Assessing cocoa aroma quality by multiple analytical approaches

Tran, Phuong Diem; Walle, Davy Van de; Clercq, Nathalie De; Winne, Ann De; Kadow, Daniel; Lieberei, Reinhard; Messens, Kathy; Tran, Dung Nhan; Dewettinck, Koen; Durme, Jim Van

doi:10.1016/j.foodres.2015.09.019

Cited by 65 publications

(61 citation statements)

References 31 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…27,28 The procedure can be handled without any GC column (replaced by a short heated deactivated capillary) 26 or with a GC column used in isothermal conditions at 240°C to 250°C. 29,30 Alternatively, virtual fingerprints may be obtained by averaging 31 or summing 32 the mass spectra on a defined time of GC-MS measurements. As mass spectra are obtained by EI on low-resolution mass analysers, usually quadrupoles or sometimes ion-traps, 33 the resulting fingerprints are complex spectra of many overlapping fragments with unit resolution.…”

Section: Ms-based Electronic Nose-direct Injection Msmentioning

confidence: 99%

“…Dynamic HS sampling can be replaced by SPME, as exemplified for off‐flavours in milks and rapid characterization of cheeses . This method is referred to as “MS‐based electronic nose” or “MS‐based e‐nose.” The procedure can be handled without any GC column (replaced by a short heated deactivated capillary) or with a GC column used in isothermal conditions at 240°C to 250°C . Alternatively, virtual fingerprints may be obtained by averaging or summing the mass spectra on a defined time of GC‐MS measurements.…”

Section: Introductionmentioning

confidence: 99%

“…Even though these mass fragments could be considered as potential descriptors of the composition of the samples HS, little chemical information can be deduced. Classification tasks are sometimes effected with a limited number of pertinent mass fragments, selected either from the most abundant mass fragments of the major peaks obtained in GC‐MS experiments conducted in parallel, or through chemometrics . These approaches using EI‐MS‐based e‐noses with quadrupole mass analysers were previously reviewed …”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Volatile compounds profiling by using proton transfer reaction‐time of flight‐mass spectrometry (PTR‐ToF‐MS). The case study of dark chocolates organoleptic differences

et al. 2019

View full text Add to dashboard Cite

Direct‐injection mass spectrometry (DIMS) techniques have evolved into powerful methods to analyse volatile organic compounds (VOCs) without the need of chromatographic separation. Combined to chemometrics, they have been used in many domains to solve sample categorization issues based on volatilome determination. In this paper, different DIMS methods that have largely outperformed conventional electronic noses (e‐noses) in classification tasks are briefly reviewed, with an emphasis on food‐related applications. A particular attention is paid to proton transfer reaction mass spectrometry (PTR‐MS), and many results obtained using the powerful PTR‐time of flight‐MS (PTR‐ToF‐MS) instrument are reviewed. Data analysis and feature selection issues are also summarized and discussed. As a case study, a challenging problem of classification of dark chocolates that has been previously assessed by sensory evaluation in four distinct categories is presented. The VOC profiles of a set of 206 chocolate samples classified in the four sensory categories were analysed by PTR‐ToF‐MS. A supervised multivariate data analysis based on partial least squares regression‐discriminant analysis allowed the construction of a classification model that showed excellent prediction capability: 97% of a test set of 62 samples were correctly predicted in the sensory categories. Tentative identification of ions aided characterisation of chocolate classes. Variable selection using dedicated methods pinpointed some volatile compounds important for the discrimination of the chocolates. Among them, the CovSel method was used for the first time on PTR‐MS data resulting in a selection of 10 features that allowed a good prediction to be achieved. Finally, challenges and future needs in the field are discussed.

show abstract

Section: Ms-based Electronic Nose-direct Injection Msmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Volatile compounds profiling by using proton transfer reaction‐time of flight‐mass spectrometry (PTR‐ToF‐MS). The case study of dark chocolates organoleptic differences

et al. 2019

View full text Add to dashboard Cite

show abstract

“…A NIR spectroscopy-electronic tongue integrated approach was accurately used for classifying cocoa bean varieties [10,11]. Other researchers showed that Mass Spectrometry (MS)fingerprinting could be a reliable and fast method for predicting the aroma potential and quality of roasted cocoa beans, the extent of processing as well as their geographical origin [12]. The influence of the roasting process conditions on the polyphenol contents of cocoa beans, nibs and chocolates have been evaluated by UPLC-DAD-ESI-MS/MS [13].…”

