Contemporary Trends in Analytical Spectroscopy and Integrating Qbd Principles for Enhanced Method Development

Analytical spectroscopy has undergone significant advancements in recent years, driven by technological innovations and the demand for more robust, efficient, and reliable methods. The integration of Quality by Design (QbD) principles in spectroscopic method development has emerged as a powerful approach to enhance performance, reliability, and regulatory compliance. Contemporary trends in UV-Visible, Infrared (IR), Near-Infrared (NIR), Raman, Nuclear Magnetic Resonance (NMR), and Mass Spectrometry showcase remarkable progress in sensitivity, selectivity, and applicability. QbD implementation in method development, optimization, and validation has led to improved robustness and reduced method failures. Hyphenated techniques, coupling spectroscopy with separation methods, have expanded the analytical capabilities, enabling more comprehensive characterization of complex samples. Chemometrics and advanced data analysis techniques play a crucial role in extracting meaningful information from large spectral datasets. Regulatory agencies increasingly recognize the value of QbD in analytical methods, encouraging its adoption in the pharmaceutical and other regulated industries. Despite these advancements, challenges remain in standardizing QbD approaches and adapting to emerging technologies. Future directions point towards increased automation, miniaturization, and real-time monitoring capabilities in spectroscopic techniques

• Publication year

  2024

• Venue

  Journal of Pharma Insights and Research

• Publication date

  2024-10-04

• Fields of study

  Not labeled

• Identifiers
  DOI 10.69613/tne5y240
• External record

  Open on Semantic Scholar

• Source metadata

  Semantic Scholar

• No claims are published for this paper.

• No concepts are published for this paper.

• Nanorobotics: Pioneering Drug Delivery and Development in Pharmaceuticals

  2024cited by this paper
• Beyond the Spectrum: Exploring Unconventional Applications of Fourier Transform Infrared (FTIR) Spectroscopy

  2024cited by this paper
• Exploring Aquasomes: A Promising Frontier in Nanotechnology-based Drug Delivery

  2024cited by this paper
• Microneedle Arrays: Advancements, Applications and Future Prospects in Pharmaceutical Delivery

  2024cited by this paper
• Formulation Development and Characterization of Vancomycin Hydrochloride Colon-Targeted Tablets Using In-Situ Polyelectrolyte Complexation Technique

  2023cited by this paper
• Novel colorimetric approach for amikacin estimation in pure powder and its pharmaceutical formulations

  2023cited by this paper
• Development and validation of an HPLC method for the simultaneous estimation of salbutamol, theophylline and ambroxol in tablet dosage form

  2023cited by this paper
• The Expanding Scope of Emulgels: Formulation, Evaluation and Medical Uses

  2023cited by this paper
• Unconventional stationary phases: Nanomaterials, nanoparticles and the future of liquid chromatography

  2023cited by this paper
• Multiset Data Analysis: Extended Multivariate Curve Resolution

  2020cited by this paper
• Introduction to Multivariate Calibration: A Practical Approach

  2018cited by this paper
• Pattern recognition in chemometrics

  2015cited by this paper
• Partial least squares discriminant analysis: taking the magic away

  2014cited by this paper
• Prediction of the geographical origin of butters by partial least square discriminant analysis (PLS-DA) applied to infrared spectroscopy (FTIR) data

  2014cited by this paper
• Pre-processing in vibrational spectroscopy - when, why and how.

  2014cited by this paper
• Recursive weighted partial least squares (rPLS): an efficient variable selection method using PLS

  2014cited by this paper
• Classification tools in chemistry. Part 1: linear models. PLS-DA

  2013cited by this paper
• Breaking with trends in pre-processing?

  2013cited by this paper
• Application of the near-infrared spectroscopy in the pharmaceutical technology.

  2012cited by this paper
• Practical Guide and Spectral Atlas for Interpretive Near

  2012cited by this paper
• Multivariate data analysis in pharmaceutics: a tutorial review.

  2011cited by this paper
• How to pre-process Raman spectra for reliable and stable models?

  2011cited by this paper
• Multivariate data analysis applied to spectroscopy: Potential application to juice and fruit quality

  2011cited by this paper
• Infrared and Raman Spectroscopy: Principles and Spectral Interpretation

  2011cited by this paper
• Near infrared and Raman spectroscopy for the in-process monitoring of pharmaceutical production processes.

  2011cited by this paper
• Interpretation of partial least squares regression models by means of target projection and selectivity ratio plots

  2010cited by this paper
• Process analytical technology : spectroscopic tools and implementation strategies for the chemical and pharmaceutical industries

  2010cited by this paper
• Mahalanobis Distance

  2009cited by this paper
• Canonical partial least squares—a unified PLS approach to classification and regression problems

  2009cited by this paper
• Review of the most common pre-processing techniques for near-infrared spectra

  2009cited by this paper
• Biomarker discovery in mass spectral profiles by means of selectivity ratio plot

  2009cited by this paper
• Near InfraRed Spectroscopy homogeneity evaluation of complex powder blends in a small-scale pharmaceutical preformulation process, a real-life application.

  2009cited by this paper
• Near-infrared chemical imaging (NIR-CI) on pharmaceutical solid dosage forms-comparing common calibration approaches.

  2008cited by this paper
• Multiblock variance partitioning: a new approach for comparing variation in multiple data blocks.

  2008cited by this paper
• Pharmaceutical applications of vibrational chemical imaging and chemometrics: a review.

