Identification of odour-active compounds of pasteurised orange juice using multidimensional gas chromatography techniques

Mastello, Raíssa Bittar; Capobiango, Michely; Chin, Sung‐Tong; Monteiro, Magali; Marriott, Philip J.

doi:10.1016/j.foodres.2015.06.014

Cited by 43 publications

(31 citation statements)

References 44 publications

(64 reference statements)

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“…For a long time, conventional detection methods such as gas chromatography [2] and gas chromatography-mass spectrometry [3] have been applied to the detection of gases in fruits and vegetables, but these instruments are expensive and time-consuming. In recent years, research in this area has used machine vision [4] and near-infrared spectroscopy [5] however, these methods were not able to characterize the bacteria.…”

Section: Introductionmentioning

confidence: 99%

Classification for Penicillium expansum Spoilage and Defect in Apples by Electronic Nose Combined with Chemometrics

Guo

Chen

et al. 2020

Sensors

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It is crucial for the efficacy of the apple storage to apply methods like electronic nose systems for detection and prediction of spoilage or infection by Penicillium expansum. Based on the acquisition of electronic nose signals, selected sensitive feature sensors of spoilage apple and all sensors were analyzed and compared by the recognition effect. Principal component analysis (PCA), principle component analysis-discriminant analysis (PCA-DA), linear discriminant analysis (LDA), partial least squares discriminate analysis (PLS-DA) and K-nearest neighbor (KNN) were used to establish the classification model of apple with different degrees of corruption. PCA-DA has the best prediction, the accuracy of training set and prediction set was 100% and 97.22%, respectively. synergy interval (SI), genetic algorithm (GA) and competitive adaptive reweighted sampling (CARS) are three selection methods used to accurately and quickly extract appropriate feature variables, while constructing a PLS model to predict plaque area. Among them, the PLS model with unique variables was optimized by CARS method, and the best prediction result of the area of the rotten apple was obtained. The best results are as follows: Rc = 0.953, root mean square error of calibration (RMSEC) = 1.28, Rp = 0.972, root mean square error of prediction (RMSEP) = 1.01. The results demonstrated that the electronic nose has a potential application in the classification of rotten apples and the quantitative detection of spoilage area.

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Section: Introductionmentioning

confidence: 99%

Classification for Penicillium expansum Spoilage and Defect in Apples by Electronic Nose Combined with Chemometrics

Guo

Chen

et al. 2020

Sensors

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“…The number of such OAs, not discriminative because they were detected by all the panelists and delivered the highest NIF values in all the samples, was inferior to 10% of the detected OAs, as recommended by Etievant and Chaintreau [41] to allow olfactive discrimination between samples in GC-O, thus validating the extract concentrations chosen for our study [40]. Other OAs were also found in all the samples at common lower levels, for instance, 2-methylpropanal (OA n • 2), ethyl propanoate (6), 3-hydroxybutan-2-one (18), 2-ethenyl-6-methylpyrazine (35), acetylpyrazine (43), 1-phenylethyl acetate (48, coeluted with another ester: methyl 2-methylpentanoate), 2,3,5-trimethyl-6-(3-methylbutyl)pyrazine (49), 1H-pyrrole-2-carbaldehyde (69), and nonanoic acid (78). Most of them were also previously identified in cocoa or chocolate categories (Table 1).…”

Section: Introductionmentioning

confidence: 99%

“…However, in the current literature, values varied from this lower level to a 50% NIF threshold [34,47,48]. Owing to the number of odor events detected by the panelists in the 12 samples, illustrating the odorous richness and sensory diversity of the dark chocolates under study, and the number of replicates per sample (12 or 10), a 50% threshold was applied, as also chosen by others [47][48][49]. This meant an OA was finally retained as…”

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confidence: 99%

Key Aroma Compounds of Dark Chocolates Differing in Organoleptic Properties: A GC-O Comparative Study

