Rapid identification of foodborne bacteria with hyperspectral microscopic imaging and artificial intelligence classification algorithms
文献类型： 外文期刊
作者： Kang, Rui 1 ; Park, Bosoon 2 ; Ouyang, Qin 2 ; Ren, Ni 1 ;
作者机构： 1.Jiangsu Acad Agr Sci, Ctr Informat, Nanjing 210031, Peoples R China
2.ARS, USDA, US Natl Poultry Res Ctr, 950 Coll Stn Rd, Athens, GA 30605 USA
关键词： Hyperspectral microscopy; Foodborne pathogen; Rapid classification; Artificial intelligence classifier
期刊名称：FOOD CONTROL （ 影响因子：5.548； 五年影响因子：5.498 ）
ISSN： 0956-7135
年卷期： 2021 年 130 卷
页码：
收录情况： SCI
摘要： 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.
- 相关文献
作者其他论文 更多>>
-
3D-GhostNet: A novel spatial-spectral algorithm to improve foodborne bacteria classification coupled with hyperspectral microscopic imaging technology
作者：Kang, Rui;Huang, Jiaxing;Kang, Rui;Ouyang, Qin;Park, Bosoon;Kang, Rui;Sun, Shangpeng;Ouyang, Qin
关键词：Hyperspectral microscope imaging; Foodborne pathogen; Rapid detection; 3D hypercube classification
-
Quantitative prediction and visualization of matcha color physicochemical indicators using hyperspectral microscope imaging technology
作者：Li, Dengshan;Chen, Quansheng;Ouyang, Qin;Park, Bosoon;Rang, Rui
关键词：Sensory quality; Quantification analysis; Multivariate calibration; Variable selection
-
A variable weight combination prediction model for climate in a greenhouse based on BiGRU-Attention and LightGBM
作者：Mao, Xiaojuan;Ren, Ni;Dai, Peiyu;Jin, Jing;Wang, Baojia;Kang, Rui;Li, Decui;Ren, Ni
关键词：Greenhouse; Climate; Bi-directional Gated Recurrent Unit; Attention; LightGBM
-
Toward Real Scenery: A Lightweight Tomato Growth Inspection Algorithm for Leaf Disease Detection and Fruit Counting
作者：Kang, Rui;Huang, Jiaxin;Ren, Ni;Kang, Rui;Zhou, Xuehai;Sun, Shangpeng
关键词：
-
Response of Flavor Substances in Tomato Fruit to Light Spectrum and Daily Light Integral
作者：Fan, Xiaoxue;Jin, Jing;Mao, Xiaojuan;Ren, Ni;Lu, Na;Xu, Wenshuo;Zhuang, Yunfei
关键词：PFALs; light quality; DLI; fruit quality; flavor substances
-
Automated segmentation of foodborne bacteria from chicken rinse with hyperspectral microscope imaging and deep learning methods
作者：Park, Bosoon;Shin, Taesung;Yoon, Seung-Chul;Kang, Rui;Fong, Alexandre;McDonogh, Barry
关键词：Hyperspectral microscopy; Foodborne pathogen; Bacterial detection; Single-cell segmentation; U-Net
-
Physicochemical indicators coupled with multivariate analysis for comprehensive evaluation of matcha sensory quality
作者：Wu, Jizhong;Ouyang, Qin;Wang, Li;Chen, Quansheng;Park, Bosoon;Kang, Rui;Wang, Zhen
关键词：Fine tea powder; Sensory attributes; Physicochemical properties; LASSO fitting; Principal component analysis
