Markov Transition Field Combined with Convolutional Neural Network Improved the Predictive Performance of Near-Infrared Spectroscopy Models for Determination of Aflatoxin B1 in Maize

Wang, Bo; Deng, Jihong; Jiang, Hui

doi:10.3390/foods11152210

Cited by 19 publications

(8 citation statements)

References 36 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Both backpropagation neural networks (BPNNs) and convolutional neural networks (CNNs) are ANNs, − abstract models constructed to imitate the working principle of human brain neurons. ANNs have a stronger feature extraction ability than traditional ML algorithms.…”

Section: Classification Modelsmentioning

confidence: 99%

Machine Learning for Infrared Spectral Classification of Polyvinyl butyrals with Identical Chemical Groups: An Example for Undergraduate Chemistry Classes

Qiu,

Lin,

Yang

et al. 2024

J. Chem. Educ.

View full text Add to dashboard Cite

Machine learning (ML) is extensively applied in chemistry, particularly in vibrational spectroscopy. However, few teaching examples effectively demonstrate the capabilities of ML in classifying polymeric materials, exhibiting subtle spectral differences that elude visual discrimination. This study presents a teaching example specifically tailored for undergraduate students to acquire the skills necessary to employ ML models in the classification of infrared spectral data from different types of polyvinyl butyrals (PVBs). The course encompasses fundamental knowledge of PVB structure and synthesis, a comprehensive spectral analysis workflow for constructing classification models, specific data processing techniques, and practical implementation of a student-synthesized PVB product in a laboratory demonstration. Assessment of students' knowledge acquisition is conducted through assignments, and student attitudes toward this course via submitted self-reflection surveys are discussed. This study underscores the efficacy of classroom examples in developing students' abilities and fostering their interest in amalgamating chemistry and artificial intelligence. The knowledge and techniques acquired in this course hold practical implications for quality control, process monitoring, and material identification in industry.

show abstract

Section: Classification Modelsmentioning

confidence: 99%

Machine Learning for Infrared Spectral Classification of Polyvinyl butyrals with Identical Chemical Groups: An Example for Undergraduate Chemistry Classes

Qiu,

Lin,

Yang

et al. 2024

J. Chem. Educ.

View full text Add to dashboard Cite

show abstract

“…Similarly, a deep learning-based approach was proposed in [ 107 ] for the in-line allergen classification of agrifood powders, combining domain-adversarial neural networks (DANN) and semisupervised generative adversarial neural networks (SGANN). In addition, two DL approaches were proposed in [ 98 , 111 ], namely a two-dimensional Markov transition field-CNN (2D-MTF-CNN) and a modified version of FCN (U-net) to monitor the aflatoxin B1 (AFB1) content in maize and identify food foreign contaminants (metallic iron, polypropylene plastic, and hair) on the surface of bread, respectively.…”

Section: Data Description and Analysismentioning

confidence: 99%

“…Figure 8. A diagram representing the chronological (from 2001 to 2022) distribution of the resulting articles.Each data-point represents an article, the colors are according to which field of study, and the lines are related to the decision-making objectiveMehl, 2001 [47];Irudayaraj, 2002 [48];Yang, 2003 [49];Gupta, 2005 [50];Gupta, 2006 [51];He, 2008 [55]; A.Scarlatos, 2008 [54]; Siripatrawan, 2008[56]; Stöckel, 2010[57]; Günes, 2013[60]; Shapaval, 2013[59]; Geng, 2017[66]; Y.Shen, 2017[67]; Lasch, 2018[68]; Guo, 2019[72]; Kaushik, 2019[69]; Liu, 2019[74]; Öner, 2019[71]; Sun, 2019[75]; Wan-dan, 2019[73]; Bertania, 2020[85]; Le, 2020[77]; Sahu, 2020[80]; Shen, 2020[84]; Wange, 2020[78]; Weng, 2020[79]; Wu, 2020[83]; Gonzalez, 2021[93]; Guo, 2021[94]; Li, 2021[95]; Magnus, 2021[91]; Nie, 2021[101]; Rahi, 2021[86]; Ricci, 2021[97]; Vakilian, 2021[96]; Wang, 2021[92]; Xu, 2021[90]; Yan, 2021[99]; Yin, 2021[98]; Adejimi, 2022[109]; Bowler, 2022[107]; Cordovana, 2022[110]; Kim, 2022[108]; Manthou, 2022[115]; Rady, 2022[106]; Wang, 2022[111]; Yan, 2022[104]; Yang, 2022…”

mentioning

confidence: 99%

Combining AI Tools with Non-Destructive Technologies for Crop-Based Food Safety: A Comprehensive Review

