Collaborative analysis on difference of apple fruits flavour using electronic nose and electronic tongue

Zhu, Danshi; Ren, Xiaojun; Wei, Liwei; Cao, Xuehui; Ge, Yonghong; Li, He; Lǐ, Jiànróng

doi:10.1016/j.scienta.2019.108879

Cited by 123 publications

(71 citation statements)

References 36 publications

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“…Odor-Banana showed only negative correlations with (E)-hex-2-enal, 2-methylhept-6-en-1-ol, and hexanal ( Figure 3); anyhow, it was projected in the upper right quadrant of the PCA, indicating a strong relation with the other parameters influencing the PC1, such as Overall odor, Odor-Pear, and Odor-Honey (Figure 3). Odor-Honey was positively correlated with hexyl hexanoate, (E)-dec-3-en-1-ol, butyl 3-hydroxybutanoate, (E)-dodec-6-en-1-ol, 1-propoxydodecane, and ethyl 3-hydroxypent-4-enoate; and negatively correlated with pentyl acetate (fruity), hexyl acetate (described as fruity, green apple, and banana [26] and significant in our Wilcoxon signed rank test (Figure 1)), hex-5-enyl acetate, 2,2,4,4,7,7-hexamethyl-1,3,3a,5,6,7a-hexahydroindene, and nonanoic acid, which gives a fruity, green apple, banana, and waxy odor [26] (Figure 3). In the same upper right quadrant of the PCA was also located Odor-Pear, which was positively correlated with 1-propoxydodecane (Figure 3), as was Odor-Banana, and very close to Odor-Banana in the PCA (Figure 2).…”

Section: Can the Altitude Influence Sensory Attributes And What Are Tmentioning

confidence: 97%

“…Anyway, it seems that compared with environment, year of production, and storage time, the variety is a more important source of compositional variations [ 24 ]. Several works have been published investigating the apple cultivar aroma profile [ 25 , 26 ]. Compounds believed to be characteristic of specific varieties have been reported; ethyl 2-methyl butanoate, 2-methyl butyl acetate, and hexyl acetate have been attributed to Fuji apples aroma, and ethyl butanoate and ethyl 2-methyl butanoate are described as active compounds in the Elstar variety [ 8 ]; however, most of them involve one or a limited number of varieties.…”

Section: Introductionmentioning

confidence: 99%

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Aroma Investigation of New and Standard Apple Varieties Grown at Two Altitudes Using Gas Chromatography-Mass Spectrometry Combined with Sensory Analysis

et al. 2020

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The aromatic profile of apples constitutes important information for the characterization and description of local products. Apple flavor is determined by perception in mouth and aroma; while the first is mainly defined by sugars and organic acids, aroma is a complex mixture of many volatile organic compounds (VOCs) whose composition is often specific to the variety. Headspace-solid phase microextraction gas chromatography coupled with mass spectrometry (HS-SPME-GC-MS) allows for the detection of detailed information of volatile constituents. In this study, eleven apple varieties (Braeburn, Fuji, Gala, Golden Delicious, Coop 39-Crimson Crisp®, Dalinette-Choupette®, Fujion, CIV323-Isaaq®, Coop43-Juliet®, SQ159-Natyra®, UEB32642-Opal®) grown in two pedoclimatic locations at different altitudes in South Tyrol (Italy) (ca. 225 m and ca. 650 m a.s.l.) were investigated. Thirty-eight VOCs were identified and combined with sensory analysis results (from 11 trained panelist) to characterize the aroma of new and standard apple varieties with a special focus on pedoclimatic location differences. The study shows strong diversification of the varieties based on their VOC profiles and sensory attributes, as expected. Moreover, investigating how the pedoclimatic location at different altitudes can influence the apple aroma profile, we identified twelve VOCs involved in these differences and provided a deeper investigation on how different altitudes can influence the apple aroma composition and perceptions combining the analytical and sensory parameters.

