A tutorial on automatic hyperparameter tuning of deep spectral modelling for regression and classification tasks

Deep 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 approaches to deep spectral modelling, currently, there is a lack of hands-on tutorial articles in this space that supply the non-expert user with practical tools to learn and implement advanced DL optimization methodologies aimed a spectral data. Hence, this tutorial article aims a reducing the gap between the non-expert user of DL in the chemometric community and the implementation of DL models for daily usage. This tutorial supplies a quick introduction to the state-of-the-art deep spectral modelling and related DL concepts and presents a set of methodologies aimed a DL hyperparameters' optimization. To this end, this tutorial shows two practical examples on how to implement and optimize two DL models for spectral regression and classification tasks. The models are implemented in python and Tensorflow and the complete code is supplied in the form of two complementary notebooks.info:eu-repo/semantics/publishedVersio

Classifying green teas with near infrared hyperspectral imaging

Tea products analysis is currently limited to high-end analytical techniques such as high-performance liquid chromatography, gas chromatography and isotope analysis. However, these techniques are time-consuming, expensive, destructive and require trained experts to perform the experiments. In the present work, an application of near infrared hyperspectral imaging for the classification of similarly appearing green tea products is demonstrated. The tea products were classified based on their origin utilising a support vector machine classifier. Results showed good accuracy (96.36 ± 0.17%) for the classification of green tea products from seven different countries of origin

Finite element modeling of snow-pack lying on a slope, considering snow as isotropic compressible viscoplastic media

A simple extension of the von Mises plasticity is proposed in which the equivalent stress is defined as a function of deviatoric and hydrostatic stresses. Non-linearity is accounted by extending NortonHoff equation for incompressible material to snow, a porous material. For developing a multi-axial constitutive equation a complementary viscoplastic potential, expressed as a function of the equivalent stress tensor, is introduced. With this potential the strain-rate tensor is obtained. Coefficients of the constitutive equation were computed with the help of experimental data. This constitutive equation is utilized to investigate the stress and velocity distribution in a snow-pack with a weak layer on a uniform slope. This weak layer has a super weak zone, responsible for initiating avalanches. Self-weight of snow is the only external force being considered. The finite-element code, based upon a plane-strain idealization, is used. Linear constitutive equation is used to give an initial guess as Newton-Raphson method has been employed for solving the system of non-linear equations. For non-linear case convergence criterion is implemented for both unknown velocities and residual forces. The effects of super-weak zone, thickness of weak layer and length of snow slab on shear stresses and deformation rates have been studied

Deep chemometrics: validation and transfer of a global deep near‐infrared fruit model to use it on a new portable instrument

Recently, 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. From a DL perspective, once a model is trained it is expected to generalise well when applied to a new batch of data. Hence, this study aims to validate the generalisability performance of the earlier developed DL model related to DM prediction in mango on a different test set measured in a local laboratory setting, with a different instrument. At first, the performance of the old DL model was presented. Later, a new DL model was crafted to cover the seasonal variability related to fruit harvest season. Finally, a DL model transfer method was performed to use the model on a new instrument. The direct application of the old DL model led to a higher error compared to the PLS model. However, the performance of the DL model was improved drastically when it was tuned to cover the seasonal variability. The updated DL model performed the best compared to the implementation of a new PLS model or updating the existing PLS model. A final root-mean-square error prediction (RMSEP) of 0.518% was reached. This result supports that, in the availability of large data sets, DL modelling can outperform chemometrics approaches.info:eu-repo/semantics/publishedVersio

Realizing transfer learning for updating deep learning models of spectral data to be used in new scenarios

This 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 DL model are complemented by adding extra fully connected (FC) layers when dealing with the new data. Such extra FC layers are expected to learn the variability of the new scenario and adjust the output of the main architecture. Furthermore, three approaches of TL were compared, first where the weights from the initial model were left untrained and the only the newly added FC layers could be retrained. The second was when the weights from the initial model could be retrained alongside the new FC layers. The third was when the weights from the initial model could be re-trained with no extra FC layers added. The TL was shown using two real cases related to near-infrared spectroscopy i.e., mango fruit analysis and melamine production monitoring. In the case of mango, the model needs to be updated to cover a new seasonal variability for dry matter prediction, while, for the melamine case, the model needs to be updated for the change in the recipe of the production material. The results showed that the proposed TL approaches successfully updated the DL models to new scenarios for both the mango and melamine cases presented. The TL performed better when the weights from the old model were retrained. Furthermore, TL outperformed three recent benchmark approaches to model updating. TL has the potential to make DL models widely useable, sharable, and scalable.info:eu-repo/semantics/publishedVersio

