Recent advances of chemometric calibration methods in modern spectroscopy: Algorithms, strategy, and related issues
HP Wang, P Chen, JW Dai, D Liu, JY Li, YP Xu… - TrAC Trends in …, 2022 - Elsevier
In recent years, modern spectral analysis techniques, such as ultraviolet–visible (UV-vis)
spectroscopy, mid-infrared (MIR) spectroscopy, near-infrared (NIR) spectroscopy, Raman …
spectroscopy, mid-infrared (MIR) spectroscopy, near-infrared (NIR) spectroscopy, Raman …
[HTML][HTML] 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 …
chemometrics domain. A major topic in the deep learning (DL) modelling of spectral data is …
[HTML][HTML] Spectral fusion modeling for soil organic carbon by a parallel input-convolutional neural network
Abstract Visible-to-near-infrared (vis–NIR) and mid-infrared (MIR) spectroscopy have been
widely utilized for the quantitative estimation of soil organic carbon (SOC). The fusion of vis …
widely utilized for the quantitative estimation of soil organic carbon (SOC). The fusion of vis …
Advances in infrared spectroscopy and hyperspectral imaging combined with artificial intelligence for the detection of cereals quality
Cereals provide humans with essential nutrients, and its quality assessment has attracted
widespread attention. Infrared (IR) spectroscopy (IRS) and hyperspectral imaging (HSI), as …
widespread attention. Infrared (IR) spectroscopy (IRS) and hyperspectral imaging (HSI), as …
Prediction of oil content in single maize kernel based on hyperspectral imaging and attention convolution neural network
An attention (A) based convolutional neural network regression (CNNR) model, namely
ACNNR, was proposed to combine hyperspectral imaging to predict oil content in single …
ACNNR, was proposed to combine hyperspectral imaging to predict oil content in single …
[HTML][HTML] Deep learning for near-infrared spectral data modelling: Hypes and benefits
Deep 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 …
experiments. Although applications are flourishing, there is also much interest currently …
[HTML][HTML] Multi-output 1-dimensional convolutional neural networks for simultaneous prediction of different traits of fruit based on near-infrared spectroscopy
In 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 …
multiple responses. A common method to deal with such a multi-response predictive …
[HTML][HTML] Chemical composition prediction in goji (Lycium barbarum) using hyperspectral imaging and multi-task 1DCNN with attention mechanism
H Hu, Y Mei, Y Wei, Z Xu, Y Zhao, H Xu, X Mao… - LWT, 2024 - Elsevier
The bioactive components of goji berries (Lycium barbarum) are crucial determinants of their
nutritional and commercial value. In this study, we combined hyperspectral imaging …
nutritional and commercial value. In this study, we combined hyperspectral imaging …
Maize seed variety identification using hyperspectral imaging and self-supervised learning: A two-stage training approach without spectral preprocessing
Rapid and non-destructive variety identification is essential for screening maize seeds for
different end-uses such as food, feed, and breeding. Hyperspectral imaging (HSI) is one of …
different end-uses such as food, feed, and breeding. Hyperspectral imaging (HSI) is one of …
A hyperspectral band selection method based on sparse band attention network for maize seed variety identification
The development of a real-time online system for rapid and nondestructive identification of
seed varieties can greatly improve production efficiency in modern agriculture …
seed varieties can greatly improve production efficiency in modern agriculture …