier Elsevier Foodborne pathogen Elsevier Hyperspectral microscopy Elsevier Rapid classification Elsevier Park, Bosoon oth Ouyang, Qin oth Ren, Ni oth Enthalten in Elsevier Science Tanaka, Hajime ELSEVIER Defining Tumour Shape Irregularity for Preoperative Risk Stratification of Clinically Localised Renal Cell Carcinoma 2022 Amsterdam [u.a.] (DE-627)ELV009139680 volume:130 year:2021 pages:0 https://doi.org/10.1016/j.foodcont.2021.108379 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OLC-PHA AR 130 2021 0 |
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10.1016/j.foodcont.2021.108379 doi /cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000001606.pica (DE-627)ELV054857678 (ELSEVIER)S0956-7135(21)00517-X DE-627 ger DE-627 rakwb eng 610 VZ Kang, Rui verfasserin aut Rapid identification of foodborne bacteria with hyperspectral microscopic imaging and artificial intelligence classification algorithms 2021transfer abstract nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier An artificial intelligence (AI) assisted hyperspectral microscopic imaging (HMI) method was successfully developed to differentiate five common foodborne pathogens simultaneously. HMI is extremely powerful for characterizing living cells, with every pixel of the cell region containing abundant spectral information. Three regions of interest (ROIs), including the whole-cell ROI, boundary ROI (outer membrane of cells), and center ROI (inner area of cells) were investigated to assess their classification performance. An artificial recurrent neural network named the long-short term memory (LSTM) network was proposed and optimized to directly process the spectra acquired from different ROIs. Compared to principal component analysis (PCA) based classifiers such as latent discriminant analysis (PCA-LDA, 66.0%), the k-nearest neighbors (PCA-KNN, 74.0%), and the support vector machine (PCA-SVM, 85.0%), our AI-based classifier achieved the highest accuracy of 92.9% for the center ROI dataset. Furthermore, AI-assisted HMI is capable of predicting spectra instantly, making it an efficient tool for foodborne pathogen identification. An artificial intelligence (AI) assisted hyperspectral microscopic imaging (HMI) method was successfully developed to differentiate five common foodborne pathogens simultaneously. HMI is extremely powerful for characterizing living cells, with every pixel of the cell region containing abundant spectral information. Three regions of interest (ROIs), including the whole-cell ROI, boundary ROI (outer membrane of cells), and center ROI (inner area of cells) were investigated to assess their classification performance. An artificial recurrent neural network named the long-short term memory (LSTM) network was proposed and optimized to directly process the spectra acquired from different ROIs. Compared to principal component analysis (PCA) based classifiers such as latent discriminant analysis (PCA-LDA, 66.0%), the k-nearest neighbors (PCA-KNN, 74.0%), and the support vector machine (PCA-SVM, 85.0%), our AI-based classifier achieved the highest accuracy of 92.9% for the center ROI dataset. Furthermore, AI-assisted HMI is capable of predicting spectra instantly, making it an efficient tool for foodborne pathogen identification. Artificial intelligence classifier Elsevier Foodborne pathogen Elsevier Hyperspectral microscopy Elsevier Rapid classification Elsevier Park, Bosoon oth Ouyang, Qin oth Ren, Ni oth Enthalten in Elsevier Science Tanaka, Hajime ELSEVIER Defining Tumour Shape Irregularity for Preoperative Risk Stratification of Clinically Localised Renal Cell Carcinoma 2022 Amsterdam [u.a.] (DE-627)ELV009139680 volume:130 year:2021 pages:0 https://doi.org/10.1016/j.foodcont.2021.108379 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OLC-PHA AR 130 2021 0 |
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10.1016/j.foodcont.2021.108379 doi /cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000001606.pica (DE-627)ELV054857678 (ELSEVIER)S0956-7135(21)00517-X DE-627 ger DE-627 rakwb eng 610 VZ Kang, Rui verfasserin aut Rapid identification of foodborne bacteria with hyperspectral microscopic imaging and artificial intelligence classification algorithms 2021transfer abstract nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier An artificial intelligence (AI) assisted hyperspectral microscopic imaging (HMI) method was successfully developed to differentiate five common foodborne pathogens simultaneously. HMI is extremely powerful for characterizing living cells, with every pixel of the cell region containing abundant spectral information. Three regions of interest (ROIs), including the whole-cell ROI, boundary ROI (outer membrane of cells), and center ROI (inner area of