A synergistic use of chemometrics and deep learning improved the predictive performance of near-infrared spectroscopy models for dry matter prediction in mango fruit

This 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 data. The data augmentation is carried out by stacking the same data pre-processed with several pre-processing techniques such as standard normal variate, 1st derivatives, 2nd derivatives and their combinations. The performance of the approach is demonstrated on a real near-infrared (NIR) data set related to dry matter (DM) prediction in mango fruit. The data set consisted of a total 11,961 spectra and reference DM measurements. The results showed that removing the outliers and augmenting spectral data improved the predictive performance of DL models. Furthermore, this innovative approach not only improved DL models but attained the lowest root mean squared error of prediction (RMSEP) on the mango data set i.e., 0.79% compared to the best known RMSEP of 0.84%. Further, by removing outliers from the test set the RMSEP decreased to 0.75%. Several chemometrics approaches can complement DL models and should be widely explored in conjunction.info:eu-repo/semantics/publishedVersio

Iterative re-weighted multilinear partial least squares modelling for robust predictive modelling

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One-dimensional surface states on a striped Ag thin film with stacking fault arrays

One-dimensional (1D) stripe structures with a periodicity of 1.3 nm are formed by introduction of stacking fault arrays into a Ag thin film. The surface states of such striped Ag thin films are studied using a low temperature scanning tunneling microscope. Standing waves running in the longitudinal direction and characteristic spectral peaks are observed by differential conductance (dI/dV) measurements, revealing the presence of 1D states on the surface stripes. Their formation can be attributed to quantum confinement of Ag(111) surface states into a stripe by stacking faults. To quantify the degree of confinement, the effective potential barrier at the stacking fault for Ag(111) surface states is estimated from independent measurements. A single quantum well model with the effective potential barrier can reproduce the main features of dI/dV spectra on stripes, while a Kronig-Penney model fails to do so. Thus the present system should be viewed as decoupled 1D states on individual stripes rather than as anisotropic 2D Bloch states extending over a stripe array.Comment: 10 pages, 6 figure

NIR hyperspectral imaging for detection of nut contamination

NIR hyperspectral imaging for detection of nut contaminatio

Close-range hyperspectral imaging of whole plants for digital phenotyping : recent applications and illumination correction approaches

Digital plant phenotyping is emerging as a key research domain at the interface of information technology and plant science. Digital phenotyping aims to deploy high-end non-destructive sensing techniques and information technology infrastructures to automate the extraction of both structural and physiological traits from plants under phenotyping experiments. One of the promising sensor technologies for plant phenotyping is hyperspectral imaging (HSI). The main benefit of utilising HSI compared to other imaging techniques is the possibility to extract simultaneously structural and physiological information on plants. The use of HSI for analysis of parts of plants, e.g. plucked leaves, has already been demonstrated. However, there are several significant challenges associated with the use of HSI for extraction of information from a whole plant, and hence this is an active area of research. These challenges are related to data processing after image acquisition. The hyperspectral data acquired of a plant suffers from variations in illumination owing to light scattering, shadowing of plant parts, multiple scattering and a complex combination of scattering and shadowing. The extent of these effects depends on the type of plants and their complex geometry. A range of approaches has been introduced to deal with these effects, however, no concrete approach is yet ready. In this article, we provide a comprehensive review of recent studies of close-range HSI of whole plants. Several studies have used HSI for plant analysis but were limited to imaging of leaves, which is considerably more straightforward than imaging of the whole plant, and thus do not relate to digital phenotyping. In this article, we discuss and compare the approaches used to deal with the effects of variation in illumination, which are an issue for imaging of whole plants. Furthermore, future possibilities to deal with these effects are also highlighted