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IR Spectroscopy: Hooke's Law Approximation of Molecular Vibration

IR Spectroscopy: Hooke's Law Approximation of Molecular Vibration

A covalently bonded heteronuclear diatomic molecule can be modeled as two vibrating masses connected by a spring. The vibrational frequency of the bond can be expressed using an equation derived from Hooke's law, which describes how the force applied to stretch or compress a spring is proportional to the displacement of the spring. In this case, the atoms behave like masses, and the bond acts like a spring.
According to Hooke's law, the vibrational frequency is directly proportional to...

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Related Experiment Video

Updated: Aug 7, 2025

Construction of Models for Nondestructive Prediction of Ingredient Contents in Blueberries by Near-infrared Spectroscopy Based on HPLC Measurements

Construction of Models for Nondestructive Prediction of Ingredient Contents in Blueberries by Near-infrared Spectroscopy Based on HPLC Measurements

Published on: June 28, 2016

Developing Prediction Models Using Near-Infrared Spectroscopy to Quantify Cannabinoid Content in Cannabis Sativa.

Jonathan Tran¹, Simone Vassiliadis¹, Aaron C Elkins¹

¹Agriculture Victoria Research, AgriBio Centre, AgriBio, Melbourne, VIC 3083, Australia.

Sensors (Basel, Switzerland)

|March 11, 2023

Summary

This summary is machine-generated.

Near-infrared (NIR) spectroscopy accurately predicts cannabis cannabinoid levels, including acidic forms like THCA and CBDA. Portable devices offer rapid, non-destructive screening for quality control in the cannabis industry.

Keywords:

cannabinoids partial least square discriminant analysis partial least square regression principal component analysis

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

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

Published on: November 8, 2019

Area of Science:

Analytical Chemistry
Spectroscopy
Cannabis Science

Background:

Cannabis cultivation for therapeutic and recreational use is expanding globally.
Accurate, non-destructive methods for determining cannabinoid content are crucial for quality control.
Existing methods often focus on decarboxylated cannabinoids (THC, CBD) rather than their acidic precursors (THCA, CBDA).

Purpose of the Study:

To develop and validate statistical models for the accurate prediction of 14 different cannabinoids, including acidic forms (THCA, CBDA), using Near-Infrared (NIR) spectroscopy.
To compare the performance of a benchtop NIR spectrometer with a portable handheld NIR device.
To evaluate the impact of cannabis sample preparation methods (finely vs. coarsely ground) on prediction accuracy.

Main Methods:

Utilized high-quality liquid chromatography-mass spectroscopy (LCMS) data for reference.
Developed statistical models including Principal Component Analysis (PCA), Partial Least Squares Regression (PLS-R), and Partial Least Squares Discriminant Analysis (PLS-DA).
Employed two NIR spectrometers: a benchtop Bruker MPA II and a handheld VIAVI MicroNIR Onsite-W.

Main Results:

Robust prediction models achieved high accuracy (99.4-100%) with the benchtop instrument.
The handheld NIR device demonstrated good performance (83.1-100% accuracy), offering portability and speed.
Coarsely ground cannabis samples yielded comparable prediction results to finely ground samples, saving preparation time.
PLS-DA models successfully classified cannabis samples into high-CBDA, high-THCA, and even-ratio categories.

Conclusions:

Portable NIR technology, when combined with LCMS data, provides accurate and rapid, non-destructive cannabinoid predictions.
This approach is suitable for high-throughput screening in cannabis quality control for cultivators, manufacturers, and regulators.
Coarse grinding of samples is an efficient preparation method without compromising predictive accuracy.