Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Experiment Videos

Analytical advantages of multivariate data processing. One, two, three, infinity?

Alejandro C Olivieri1

  • 1Departamento de Química Analítica, Facultad de Ciencias Bioquímicas y Farmacéuticas, Universidad Nacional de Rosario, Rosario, Argentina. aolivier@fbioyf.unr.edu.ar

Analytical Chemistry
|July 11, 2008
PubMed
Summary
This summary is machine-generated.

Related Concept Videos

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Old advice on multivariate calibration: still in force, but not always followed. A tutorial.

Analytica chimica acta·2026
Same author

Hazards of using multivariate curve resolution for processing first-order spectral data. A rotational ambiguity analysis.

Analytica chimica acta·2025
Same author

How does sensor-wise N-BANDS relate to the of area of feasible solutions?

Analytica chimica acta·2025
Same author

Feasible band boundaries computation in bilinear matrix decomposition using essential data.

Analytica chimica acta·2025
Same author

Novel Data Fusion Strategy for Second-Order Data: Multivariate Curve Resolution for the Determination of Pharmaceuticals by Means of Fluorimetric Measurements.

Analytical chemistry·2025
Same author

Cement classification and characterization using Non-Invasive techniques.

Talanta·2024
Same journal

Strain-Level Food Surveillance of <i>Escherichia coli</i> Using a Specific-Nonspecific Hybrid Sensor Array Strategy.

Analytical chemistry·2026
Same journal

A Field-Portable Fe(IV)-Mediated Competitive Quenching Chemiluminescence Platform with a Synchronous Y-Shaped Flow-through Cell for Broad-Spectrum Quantification of Volatile Phenols.

Analytical chemistry·2026
Same journal

Single-Molecule Characterization of CRISPR-Cas12a for Amplification-Free Genetic Testing.

Analytical chemistry·2026
Same journal

Integrated Acoustofluidic Manipulation and Oscillation-Stabilized Magnetic Relaxation Biosensing for <i>Salmonella</i> Detection.

Analytical chemistry·2026
Same journal

A Self-Powered Sensing Platform Based on the Janus Heterostructure for Machine Learning-Assisted Dual-Mode Detection of 17β-Estradiol.

Analytical chemistry·2026
Same journal

Large Language Model-Generated Dietary Metabolite Biomarker Database Drives Deep Annotation of the Human Diet Metabolome.

Analytical chemistry·2026
See all related articles

Modern analytical instruments generate vast multidimensional data, necessitating advanced processing. Multivariate calibration offers significant advantages, with second-order data enabling accurate quantitation of unknown components.

Area of Science:

  • Analytical Chemistry
  • Chemometrics
  • Data Science

Background:

  • Modern analytical instrumentation generates large volumes of multidimensional data.
  • Existing data processing techniques are diverse, but share common underlying principles.
  • Advancements in chemometrics have led to sophisticated algorithms for data analysis.

Purpose of the Study:

  • To classify and understand the common aspects of various data processing algorithms.
  • To highlight the analytical advantages of moving from univariate to multivariate data analysis.
  • To explore the benefits of second-order (or higher) data processing in analytical chemistry.

Main Methods:

  • Review and classification of existing multivariate calibration algorithms.
  • Comparison of data processing techniques based on data dimensionality (univariate, vector, second-order).

Related Experiment Videos

  • Analysis of the analytical advantages offered by different orders of data processing.

    • Main Results:

    • Univariate calibration uses single data points per sample.
    • First-order advantage (vector data) allows flagging of new samples outside the calibration set.
    • Second-order advantage (second-order data) enables identification, modeling, and accurate quantitation of unknown components and analytes.

    Conclusions:

    • Multivariate calibration offers significant gains in sensitivity and selectivity over univariate methods.
    • Second-order data processing provides substantial analytical benefits, including accurate analyte quantitation.
    • Further research is needed to explore potential advantages of third-order and higher-order data processing.