In recent years, modern spectral analysis techniques, such as ultraviolet–visible (UV-vis)
spectroscopy, mid-infrared (MIR) spectroscopy, near-infrared (NIR) spectroscopy, Raman …

Raman spectroscopy is increasingly being used in biology, forensics, diagnostics,
pharmaceutics and food science applications. This growth is triggered not only by …

Data generated by analytical instruments, such as spectrometers, may contain unwanted
variation due to measurement mode, sample state and other external physical, chemical and …

Abstract Machine learning is shaping up our lives in many ways. In analytical sciences,
machine learning provides an unprecedented opportunity to extract information from …

Partial least squares-discriminant analysis (PLS-DA) is a versatile algorithm that can be
used for predictive and descriptive modelling as well as for discriminative variable selection …

Learning patterns from spectra is critical for the development of chemometric analysis of
spectroscopic data. Conventional two-stage calibration approaches consist of data …

Background Rapid and non-destructive infrared spectroscopy has been applied to both
internal and external quality evaluations of food and agro-products. Various linear and …

Food systems are negatively affected by food frauds with food recalls challenging the
system's sustainability and consumer confidence in food safety. Coffee, an economically …

The development of chemometrics aims to provide an effective analysis approach for data
generated by advanced analytical instruments. The success of existing analytical …

In this work we show that convolutional neural networks (CNNs) can be efficiently used to
classify vibrational spectroscopic data and identify important spectral regions. CNNs are the …