Coffee is considered a stable product with a long shelf-life, although, after roasting, it is still an effective 'chemical reactor' because of the high reactivity of its components. During storage, roasted coffee undergoes chemical and physical changes that can affect its quality. The changes in sensory properties are generally attributed to the loss of volatile compounds that are characteristic of the aroma of roasted coffee, and the appearance of oxidation products that can cause off-flavours. Storage conditions are closely related to these sensory changes. Several authors have shown that storage temperature is a fundamental parameter for maintaining the quality of the product over time [1-5]. The presence of oxygen, which is, in turn, related to packaging technology, and moisture are another important elements to consider in the deterioration/ alteration of coffee aroma. Despite their number, the studies in this field have always focused on one or two compounds in the expression of coffee staling and not on the synergism between the components of the whole coffee volatilome [2]. Due to the complexity and dynamics of the chemistry involved, coffee oxidation studies have mainly been conducted on a single species, package or condition. This study investigates the volatilome of good quality and oxidised coffee from different packaging (i.e. standard with metallic barrier and Eco-caps) by combining HS-SPME-GC-MS/FPD with a machine learning approach to define a potential fingerprint that can describe the oxidised note of roasted coffee.

Identification of potential aroma markers of coffee oxidized note

Giulia Strocchi
First
;
Eloisa Bagnulo;Manuela R. Ruosi;Giulia Ravaioli;Francesca Trapani;Carlo Bicchi;Erica Liberto
2022-01-01

Abstract

Coffee is considered a stable product with a long shelf-life, although, after roasting, it is still an effective 'chemical reactor' because of the high reactivity of its components. During storage, roasted coffee undergoes chemical and physical changes that can affect its quality. The changes in sensory properties are generally attributed to the loss of volatile compounds that are characteristic of the aroma of roasted coffee, and the appearance of oxidation products that can cause off-flavours. Storage conditions are closely related to these sensory changes. Several authors have shown that storage temperature is a fundamental parameter for maintaining the quality of the product over time [1-5]. The presence of oxygen, which is, in turn, related to packaging technology, and moisture are another important elements to consider in the deterioration/ alteration of coffee aroma. Despite their number, the studies in this field have always focused on one or two compounds in the expression of coffee staling and not on the synergism between the components of the whole coffee volatilome [2]. Due to the complexity and dynamics of the chemistry involved, coffee oxidation studies have mainly been conducted on a single species, package or condition. This study investigates the volatilome of good quality and oxidised coffee from different packaging (i.e. standard with metallic barrier and Eco-caps) by combining HS-SPME-GC-MS/FPD with a machine learning approach to define a potential fingerprint that can describe the oxidised note of roasted coffee.
2022
7 MS Food Day
Firenze
5-7 Ottobre 2022
7 MS Food Day
83
85
https://www.spettrometriadimassa.it/Congressi/7MS-FoodDay/7MS_Food_Day_posters.pdf
Coffee volatilome, HS-SPME-GC-MS/FPD, machine learning
Giulia Strocchi, Eloisa Bagnulo, Manuela R. Ruosi, Giulia Ravaioli, Francesca Trapani, Carlo Bicchi, Gloria Pellegrino, Erica Liberto
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/2318/1882766
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