Deep learning for near-infrared spectral data modelling: Hypes and benefits

Mishra, Puneet; Passos, Dário; Marini, Federico; Xu, Junli; Amigo, José Manuel; Gowen, Aoife; Jansen, Jeroen J.; Biancolillo, Alessandra; Roger, Jean Michel; Rutledge, Douglas; Nordon, Alison

doi:10.1016/j.trac.2022.116804

Cited by 70 publications

(15 citation statements)

References 39 publications

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“…For example, while spectral pre-treatment techniques have largely been developed to accentuate information specifically for PLS regression modelling, there is some suggestion that CNN models may require less pre-treatment. 9 CNN applications for the assessment of fruit quality using NIR spectroscopy are then examined, with advantages and limitations of the technique deduced and areas for refinement and improvement suggested.…”

Section: Scopementioning

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

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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.

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Section: Scopementioning

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

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“…These early results further sparked interest and concerns around the topic. Mishra et al 1 and Zhang et al 2 provided recent reviews about the current state of DL applied to NIR data modelling. These reviews cover most of the recent results including the most used DL architectures, data-augmentation proposals, interpretability of the models, implementations of model automatic optimization and some of the open questions.…”

Section: What Is Already Achieved?mentioning

confidence: 99%

“…Mishra et al. 1 and Zhang et al. 2 provided recent reviews about the current state of DL applied to NIR data modelling.…”

Section: What Is Already Achieved?mentioning

confidence: 99%

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Perspectives on deep learning for near-infrared spectral data modelling

Passos

Mishra

2022

NIR news

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Deep learning for near-infrared spectral data is a recent topic of interest for near-infrared practitioners. In recent years, applications of deep learning are flourishing from analyses of point spectrometer data to hyperspectral image analysis. However, there are also some cases where simple partial least-squares based models are sufficient. This paper provides a concise view of the state of the art of deep learning for near-infrared data modelling, particularly discussing when deep learning is useful. Discussion is also provided on what is already achieved and what ideas would be interesting to pursue regarding deep learning modelling of near-infrared data.

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“…[22][23][24][25] There was also research that demonstrated that the appropriate CNN model could transform the input data to a suitable form for prediction and extract sufficient abstract features from raw spectrum so that the performance of quantitative analyzing tasks of spectroscopic data could be improved without data preprocessing. [26][27][28][29][30][31][32] The potential of an end-to-end analysis system based on raw spectral data without preprocessing is attractive because inappropriate preprocessing methods might remove useful information and computational resources could be saved by avoiding redundant optimization. However, most research on deep learning with HSI has been dependent on the stability of neural networks due to the lack of transparency and interpretability for neural networks, 15,33 so it might be beneficial to analyze the effects of normalization HSI data on the trained neural networks directly to have a better understanding of deep learning mechanism.…”

Section: Introductionmentioning

confidence: 99%

Optimizing Hyperspectral Imaging Classification Performance with CNN and Batch Normalization

Zhang,

Abdulla

2023

Applied Spectroscopy Practica

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Background: Hyperspectral imaging systems face numerous challenges in acquiring accurate spatial-spectral hypercubes due to sample surface heterogeneity, environmental instability, and instrumental noise. Preprocessing strategies such as outlier detection, calibration, smoothing, and normalization are typically employed to address these issues, selecting appropriate techniques based on prediction performance evaluation. However, the risk of misusing inappropriate preprocessing methods remains a concern. Methods: In this study, we evaluate the impact of five normalization methods on the classification performance of six different classifiers using honey hyperspectral images. Our results show that different classifiers have varying compatible normalization techniques and that using Batch Normalization with Convolutional Neural Networks (CNN) can significantly improve classification performance and diminish the variations among other normalization techniques. The CNN with Batch Normalization can achieve a macro average F1 score of ≥0.99 with four different normalization methods and ≥0.97 without normalization. Furthermore, we analyze kernel weights' distribution in the CNN models' final convolutional layers using statistical measurements and kernel density estimation (KDE) graphs. Results: We find that the performance improvements resulting from adding BatchNorm layers are associated with kernel weight range, kurtosis, and density around 0. However, the differences among normalization methods do not show a strong correlation with kernel weight distribution. In conclusion, our findings demonstrate that the CNN with Batch Normalization layers can achieve better prediction results and avoid the risk of inappropriate normalization.

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Deep learning for near-infrared spectral data modelling: Hypes and benefits

Cited by 70 publications

References 39 publications

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

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

Perspectives on deep learning for near-infrared spectral data modelling

Optimizing Hyperspectral Imaging Classification Performance with CNN and Batch Normalization

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