Classification of foodborne bacteria using hyperspectral microscope imaging technology coupled with convolutional neural networks‡

Kang, Rui; Park, Bosoon; Eady, Matthew; Ouyang, Qin; Chen, Kunjie

doi:10.1007/s00253-020-10387-4

Cited by 32 publications

(16 citation statements)

References 30 publications

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“…The CNN algorithm is a subset of deep neural networks and deep learning paradigms [ 22 ], and has proven its effectiveness as an image, speech recognition, face detection, futures extraction algorithm. The novel research confirms that CNNs have advantages in series forecasting, a data-driven approach for diagnostic and fault classification of various industrial processes and applications [ 23 , 24 , 25 , 26 , 27 ].…”

Section: Introductionsupporting

confidence: 63%

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Automated Inorganic Pigment Classification in Plastic Material Using Terahertz Spectroscopy

Sarjaš

Pongrac

Gleich

2021

Sensors

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This paper presents an automatic classification of plastic material’s inorganic pigment using terahertz spectroscopy and convolutional neural networks (CNN). The plastic materials were placed between the THz transmitter and receiver, and the acquired THz signals were classified using a supervised learning approach. A THz frequency band between 0.1–1.2 THz produced a one-dimensional (1D) vector that is almost impossible to classify directly using supervised learning. This paper proposes a novel pre-processing of 1D THz data that transforms 1D data into 2D data, which are processed efficiently using a convolutional neural network. The proposed pre-processing algorithm consists of four steps: peak detection, envelope extraction, and a down-sampling procedure. The last main step introduces the windowing with spectrum dilatation that reorders 1D data into 2D data that can be considered as an image. The spectrum dilation techniques ensure the classifier’s robustness by suppressing measurement bias, reducing the complexity of the THz dataset with negligible loss of accuracy, and speeding up the network classification. The experimental results showed that the proposed approach achieved high accuracy using a CNN classifier, and outperforms 1D classification of THz data using support vector machine, naive Bayes, and other popular classification algorithms.

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Section: Introductionsupporting

confidence: 63%

“…The comparisons with support vector machine, naive Bayes, classification tree, and discriminant analysis [ 24 , 25 , 26 ] are presented in Table 7 and Figure 14 . All classification algorithms SVM, NB, CT, and DA used 1D data.…”

Section: Resultsmentioning

confidence: 99%

Automated Inorganic Pigment Classification in Plastic Material Using Terahertz Spectroscopy

Sarjaš

Pongrac

Gleich

2021

Sensors

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“…However, both the quality and quantity of data are insufficient for creating effective ML model. Some researchers ([ 45 , 62 , 64 , 65 ]) merge one ML technique with other ML techniques, like SVM with radial basis function, CNN with random projection and DL with LSTM, ResNet and 1D-CNN to produce hybrid model for bacterial image classification. However the desired efficiency of the model is yet to be attained due to poor data quality.…”

Section: Discussionmentioning

confidence: 99%

“…In the same year, DL based classifier was implemented by the same authors [ 65 ] for classification of food-borne bacterial species at the cellular level by using HMI technology combined with CNN. The proposed method implemented 1D-CNN architecture, KNN and SVM for classification.…”

Section: In Bacterial Image Analysismentioning

confidence: 99%

Automated Bacterial Classifications Using Machine Learning Based Computational Techniques: Architectures, Challenges and Open Research Issues

Kotwal

Rani

Arif

et al. 2021

Arch Computat Methods Eng

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Bacteria are important in a variety of practical domains, including industry, agriculture, medicine etc. A very few species of bacteria are favourable to humans. Whereas, majority of them are extremely dangerous and causes variety of life threatening illness to different living organisms. Traditionally, this class of microbes is detected and classified using different approaches like gram staining, biochemical testing, motility testing etc. However with the availability of large amount of data and technical advances in the field of medical and computer science, the machine learning methods have been widely used and have shown tremendous performance in automatic detection of bacteria. The inclusion of latest technology employing different Artificial Intelligence techniques are greatly assisting microbiologist in solving extremely complex problems in this domain. This paper presents a review of the literature on various machine learning approaches that have been used to classify bacteria, for the period 1998–2020. The resources include research papers and book chapters from different publishers of national and international repute such as Elsevier, Springer, IEEE, PLOS, etc. The study carried out a detailed and critical analysis of penetrating different Machine learning methodologies in the field of bacterial classification along with their limitations and future scope. In addition, different opportunities and challenges in implementing these techniques in the concerned field are also presented to provide a deep insight to the researchers working in this field.

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“…In particular, CNN-1D is considered ideal for processing signals and prediction models, such as medical electrocardiography signals [ 19 ], environmental sounds [ 20 ], and human activity recognition [ 21 ]. Additionally, CNN-1D classified the spectral data of foodborne bacteria using hyperspectral microscope imaging technology with higher accuracy (90%) than that of the machine learning methods [ 22 ].…”

Section: Introductionmentioning

confidence: 99%

Non-Destructive Detection Pilot Study of Vegetable Organic Residues Using VNIR Hyperspectral Imaging and Deep Learning Techniques

Seo

Kim

Lim

et al. 2021

Sensors

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Contamination is a critical issue that affects food consumption adversely. Therefore, efficient detection and classification of food contaminants are essential to ensure food safety. This study applied a visible and near-infrared (VNIR) hyperspectral imaging technique to detect and classify organic residues on the metallic surfaces of food processing machinery. The experimental analysis was performed by diluting both potato and spinach juices to six different concentration levels using distilled water. The 3D hypercube data were acquired in the range of 400–1000 nm using a line-scan VNIR hyperspectral imaging system. Each diluted residue in the spectral domain was detected and classified using six classification methods, including a 1D convolutional neural network (CNN-1D) and five pre-processing methods. Among them, CNN-1D exhibited the highest classification accuracy, with a 0.99 and 0.98 calibration result and a 0.94 validation result for both spinach and potato residues. Therefore, in comparison with the validation accuracy of the support vector machine classifier (0.9 and 0.92 for spinach and potato, respectively), the CNN-1D technique demonstrated improved performance. Hence, the VNIR hyperspectral imaging technique with deep learning can potentially afford rapid and non-destructive detection and classification of organic residues in food facilities.

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Classification of foodborne bacteria using hyperspectral microscope imaging technology coupled with convolutional neural networks‡

Cited by 32 publications

References 30 publications

Automated Inorganic Pigment Classification in Plastic Material Using Terahertz Spectroscopy

Automated Inorganic Pigment Classification in Plastic Material Using Terahertz Spectroscopy

Automated Bacterial Classifications Using Machine Learning Based Computational Techniques: Architectures, Challenges and Open Research Issues

Non-Destructive Detection Pilot Study of Vegetable Organic Residues Using VNIR Hyperspectral Imaging and Deep Learning Techniques

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