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

Glassware Calibration01:11

Glassware Calibration

1.3K
Accurate calibration of glassware, such as volumetric flasks, pipettes, and burettes, is essential to ensure accurate measurements in the analytical laboratory. Calibration helps maintain consistency across measurements and prevents errors arising from inaccurate volumes.
Volumetric flasks: Volumetric flasks are designed to prepare aqueous solutions of precise volumes accurately with a calibration line on the neck. To calibrate a volumetric flask, it is important to fill it with distilled...
1.3K
Instrument Calibration01:12

Instrument Calibration

695
Instrument calibration is essential for ensuring that instruments produce accurate and consistent results. It is vital in manufacturing, healthcare, testing laboratories, and scientific research. Calibration processes are specific to each instrument and help enhance data accuracy. Each instrument has a unique calibration process tailored to its design and function to improve data accuracy.
Analytical Balance Calibration
An analytical balance measures mass and requires regular calibration to...
695
Plotting and Calibrating the Root Locus01:19

Plotting and Calibrating the Root Locus

453
Root loci often diverge as system poles shift from the real axis to the complex plane. Key points in this transition are the breakaway and break-in points, indicating where the root locus leaves and reenters the real axis. The branches of the root locus form an angle of 180/n degrees with the real axis, where n is the number of branches at a breakaway or break-in point.
The maximum gain occurs at the breakaway points between open-loop poles on the real axis, while the minimum gain is...
453
Calibration Curves: Correlation Coefficient01:10

Calibration Curves: Correlation Coefficient

4.6K
In a linear calibration curve, there is a value called the calibration coefficient, denoted by 'r,' which measures the strength and the direction of association between two variables. The correlation coefficient value ranges from −1 to +1. A value of +1 indicates a perfect positive linear correlation, −1 denotes a perfect negative correlation, and 0 implies no correlation between the two variables. A positive correlation value establishes that as one variable increases, the...
4.6K
Calibration Curves: Linear Least Squares01:20

Calibration Curves: Linear Least Squares

4.3K
A calibration curve is a plot of the instrument's response against a series of known concentrations of a substance. This curve is used to set the instrument response levels, using the substance and its concentrations as standards. Alternatively, or additionally, an equation is fitted to the calibration curve plot and subsequently used to calculate the unknown concentrations of other samples reliably.
For data that follow a straight line, the standard method for fitting is the linear...
4.3K
Optimal Foraging00:48

Optimal Foraging

13.7K
How animals obtain and eat their food is called foraging behavior. Foraging can include searching for plants and hunting for prey and depends on the species and environment.
13.7K

You might also read

Related Articles

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

Sort by
Same author

Digital media pattern design compression and optimization method based on K-means clustering and LLE dimensionality reduction.

PloS one·2026
Same author

The LPA1-ITA1-BRXL4 module regulates shoot gravitropism and tiller angle in rice.

Plant communications·2026
Same author

Microbiomics and metabolomics explored the characteristics of gut microbiota and metabolites in patients with aortic dissection.

Frontiers in cellular and infection microbiology·2025
Same author

Large-scale genomic deletion in <i>spl39</i> activates immune responses and confers resistance to rice bacterial blight.

Frontiers in plant science·2025
Same author

Mutation of rice EARLY LEAF LESION AND SENESCENCE 1 (ELS1), which encodes an anthranilate synthase α-subunit, induces ROS accumulation and cell death through activating the tryptophan synthesis pathway in rice.

The Plant journal : for cell and molecular biology·2024
Same author

Mutation of GLR2 confers enhanced glufosinate resistance and salt tolerance in rice.

Plant physiology·2024

Related Experiment Video

Updated: Jan 24, 2026

Diffuse Reflectance Infrared Spectroscopic Identification of Dispersant/Particle Bonding Mechanisms in Functional Inks
10:31

Diffuse Reflectance Infrared Spectroscopic Identification of Dispersant/Particle Bonding Mechanisms in Functional Inks

Published on: May 8, 2015

14.2K

A calibration transfer optimized single kernel near-infrared spectroscopic method.

