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

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...
IR and UV–Vis Spectroscopy of Aldehydes and Ketones01:29

IR and UV–Vis Spectroscopy of Aldehydes and Ketones

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 C=O stretching, is...
Imaging Biological Samples with Optical Microscopy01:18

Imaging Biological Samples with Optical Microscopy

Optical microscopy uses optic principles to provide detailed images of samples. Antonie van Leeuwenhoek designed the first compound optical microscope in the 17th century to visualize blood cells, bacteria, and yeast cells. In 1830, Joseph Jackson Lister created an essentially modern light microscope. The 20th century saw the development of microscopes with enhanced magnification and resolution.
In optical microscopy, the specimen to be viewed is placed on a glass slide and clipped on the stage...
Differential Staining Technique01:26

Differential Staining Technique

Differential staining is an essential microbiological technique that exploits variations in cell wall structures to classify and identify microorganisms. It facilitates the distinction of bacteria, aiding in diagnostic and research applications. Two of the most widely used differential staining methods are Gram staining and acid-fast staining, both of which rely on the chemical and structural differences in bacterial cell walls.Gram Staining TechniqueGram staining differentiates bacteria by...
IR and UV–Vis Spectroscopy of Carboxylic Acids01:28

IR and UV–Vis Spectroscopy of Carboxylic Acids

In IR spectroscopy of carboxylic acids, the C=O bond shows a characteristic band between 1710 and 1760 cm⁻¹, and the O–H bond exhibits a broad band between 2500 and 3300 cm⁻¹.
However, the stretching absorptions for the C=O bond vary depending on the structure of carboxylic acids. The C=O bond of the free carboxylic acids shows a higher stretching frequency, 1760 cm−1, while H-bonded carboxylic acids (dimers) exhibit stretching absorptions at a lower frequency, 1710 cm−1. The C=O bond of the...
IR Spectrometers01:25

IR Spectrometers

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...

You might also read

Related Articles

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

Sort by
Same author

Combined application of Bacillus velezensis JDB15 and Trichoderma harzianum JDL4 suppresses banana Fusarium wilt under controlled conditions.

Pest management science·2026
Same author

Three-port laparoscopic cholecystectomy with common bile duct exploration in situs inverse totalis: a case report and literature review.

Frontiers in surgery·2026
Same author

Pressure-induced softening of locust bean gum hydrogels: A counterintuitive alternative to freeze-thaw stiffening.

Proceedings of the National Academy of Sciences of the United States of America·2026
Same author

Development and validation of the Maladaptive Eating Behavior Questionnaire after Metabolic and Bariatric Surgery.

Obesity surgery·2026
Same author

Chemodivergent aminocarbonylation enabled by oxygen vacancy-engineered Pd-doped In<sub>2</sub>O<sub>3</sub> nanocatalysts.

Science advances·2026
Same author

Electron-Induced Reactions in Solid Carbon Dioxide: Lithography and Methane Formation under High-Energy Irradiation.

ACS applied materials & interfaces·2026

Related Experiment Video

Updated: May 28, 2026

Fruit Volatile Analysis Using an Electronic Nose
11:02

Fruit Volatile Analysis Using an Electronic Nose

Published on: March 30, 2012

Nondestructive Detection of Moldy Pear Core for Fruit Quality Control Using Vis/NIR Spectroscopy and Enhanced Image

Congkai Liu1, Kang Zhao1, Yunhao Zhang1

  • 1School of Electrical Engineering, University of Jinan, Jinan 250022, China.

Foods (Basel, Switzerland)
|May 27, 2026
PubMed
Summary

This study introduces a nondestructive method using Vis/NIR spectral signals to detect moldy pear cores. The advanced Laplacian pyramid Markov transition field-Vision Transformer (LPMTF-ViT) model achieved high accuracy, improving fruit quality control.

Keywords:
deep learningmoldy pear corenondestructive detectionvisible near-infrared spectroscopy

More Related Videos

RGB and Spectral Root Imaging for Plant Phenotyping and Physiological Research: Experimental Setup and Imaging Protocols
11:37

RGB and Spectral Root Imaging for Plant Phenotyping and Physiological Research: Experimental Setup and Imaging Protocols

Published on: August 8, 2017

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

Related Experiment Videos

Last Updated: May 28, 2026

Fruit Volatile Analysis Using an Electronic Nose
11:02

Fruit Volatile Analysis Using an Electronic Nose

Published on: March 30, 2012

RGB and Spectral Root Imaging for Plant Phenotyping and Physiological Research: Experimental Setup and Imaging Protocols
11:37

RGB and Spectral Root Imaging for Plant Phenotyping and Physiological Research: Experimental Setup and Imaging Protocols

Published on: August 8, 2017

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
  • Food Science
  • Computer Vision

Background:

  • Moldy pear core is a significant internal defect impacting fruit quality.
  • Nondestructive detection methods are crucial for post-harvest quality control and automated sorting.

Purpose of the Study:

  • To develop a nondestructive detection method for Korla pear moldy core using Vis/NIR spectral signals.
  • To compare the efficacy of traditional machine learning and advanced deep learning models for this detection task.

Main Methods:

  • Collected Vis/NIR spectral signals from pears and classified them into healthy, slightly moldy, and severely moldy categories.
  • Evaluated traditional methods (US, RF, SVM), 1D-ResNet, and 2D approaches (IGAF, LPMTF) with DBN, MobileNetv3, and ViT.
  • The LPMTF-ViT combination was investigated for its performance.

Main Results:

  • The LPMTF-ViT model achieved the highest test accuracy (98.98%) and external validation accuracy (94.44%).
  • This deep learning approach significantly outperformed traditional machine learning and 1D-ResNet.
  • The method demonstrated effective early-stage detection of internal fruit defects.

Conclusions:

  • The LPMTF-ViT model offers a highly accurate and effective solution for nondestructive detection of moldy pear cores.
  • This approach provides technical support for automated industrial inspection systems.
  • It has the potential to reduce post-harvest losses and enhance fruit supply chain quality control.