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Related Concept Videos

Applications of IR Spectroscopy: Overview01:11

Applications of IR Spectroscopy: Overview

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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,...
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Infrared (IR) Spectroscopy: Overview01:09

Infrared (IR) Spectroscopy: Overview

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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...
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IR Spectrometers01:25

IR Spectrometers

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There are two main infrared (IR) spectrophotometers: dispersive IR spectrometers and Fourier transform infrared (FTIR) spectrometers. In a dispersive IR spectrometer, a beam of infrared radiation produced by a hot wire is divided into two parallel equal-intensity beams using mirrors. One beam passes through the sample, while another is a reference beam. The beams then move through the monochromator, which separates the radiations into a continuous spectrum of different frequencies. The...
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IR and UV–Vis Spectroscopy of Aldehydes and Ketones01:29

IR and UV–Vis Spectroscopy of Aldehydes and Ketones

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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...
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UV–Vis Spectroscopy of Conjugated Systems01:32

UV–Vis Spectroscopy of Conjugated Systems

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Organic compounds with conjugated double bonds show strong absorption features in the UV–visible region of the electromagnetic spectrum attributed to π → π* electronic excitations. Generally, a UV–vis absorption spectrum is recorded as a plot of absorbance vs wavelength. The wavelength of maximum absorbance, which manifests as a peak in the absorption spectrum, is denoted as λmax.
One of the factors influencing λmax is the extent of conjugation in...
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NMR Spectroscopy of Aromatic Compounds01:14

NMR Spectroscopy of Aromatic Compounds

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Aromatic compounds can be identified or analyzed using proton NMR and carbon‐13 NMR. Typically, aromatic hydrogens or hydrogens directly bonded to the aromatic rings are strongly deshielded by the aromatic ring current. Therefore, they absorb in the range of 6.5–8.0 ppm in proton NMR spectra. For instance, aromatic hydrogens directly bonded to the benzene ring absorb at 7.3 ppm. However, aromatic hydrogens of larger rings absorb farther upfield or downfield than the ideal range.
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Related Experiment Video

Updated: Dec 25, 2025

O-cresol Concentration Online Measurement Based On Near Infrared Spectroscopy Via Partial Least Square Regression
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Open-source python module for automated preprocessing of near infrared spectroscopic data.

Jari Torniainen1, Isaac O Afara1, Mithilesh Prakash1

  • 1Department of Applied Physics, University of Eastern Finland, Kuopio, Finland; Diagnostic Imaging Center, Kuopio University Hospital, Kuopio, Finland.

Analytica Chimica Acta
|March 31, 2020
PubMed
Summary

Near infrared spectroscopy (NIRS) analysis is improved by the open-source Python module, nippy. This tool semi-automatically compares preprocessing methods, optimizing NIRS models and reducing trial-and-error.

Keywords:
ChemometricsNear infrared spectroscopyPreprocessing

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Area of Science:

  • Analytical Chemistry
  • Spectroscopy
  • Chemometrics

Background:

  • Near infrared spectroscopy (NIRS) is a versatile analytical technique used across agriculture, pharmacology, medicine, and petrochemistry.
  • NIRS spectra often exhibit broad, overlapping absorption bands, necessitating advanced multivariate analysis.
  • Noise from instrumentation, scattering, and non-laboratory measurements complicates NIRS data analysis.

Purpose of the Study:

  • To address the challenge of selecting optimal preprocessing methods for NIRS data.
  • To introduce 'nippy', an open-source Python module for semi-automatic comparison of NIRS preprocessing techniques.
  • To facilitate more efficient and robust NIRS data analysis through automated method selection.

Main Methods:

  • Development of the 'nippy' Python module for NIRS preprocessing.
  • Implementation of semi-automatic comparison of various NIRS preprocessing techniques.
  • Demonstration of 'nippy' usage with two public NIRS datasets.

Main Results:

  • 'nippy' enables a more systematic approach to optimizing NIRS preprocessing compared to trial-and-error.
  • The module facilitates the evaluation of multiple preprocessing strategies for improved multivariate model performance.
  • Successful application of 'nippy' demonstrated on public datasets, showcasing its practical utility.

Conclusions:

  • Automated comparison of preprocessing methods is crucial for advancing NIRS data analysis.
  • 'nippy' provides a valuable tool for researchers and practitioners seeking to optimize their NIRS models.
  • The open-source nature of 'nippy' promotes wider adoption and further development in the NIRS community.