Raki,

Aalaila,

Taktour

et al. 2023

Foods

View full text Add to dashboard Cite

On a global scale, food safety and security aspects entail consideration throughout the farm-to-fork continuum, considering food’s supply chain. Generally, the agrifood system is a multiplex network of interconnected features and processes, with a hard predictive rate, where maintaining the food’s safety is an indispensable element and is part of the Sustainable Development Goals (SDGs). It has led the scientific community to develop advanced applied analytical methods, such as machine learning (ML) and deep learning (DL) techniques applied for assessing foodborne diseases. The main objective of this paper is to contribute to the development of the consensus version of ongoing research about the application of Artificial Intelligence (AI) tools in the domain of food-crop safety from an analytical point of view. Writing a comprehensive review for a more specific topic can also be challenging, especially when searching within the literature. To our knowledge, this review is the first to address this issue. This work consisted of conducting a unique and exhaustive study of the literature, using our TriScope Keywords-based Synthesis methodology. All available literature related to our topic was investigated according to our criteria of inclusion and exclusion. The final count of data papers was subject to deep reading and analysis to extract the necessary information to answer our research questions. Although many studies have been conducted, limited attention has been paid to outlining the applications of AI tools combined with analytical strategies for crop-based food safety specifically.

show abstract

“…This results in researchers exploring new methods to improve NIR in this application. Other application 'hot spots' include grain, 28,37,[48][49][50][51][52] organic matter such as leaves, wood and beans, [53][54][55][56][57][58][59] food powders, 37,60,61 oil 62 and brain, 63 with 7, 7, 3, one and one papers, respectively.…”

Section: Cnn For Nir Spectroscopymentioning

confidence: 99%

Review: The evolution of chemometrics coupled with near infrared spectroscopy for fruit quality evaluation. II. The rise of convolutional neural networks

Walsh

Neupane

Koirala

et al. 2023

Journal of Near Infrared Spectroscopy

View full text Add to dashboard Cite

The Part 1 prequel to this review evaluated the evolution of modelling techniques used in evaluation of fruit quality over the past three decades and noted a progression towards the use of artificial neural networks (ANNs) and convolutional neural networks (CNNs). In this review, Part 2, the use of CNNs for NIR fruit quality evaluation is explored, given the success of CNNs in various other fields, such as image, video, speech, and audio processing, and the availability of large (open source) datasets of fruit spectra and reference quality attribute, which is required for the training of CNN models. The review provides an overview of deep learning and the CNN architectures and techniques used in NIR spectroscopy for regression modelling, with advantages and disadvantages identified. Studies using CNN for NIR based fruit quality evaluation are then critically examined. Eight publications have presented on models using the same open-source mango dry matter calibration and test set, enabling inter-method comparisons. CNN models have been demonstrated to be accurate, precise and robust. Techniques of transfer learning for CNN models offer an alternative solution to model updating and calibration transfer methods applied in traditional chemometrics. The review has highlighted crucial areas that require resolution and exploration in this application through future research, including, (i) data requirements for training a CNN (ii) optimal spectral pre-processing for CNN (iii) CNN architecture and hyper-parameter selection and tuning for fruit quality evaluation (iv) CNN model interpretability and explainability. Future studies must conduct clearer comparison to partial least squares (PLS) regression and shallow ANNs to better assess the prospective benefit of using CNN, a more complex model. The potential for visualisation of spectra relevance to the CNN model using techniques such as GradCam, currently employed in visualising 2D-CNN models, remains to be explored.

show abstract

Markov Transition Field Combined with Convolutional Neural Network Improved the Predictive Performance of Near-Infrared Spectroscopy Models for Determination of Aflatoxin B1 in Maize

Cited by 19 publications

References 36 publications

Machine Learning for Infrared Spectral Classification of Polyvinyl butyrals with Identical Chemical Groups: An Example for Undergraduate Chemistry Classes

Machine Learning for Infrared Spectral Classification of Polyvinyl butyrals with Identical Chemical Groups: An Example for Undergraduate Chemistry Classes

Combining AI Tools with Non-Destructive Technologies for Crop-Based Food Safety: A Comprehensive Review

Review: The evolution of chemometrics coupled with near infrared spectroscopy for fruit quality evaluation. II. The rise of convolutional neural networks

Contact Info

Product

Resources

About