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. 2023 Dec 7;28(24):7999.
doi: 10.3390/molecules28247999.

A Multivariate Analysis-Driven Workflow to Tackle Uncertainties in Miniaturized NIR Data

Giulia Gorla  1 , Paolo Taborelli  1 , Barbara Giussani  1
Affiliations

A Multivariate Analysis-Driven Workflow to Tackle Uncertainties in Miniaturized NIR Data

Giulia Gorla et al. Molecules. .

Abstract

This study focuses on exploring and understanding measurement errors in analytical procedures involving miniaturized near-infrared instruments. Despite recent spreading in different application fields, there remains a lack of emphasis on the accuracy and reliability of these devices, which is a critical concern for accurate scientific outcomes. The study investigates multivariate measurement errors, revealing their complex nature and the influence that preprocessing techniques can have. The research introduces a possible workflow for practical error analysis in experiments involving diverse samples and instruments. Notably, it investigates how sample characteristics impact errors in the case of solid pills and tablets, typical pharmaceutical samples. ASCA was used for understanding critical instrumental factors and the potential and limitations of the method in the current application were discussed. The joint interpretation of multivariate error matrices and their resume through image histograms and K index are discussed in order to evaluate the impact of common preprocessing methods and to assess their influence on signals.

Keywords: ASCA; data uncertainty; image analysis; miniaturized NIR; multivariate error.

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Conflict of interest statement

The authors declare no conflict of interest.

Figures

Figure 1
Figure 1
Mean spectra (a), relative standard deviation (b) and signal to noise ratio (c) for the spectra of all samples acquired under charge (90 experimental replicates) from left to right: AvaSpec-Mini-Nir Integrating sphere; AvaSpec-Mini-Nir Optical fiber; and NeoSpectra Scanner.
Figure 2
Figure 2
ASCA sub-models examples. Scores of the ASCA sub−model for the factor (a) session (b) replicates (c,d) timing of background. Instruments: (a) AvaSpec-Mini-NIR equipped with integrating sphere (b) NeoSpectra Scanner (c,d) AvaSpec-Mini-NIR equipped with optical fiber. Samples: (a) Sample 3 (b) Sample 2 (c,d) Sample 4.
Figure 3
Figure 3
Multivariate error covariance matrix, error covariance matrix diagonal, correlation matrix and image histogram of the correlation matrix for Sample 1 acquired with AvaSpec-Mini NIR with integrating sphere: (a) raw data, (b) SNV, (c) first derivative.
Figure 4
Figure 4
Multivariate error covariance matrix, error covariance matrix diagonal, correlation matrix and image histogram of the correlation matrix for Sample 4 acquired with AvaSpec-Mini NIR with optical fiber: (a) raw data, (b) SNV, (c) first derivative.
Figure 5
Figure 5
Multivariate error covariance matrix, error covariance matrix diagonal, correlation matrix and image histogram of the correlation matrix for Sample 1 acquired with NeoSpectra Scanner: (a) raw data, (b) SNV, (c) first derivative.
Figure 6
Figure 6
Multivariate error covariance matrix, error covariance matrix diagonal, correlation matrix and image histogram of the correlation matrix for Sample 2 acquired with NeoSpectra Scanner: (a) raw data (b) SNV (c) first derivative.
See this image and copyright information in PMC

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