Zhuopin Xu1, Shuang Fan1, Jing Liu2

  • 1Key Laboratory of High Magnetic Field and Ion Beam Physical Biology, Hefei Institutes of Physical Science, Chinese Academy of Sciences, 350 Shushanhu Road, Hefei, Anhui 230031, People's Republic of China; University of Science and Technology of China, No. 96 Jinzhai Road, Hefei, Anhui 230026, People's Republic of China.

Spectrochimica Acta. Part A, Molecular and Biomolecular Spectroscopy
|May 27, 2019
PubMed
Summary
This summary is machine-generated.

A new calibration transfer method improves single kernel near-infrared spectroscopy (SKNIRS) for seed quality screening. This technique accurately predicts protein content in single rice kernels by transferring spectral data between different rice forms.

Keywords:
Calibration transferProtein contentSingle kernel near-infrared spectroscopySpectral space transformation

More Related Videos

High-definition Fourier Transform Infrared FT-IR Spectroscopic Imaging of Human Tissue Sections towards Improving Pathology
11:05

High-definition Fourier Transform Infrared FT-IR Spectroscopic Imaging of Human Tissue Sections towards Improving Pathology

Published on: January 21, 2015

33.8K
Purification and Reconstitution of TRPV1 for Spectroscopic Analysis
11:53

Purification and Reconstitution of TRPV1 for Spectroscopic Analysis

Published on: July 3, 2018

8.4K

Related Experiment Videos

Last Updated: Jan 24, 2026

Diffuse Reflectance Infrared Spectroscopic Identification of Dispersant/Particle Bonding Mechanisms in Functional Inks
10:31

Diffuse Reflectance Infrared Spectroscopic Identification of Dispersant/Particle Bonding Mechanisms in Functional Inks

Published on: May 8, 2015

14.2K
High-definition Fourier Transform Infrared FT-IR Spectroscopic Imaging of Human Tissue Sections towards Improving Pathology
11:05

High-definition Fourier Transform Infrared FT-IR Spectroscopic Imaging of Human Tissue Sections towards Improving Pathology

Published on: January 21, 2015

33.8K
Purification and Reconstitution of TRPV1 for Spectroscopic Analysis
11:53

Purification and Reconstitution of TRPV1 for Spectroscopic Analysis

Published on: July 3, 2018

8.4K

Area of Science:

  • Agricultural Science
  • Analytical Chemistry
  • Spectroscopy

Background:

  • Single kernel near-infrared spectroscopy (SKNIRS) offers potential for seed quality screening in breeding.
  • Current SKNIRS applications are limited by seed physical characteristics and detection accuracy issues.
  • Existing methods show lower accuracy for whole seeds compared to processed samples.

Purpose of the Study:

  • To develop a calibration transfer-optimized SKNIRS method for accurate single seed chemical composition analysis.
  • To enable the use of calibration models from dehusked seeds or seed flour for whole single seeds.
  • To apply and validate this method for determining protein content in individual rice kernels.

Main Methods:

  • Collected near-infrared transmission spectra and protein content for 201 individual rice seeds in three forms: single rice kernel (SRK), single brown rice kernel (SBK), and rice flour (RF).
  • Optimized partial least squares (PLS) regression models using spectral range 950-1250 nm, standard normal variate (SNV) transformation, and 9 PLS factors.
  • Applied a calibration transfer algorithm, spectral space transformation (SST), to transfer SRK spectra to SBK and RF models for protein content prediction.

Main Results:

  • The direct method for SRK protein prediction yielded R=0.971 and RMSEP=0.423.
  • The proposed calibration transfer method achieved R=0.962 (SBK) and R=0.975 (RF), with RMSEP=0.480 (SBK) and RMSEP=0.401 (RF).
  • Results from the proposed method closely matched the direct method, demonstrating successful spectral data transfer among rice forms.

Conclusions:

  • The proposed calibration transfer method effectively overcomes challenges in analyzing individual seeds using SKNIRS.
  • Spectral data can be reliably transferred between different forms of rice (SRK, SBK, RF) using calibration transfer techniques.
  • This approach enhances the accuracy and applicability of SKNIRS for seed quality assessment in breeding programs.