cells) were investigated to assess their classification performance. An artificial recurrent neural network named the long-short term memory (LSTM) network was proposed and optimized to directly process the spectra acquired from different ROIs. Compared to principal component analysis (PCA) based classifiers such as latent discriminant analysis (PCA-LDA, 66.0%), the k-nearest neighbors (PCA-KNN, 74.0%), and the support vector machine (PCA-SVM, 85.0%), our AI-based classifier achieved the highest accuracy of 92.9% for the center ROI dataset. Furthermore, AI-assisted HMI is capable of predicting spectra instantly, making it an efficient tool for foodborne pathogen identification. An artificial intelligence (AI) assisted hyperspectral microscopic imaging (HMI) method was successfully developed to differentiate five common foodborne pathogens simultaneously. HMI is extremely powerful for characterizing living cells, with every pixel of the cell region containing abundant spectral information. Three regions of interest (ROIs), including the whole-cell ROI, boundary ROI (outer membrane of cells), and center ROI (inner area of cells) were investigated to assess their classification performance. An artificial recurrent neural network named the long-short term memory (LSTM) network was proposed and optimized to directly process the spectra acquired from different ROIs. Compared to principal component analysis (PCA) based classifiers such as latent discriminant analysis (PCA-LDA, 66.0%), the k-nearest neighbors (PCA-KNN, 74.0%), and the support vector machine (PCA-SVM, 85.0%), our AI-based classifier achieved the highest accuracy of 92.9% for the center ROI dataset. Furthermore, AI-assisted HMI is capable of predicting spectra instantly, making it an efficient tool for foodborne pathogen identification. Artificial intelligence classifier Elsevier Foodborne pathogen Elsevier Hyperspectral microscopy Elsevier Rapid classification Elsevier Park, Bosoon oth Ouyang, Qin oth Ren, Ni oth Enthalten in Elsevier Science Tanaka, Hajime ELSEVIER Defining Tumour Shape Irregularity for Preoperative Risk Stratification of Clinically Localised Renal Cell Carcinoma 2022 Amsterdam [u.a.] (DE-627)ELV009139680 volume:130 year:2021 pages:0 https://doi.org/10.1016/j.foodcont.2021.108379 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OLC-PHA AR 130 2021 0 |
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Enthalten in Defining Tumour Shape Irregularity for Preoperative Risk Stratification of Clinically Localised Renal Cell Carcinoma Amsterdam [u.a.] volume:130 year:2021 pages:0 |
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| author |
Kang, Rui |
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Kang, Rui ddc 610 Elsevier Artificial intelligence classifier Elsevier Foodborne pathogen Elsevier Hyperspectral microscopy Elsevier Rapid classification Rapid identification of foodborne bacteria with hyperspectral microscopic imaging and artificial intelligence classification algorithms |
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610 VZ Rapid identification of foodborne bacteria with hyperspectral microscopic imaging and artificial intelligence classification algorithms Artificial intelligence classifier Elsevier Foodborne pathogen Elsevier Hyperspectral microscopy Elsevier Rapid classification Elsevier |
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ddc 610 Elsevier Artificial intelligence classifier Elsevier Foodborne pathogen Elsevier Hyperspectral microscopy Elsevier Rapid classification |
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ddc 610 Elsevier Artificial intelligence classifier Elsevier Foodborne pathogen Elsevier Hyperspectral microscopy Elsevier Rapid classification |
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ddc 610 Elsevier Artificial intelligence classifier Elsevier Foodborne pathogen Elsevier Hyperspectral microscopy Elsevier Rapid classification |
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Defining Tumour Shape Irregularity for Preoperative Risk Stratification of Clinically Localised Renal Cell Carcinoma |
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Defining Tumour Shape Irregularity for Preoperative Risk Stratification of Clinically Localised Renal Cell Carcinoma |
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Rapid identification of foodborne bacteria with hyperspectral microscopic imaging and artificial intelligence classification algorithms |
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Rapid identification of foodborne bacteria with hyperspectral microscopic imaging and artificial intelligence classification algorithms |
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Kang, Rui |
