An empirical study was carried out to compare different implementations of
ensemble models aimed at improving prediction in spectroscopic data. A wide
range of candidate models were fitted to benchmark datasets from regression and
classification settings. A statistical analysis using linear mixed model was
carried out on prediction performance criteria resulting from model fits over
random splits of the data. The results showed that the ensemble classifiers
were able to consistently outperform candidate models in our application

比较不同实现的集成模型在改进光谱数据预测方面的效果的经验性研究表明，集成分类器能够在我们的应用中始终超越候选模型。

利用集成模型分析光谱数据的益处

What is to be gained by ensemble models in analysis of spectroscopic  data?

Deep learning methods are used on spectroscopic data to predict drug content
in tablets from near infrared (NIR) spectra. Using convolutional neural
networks (CNNs), features are ex- tracted from the spectroscopic data. Extended
multiplicative scatter correction (EMSC) and a novel spectral data augmentation
method are benchmarked as preprocessing steps. The learned models perform
better or on par with hypothetical optimal partial least squares (PLS) models
for all combinations of preprocessing. Data augmentation with subsequent EMSC
in combination gave the best results. The deep learning model CNNs also
outperform the PLS models in an extrapolation chal- lenge created using data
from a second instrument and from an analyte concentration not covered by the
training data. Qualitative investigations of the CNNs kernel activations show
their resemblance to wellknown data processing methods such as smoothing,
slope/derivative, thresholds and spectral region selection.

使用深度学习方法，利用近红外光谱数据，通过卷积神经网络从光谱数据中提取特征，数据预处理使用了标准的扩展乘性散射校正和新的光谱数据增强方法进行基准测试，结果表明数据扩增并结合扩展乘性散射校正的组合效果最佳。卷积神经网络模型在使用第二台仪器和未被训练数据覆盖的分析物浓度创建的外推挑战中表现优异，同时定性研究表明 CNNs 核激活与著名的数据处理方法（如平滑、斜率 / 导数、阈值和光谱区域选择）相似。

基于卷积神经网络的光谱数据数据增强与深度化学计量学研究

Data Augmentation of Spectral Data for Convolutional Neural Network  (CNN) Based Deep Chemometrics

Conventional algorithms for rejecting cosmic-rays in single CCD exposures
rely on the contrast between cosmic-rays and their surroundings, and may
produce erroneous results if the Point Spread Function (PSF) is smaller than
the largest cosmic-rays. This paper describes a robust algorithm for cosmic-ray
rejection, based on a variation of Laplacian edge detection. The algorithm
identifies cosmic-rays of arbitrary shapes and sizes by the sharpness of their
edges, and reliably discriminates between poorly sampled point sources and
cosmic-rays. Examples of its performance are given for spectroscopic and
imaging data, including HST WFPC2 images.

本文提出了一种基于 Laplacian 边缘检测的抗宇宙射线算法，可以识别各种形状和大小的宇宙射线，并可靠地区分采样不好的点源和宇宙射线。在本文中，我们展示了该算法在分光和成像数据中的表现，包括 HST WFPC2 图像。