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 Concept Videos

IR Frequency Region: Fingerprint Region01:03

IR Frequency Region: Fingerprint Region

2.3K
IR spectra are divided into two main regions: the diagnostic region and the fingerprint region. The diagnostic region of the spectrum lies above 1500 cm−1. The absorptions resulting from single-bond vibrations of the N–H, C–H, and O–H stretch at higher wavenumbers and appear on the left side of the spectrum. The stretching absorptions of the C≡C and C≡N occur between 2100–2300 cm−1. In contrast, those arising from stretching absorptions of the...
2.3K
Infrared (IR) Spectroscopy: Overview01:09

Infrared (IR) Spectroscopy: Overview

7.2K
When electromagnetic radiation passes through a material, atoms or molecules transition from a lower to a higher energy state by absorbing radiation corresponding to the energy difference between the two states. The absorption of infrared (IR) radiation causes transitions between vibrational energy levels in a molecule. Therefore, IR spectroscopy is a useful analytical tool for determining the molecular structure of molecules.
Different compounds display unique properties due to their...
7.2K
IR and UV–Vis Spectroscopy of Aldehydes and Ketones01:29

IR and UV–Vis Spectroscopy of Aldehydes and Ketones

7.9K
Infrared spectroscopy, also known as vibrational spectroscopy, is mainly used to determine the types of bonds and functional groups in molecules. In aldehydes and ketones, the carbonyl (C=O) bond shows an absorption around 1710 cm-1. The C=O bond vibration of an aldehyde occurs at lower frequencies than that of a ketone. In addition to the C=O absorption in an aldehyde, the aldehydic C–H bond also gives two peaks in the 2700–2800 cm-1 range. This absorption, coupled with the...
7.9K
Applications of IR Spectroscopy: Overview01:11

Applications of IR Spectroscopy: Overview

2.8K
The non-destructive nature and ability to provide valuable chemical information make IR spectroscopy a versatile technique with broad applications in various scientific and industrial fields. IR spectroscopy is commonly used to identify and characterize organic and inorganic compounds. It provides information about the functional groups present in a molecule and the bonding between atoms. This helps in the structural elucidation of compounds during organic synthesis, pharmaceutical research,...
2.8K
IR Spectroscopy: Molecular Vibration Overview01:24

IR Spectroscopy: Molecular Vibration Overview

6.1K
When Infrared (IR) radiation passes through a covalently bonded molecule, the bonds transition from lower to higher vibrational levels. The fundamental vibrational motions that result in infrared absorption can be classified as stretching or bending vibrations.
Stretching vibrations are vibrational motions that occur along the bond line, changing the bond length or distance between two bonded atoms. They are further distinguished as symmetric or asymmetric. In symmetric stretching, the...
6.1K
Attenuated Total Reflectance (ATR) Infrared Spectroscopy: Overview01:13

Attenuated Total Reflectance (ATR) Infrared Spectroscopy: Overview

1.5K
Attenuated total reflectance (ATR) infrared spectroscopy is a powerful analytical technique used to study the composition of materials. It is widely employed in chemistry, materials science, forensic science, and other fields where sample characterization is required. ATR has several advantages over traditional transmission IR spectroscopy, including the requirement of little to no sample preparation and the ability to analyze a wide range of samples.
The ATR process begins by directing a beam...
1.5K

You might also read

Related Articles

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

Sort by
Same author

<b>New Middle Jurassic Osmylopsychopidae (Neuroptera) from Northeastern China</b>.

Zootaxa·2026
Same author

<b>New hemeroscopid (Odonata: Hemeroscopidae) from Lower Cretaceous Jinju Formation (South Korea) illustrating variation of wing vannal region among hemeroscopids and prohemeroscopids</b>.

Zootaxa·2026
Same author

<b>A new Jurassic neuropteran larva (Neuroptera: Osmyloidea)</b>.

Zootaxa·2026
Same author

<b>A new mid-Cretaceous Apsilocephalidae (Diptera) with elongated mouthparts from Kachin amber</b>.