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Defining Tumour Shape Irregularity for Preoperative Risk Stratification of Clinically Localised Renal Cell Carcinoma |
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Defining Tumour Shape Irregularity for Preoperative Risk Stratification of Clinically Localised Renal Cell Carcinoma |
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10.1016/j.foodcont.2021.108379 |
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rapid identification of foodborne bacteria with hyperspectral microscopic imaging and artificial intelligence classification algorithms |
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Rapid identification of foodborne bacteria with hyperspectral microscopic imaging and artificial intelligence classification algorithms |
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An artificial intelligence (AI) assisted hyperspectral microscopic imaging (HMI) method was successfully developed to differentiate five common foodborne pathogens simultaneously. HMI is extremely powerful for characterizing living cells, with every pixel of the cell region containing abundant spectral information. Three regions of interest (ROIs), including the whole-cell ROI, boundary ROI (outer membrane of cells), and center ROI (inner area of cells) were investigated to assess their classification performance. An artificial recurrent neural network named the long-short term memory (LSTM) network was proposed and optimized to directly process the spectra acquired from different ROIs. Compared to principal component analysis (PCA) based classifiers such as latent discriminant analysis (PCA-LDA, 66.0%), the k-nearest neighbors (PCA-KNN, 74.0%), and the support vector machine (PCA-SVM, 85.0%), our AI-based classifier achieved the highest accuracy of 92.9% for the center ROI dataset. Furthermore, AI-assisted HMI is capable of predicting spectra instantly, making it an efficient tool for foodborne pathogen identification. |
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An artificial intelligence (AI) assisted hyperspectral microscopic imaging (HMI) method was successfully developed to differentiate five common foodborne pathogens simultaneously. HMI is extremely powerful for characterizing living cells, with every pixel of the cell region containing abundant spectral information. Three regions of interest (ROIs), including the whole-cell ROI, boundary ROI (outer membrane of cells), and center ROI (inner area of cells) were investigated to assess their classification performance. An artificial recurrent neural network named the long-short term memory (LSTM) network was proposed and optimized to directly process the spectra acquired from different ROIs. Compared to principal component analysis (PCA) based classifiers such as latent discriminant analysis (PCA-LDA, 66.0%), the k-nearest neighbors (PCA-KNN, 74.0%), and the support vector machine (PCA-SVM, 85.0%), our AI-based classifier achieved the highest accuracy of 92.9% for the center ROI dataset. Furthermore, AI-assisted HMI is capable of predicting spectra instantly, making it an efficient tool for foodborne pathogen identification. |
| abstract_unstemmed |
An artificial intelligence (AI) assisted hyperspectral microscopic imaging (HMI) method was successfully developed to differentiate five common foodborne pathogens simultaneously. HMI is extremely powerful for characterizing living cells, with every pixel of the cell region containing abundant spectral information. Three regions of interest (ROIs), including the whole-cell ROI, boundary ROI (outer membrane of cells), and center ROI (inner area of cells) were investigated to assess their classification performance. An artificial recurrent neural network named the long-short term memory (LSTM) network was proposed and optimized to directly process the spectra acquired from different ROIs. Compared to principal component analysis (PCA) based classifiers such as latent discriminant analysis (PCA-LDA, 66.0%), the k-nearest neighbors (PCA-KNN, 74.0%), and the support vector machine (PCA-SVM, 85.0%), our AI-based classifier achieved the highest accuracy of 92.9% for the center ROI dataset. Furthermore, AI-assisted HMI is capable of predicting spectra instantly, making it an efficient tool for foodborne pathogen identification. |
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Rapid identification of foodborne bacteria with hyperspectral microscopic imaging and artificial intelligence classification algorithms |
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https://doi.org/10.1016/j.foodcont.2021.108379 |
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Park, Bosoon Ouyang, Qin Ren, Ni |
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