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In IR spectroscopy of carboxylic acids, the C=O bond shows a characteristic band between 1710 and 1760 cm⁻¹, and the O–H bond exhibits a broad band between 2500 and 3300 cm⁻¹.
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Related Experiment Video

Updated: Sep 16, 2025

Construction of Models for Nondestructive Prediction of Ingredient Contents in Blueberries by Near-infrared Spectroscopy Based on HPLC Measurements
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Mid-Infrared Spectroscopy for Predicting Goat Milk Coagulation Properties.

Arianna Goi1, Silvia Magro1, Luigi Lanni2

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

Foods (Basel, Switzerland)
|July 12, 2025
PubMed
Summary

Mid-infrared spectroscopy (MIRS) shows potential for predicting goat milk coagulation properties, crucial for cheesemaking. Only rennet coagulation time prediction was accurate enough for screening, indicating MIRS can help improve dairy-goat sector milk suitability.

Keywords:
goatmid-infrared spectroscopymilktechnological properties

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Area of Science:

  • Dairy Science
  • Analytical Chemistry
  • Agricultural Technology

Background:

  • Milk coagulation properties (MCPs) are vital for goat cheese production and quality.
  • Traditional MCP assessment uses time-consuming lactodynamographic analysis.
  • Mid-infrared spectroscopy (MIRS) presents a rapid, large-scale alternative for evaluating milk's technological traits.

Purpose of the Study:

  • To evaluate the potential of MIRS for predicting MCPs in goat milk.
  • To assess MIRS's accuracy in classifying milk samples based on coagulation aptitude.
  • To determine if MIRS can be a viable tool for the dairy-goat sector.

Main Methods:

  • Collected 501 bulk goat milk samples from various farms.
  • Paired reference MCP measurements with mid-infrared spectra.
  • Developed prediction models using partial least squares regression and partial least squares discriminant analysis.

Main Results:

  • The MIRS model for rennet coagulation time achieved sufficient accuracy for screening (R²CrV = 0.68, R²Ext = 0.66, RPD = 2.05).
  • Models for curd-firming time and curd firmness showed lower performance.
  • Classification of high coagulation aptitude yielded balanced accuracy values of 0.81 (calibration) and 0.74 (validation).

Conclusions:

  • MIRS shows promise for predicting certain MCPs, particularly rennet coagulation time, in goat milk.
  • Further model refinement and larger datasets are needed for broader application.
  • MIRS could become a valuable tool for monitoring and enhancing milk suitability for cheesemaking in the dairy-goat industry.