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

Multiple Regression

Multiple Regression

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...

Attenuated Total Reflectance (ATR) Infrared Spectroscopy: Overview

Attenuated Total Reflectance (ATR) Infrared Spectroscopy: Overview

Attenuated total reflectance (ATR) infrared spectroscopy is a powerful analytical technique used to study the composition of materials. It is widely employed in chemistry, materials science, forensic science, and other fields where sample characterization is required. ATR has several advantages over traditional transmission IR spectroscopy, including the requirement of little to no sample preparation and the ability to analyze a wide range of samples.
The ATR process begins by directing a beam...

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.

Infrared (IR) Spectroscopy: Overview

Infrared (IR) Spectroscopy: Overview

When electromagnetic radiation passes through a material, atoms or molecules transition from a lower to a higher energy state by absorbing radiation corresponding to the energy difference between the two states. The absorption of infrared (IR) radiation causes transitions between vibrational energy levels in a molecule. Therefore, IR spectroscopy is a useful analytical tool for determining the molecular structure of molecules.
Different compounds display unique properties due to their...

Applications of IR Spectroscopy: Overview

Applications of IR Spectroscopy: Overview

The non-destructive nature and ability to provide valuable chemical information make IR spectroscopy a versatile technique with broad applications in various scientific and industrial fields. IR spectroscopy is commonly used to identify and characterize organic and inorganic compounds. It provides information about the functional groups present in a molecule and the bonding between atoms. This helps in the structural elucidation of compounds during organic synthesis, pharmaceutical research,...

Calibration Curves: Linear Least Squares

Calibration Curves: Linear Least Squares

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...

You might also read

Related Articles

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

Sort by

Same author

Assessing climatic effects on milk yield traits using sinusoidal modelling in dairy cows.

Animal : an international journal of animal bioscience·2026

Same author

Field application of milk infrared-based equations to predict blood energy metabolites and minerals: effects of cattle breed and dairy system interactions.

Animal : an international journal of animal bioscience·2026

Same author

A multivariate approach to exploring interrelationships among milk fatty acids across ruminant species.

Journal of dairy science·2026

Same author

DNA metabarcoding for the identification and relative abundance assessment of general and potentially pathogenic bacteria in Sardinian sheep cheese processing environments.

International journal of food microbiology·2025

Same author

Biogenic and fossil main greenhouse gas emissions of dairy, beef, pig and poultry systems.

Animal : an international journal of animal bioscience·2025

Same author

Generative AI for predictive breeding: hopes and caveats.

TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik·2025

Search research articles

Related Experiment Video

Updated: Apr 3, 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

Bayesian regression models outperform partial least squares methods for predicting milk components and technological

A Ferragina¹, G de los Campos², A I Vazquez³

¹Department of Agronomy, Food, Natural Resources, Animals and Environment (DAFNAE), University of Padova, Viale dell'Università 16, 35020 Legnaro, Italy.

Journal of Dairy Science

|September 21, 2015

Summary

Bayesian models, particularly Bayes A and Bayes B, significantly improved prediction accuracy for difficult-to-predict dairy traits like fatty acid composition and cheese yield compared to traditional methods. These models offer powerful, accessible tools for developing accurate calibration equations using open-source software.

Keywords:

Bayesian method cheese yield fatty acid infrared spectroscopy milk trait

More Related Videos

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

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

Published on: November 8, 2019

Author Spotlight: UAV Remote Sensing for Efficient Invasive Plant Biomass Estimation

Author Spotlight: UAV Remote Sensing for Efficient Invasive Plant Biomass Estimation

Published on: February 9, 2024

Related Experiment Videos

Last Updated: Apr 3, 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

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

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

Published on: November 8, 2019

Author Spotlight: UAV Remote Sensing for Efficient Invasive Plant Biomass Estimation

Author Spotlight: UAV Remote Sensing for Efficient Invasive Plant Biomass Estimation

Published on: February 9, 2024

Area of Science:

Genomic selection and dairy science
Chemometrics and spectral data analysis
Statistical modeling in animal breeding

Background:

Predicting complex dairy traits like fatty acid profiles and technological properties is challenging with current methods.
Fourier-transform infrared (FTIR) spectroscopy offers a high-throughput approach for dairy analysis.
Existing calibration models, such as partial least squares (PLS), have limitations in accuracy for these traits.

Purpose of the Study:

To evaluate the performance of Bayesian models for predicting difficult-to-predict dairy traits using FTIR spectral data.
To compare the predictive accuracy of Bayesian models against established methods like PLS and modified PLS (MPLS).
To determine if Bayesian models with shrinkage and variable selection capabilities enhance prediction accuracy.

Main Methods:

Utilized FTIR spectral data from 1,264 individual milk samples from Brown Swiss cows.
Applied three Bayesian models (Bayes RR, Bayes A, Bayes B) and two reference models (PLS, MPLS) for calibration equation development.
Employed a training-testing validation procedure with 25 replicates to estimate prediction accuracy.

Main Results:

Bayesian models (MPLS, Bayes A, Bayes B) demonstrated significantly greater prediction accuracy than PLS.
Prediction accuracy improved with external validation and when using Bayesian methods over PLS and MPLS.
Bayes A and Bayes B achieved the highest validation R² values, with 0.75 for C10:0 fatty acid and 0.82 for fresh cheese yield.

Conclusions:

Bayesian models are powerful and effective tools for developing accurate calibration equations for dairy traits.
These models facilitate shrinkage and selection of informative wavelengths, improving trait prediction.
The use of open-source software like R package BGLR makes these advanced modeling techniques accessible for broader application.