Portable NIR spectrometer for quick identification of fat bloom in chocolates

Area of Science:

• Food Science
• Analytical Chemistry
• Spectroscopy

Background:

• Cocoa butter's polymorphic changes cause fat bloom, a surface defect in chocolate.
• Temperature variations during chocolate transport and storage exacerbate fat bloom.
• Detecting fat bloom is crucial for maintaining chocolate quality and consumer satisfaction.

Purpose of the Study:

• To evaluate portable Near-Infrared (NIR) spectroscopy combined with chemometrics for fat bloom detection.
• To discriminate between different chocolate types (milk, white, 40% cocoa, 70% cocoa) subjected to temperature abuse.
• To identify specific temperature conditions causing fat bloom in chocolate.

Main Methods:

• Utilized a portable NIR spectrometer for spectral data acquisition.
• Applied Principal Component Analysis (PCA) for sample classification based on temperature.
• Employed Partial Least Squares-Discriminant Analysis (PLS-DA) for model development and accuracy assessment.

Main Results:

• PCA successfully separated chocolate samples into three distinct classes: control (20°C), 35°C, and 40°C.
• PLS-DA models achieved high accuracy (80-100%) in discriminating temperature-abused samples.
• Identified key NIR wavelengths influencing the discrimination models for different chocolate types.

Conclusions:

• Portable NIR spectroscopy is a viable tool for detecting fat bloom in chocolate.
• Chemometric analysis enables accurate classification of chocolates based on temperature history.
• This method aids in quality control and identifying potential issues in chocolate supply chains.