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

Difference from Background: Limit of Detection

Difference from Background: Limit of Detection

The limit of detection (LOD) is the smallest amount of analyte that can be distinguished from the background noise. The LOD value corresponds to the concentration at which the analyte signal is three times larger than the standard deviation of the blank signal. Below this value, the analyte signal cannot be differentiated from the background noise. It is calculated by dividing the calibration slope by 3 times the standard deviation of the blank signals.
The LOD indicates the presence or absence...

You might also read

Related Articles

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

Sort by

Same author

Rapid Identification of Counterfeit Biopharmaceuticals using Portable Fourier Transform Infrared Spectroscopy.

AAPS PharmSciTech·2026

Same author

A Multitechnique Spectroscopy Platform for Monitoring Heterogeneities in the Higher-Order Structure of mAb Therapeutics.

Molecular pharmaceutics·2026

Same author

Cytokine expression mediated immunogenic response of therapeutic IgG aggregates obtained by forced degradative stress factors and the role of a deep neural network in its prediction.

Biochemical and biophysical research communications·2026

Same author

Best Practices for Performing Analytical and Functional Biosimilarity Assessment of Recombinant Monoclonal Antibody Biosimilars.

The AAPS journal·2026

Same author

Continuous Processing for Manufacturing of Antibody-Drug Conjugates (ADCs).

Bioconjugate chemistry·2026

Same author

Do We Really Need End-To-End Continuous Processing for Biomanufacturing of Monoclonal Antibodies?

Biotechnology and bioengineering·2025

Same journal

Postbiotics and the Future of Functional Foods: Delivery Innovations for Targeted Health Benefits.

Journal of food science·2026

Same journal

Differences in Collagen Characteristics and Gene Expression in Longissimus Lumborum between Xia'Nan and Chinese Simmental Bulls and Their Impact on Meat Tenderness.

Journal of food science·2026

Same journal

Classification and Soluble Solids Content Prediction of Intact Tamarind Using Short-Wave Infrared Spectroscopy and Chemometric Models.

Journal of food science·2026

Same journal

The Rheological, Cooking, and Digestion Characteristics of Meat Noodles as Affected by the Composite Formulation of Gluten-Myofibrillar Proteins.

Journal of food science·2026

Same journal

Donkey Milk Exosomes Protect Against Dextran Sulfate Sodium-Induced Colitis by Delivering Anti-Inflammatory miRNAs and Reshaping Gut Microbiota.

Journal of food science·2026

Same journal

Effect of Sedum aizoon L. Powder Addition on the Rheological Properties and Gluten Structure Characteristics of Dough and the In Vitro Digestibility of Noodles.

Journal of food science·2026

See all related articles

Search research articles

Related Experiment Video

Updated: Apr 26, 2026

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

A Prediction Framework for Quantification of Milk Adulterants Using a NIR-Coupled Boosting Algorithm.

Naveen G Jesubalan¹, Hemlata Chhabra², Anurag S Rathore^1,2

¹School of Interdisciplinary Research, Indian Institute of Technology, Delhi, India.

Journal of Food Science

|March 10, 2026

Summary

This summary is machine-generated.

This study introduces a novel soft sensor using near-infrared (NIR) spectroscopy and chemometrics to detect common milk adulterants like urea and sugar in real-time. The developed sensor offers a rapid, non-destructive, and reliable method for enhanced food safety monitoring.

Keywords:

NIR spectroscopy chemometrics ensemble learning milk adulterants soft sensors

More Related Videos

A Method for Targeted 16S Sequencing of Human Milk Samples

A Method for Targeted 16S Sequencing of Human Milk Samples

Published on: March 23, 2018

Individualized Reconstitution of Human Milk Microbiota: A Feasible Approach in Real-World Settings

Individualized Reconstitution of Human Milk Microbiota: A Feasible Approach in Real-World Settings

Published on: February 7, 2025

Related Experiment Videos

Last Updated: Apr 26, 2026

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

A Method for Targeted 16S Sequencing of Human Milk Samples

A Method for Targeted 16S Sequencing of Human Milk Samples

Published on: March 23, 2018

Individualized Reconstitution of Human Milk Microbiota: A Feasible Approach in Real-World Settings

Individualized Reconstitution of Human Milk Microbiota: A Feasible Approach in Real-World Settings

Published on: February 7, 2025

Area of Science:

Analytical Chemistry
Food Science
Spectroscopy

Background:

Milk adulteration is a critical global food safety issue affecting billions.
Accurate and rapid detection of adulterants is essential for public health.

Purpose of the Study:

To develop a soft sensor for real-time quantification of common milk adulterants.
To integrate near-infrared (NIR) spectroscopy with advanced chemometrics for enhanced detection.

Main Methods:

Utilized near-infrared (NIR) spectroscopy combined with a chemometric framework.
Employed Orthogonal Partial Least Squares (OPLS) and XGBoost algorithm for predictive modeling.
Implemented K-means random clustering for experimental trial organization.

Main Results:

The soft sensor achieved high predictive performance for urea, ammonium sulfate, sugar, and hydrogen peroxide.
Demonstrated excellent correlation coefficients (CV-R² > 0.94) and R² values (> 0.95) for all tested adulterants.
Provided accurate real-time quantification with an average error rate below 10%.

Conclusions:

The developed NIR-chemometric soft sensor is a rapid, non-destructive, and reliable tool for detecting multiple milk adulterants.
This technology holds significant potential for improving real-time food safety monitoring systems.
The study highlights the efficacy of integrating spectroscopy with advanced machine learning for food analysis.