Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Stereotype Content Model

Stereotype Content Model

The Stereotype Content Model (SCM) was first proposed by Susan Fiske and her colleagues (Fiske, Cuddy, Glick & Xu, 2002; see also Fiske, 2012 and Fiske, 2017). The SCM specifies that when someone encounters a new group, they will stereotype them based on two metrics: warmth—or that group’s perceived intent, and how likely they are to provide help or inflict harm—and competence—or their ability to carry out that objective. Depending on the warmth-competence...

Predicting Molecular Geometry

Predicting Molecular Geometry

VSEPR Theory for Determination of Electron Pair Geometries

Prediction Intervals

Prediction Intervals

The interval estimate of any variable is known as the prediction interval. It helps decide if a point estimate is dependable.
However, the point estimate is most likely not the exact value of the population parameter, but close to it. After calculating point estimates, we construct interval estimates, called confidence intervals or prediction intervals. This prediction interval comprises a range of values unlike the point estimate and is a better predictor of the observed sample value, y.

End Point Prediction: Gran Plot

End Point Prediction: Gran Plot

A Gran plot is used to predict the equivalence volume or endpoint of a potentiometric or acid-base titration without reaching the endpoint. Typically, titration data is collected as a function of the titrant's volume up to a point less than the equivalence volume and then transformed into a linear format. The straight line is extended to the x-axis, indicating the necessary titrant volume to achieve the equivalence point.
For potentiometric titration, the Gran plot is created by plotting...

Sensitivity, Specificity, and Predicted Value

Sensitivity, Specificity, and Predicted Value

In healthcare diagnostics, laboratory tests play a crucial role in identifying and diagnosing a wide range of medical conditions. However, interpreting test results is not always straightforward. An abnormal test result does not always confirm the presence of a disease, just as a normal result does not guarantee its absence. To assess the reliability of these diagnostic tools, healthcare practitioners rely on two key statistical indicators: sensitivity and specificity.
Sensitivity is the...

Predicting Reaction Outcomes

Predicting Reaction Outcomes

Kinetics describes the rate and path by which a reaction occurs. In contrast, thermodynamics deals with state functions and describes the properties, behavior, and components of a system. It is not concerned with the path taken by the process and cannot address the rate at which a reaction occurs. Although it does provide information about what can happen during a reaction process, it does not describe the detailed steps of what appears on an atomic or a molecular level. On the other hand,...

You might also read

Related Articles

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

Sort by

Same author

Role of Endogenous and Exogenous Phenolic Compounds on the Formation of Acrylamide and Fluorescent Advanced Glycation End Products in Heated Quinoa and Cañihua Flours.

Foods (Basel, Switzerland)·2026

Same author

Estimating Acrylamide and 5-Hydroxymethylfurfural Levels in Crackers Using Computer Vision: Effects on Consumer Acceptance.

Foods (Basel, Switzerland)·2026

Same author

Rheological and Physicochemical Characterization of Structured Chia Oil: A Novel Approach Using a Low-Content Shellac Wax/Beeswax Blend as Oleogelant.

Gels (Basel, Switzerland)·2025

Same author

Investigating the Microstructural and Textural Properties of Cookies Using Plant-Based Bigel as an Alternative to Commercial Solid Fat.

Gels (Basel, Switzerland)·2025

Same author

Reduction of the formation and toxicity of heterocyclic aromatic amines (PhIP, IQ, MeIQ, MeIQx) in food: potential of nucleophilic compounds as mitigating agents.

Critical reviews in food science and nutrition·2025

Same author

Ultrasound-Assisted Extraction and Microencapsulation of <i>Durvillaea incurvata</i> Polyphenols: Toward a Stable Anti-Inflammatory Ingredient for Functional Foods.

Foods (Basel, Switzerland)·2025

Search research articles

Related Experiment Video

Updated: Jan 22, 2026

A Neuronal and Astrocyte Co-Culture Assay for High Content Analysis of Neurotoxicity

A Neuronal and Astrocyte Co-Culture Assay for High Content Analysis of Neurotoxicity

Published on: May 5, 2009

Predicting furan content in a fried dough system using image analysis.

Gabriel A Leiva-Valenzuela¹, Marcela Quilaqueo², María Salomé Mariotti-Celis³

¹Department of Chemical and Bioprocess Engineering, Pontificia Universidad Católica de Chile, Avenida Vicuña Mackenna 4860, Macul, Santiago, Chile.

|July 5, 2019

Summary

Predicting furan content in fried dough is possible using computer vision. Image textural features, analyzed with partial least squares regression, provided the most accurate predictions for furan levels.

Keywords:

Furan Image analysis Non-enzymatic browning Prediction of furan

More Related Videos

A Rhodopsin Transport Assay by High-Content Imaging Analysis

A Rhodopsin Transport Assay by High-Content Imaging Analysis

Published on: January 16, 2019

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

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: Jan 22, 2026

A Neuronal and Astrocyte Co-Culture Assay for High Content Analysis of Neurotoxicity

A Neuronal and Astrocyte Co-Culture Assay for High Content Analysis of Neurotoxicity

Published on: May 5, 2009

A Rhodopsin Transport Assay by High-Content Imaging Analysis

A Rhodopsin Transport Assay by High-Content Imaging Analysis

Published on: January 16, 2019

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

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:

Food Science
Analytical Chemistry
Computer Vision

Background:

Furan compounds are formed during the thermal processing of food.
Accurate prediction of furan content is crucial for food safety and quality control.
Traditional methods for furan quantification can be time-consuming.

Purpose of the Study:

To evaluate computer vision models for predicting furan content in fried dough.
To compare the effectiveness of chromatic and textural image features for furan prediction.
To utilize partial least squares regression for model development.

Main Methods:

Starch dough systems were fried at varying temperatures (150-190°C) and times (5-13 min).
Furan content was measured using gas chromatography/mass spectrometry (GC/MS).
Color images were captured and processed to extract 2914 features, followed by feature selection algorithms.

Main Results:

Good prediction of furan content was achieved using chromatic image features (Rp=0.86).
Optimal prediction was obtained with image textural features after reducing the feature set to 10 (Rp=0.93).
Partial least squares regression models demonstrated the feasibility of image-based prediction.

Conclusions:

Computer vision, particularly using image textural features, offers a promising non-destructive method for predicting furan content in fried dough.
This approach can potentially streamline the assessment of furan levels in food products.
Further research can explore the application of these methods to a wider range of food systems.