Rapid identification of foodborne bacteria with hyperspectral microscopic imaging and artificial intelligence classification algorithms

Kang, Rui; Park, Bosoon; Ouyang, Qin; Ren, Ni

doi:10.1016/j.foodcont.2021.108379

Cited by 23 publications

(15 citation statements)

References 27 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…An AI-assisted pathogen detection system, with designed HMI and Buffer Net, was developed for rapid, accurate and low-price automatic identification in this paper. Before us, Seo et al and Rui et al used HMI to classify food-borne bacteria [ 17 , 18 , 19 , 20 ]. Nevertheless, our method was better than the previous studies in terms of data or algorithm.…”

Section: Discussionmentioning

confidence: 99%

“…For faster identification of pathogens, HSI and microscopic techniques were combined to develop microscopic hyperspectral imaging (HMI). HMI was exploited to detect food-borne pathogens at the cell level [ 17 , 18 , 19 , 20 ]. However, the narrow range of wavelengths (450–800 nm) and coast spectral resolution (4 nm) are insufficient for classifying infectious pathogens.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

A Deep-Learning Based System for Rapid Genus Identification of Pathogens under Hyperspectral Microscopic Images

Tao

Tang

et al. 2022

Cells

View full text Add to dashboard Cite

Infectious diseases have always been a major threat to the survival of humanity. Additionally, they bring an enormous economic burden to society. The conventional methods for bacteria identification are expensive, time-consuming and laborious. Therefore, it is of great importance to automatically rapidly identify pathogenic bacteria in a short time. Here, we constructed an AI-assisted system for automating rapid bacteria genus identification, combining the hyperspectral microscopic technology and a deep-learning-based algorithm Buffer Net. After being trained and validated in the self-built dataset, which consists of 11 genera with over 130,000 hyperspectral images, the accuracy of the algorithm could achieve 94.9%, which outperformed 1D-CNN, 2D-CNN and 3D-ResNet. The AI-assisted system we developed has great potential in assisting clinicians in identifying pathogenic bacteria at the single-cell level with high accuracy in a cheap, rapid and automatic way. Since the AI-assisted system can identify the pathogenic genus rapidly (about 30 s per hyperspectral microscopic image) at the single-cell level, it can shorten the time or even eliminate the demand for cultivating. Additionally, the system is user-friendly for novices.

show abstract

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Deep-Learning Based System for Rapid Genus Identification of Pathogens under Hyperspectral Microscopic Images

Tao

Tang

et al. 2022

Cells

View full text Add to dashboard Cite

show abstract

“…The authors in [47] investigate the use of LSTM for identification and classification of food-borne bacteria. The authors make use of Hyper Spectral Microscopic Images, which were pre-processed and fed to an LSTM model as well as to three other models: PCA KNN, SCM, and LDA.…”

Section: ) Inception Netmentioning

confidence: 99%

“…The authors in [47] make use of live spectral analysis to train an RNN-based LSTM model. The authors in [46] propose an RNN-based model called LSTM to identify bacteria from marine water.…”

Section: Rq 12 Which Types Of Learning Have Been Applied?mentioning

confidence: 99%

“…Bacteria were segmented from a 512x512 image background with the help of a binarization image-processing technique. The authors in [47] made use of a pre-processed industrial dataset of Hyper Spectral Microscopic Images to classify food-borne bacteria into five classes. RQ 2.2 What performance evaluation metrics do the studies use to evaluate the performance of deep-learning-based techniques for the detection and classification of bacterial colonies?…”

Section: Rq 12 Which Types Of Learning Have Been Applied?mentioning

confidence: 99%

See 1 more Smart Citation

A Systematic Literature Review of Deep Learning-Based Detection and Classification Methods for Bacterial Colonies

Nagro¹

2022

IJACSA

View full text Add to dashboard Cite

Deep learning is an area of machine learning that has substantial potential in various fields of study such as image processing and computer vision. A large number of studies are published annually on deep learning techniques. The focus of this paper is on bacteria detection, identification, and classification. This paper presents a systematic literature review that synthesizes the evidence related to bacteria colony identification and detection published in the year 2021. The aim is to aggregate, analyse, and summarize the evidence related to deep learning detection, identification, and classification of bacteria and bacteria colonies. The significance is that the review will help experts and technicians to understand how deep learning techniques can apply in this regard and potentially further support more accurate detection of bacteria types. A total of 38 studies are analysed. The majority of the published studies focus on supervised-learningbased convolutional neural networks. Furthermore, a large number of studies make use of laboratory-prepared datasets as compared to open-source and industrial datasets. The results also indicate a lack of tools, which is a barrier in adapting academic research in industrial settings.

show abstract

Hyperspectral upgrade solution for biomicroscope combined with Transformer network to classify infectious bacteria

Lu,

Zhang,

Wang

et al. 2024

Journal of Biophotonics

View full text Add to dashboard Cite

Infectious diseases caused by bacterial pathogens pose a significant public health threat, emphasizing the need for swift and accurate bacterial species detection methods. Hyperspectral microscopic imaging (HMI) offers nondestructive, rapid, and data‐rich advantages, making it a promising tool for microbial detection. In this research, we present a highly compatible and cost‐effective approach to extend a standard biomicroscope system into a hyperspectral biomicroscope using a prism‐grating‐prism configuration. Using this prototype, we generate 600 hyperspectral data cubes for Listeria, Bacillus typhi, Bacillus pestis, and Bacillus anthracis. Additionally, we propose a Transformer‐based classification network that achieves a 99.44% accuracy in classifying these infectious pathogens, outperforming traditional methods. Our results suggest that the successful combination of HMI and the optimized Transformer‐based classification network highlights the potential for rapid and precise detection of infectious disease pathogens .

show abstract

Rapid identification of foodborne bacteria with hyperspectral microscopic imaging and artificial intelligence classification algorithms

Cited by 23 publications

References 27 publications

A Deep-Learning Based System for Rapid Genus Identification of Pathogens under Hyperspectral Microscopic Images

A Deep-Learning Based System for Rapid Genus Identification of Pathogens under Hyperspectral Microscopic Images

A Systematic Literature Review of Deep Learning-Based Detection and Classification Methods for Bacterial Colonies

Hyperspectral upgrade solution for biomicroscope combined with Transformer network to classify infectious bacteria

Contact Info

Product

Resources

About