Non-destructive method to classify walnut kernel freshness from volatile organic compound (VOC) emissions using gas...

Area of Science:

• Food Science
• Analytical Chemistry
• Agricultural Technology

Background:

• Volatile organic compounds (VOCs) are crucial indicators of horticultural commodity quality and degradation.
• Assessing walnut freshness is vital for maintaining product quality and preventing spoilage.

Purpose of the Study:

• To investigate the potential of gas chromatography-differential mobility spectrometry (GC-DMS) for analyzing VOCs emitted by walnuts.
• To develop a method for predicting walnut rancidity and classifying freshness grades using GC-DMS data.
• To identify specific VOCs associated with walnut degradation.

Main Methods:

• Walnut kernels were classified into freshness grades using a peroxide assay.
• Volatile organic compounds (VOCs) were analyzed using gas chromatography-differential mobility spectrometry (GC-DMS).
• A partial least square regression (PLSR) model was employed to predict walnut freshness from GC-DMS data.
• Gas chromatography-mass spectrometry (GC-MS) was used to identify key VOCs.

Main Results:

• The PLSR model achieved 80% accuracy in predicting walnut grade, with a minimal root mean squared error of 0.42.
• GC-DMS analysis of VOCs effectively correlated with walnut rancidity levels.
• GC-MS analysis confirmed the presence of volatiles that increase with walnut rancidity, aligning with GC-DMS findings.

Conclusions:

• GC-DMS coupled with machine learning offers a highly effective method for classifying walnut grades in the food industry.
• This approach provides a rapid and accurate assessment of walnut freshness, aiding quality control.
• The study demonstrates the utility of VOC analysis for non-destructive quality assessment of nuts.