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Related Concept Videos

IR Frequency Region: Fingerprint Region01:03

IR Frequency Region: Fingerprint Region

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 C=O, C=N, and C=C occur between 1600–1850 cm−1.
The...
Applications of IR Spectroscopy: Overview01:11

Applications of IR Spectroscopy: Overview

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,...
Rapid Identification of Pathogens01:25

Rapid Identification of Pathogens

MALDI-TOF MS has transformed clinical microbiology by offering a rapid and reliable method for pathogen identification. The traditional approach to microbial identification typically involves time-consuming culture techniques and biochemical tests, which can delay the initiation of appropriate antimicrobial therapy. MALDI-TOF MS avoids these delays by using characteristic ribosomal protein mass patterns of microbial cells, enabling accurate species-level identification within minutes.Principle...
Plant Breeding and Biotechnology01:59

Plant Breeding and Biotechnology

Crop cultivation has a long history in human civilization, with records showing the cultivation of cereal plants beginning at around 8000 BC. This early plant breeding was developed primarily to provide a steady supply of food.
IR Spectrum01:19

IR Spectrum

When infrared (IR) radiation passes through a molecule, the bonds stretch or bend by absorbing the radiation. This absorption creates the molecule's absorption spectrum, which is the plot of its percentage transmittance versus wavenumber.
Transmittance is defined as the ratio of the radiant power passing through a sample to that from the radiation's source. Multiplying the transmittance by 100 gives the percent transmittance (%T), which varies between 100% (no absorption) and 0% (complete...
Methods of Classification and Identification01:28

Methods of Classification and Identification

Bacterial identification relies on a diverse array of techniques to classify and understand microorganisms, each tailored to uncover specific characteristics. Traditional morphological approaches, while still valuable, are limited for closely related or structurally simple organisms. Modern methods integrate biochemical, serological, genetic, and advanced molecular tools to achieve greater accuracy.Morphological and Biochemical TechniquesMorphological characteristics, such as cell shape and...

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Related Experiment Video

Updated: Jun 13, 2026

Construction of Models for Nondestructive Prediction of Ingredient Contents in Blueberries by Near-infrared Spectroscopy Based on HPLC Measurements
10:25

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

Published on: June 28, 2016

Application of Terahertz Technology in Food Safety: Rice Origin-Variety Classification Based on Spectral Analysis and

Dongdong Ye1,2, Xiaochang Yuan1, Jianfei Xu3

  • 1School of Artificial Intelligence, Anhui Polytechnic University, Wuhu 241000, China.

Foods (Basel, Switzerland)
|June 12, 2026
PubMed
Summary
This summary is machine-generated.

This study introduces terahertz time-domain spectroscopy and machine learning to classify rice origin and variety. This non-destructive method achieved up to 93.3% accuracy, improving food safety and quality control.

Keywords:
machine learningorigin categoryriceterahertz time-domain spectroscopy

Related Experiment Videos

Last Updated: Jun 13, 2026

Construction of Models for Nondestructive Prediction of Ingredient Contents in Blueberries by Near-infrared Spectroscopy Based on HPLC Measurements
10:25

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

Published on: June 28, 2016

Area of Science:

  • Agricultural Science
  • Spectroscopy
  • Machine Learning

Background:

  • Food security is crucial for social stability, with rice quality and origin impacting consumer health.
  • Traditional rice classification methods are subjective and prone to error.
  • Developing objective, accurate methods for rice authentication is essential.

Purpose of the Study:

  • To propose and validate a novel method for classifying rice by origin and variety using terahertz time-domain spectroscopy (THz-TDS).
  • To integrate THz-TDS with machine learning algorithms for enhanced rice analysis.
  • To assess the accuracy and feasibility of the proposed classification system.

Main Methods:

  • Terahertz time-domain spectroscopy (THz-TDS) was employed for non-destructive detection of rice samples.
  • Data preprocessing involved Savitzky-Golay smoothing, wavelet denoising, and moving average smoothing to extract refractive index and absorption coefficients.
  • Machine learning models, including principal component analysis (PCA), partial least squares discriminant analysis (PLS-DA), and least-squares support vector machine (LS-SVM), were utilized for classification.

Main Results:

  • A combined model using PCA for dimensionality reduction and LS-SVM, following Savitzky-Golay smoothing, achieved the highest prediction accuracy of 93.3% for four representative rice samples.
  • In an extended test with 20 rice types, this optimized model demonstrated an identification accuracy of 89.6%.
  • Principal component analysis (PCA) alone showed limited classification performance.

Conclusions:

  • Terahertz time-domain spectroscopy (THz-TDS) is a viable technology for non-destructive food analysis.
  • Optimized machine learning algorithms, particularly LS-SVM combined with PCA and appropriate preprocessing, significantly enhance the accuracy of classifying rice by origin and variety.
  • This approach offers a reliable and efficient alternative to traditional methods for ensuring rice quality and authenticity.