Analysis of the volatile profiles of kiwifruits experiencing soft rot using E-nose and HS-SPME/GC–MS

Wang, Yujiao; Wang, Dan; Lv, Zhenzhen; Zeng, Qingxiao; Fu, Xiali; Chen, Qiyang; Luo, Zhongwei; Luo, Cheng; Wang, Dachuan; Zhang, Wen

doi:10.1016/j.lwt.2022.114405

Cited by 19 publications

(5 citation statements)

References 34 publications

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“…An electronic nose (E-nose) is a simple, efficient, non-destructive, and stable device that imitates the human olfactory system. It has been used for assessing fruit quality and ripeness and in other applications [23,24]. In our research, W5S, W1S, and W1W could explain up to 99.99% of the total contributions, indicating that these non-aromatic substance classes (nitrogen oxides, methane, and hydrogen sulphide) distinguished the peach fruit aromas in our research.…”

Section: Discussionsupporting

confidence: 51%

Sensory Determination of Peach and Nectarine Germplasms with Instrumental Analysis

Sun,

Ma,

Cai

et al. 2023

Foods

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The flavour and mouthfeel of peaches are crucial qualities of peach germplasm resources that significantly influence consumer preferences. In this study, we utilized 212 peach germplasm resources from the Nanjing Peach Resource Repository, National Fruit Germplasm facility, Jiangsu Academy of Agricultural Sciences as materials for sensory analysis, electronic nose analysis, and composition analysis via high-performance liquid chromatography (HPLC). In the sensory analysis, we divided 212 peach germplasms into three clusters based on hierarchical cluster analysis (d = 5). No.27, No.151, and No.46 emerged as the most representative of these clusters. The electronic nose was used to conduct an evaluation of the aroma profiles of the 212 peach germplasms, revealing that the primary distinguishing factors of peach aroma can be attributed to three sensors: W1S (methane), W1W (terpenes and organosulfur compounds), and W5S (hydrocarbons and aromatic compounds). The primary differences in the aromatic substances were characterized by sensors W2W (aromatic compounds, sulphur, and chlorine compounds) and W1C (aromatic benzene). The HPLC analysis indicated that the persistence of peach sensory characteristics was positively correlated with acids and sourness and negatively correlated with sweetness and the ratio of sugar to acids. The overall impression of the 212 peach germplasms revealed a negative correlation with acids, while a positive correlation was observed between the overall impression and the ratio of sugar to acids. Therefore, this study substantially contributes to the preliminary screening of the analysed specific characteristics of peach germplasms such as No.27, No.46, No.151, and No.211. These selections may provide valuable information for the potential creation of superior germplasm resources.

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Section: Discussionsupporting

confidence: 51%

Sensory Determination of Peach and Nectarine Germplasms with Instrumental Analysis

Sun,

Ma,

Cai

et al. 2023

Foods

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“…Principal component analysis (PCA) has the ability to determine complex and difficult‐to‐find variables and evaluate the differences among the samples (Li et al., 2019). In previous studies, aroma profiles of microwave‐dried perilla leaves (Jin et al., 2023), red‐cooked chickens (Sun, Qiao, et al., 2023; Sun, Yu, et al., 2023), and kiwifruits experiencing soft rot (Wang et al., 2023) were successfully characterized by PCA. Hence, PCA was applied for the analysis of QS samples with different jujube powder additions based on the data from e‐nose.…”

Section: Resultsmentioning

confidence: 99%

Effect of jujube powder addition on the aroma profile of quinoa snacks (QS)

Song,

Liu,

Wang

et al. 2024

Food Science & Nutrition

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Quinoa is a full‐nutrition food; however, its poor flavor and small size make it not the best food option for direct consumption. In this study, a quinoa snack (QS, a cake) was developed, and the aroma profile of the products was improved by adding jujube fruit powder (made from dried jujube fruits, from 5% to 30%). Gas chromatography mass spectrum (GC–MS) combined with electronic nose (e‐nose) was applied for characterizing the aroma profiles of QS samples. Results showed a total of 26 aroma compounds were identified in QS samples by GC–MS, and 3‐methylbutanol (from 1525 μg/kg in QS‐30 to 3487 μg/kg in QS‐0), ethanol (from 1126 μg/kg in QS‐0 to 3581 μg/kg in QS‐30), hexanal (from 125.6 μg/kg in QS‐30 to 984.1 μg/kg in QS‐0), and acetaldehyde (from 531.9 μg/kg in QS‐30 to 191.1 μg/kg in QS‐0) were common. The e‐nose response of W1S (sensitive to methane, from 17.50 of QS‐0 to 93.85 of QS‐30) and W1W (sensitive to sulfur‐organic compounds of e‐nose, from 15.57 of QS‐0 to 39.50 of QS‐30) were significantly higher, and significant differences were presented among QS samples. In conclusion, the aroma profile of the QS sample was significantly (p < .05) enhanced by the addition of jujube powder, and QS‐30 with the highest jujube content (30%) presented the strongest aroma profile. Moreover, QS samples with different additions of jujube powders could be well distinguished by principal component analysis (PCA), and the combination of e‐nose and GC–MS was effective in the volatile profile analysis of QS samples.

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“…The electronic nose (E-nose), an odor-based, rapid, portable, and sensitive technology [19], has been introduced as a promising solution for investigating the rotting process in different agricultural products. Wang et al [20] pointed out that rotten kiwifruit can produce lipids, alcohols, and aromatic hydrocarbons through metabolic processes which change the composition of volatile compounds in kiwifruit, and that there is a close correlation between E-nose signals and metabolites (p < 0.01). Wang et al divided rotting kiwifruit into latent, early, middle, and late stages based on different storage times, and kiwifruit in the latent stage of rot could not be recognized [21].…”

Section: Introductionmentioning

confidence: 99%

Early Identification of Rotten Potatoes Using an Electronic Nose Based on Feature Discretization and Ensemble Convolutional Neural Network

Lin,

Wei,

Chen

et al. 2024

Sensors

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The early identification of rotten potatoes is one of the most important challenges in a storage facility because of the inconspicuous symptoms of rot, the high density of storage, and environmental factors (such as temperature, humidity, and ambient gases). An electronic nose system based on an ensemble convolutional neural network (ECNN, a powerful feature extraction method) was developed to detect potatoes with different degrees of rot. Three types of potatoes were detected: normal samples, slightly rotten samples, and totally rotten samples. A feature discretization method was proposed to optimize the impact of ambient gases on electronic nose signals by eliminating redundant information from the features. The ECNN based on original features presented good results for the prediction of rotten potatoes in both laboratory and storage environments, and the accuracy of the prediction results was 94.70% and 90.76%, respectively. Moreover, the application of the feature discretization method significantly improved the prediction results, and the accuracy of prediction results improved by 1.59% and 3.73%, respectively. Above all, the electronic nose system performed well in the identification of three types of potatoes by using the ECNN, and the proposed feature discretization method was helpful in reducing the interference of ambient gases.

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Analysis of the volatile profiles of kiwifruits experiencing soft rot using E-nose and HS-SPME/GC–MS

Cited by 19 publications

References 34 publications

Sensory Determination of Peach and Nectarine Germplasms with Instrumental Analysis

Sensory Determination of Peach and Nectarine Germplasms with Instrumental Analysis

Effect of jujube powder addition on the aroma profile of quinoa snacks (QS)

Early Identification of Rotten Potatoes Using an Electronic Nose Based on Feature Discretization and Ensemble Convolutional Neural Network

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