14 documents found, page 1 of 2
Deep learning for near-infrared spectral data modelling: Hypes and benefits
Mishra, Puneet; Passos, Dário; Marini, Federico; Xu, Junli; Amigo, Jose M.; Gowen, Aoife A.; Jansen, Jeroen J.; Biancolillo, AlessandraDeep learning (DL) is emerging as a new tool to model spectral data acquired in analytical experiments. Although applications are flourishing, there is also much interest currently observed in the scientific community on the use of DL for spectral data modelling. This paper provides a critical and compre-hensive review of the major benefits, and potential pitfalls, of current DL tecnhiques used for spectral dat...
A deep learning approach to improving spectral analysis of fruit quality under ...
Yang, Jie; Luo, Xuan; Zhang, Xiaolei; Passos, Dário; Xie, Lijuan; Rao, Xiuqin; Xu, Huirong; Ting, K.C.; Lin, Tao; Ying, YibinModel updating for developed calibrations is critical for robust spectral analysis in fruit quality control. Existing methods have limitations that usually need sufficient samples for model recalibration and are mainly designed for conventional linear models. This study proposes a model fine-tuning approach to update nonlinear deep learning models using limited sample sizes for fruit detection under interseason...
Multi-output 1-dimensional convolutional neural networks for simultaneous predi...
Mishra, Puneet; Passos, DárioIn spectral data predictive modelling of fresh fruit, often the models are calibrated to predict multiple responses. A common method to deal with such a multi-response predictive modelling is the partial least-squares (PLS2) regression. Recently, deep learning (DL) has shown to outperform partial least-squares (PLS) approaches for single fruit traits prediction. The DL can also be adapted to perform multi-respo...
A tutorial on automatic hyperparameter tuning of deep spectral modelling for re...
Passos, Dário; Mishra, PuneetDeep spectral modelling for regression and classification is gaining popularity in the chemometrics domain. A major topic in the deep learning (DL) modelling of spectral data is the choice and optimization of the deep neural network architecture suitable for the specific task of spectral modelling. Although there are several recent research articles already available in the chemometric domain showing advanced a...
Deep calibration transfer: transferring deep learning models between infrared s...
Mishra, Puneet; Passos, DárioCalibration transfer (CT) is required when a model developed on one instrument needs to be transferred and used on a new instrument. Several methods are available in the chemometrics domain to transfer the multivariate calibrations developed using modelling techniques such as partial least-square regression. However, recently deep learning (DL) models are gaining popularity to model spectral data. The tradition...
Realizing transfer learning for updating deep learning models of spectral data ...
Mishra, Puneet; Passos, DárioThis study presents the concept of transfer learning (TL) to the chemometrics community for updating DL models related to spectral data, particularly when a pre-trained DL model needs to be used in a scenario having unseen variability. This is the typical situation where classical chemometrics models require some form of re-calibration or update. In TL, the network architecture and weights from the pre-trained ...
Deep chemometrics: validation and transfer of a global deep near‐infrared fruit...
Mishra, Puneet; Passos, DárioRecently, a large near-infrared spectroscopy data set for mango fruit quality assessment was made available online. Based on that data, a deep learning (DL) model outperformed all major chemometrics and machine learning approaches. However, in earlier studies, the model validation was limited to the test set from the same data set which was measured with the same instru ment on samples from a similar origin. Fr...
An automated deep learning pipeline based on advanced optimisations for leverag...
Passos, Dário; Mishra, PuneetNa modelagem de deep learning (DL) para dados espectrais, um grande desafio está relacionado à escolha da arquitetura de rede DL e à seleção dos melhores hiperparmetros. Muitas vezes, pequenas mudanças na arquitetura neural ou seu hiperparômetro podem ter uma influência direta no desempenho do modelo, tornando sua robustez questionável. Para lidar com isso, este estudo apresenta uma modelagem automatizada de ap...
Deep multiblock predictive modelling using parallel input convolutional neural ...
Mishra, Puneet; Passos, DárioIn the domain of chemometrics, multiblock data analysis is widely performed for exploring or fusing data from multiple sources. Commonly used methods for multiblock predictive analysis are the extensions of latent space modelling approaches. However, recently, deep learning (DL) approaches such as convolutional neural networks (CNNs) have outperformed the single block traditional latent space modelling chemomet...
A synergistic use of chemometrics and deep learning improved the predictive per...
Mishra, Puneet; Passos, DárioThis study provides an innovative approach to improve deep learning (DL) models for spectral data processing with the use of chemometrics knowledge. The technique proposes pre-filtering the outliers using the Hotelling’s T2 and Q statistics obtained with partial least-square (PLS) analysis and spectral data augmentation in the variable domain to improve the predictive performance of DL models made on spectral d...