Deuscher

Gourrat

Repoux³

et al. 2020

Molecules

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Dark chocolate samples were previously classified into four sensory categories. The classification was modelled based on volatile compounds analyzed by direct introduction mass spectrometry of the chocolates' headspace. The purpose of the study was to identify the most discriminant odor-active compounds that should characterize the four sensory categories. To address the problem, a gas chromatography-olfactometry (GC-O) study was conducted by 12 assessors using a comparative detection frequency analysis (cDFA) approach on 12 exemplary samples. A nasal impact frequency (NIF) difference threshold combined with a statistical approach (Khi 2 test on k proportions) revealed 38 discriminative key odorants able to differentiate the samples and to characterize the sensory categories. A heatmap emphasized the 19 most discriminant key odorants, among which heterocyclic molecules (furanones, pyranones, lactones, one pyrrole, and one pyrazine) played a prominent role with secondary alcohols, acids, and esters. The initial sensory classes were retrieved using the discriminant key volatiles in a correspondence analysis (CA) and a hierarchical cluster analysis (HCA). Among the 38 discriminant key odorants, although previously identified in cocoa products, 21 were formally described for the first time as key aroma compounds of dark chocolate. Moreover, 13 key odorants were described for the first time in a cocoa product.Molecules 2020, 25, 1809 2 of 32 Thus, quality attributes of dark chocolate produced from Vietnamese cocoa were recently investigated [2], as well as the influence of the cocoa variety on the fermentation step studied [3][4][5], influence of the cocoa origin on the cocoa flavor examined [6][7][8][9], and the link between the origin and process searched for [10]. The process, starting with fermentation [11][12][13][14][15][16] and followed by drying [17] and roasting [18][19][20][21][22][23] steps, has been the subject of many studies. Specialized reviews and treatises gathered such knowledge [1,[24][25][26].Among these investigations, studies aiming to identify the only aroma-active compounds in dark chocolate (the dark chocolate key aroma compounds) are scarce. Nevertheless, some important gas chromatography-olfactometry (GC-O) studies, completed by GC-MS, allowed identification of the major dark chocolate key aroma compounds. Thus, the influence of conching was examined, with the aim of identifying the key odorant compounds in dark chocolate [27]. Aroma-active compounds in dark and milk chocolate in relation to sensory perception were investigated [28], and recently, key aroma compounds in two commercial dark chocolates with high cocoa contents were characterized [29]. As already cited, the major results are reported in reviews on cocoa and cocoa products [24][25][26] as well as in a specialized treatise dedicated to chocolate [1].Direct injection mass spectrometry (DIMS) methods, such as proton transfer reaction mass spectrometry (PTR-MS), have been used in some studies conducted on dark chocolates. Their obje...

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“…These latter two compounds are typical reaction products of acid-catalysed degradation of limonene and β-linalool, formed at high temperatures and may be recognized as compounds causing off-flavour when present in high concentrations (Bazemore, Goodner & Rouseff 1999;Mastello et al, 2015;Perez-Cacho & Rouseff, 2008b). A temperature dependent increase of both compounds was found in PEF treated juice, when the maximum temperatures of the treatment exceeded 69 °C (juice B).…”

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confidence: 99%

“…compounds that have been shown to be aroma active in GC-Olfactometry studies and that have been identified as relevant for orange juice flavour by two or more independent research groups. d Impact on processed orange juice, from GC-Olfactometry studies, according to Perez-Cacho & Rouseff 2008b and Bazemore, Goodner and Rouseff 1999. e Impact on processed orange juice, from GC-Olfactometry studies, according to Rouseff 2008b andMastello et al 2015. f reported by Shaw 1991, not …”

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confidence: 99%

Moderate and high intensity pulsed electric fields : Effect on microbial inactivation, shelf life and quality of fruit juices

Timmermans¹

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Identification of odour-active compounds of pasteurised orange juice using multidimensional gas chromatography techniques

Cited by 43 publications

References 44 publications

Classification for Penicillium expansum Spoilage and Defect in Apples by Electronic Nose Combined with Chemometrics

Classification for Penicillium expansum Spoilage and Defect in Apples by Electronic Nose Combined with Chemometrics

Key Aroma Compounds of Dark Chocolates Differing in Organoleptic Properties: A GC-O Comparative Study

Moderate and high intensity pulsed electric fields : Effect on microbial inactivation, shelf life and quality of fruit juices

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