Application of chemometric algorithms to MALDI mass spectrometry imaging of pharmaceutical tablets

Gut, Yoann; Boiret, Mathieu; Bultel, Laurent; Renaud, Tristan; Chetouani, Aladine; Hafiane, Adel; Ginot, Yves‐Michel; Jennane, Rachid

doi:10.1016/j.jpba.2014.11.047

Cited by 40 publications

(27 citation statements)

References 32 publications

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“…ICA has been used extensively in a wide range of applications, including facial recognition (Bartlett, Movellan, & Sejnowski, ), functional magnetic resonance imaging (fMRI; Calhoun, Liu, & Adalı, ), and remote sensing (Du, Kopriva, & Szu, ; Wang & Chang, ). In IMS research, ICA has been used in the analysis of MALDI IMS measurements (Hanselmann et al, ; Siy et al, ; Verbeeck, ; Gut et al, ), but, to our knowledge, not yet in other IMS varieties.…”

Section: Factorizationmentioning

confidence: 99%

Unsupervised machine learning for exploratory data analysis in imaging mass spectrometry

Verbeeck

Caprioli

Plas

2019

Mass Spectrometry Reviews

201

190

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Imaging mass spectrometry (IMS) is a rapidly advancing molecular imaging modality that can map the spatial distribution of molecules with high chemical specificity. IMS does not require prior tagging of molecular targets and is able to measure a large number of ions concurrently in a single experiment. While this makes it particularly suited for exploratory analysis, the large amount and high‐dimensional nature of data generated by IMS techniques make automated computational analysis indispensable. Research into computational methods for IMS data has touched upon different aspects, including spectral preprocessing, data formats, dimensionality reduction, spatial registration, sample classification, differential analysis between IMS experiments, and data‐driven fusion methods to extract patterns corroborated by both IMS and other imaging modalities. In this work, we review unsupervised machine learning methods for exploratory analysis of IMS data, with particular focus on (a) factorization, (b) clustering, and (c) manifold learning. To provide a view across the various IMS modalities, we have attempted to include examples from a range of approaches including matrix assisted laser desorption/ionization, desorption electrospray ionization, and secondary ion mass spectrometry‐based IMS. This review aims to be an entry point for both (i) analytical chemists and mass spectrometry experts who want to explore computational techniques; and (ii) computer scientists and data mining specialists who want to enter the IMS field. © 2019 The Authors. Mass Spectrometry Reviews published by Wiley Periodicals, Inc. Mass SpecRev 00:1–47, 2019.

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Section: Factorizationmentioning

confidence: 99%

Unsupervised machine learning for exploratory data analysis in imaging mass spectrometry

Verbeeck

Caprioli

Plas

2019

Mass Spectrometry Reviews

201

190

View full text Add to dashboard Cite

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“…The matrix absorbs much of the energy from the incident laser and provides a very "soft" ionization for the analyte compounds. To produce a good spectra, the matrix must absorb light at the laser wavelength and must not react with the analyte [22]. MALDI MSI is capable of imaging molecules small and large, making it a very versatile technique for pharmaceutical drugs.…”

Section: Matrix Assisted Laser Desorption/ Ioni-zation (Maldi) Mass Smentioning

confidence: 99%

“…A study to examine the distribution of active ingredient and excipients in perindopril tablets was carried out using MALDI MSI with multivariate chemometric analysis [22]. The tablet was first eroded to obtain a flat surface for higher quality and reproducibility of spectra acquisition.…”

Section: Matrix Assisted Laser Desorption/ Ioni-zation (Maldi) Mass Smentioning

confidence: 99%

“…This enables the study of any fragment within the mass spectrum, or to extract a spectrum from any region of interest from an image. In addition to that, depth profiling can offer very sensitive compositional and molecular information as a function of depth as well as provide full 3-dimensional distributions within the analysed volume [22,23]. Time-of-flight instruments have resolving powers ranging from 10,000 to 100,000 and can routinely achieve less than 5 ppm mass accuracy [21].…”

Section: Time Of Flight Secondary Ion Mass Spectrometry (Tof-sims) Immentioning

confidence: 99%

“…Cross-sections of three different controlled release pellet formulations (paracetamol, theophylline and prednisone) were studied with ToF-SIMS imaging and the distribution of drug, excipients and coating characterised [22]. This methodology has also been successfully used to determine the distribution of different drug forms (hydrated and non-hydrated) within a controlled release pellet.…”

Section: Time Of Flight Secondary Ion Mass Spectrometry (Tof-sims) Immentioning

confidence: 99%

See 2 more Smart Citations

Recent Advances in Solid-State Analysis of Pharmaceuticals

Bukhari¹,

Hwei²,

Jantan³

2015

PHARMSCI

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Current analytical techniques for characterizing solid-state pharmaceuticals include powder x-ray diffraction, differential scanning calorimetry, thermogravimetric analysis, infrared spectroscopy, Raman spectroscopy, electron microscopy and nuclear magnetic resonance. Powder x-ray diffraction and differential scanning calorimetry are mainstream techniques but they lack spatial resolution. Scanning electron microscopy and micro-Raman spectroscopy provide good chemical and optical characterization but they are not capable of analysing very small nanoparticles. Transmission electron microscopy and nano-thermal analysis can provide explicit characterization of nanoparticles but they are invasive. Nuclear magnetic resonance offers good spatial resolution but its use is mainly limited by poor sensitivity and high costs. In view of the many challenges posed by existing methods, new and novel techniques are being continually researched and developed to cater to the growing number of solid formulations in the pipeline and in the market. Some of the recent advances attained in the solid-state analysis of pharmaceutical are summarized in this review article.

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Desorption Electrospray Ionization Mass Spectrometry Imaging for Tissue Analysis

Song

Chen

2020

Encyclopedia of Analytical Chemistry

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Mass spectrometry imaging (MSI) has become a significant technique of mass spectrometry (MS) to visualize the spatial distribution of molecules such as drugs, metabolites, lipids, and proteins by their molecular masses. As a representative ambient ionization method that requires minimal or no sample preparation, desorption electrospray ionization mass spectrometry (DESI‐MS) has been shown to have its unique strength in MSI. This article provides an overview of this powerful DESI‐MSI technique, including its basic principle, instrumentation, sample preparation, method optimization, 2D and 3D imaging, quantitation, statistical analysis, software, and its applications in pharmaceutical, clinical, biomedical, plant, microbial, and forensic researches.

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Application of chemometric algorithms to MALDI mass spectrometry imaging of pharmaceutical tablets

Cited by 40 publications

References 32 publications

Unsupervised machine learning for exploratory data analysis in imaging mass spectrometry

Unsupervised machine learning for exploratory data analysis in imaging mass spectrometry

Recent Advances in Solid-State Analysis of Pharmaceuticals

Desorption Electrospray Ionization Mass Spectrometry Imaging for Tissue Analysis

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