Unraveling the difference in aroma characteristics of tomato flesh with different colors using HS-SPME-GC–MS/MS and E-nose combined with multivariate data analysis

TL;DR

This study identifies 154 volatile compounds in tomatoes of different colors and finds 16 key compounds that may explain flavor differences, using advanced analytical and machine learning techniques.

Contribution

The study combines HS-SPME-GC–MS/MS, E-nose, and LightGBM to identify color-specific aroma compounds in tomatoes, revealing 16 VOCs linked to fruit color.

Findings

154 volatile organic compounds were identified in 16 tomato varieties with four different fruit colors.

Twenty-six VOCs were found to significantly contribute to tomato aroma based on odor activity values.

Multivariate analysis identified 16 VOCs strongly associated with fruit color, aiding flavor evaluation and improvement.

Abstract

Aroma profiles of tomato flesh from 16 tomato varieties with 4 different fruit colors were characterized by headspace-solid phase microextraction coupled with gas chromatography-triple quadrupole mass spectrometry (HS-SPME-GC–MS/MS) and electronic nose (E-nose). A total of 154 volatile organic compounds (VOCs) were qualitatively and semi-quantitatively identified, including 29 aldehydes, 22 hydrocarbons, 21 alcohols, 32 unknown compounds, and others, with aldehydes being the most abundant. Twenty-six characteristic VOCs might be major contributors to tomato flavor by relative odor activity value analysis. The correlation between E-nose sensor responses and GC–MS/MS volatile profiles was also examined. The machine learning models were constructed to show potential for distinguishing the fruit colors of the tomato. Finally, 16 fruit color-indicating VOCs were selected via multivariate data analysis. The present study will contribute to flavor evaluation and provide a chemical basis for future tomato flavor improvement, to be accompanied by sensory validation.

•HS-SPME-GC–MS/MS identified 154 VOCs in the flesh of 16 tomato varieties.•Twenty-six characteristic volatiles contributed to various aroma notes.•E-nose and GC–MS/MS combined with LightGBM showed potential to classify fruit colors.•Multivariate data analysis uncovered 16 fruit color-indicative VOCs.

HS-SPME-GC–MS/MS identified 154 VOCs in the flesh of 16 tomato varieties.

Twenty-six characteristic volatiles contributed to various aroma notes.

E-nose and GC–MS/MS combined with LightGBM showed potential to classify fruit colors.

Multivariate data analysis uncovered 16 fruit color-indicative VOCs.

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