A new expression is developed which allows estimating the sensitivity for the whole family of multivariate calibration algorithms based on partial least-squares regression combined with residual multilinearization. The ...

In this chapter, the estimation of relevant analytical figures of merit is reviewed, from univariate to multivariate and multi-way calibration: sensitivity, limit of detection and other analytical parameters. After a brief ...

With a growing number of analytical methodologies incorporating multivariate calibration models, the development of suitable estimators of their analytical figures of merit is playing an important role in modern analytical ...

Generalized analytical sensitivity (γ) is proposed as a new figure of merit, which can be estimated from a multivariate calibration data set. It can be confidently applied to compare different calibration methodologies, ...

In recent years, convolutional neural networks and deep neural networks have been used extensively in various fields of analytical chemistry. The use of these models for calibration tasks has been highly effective; however, ...

As previously presented in this book, measuring and processing multiway data provides analytical chemists with a number of advantages, such as (1) improved sensitivity, derived from noise averaging multiple measurements ...