Zootaxa·2026
Same author

Reconstructing vegetation biomass in the Middle Jurassic Yanliao Biota from insect fossil assemblages.

National science review·2026
Same author

Psoralidin enhances the sensitivity of breast cancer to cisplatin by targeting Nr1i2.

Scientific reports·2026

Related Experiment Video

Updated: Mar 27, 2026

O-cresol Concentration Online Measurement Based On Near Infrared Spectroscopy Via Partial Least Square Regression
06:50

O-cresol Concentration Online Measurement Based On Near Infrared Spectroscopy Via Partial Least Square Regression

Published on: November 8, 2019

7.1K

An Ensemble Successive Project Algorithm for Liquor Detection Using Near Infrared Sensor.

Fangfang Qu1, Dong Ren2, Jihua Wang3,4

  • 1College of Computer and Information Technology, Three Gorges University, Yichang 443002, China. quff1128@163.com.

Sensors (Basel, Switzerland)
|January 14, 2016
PubMed
Summary

A new evaluated bootstrap ensemble successive projections algorithm (EBSPA) improves near-infrared (NIR) spectral analysis for agricultural products. This method enhances variable selection stability and prediction accuracy for quality analysis.

Keywords:
information processingnear infrared sensorsspectroscopysuccessive projections algorithmvariable selection

More Related Videos

Ultrafast Time-resolved Near-IR Stimulated Raman Measurements of Functional &#960;-conjugate Systems
09:57

Ultrafast Time-resolved Near-IR Stimulated Raman Measurements of Functional π-conjugate Systems

Published on: February 10, 2020

7.7K
Infrared Degenerate Four-wave Mixing with Upconversion Detection for Quantitative Gas Sensing
10:42

Infrared Degenerate Four-wave Mixing with Upconversion Detection for Quantitative Gas Sensing

Published on: March 22, 2019

6.7K

Related Experiment Videos

Last Updated: Mar 27, 2026

O-cresol Concentration Online Measurement Based On Near Infrared Spectroscopy Via Partial Least Square Regression
06:50

O-cresol Concentration Online Measurement Based On Near Infrared Spectroscopy Via Partial Least Square Regression

Published on: November 8, 2019

7.1K
Ultrafast Time-resolved Near-IR Stimulated Raman Measurements of Functional &#960;-conjugate Systems
09:57

Ultrafast Time-resolved Near-IR Stimulated Raman Measurements of Functional π-conjugate Systems

Published on: February 10, 2020

7.7K
Infrared Degenerate Four-wave Mixing with Upconversion Detection for Quantitative Gas Sensing
10:42

Infrared Degenerate Four-wave Mixing with Upconversion Detection for Quantitative Gas Sensing

Published on: March 22, 2019

6.7K

Area of Science:

  • Analytical Chemistry
  • Chemometrics
  • Spectroscopy

Background:

  • Near-infrared (NIR) spectroscopy is vital for agricultural product quality analysis.
  • Existing successive projections algorithm (SPA) methods struggle with small sample sizes and variable relevance.

Purpose of the Study:

  • To develop an improved variable selection method for NIR spectral analysis.
  • To address the limitations of SPA in terms of stability and analyte association.

Main Methods:

  • Proposed an evaluated bootstrap ensemble SPA (EBSPA) incorporating a variable evaluation index (EI).
  • Applied EBSPA to quantitative prediction of alcohol concentration in liquor using NIR sensor data.
  • Compared EBSPA with other variable selection methods, including partial least squares (PLS).

Main Results:

  • EBSPA demonstrated superior generalization performance and stability compared to traditional SPA.
  • The selected variables from NIR sensor data had clear physical meanings.
  • The method effectively reduced variables and improved prediction accuracy.

Conclusions:

  • EBSPA overcomes the inherent defects of SPA, offering a more robust approach.
  • The enhanced method provides a reliable tool for accurate quality assessment of agricultural products.
  • Clear physical interpretability of selected variables aids in understanding the analytical process.