Section: Introductionmentioning

confidence: 99%

“…A classification chemometric model based on HR-MS 1H NMR data has been also proposed for assessing the geographical origin of cocoa beans [19]. Finally, cocoa fermentation quality and aroma formation potential have been evaluated by gas chromatography-mass spectrometry (GC-MS) [12], [20]. Indeed, advanced chemical untargeted and targeted fingerprinting together with twodimensional GC-MS may be used as a pattern recognition tool for classification, discrimination, and sensory-quality assessment of high-quality cocoa at early processing stages [21].…”

Section: Introductionmentioning

confidence: 99%

Electronic Nose Coupled with Linear and Nonlinear Supervised Learning Methods for Rapid Discriminating Quality Grades of Superior Java Cocoa Beans

Hidayat¹,

Rusman²,

Julian³

et al. 2019

IJIES

View full text Add to dashboard Cite

An electronic nose (E-nose), comprising eight metal oxide semiconductor (MOS) gas sensors and a moisture-temperature sensor, was used for classifying three quality grades of superior java cocoa beans, namely fine cocoa dark bean < 20%, fine cocoa dark bean > 60%, and bulk cocoa bean that is a harder task compared to the discrimination of high versus low-quality cocoa beans. The E-nose signals were pre-processed using the maximum value method. The capability for discriminating the quality grade of the cocoa beans was checked by applying multivariate statistical tools, namely, linear discriminant analysis (LDA), support vector machine (SVM) and artificial neural networks (ANN). For this, the experimental dataset was split into two subsets, one for training (i.e., establishing the classification models) and the other for external-validation purposes. Furthermore, hyperparameter optimization and K-fold cross-validation variant were implemented during the model training procedure to select the best classification models and to avoid over-fitting issues. The best predictive classification performance was obtained with the E-nose-MLP-ANN procedure, which allowed 99% of correct classifications (overall accuracy) for the training dataset and 95% of correct classifications (overall accuracy) for the external-validation dataset. The satisfactory results clearly demonstrated that the E-nose could be applied as a quality control tool in the cocoa industry, requiring minimum and simple sample preparation.

show abstract

Chemical and flavor profile changes of cocoa beans (Theobroma cacao L.) during primary fermentation

Fang

Zhong

et al. 2020

Food Science & Nutrition

View full text Add to dashboard Cite

This survey reports for the first time the changed of quality of fermented cocoa (Theobroma cacao L.) beans. The quality evaluation and simultaneous detection of amino acids, flavor, procyanidin, color, fat, protein, antioxidant activity, and enthalpy were obtained for different fermentation stages of cocoa beans. The results showed that total essential amino acids contents ranged from 2.64 g/100 g to 3.68 g/100 g. A total of 88 compounds identified at the end of the fermentation belonged to alcohols, acids, esters, ketones, pyrazines, aldehydes, and terpenoids. One of the chemical groups that were present in highest abundance in the consummation treatments was acids, representing 56.04% of the total extracted area, followed by alcohols (22.95%) and ketones (9.40%). The colors of the beans in different fermentation stages were different, from deep purple to deep red‐brown. Fermented cocoa beans were shown to be 53.45% and 13.51% bean butter and protein content, respectively. The value of denaturation enthalpy (ΔH) ranged from 30.4 (J/g) to 43.38 (J/g). The 3‐day fermented sample had the highest ΔH (43.38 J/g). When the fermentation process was complete, the procyanidin concentration of the beans decreased, with the final yield of procyanidin at 6.2%. During fermentation, the antioxidant capacity of beans gradually reduced. The fermenting of cocoa beans had a significant effect on the quality formation. The findings of this study constitute a basis for further investigations on the quality formation of cocoa during fermentation.

show abstract

Assessing cocoa aroma quality by multiple analytical approaches

Cited by 65 publications

References 31 publications

Volatile compounds profiling by using proton transfer reaction‐time of flight‐mass spectrometry (PTR‐ToF‐MS). The case study of dark chocolates organoleptic differences

Volatile compounds profiling by using proton transfer reaction‐time of flight‐mass spectrometry (PTR‐ToF‐MS). The case study of dark chocolates organoleptic differences

Electronic Nose Coupled with Linear and Nonlinear Supervised Learning Methods for Rapid Discriminating Quality Grades of Superior Java Cocoa Beans

Chemical and flavor profile changes of cocoa beans (Theobroma cacao L.) during primary fermentation

Contact Info

Product

Resources

About