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Section: Can the Altitude Influence Sensory Attributes And What Are Tmentioning

confidence: 97%

Section: Introductionmentioning

confidence: 99%

Aroma Investigation of New and Standard Apple Varieties Grown at Two Altitudes Using Gas Chromatography-Mass Spectrometry Combined with Sensory Analysis

et al. 2020

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“…2 Number of peaks 3. Already reported in Kheshti et al[42], Mao et al[43], Medina et al[44], Perestrelo et al[50], López-Fructuoso and Echeverría-Cortada[51], and Zhu et al[52] 4. Retention time 5.…”

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confidence: 93%

Influence of Cryoconcentration on Quality Attributes of Apple Juice (Malus Domestica cv. Red Fuji)

et al. 2020

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Apple juice was subjected to centrifugal block cryoconcentration (CBCC) for three cycles and their effect on the physicochemical properties, bioactive compounds, antioxidant activity, volatile profile, and sensory analysis was investigated. In the final cycle, the solutes were approximately four-fold of the initial condition (≈14 °Brix) and the color (ΔE* ≈ 25.0) was darker than the fresh juice, with bioactive compound concentration values close to 819 mg GAE/100 g d.m., 248 and 345 mg CEQ/100 g d.m. for total polyphenol, flavonoid, and flavanol content, respectively, equivalent to a retention of over 60%. DPPH and FRAP assays presented high antioxidant activities, with values of approximately 1803 μmol TE/100 g d.m. and 2936 μmol TE/100 g d.m, respectively. The cryoconcentrate showed a similar aromatic profile to the fresh juice, with 29 and 28 volatile compounds identified, respectively. The centrifugal force allowed to obtain excellent process parameters, with 73%, 0.87 (kg/kg), and 85% for efficiency, solute yield, and percentage of concentrate, respectively. Sensory evaluation shows that the odor, aroma, and flavor of fresh sample were remained in the reconstituted cryoconcentrate sample, with good qualifications (four points in a five-score hedonic scale) by trained panelists. Therefore, CBCC can preserve important quality attributes from apple juice.

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“…This study concluded that the resulting OXIDE levels could affect the young and ripe of fruit. Electronic noses can also distinguish odor differences between different fruit varieties such as apples [4]. The use of E-Nose allows complex aroma changes in the fruit to be monitored [5].…”

Section: E-nose To Determine the Level Of Fruit Sweetnessmentioning

confidence: 99%

Optimizing Machine Learning Parameters for Classifying the Sweetness of Pineapple Aroma Using Electronic Nose

Hasan¹,

Sarno²,

Sabilla³

2020

IJIES

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Electronic nose (e-nose) has been widely used to distinguish various scents in food. The output of e-nose is a signal that can be identified, compared, and analyzed. However, many researchers use e-nose without using standardization tools, therefore e-nose is still often questioned for its validity. This paper proposes an electronic nose (e-nose) to classify the sweetness of pineapples. The standard sweetness levels are measured by using a Brix meter as a standardization tool. The e-nose consists of a series of gas sensors MQ Series which are connected to the Arduino micro-controller. The sweetness levels measured by the Brix meter are then ordered into low, medium, high sweetness groups. These sweetness groups are used as label ground-truth for the e-nose. Signal processing PCA and mother wavelet is employed to reduce noise from the e-nose signals. The signal processing methods obtain optimal parameters to find the characteristics of each signal. Machine learning methods were successfully carried out with optimized parameters for the classification of three levels of sweetness of pineapple. The best accuracy is 82% using KNN with 3 neighbors.

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Collaborative analysis on difference of apple fruits flavour using electronic nose and electronic tongue

Cited by 123 publications

References 36 publications

Aroma Investigation of New and Standard Apple Varieties Grown at Two Altitudes Using Gas Chromatography-Mass Spectrometry Combined with Sensory Analysis

Aroma Investigation of New and Standard Apple Varieties Grown at Two Altitudes Using Gas Chromatography-Mass Spectrometry Combined with Sensory Analysis

Influence of Cryoconcentration on Quality Attributes of Apple Juice (Malus Domestica cv. Red Fuji)

Optimizing Machine Learning Parameters for Classifying the Sweetness of Pineapple Aroma Using Electronic Nose

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