  2008cited by this paper
• Nondestructive measurement of fruit and vegetable quality by means of NIR spectroscopy: A review

  2007cited by this paper
• The pls Package: Principal Component and Partial Least Squares Regression in R

  2007cited by this paper
• Proposed minimum reporting standards for data analysis in metabolomics

  2007cited by this paper
• Application of near infrared reflectance (NIR) and fluorescence spectroscopy to analysis of microbiological and chemical properties of arctic soil

  2007cited by this paper
• Near-infrared spectroscopy in food science and technology

  2007cited by this paper
• Multivariate calibration.

  2007cited by this paper
• Near-infrared spectroscopy applications in pharmaceutical analysis.

  2007cited by this paper
• A review of near infrared spectroscopy and chemometrics in pharmaceutical technologies.

  2007cited by this paper
• Chemometrics: achievements and prospects

  2006cited by this paper
• Automated alignment of chromatographic data

  2006cited by this paper
• OPLS discriminant analysis: combining the strengths of PLS‐DA and SIMCA classification

  2006cited by this paper
• Multivariate Curve Resolution (MCR) from 2000: Progress in Concepts and Applications

  2006cited by this paper
• NIR spectrometry for counterfeit drug detection - A feasibility study

  2005cited by this paper
• ANOVA-simultaneous component analysis (ASCA): a new tool for analyzing designed metabolomics data

  2005cited by this paper
• Characterizing process effects on pharmaceutical solid forms using near-infrared spectroscopy and infrared imaging.

  2005cited by this paper
• Addendum: Regularization and variable selection via the elastic net

  2005cited by this paper
• PLS post‐processing by similarity transformation (PLS + ST): a simple alternative to OPLS

  2005cited by this paper
• Hyperspectral NIR image regression part I: calibration and correction

  2005cited by this paper
• A graphical user-friendly interface for MCR-ALS: a new tool for multivariate curve resolution in MATLAB

  2005cited by this paper
• Near-infrared spectroscopy and imaging: basic principles and pharmaceutical applications.

  2005cited by this paper
• Parametric time warping.

  2004cited by this paper
• Chemometrics: Data Analysis for the Laboratory and Chemical Plant

  2004cited by this paper
• Book Reviews: A User-Friendly Guide to Multivariate Calibration and Classification

  2004cited by this paper
• A framework for sequential multiblock component methods

  2003cited by this paper
• Principal Component Analysis

  2003cited by this paper
• Partial least squares for discrimination

  2003cited by this paper
• Multivariate calibration: What is in chemometrics for the analytical chemist?

  2003cited by this paper
• Near Infrared Spectroscopy: fundamentals, practical aspects and analytical applications

  2003cited by this paper
• Pre‐whitening of data by covariance‐weighted pre‐processing

  2003cited by this paper
• Centering and scaling in component analysis

  2003cited by this paper
• O2‐PLS, a two‐block (X–Y) latent variable regression (LVR) method with an integral OSC filter

  2003cited by this paper
• Orthogonal projections to latent structures (O‐PLS)

  2002cited by this paper
• NIR spectroscopy: a rapid-response analytical tool

  2002cited by this paper
• Representative subset selection

  2002cited by this paper
• Near-infrared spectroscopy:principles,instruments,applications

  2002cited by this paper
• Transfer of multivariate calibration models: a review

  2002cited by this paper
• Understanding the collinearity problem in regression and discriminant analysis

  2001cited by this paper
• PLS-regression: a basic tool of chemometrics

  2001cited by this paper
• The N-way Toolbox for MATLAB

  2000cited by this paper
• Orthogonal Signal Correction

  1999cited by this paper
• The influence of data pre-processing in the pattern recognition of excipients near-infrared spectra.

  1999cited by this paper
• Review of Process and Non-invasive Near-Infrared and Infrared Spectroscopy: 1993-1999

  1999cited by this paper
• An evaluation of orthogonal signal correction applied to calibration transfer of near infrared spectra

  1998cited by this paper
• Orthogonal signal correction of near-infrared spectra

  1998cited by this paper
• Analysis of multiblock and hierarchical PCA and PLS models

  1998cited by this paper
• Chemometrics: A Practical Guide

  1998cited by this paper
• Near-infrared spectroscopy in the pharmaceutical industry.

  1998cited by this paper
• PARAFAC. Tutorial and applications

  1997cited by this paper
• Regression Shrinkage and Selection via the Lasso

  1996cited by this paper
• Hierarchical multiblock PLS and PC models for easier model interpretation and as an alternative to variable selection

  1996cited by this paper
• Practical Guide to Chemometrics

  1995cited by this paper
• The process chemometrics approach to process monitoring and fault detection

  1995cited by this paper
• Near infrared spectroscopy

  1994cited by this paper
• SIMPLS: an alternative approach to partial least squares regression

  1993cited by this paper
• PLS: Partial Least Squares Projections to Latent Structures

  1993cited by this paper
• Handbook of Near-Infrared Analysis

  1992cited by this paper
• Population Definition, Sample Selection, and Calibration Procedures for Near Infrared Reflectance Spectroscopy

  1991cited by this paper
• Interactive self-modeling mixture analysis

  1991cited by this paper
• Locally weighted regression and scatter correction for near-infrared reflectance data

  1990cited by this paper
• Interpretation of latent-variable regression models

  1989cited by this paper
• Nonlinear PLS modeling

  1989cited by this paper
• Standard Normal Variate Transformation and De-Trending of Near-Infrared Diffuse Reflectance Spectra

  1989cited by this paper
• Principal component regression in NIR analysis: Viewpoints, background details and selection of components

  1988cited by this paper
• Partial least-squares methods for spectral analyses. 1. Relation to other quantitative calibration methods and the extraction of qualitative information

  1988cited by this paper
• The Effect of Multiplicative Scatter Correction (MSC) and Linearity Improvement in NIR Spectroscopy

  1988cited by this paper

• No citing papers are available for this paper.