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

236
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...
236
Frequency-dependent Selection01:21

Frequency-dependent Selection

22.3K
When the fitness of a trait is influenced by how common it is (i.e., its frequency) relative to different traits within a population, this is referred to as frequency-dependent selection. Frequency-dependent selection may occur between species or within a single species. This type of selection can either be positive—with more common phenotypes having higher fitness—or negative, with rarer phenotypes conferring increased fitness.
22.3K
Multiple Regression01:25

Multiple Regression

3.2K
Multiple regression assesses a linear relationship between one response or dependent variable and two or more independent variables. It has many practical applications.
Farmers can use multiple regression to determine the crop yield based on more than one factor, such as water availability, fertilizer, soil properties, etc. Here, the crop yield is the response or dependent variable as it depends on the other independent variables. The analysis requires the construction of a scatter plot...
3.2K
Pharmacokinetic Models: Comparison and Selection Criterion01:26

Pharmacokinetic Models: Comparison and Selection Criterion

160
Physiological and compartmental models are valuable tools used in studying biological systems. These models rely on differential equations to maintain mass balance within the system, ensuring an accurate representation of the dynamic processes at play.
Physiological models take a detailed approach by considering specific molecular processes. They can predict drug distribution, metabolism, and elimination changes, providing a comprehensive understanding of how drugs interact with the body.
160
IR and UV–Vis Spectroscopy of Aldehydes and Ketones01:29

IR and UV–Vis Spectroscopy of Aldehydes and Ketones

6.1K
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...
6.1K
Regression Analysis01:11

Regression Analysis

6.2K
Regression analysis is a statistical tool that describes a mathematical relationship between a dependent variable and one or more independent variables.
In regression analysis, a regression equation is determined based on the line of best fit– a line that best fits the data points plotted in a graph. This line is also called the regression line. The algebraic equation for the regression line is called the regression equation. It is represented as:
6.2K

You might also read

Related Articles

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

Sort by
Same author

Characterization of Coffee Residues and Derived Biochar via Fourier-Transform Infrared Spectroscopy: Current Status and Outlook.

Critical reviews in analytical chemistry·2026
Same author

Explainable AI for hyperspectral imaging in food quality decision support: interpretability, reliability and future directions.

Critical reviews in food science and nutrition·2026
Same author

Fully portable smartphone-integrated device coupled with nanozyme-based assay for sensitive detection of TBHQ in edible oils.

Food chemistry·2026
Same author

Comparative analysis of explainable machine learning integrated with hyperspectral imaging for early prediction of wheat yield.

Talanta·2026
Same author

Explainable AI-Guided Hyperspectral Feature Selection in Fruit Quality Assessment and Spatial Visualization.

Journal of food science·2026
Same author

Hyperspectral imaging dataset for non-destructive fertility and structural evaluation of chicken eggs.

Scientific data·2026

Related Experiment Video

Updated: Sep 28, 2025

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

10.7K

Effect of variable selection algorithms on model performance for predicting moisture content in biological materials

Mohammed Kamruzzaman1, Dipsikha Kalita2, Md Toukir Ahmed1

  • 1Department of Agricultural and Biological Engineering, University of Illinois at Urbana-Champaign, Urbana, IL, 61801, USA.

Analytica Chimica Acta
|March 28, 2022
PubMed
Summary

Variable selection algorithms improve moisture content prediction in red meat and corn. Competitive adaptive reweighted sampling-partial least squares regression (CARS-PLSR) demonstrated the best performance, enabling efficient multispectral system design.

Keywords:
CornHyperspectral imagingMeatMoisture contentSpectroscopyVariable selection

More Related Videos

O-cresol Concentration Online Measurement Based On Near Infrared Spectroscopy Via Partial Least Square Regression
06:50

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

Published on: November 8, 2019

6.7K
Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances
07:35

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Published on: October 11, 2018

7.7K

Related Experiment Videos

Last Updated: Sep 28, 2025

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

10.7K
O-cresol Concentration Online Measurement Based On Near Infrared Spectroscopy Via Partial Least Square Regression
06:50

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

Published on: November 8, 2019

6.7K
Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances
07:35

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Published on: October 11, 2018

7.7K

Area of Science:

  • Agricultural Science
  • Food Science
  • Analytical Chemistry

Background:

  • Multicollinearity in spectral data necessitates variable selection for accurate predictive modeling.
  • Reducing spectral data dimensionality enhances model performance and prediction speed.
  • Developing real-time multispectral systems requires efficient variable selection methods.

Purpose of the Study:

  • To compare the efficacy of six variable selection algorithms (RC, VIP, GA, CARS, SPA, SWR) on predictive model performance.
  • To determine the optimal variable selection method for predicting moisture content in red meat and corn.
  • To identify key spectral wavelengths for designing cost-effective, real-time multispectral systems.

Main Methods:

  • Visible and Near-Infrared (VNIR) hyperspectral imaging (400-1000 nm) for red meat.
  • Near-Infrared (NIR) spectroscopy (1100-2498 nm) for corn.
  • Evaluation of six variable selection algorithms: Regression Coefficient (RC), Variable Importance in Projection (VIP), Genetic Algorithm (GA), Competitive Adaptive Reweighted Sampling (CARS), Successive Projection Algorithm (SPA), and Stepwise Regression (SWR).
  • Partial Least Squares Regression (PLSR) was used as the modeling technique.

Main Results:

  • The Competitive Adaptive Reweighted Sampling-Partial Least Squares Regression (CARS-PLSR) model exhibited superior performance for predicting moisture content in both red meat and corn.
  • Variable selection significantly improved model prediction ability and reduced data complexity.
  • Specific feature wavelengths were identified, crucial for developing targeted multispectral systems.

Conclusions:

  • Variable selection is essential for developing robust and efficient predictive models in food analysis.
  • CARS-PLSR is a highly effective method for moisture content prediction using spectral data.
  • The identified feature wavelengths facilitate the design of practical, low-cost, real-time multispectral systems for quality assessment.