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

Moisture Content and Bulking of Aggregate01:10

Moisture Content and Bulking of Aggregate

329
The moisture content of aggregates is a crucial factor in construction, particularly in concrete mixing, as it influences the total water required in the mix. Moisture content represents the water coated on the exterior surface of the aggregate existing in a saturated and surface-dry condition. The total water content of a moist aggregate is the sum of its moisture content and water absorption.
When aggregates are exposed to rain or sit in stockpiles, they absorb moisture, which must be...
329
Physical Properties Affecting Solubility02:19

Physical Properties Affecting Solubility

25.4K
Solutions of Gases in Liquids
As for any solution, the solubility of a gas in a liquid is affected by the attractive intermolecular forces between solute and solvent species. Unlike solid and liquid solutes, however, there is no solute-solute intermolecular attraction to overcome when a gaseous solute dissolves in a liquid solvent since the atoms or molecules comprising a gas are far separated and experience negligible interactions. Consequently, solute-solvent interactions are the sole...
25.4K
Precipitation Titration Curve: Analysis01:21

Precipitation Titration Curve: Analysis

1.5K
The precipitation titration curve demonstrates the change in concentration of one reactant with the volume of titrant added. During the titration of chloride ions with silver nitrate, the precipitation titration curve is divided into three regions: before, at, and after the equivalence point. Before the equivalence point, low redissolution of the sparingly soluble silver chloride precipitate gives a low silver ion concentration. However, in the second region, representing the equivalence point,...
1.5K
Adaptations that Reduce Water Loss01:57

Adaptations that Reduce Water Loss

27.2K
Though evaporation from plant leaves drives transpiration, it also results in loss of water. Because water is critical for photosynthetic reactions and other cellular processes, evolutionary pressures on plants in different environments have driven the acquisition of adaptations that reduce water loss.
27.2K
Solubility Equilibria: Overview01:09

Solubility Equilibria: Overview

1.1K
When a substance such as sodium chloride is added to water, it dissolves, forming an aqueous solution. The extent of dissolution is called solubility. The process of dissolution can exist in equilibrium, just like other chemical processes. Solubility equilibria are also called precipitation equilibria because the process of solubility can be reversible. The reverse of the solubility process is called precipitation.
Solubility is important in biological and environmental processes. A notable...
1.1K
Factors Affecting Solubility04:01

Factors Affecting Solubility

35.6K
Compared with pure water, the solubility of an ionic compound is less in aqueous solutions containing a common ion (one also produced by dissolution of the ionic compound). This is an example of a phenomenon known as the common ion effect, which is a consequence of the law of mass action that may be explained using Le Chȃtelier’s principle. Consider the dissolution of silver iodide:
35.6K

You might also read

Related Articles

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

Sort by
Same author

KlebPhaCol: a community-driven resource for Klebsiella research identified a novel phage family.

Nucleic acids research·2025
Same author

Archival and Newly Isolated Historical <i>Bacillus anthracis</i> Strains Populate the Deeper Phylogeny of the A.Br.075(Sterne) Clade.

Pathogens (Basel, Switzerland)·2025
Same author

Alleviation of severe chronic arthritic pain using polyvalent immunoglobulins (KMP01): Two case reports.

International journal of clinical pharmacology and therapeutics·2024
Same author

A Heat Emergency: Urban Heat Exposure and Access to Refuge in Richmond, VA.

GeoHealth·2024
Same author

Cell-free biosynthesis combined with deep learning accelerates de novo-development of antimicrobial peptides.

Nature communications·2023
Same author

Recombinant Reporter Phage rTUN1::<i>nLuc</i> Enables Rapid Detection and Real-Time Antibiotic Susceptibility Testing of <i>Klebsiella pneumoniae</i> K64 Strains.

ACS sensors·2023

Related Experiment Video

Updated: Nov 18, 2025

Staining the Cytoplasmic Ca2+ with Fluo-4/AM in Apple Pulp
08:05

Staining the Cytoplasmic Ca2+ with Fluo-4/AM in Apple Pulp

Published on: November 6, 2021

4.8K

Modelling Soluble Solids Content Accumulation in 'Braeburn' Apples.

Konni Biegert1, Daniel Stöckeler2, Roy J McCormick1

  • 1Kompetenzzentrum Obstbau Bodensee, Fachgebiet Ertragsphysiologie, 88213 Ravensburg, Germany.

Plants (Basel, Switzerland)
|February 10, 2021
PubMed
Summary

Non-destructive optical sensors accurately predict apple soluble solids content (SSC), reducing reliance on chemical analysis. This technology offers practical orchard applications for understanding apple physiology and quality.

Keywords:
Vis/NIRapple maturationprecision horticulturerepeated longitudinal measurements

More Related Videos

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

10.9K
Quantifying Plant Soluble Protein and Digestible Carbohydrate Content, Using Corn Zea mays As an Exemplar
07:19

Quantifying Plant Soluble Protein and Digestible Carbohydrate Content, Using Corn Zea mays As an Exemplar

Published on: August 6, 2018

20.8K

Related Experiment Videos

Last Updated: Nov 18, 2025

Staining the Cytoplasmic Ca2+ with Fluo-4/AM in Apple Pulp
08:05

Staining the Cytoplasmic Ca2+ with Fluo-4/AM in Apple Pulp

Published on: November 6, 2021

4.8K
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

10.9K
Quantifying Plant Soluble Protein and Digestible Carbohydrate Content, Using Corn Zea mays As an Exemplar
07:19

Quantifying Plant Soluble Protein and Digestible Carbohydrate Content, Using Corn Zea mays As an Exemplar

Published on: August 6, 2018

20.8K

Area of Science:

  • Horticultural Science
  • Agricultural Engineering
  • Spectroscopy

Background:

  • Accurate measurement of soluble solids content (SSC) is crucial for apple quality assessment.
  • Traditional methods for determining SSC are destructive and labor-intensive.
  • Optical sensor technology offers a potential non-destructive alternative for real-time quality monitoring.

Purpose of the Study:

  • To evaluate the efficacy of visible and near-infrared spectroscopy for non-destructive SSC determination in apples.
  • To develop and validate multivariate calibration models for SSC prediction.
  • To explore the application of SSC predictions in orchard management and understanding apple physiology.

Main Methods:

  • Visible and near-infrared spectral data (729-975 nm) were collected from 'Braeburn' apples over three growing seasons (2016-2018).
  • Partial Least Square Regression (PLSR) was employed to build multivariate calibration models relating spectral data to SSC values.
  • Monte Carlo simulations were used to assess model robustness concerning sample size, seasonal variations, and laboratory errors.
  • Longitudinal linear mixed-effect models were utilized to analyze crop load and fruit position effects on SSC.

Main Results:

  • PLSR models demonstrated a minor dependence on the number and accuracy of wet chemistry calibration measurements.
  • Non-destructive optical measurements showed a mean difference of only 0.5% SSC compared to standard destructive lab testing.
  • High crop loads were associated with lower SSC values at harvest.
  • Fruit from the top part of the tree exhibited higher SSC values.

Conclusions:

  • Non-destructive optical sensing is a viable and accurate method for determining apple SSC in orchard settings.
  • The developed PLSR models are robust and reliable for practical application, minimizing the need for extensive chemical analysis.
  • Understanding factors like crop load and fruit position through SSC monitoring can enhance apple production and quality management.