Non-Destructive Detection of Damaged Strawberries after Impact Based on Analyzing Volatile Organic Compounds

Cao, Yang; Zhang, Yuchen; Lin, Menghua; Wu, Di

doi:10.3390/s22020427

Cited by 12 publications

(9 citation statements)

References 45 publications

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“…It is composed of three parts: sensor, signal processing system and pattern recognition system. The overall information of the tested sample is obtained through the sensor and pattern recognition system (Cao et al, 2022). In the field of fruit detection, the electronic nose is mainly used to detect and judge the storage period, freshness, maturity and whether the fruit is infected by pests and diseases (Adedeji et al, 2020;Salehi, 2020).…”

Section: Study On Fruit Quality Detection Based On Electronic Nose Te...mentioning

confidence: 99%

A bibliometric and visual analysis of fruit quality detection research

Luo

ZHANG

et al. 2022

Food Sci. Technol

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To understand the research status and current dynamics of fruit quality detection and objectively reflect the influence of different countries, research institutions and authors in this field, the software CiteSpace was used to bibliometrically and visually analyze the relevant literature on fruit quality detection in Web of Science (WoS) database from 1982 to 2021. The results showed that the number of publications on fruit quality detection showed a trend of slow in the early stage, rapid in the middle stage and intensified in the late stage. China has the largest number of international publications, accounting for 21.80% of the WoS database, but its intermediary centrality is inferior to that of the United States, Germany, Italy and Spain; the United States is at the core of international cooperation, establishing cooperative relations with most countries; through keywords co-occurrence network and keyword clustering, it is concluded that the research mainly focuses on fruit quality detection based on spectral technology or electronic nose technology, fruit composition detection based on high-performance liquid chromatography (HPLC), fruit edible safety detection, and genetic analysis of fruit quality-related traits. Bibliometrics and visual analysis of this fruit quality detection research can provide a certain reference for relevant researchers.

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Section: Study On Fruit Quality Detection Based On Electronic Nose Te...mentioning

confidence: 99%

A bibliometric and visual analysis of fruit quality detection research

Luo

ZHANG

et al. 2022

Food Sci. Technol

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“…The electronic nose is composed of a gas sensor array, which collects electrical signals and responds to the whole set of volatile compounds in a unique digital pattern (fingerprint) (Tan & Xu, 2020) in association with an appropriate pattern recognition statistical system. The discrimination of aroma compounds has attracted increasing attention from producers and consumers since brand awareness is getting stronger (Cao et al, 2022; Feltes et al, 2023). To the best of our knowledge, the e‐nose system has not been used to discriminate between different artificial strawberry aromas.…”

Section: Introductionmentioning

confidence: 99%

“…To the best of our knowledge, the e‐nose system has not been used to discriminate between different artificial strawberry aromas. Moreover, in previous studies, it was mainly conducted to discriminate between different types of artificial aromas (Ballen et al, 2019; Galvagni et al, 2020; Graboski, Ballen, et al, 2018; Graboski, Galvagni, et al, 2018; Tiggemann et al, 2016), classify fruit grades (Baietto & Wilson, 2015), predict fruit quality (Xing et al, 2018), and detect mechanical damage in strawberries (Cao et al, 2022; Rao et al, 2020).…”

Section: Introductionmentioning

confidence: 99%

Discrimination of artificial strawberry aroma by electronic nose based on nanocomposites

Feltes,

Ballen,

Soares

et al. 2023

J Food Process Engineering

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An electronic nose containing an array of gas sensors with interdigitated electrodes coated with nanocomposites based on polyaniline (Pani) combined with carboxylated multiwalled carbon nanotubes doped with different acids was used to discriminate artificial strawberry aromas. Principal component analysis (PCA) and linear discriminant analysis (LDA) were used to investigate if the electronic nose was able to distinguish between different strawberry aromas. In addition, a nonlinear projection of the interactive document map technique (IDMAP) was applied to evaluate the distinction ability of the gas sensor array. The sensing layers showed tubular characteristics for Pani/MWCNT_COOH morphology and the group's representative doped state. The main and most common compound identified by gas chromatography in strawberry aromas was ethyl butyrate. The gas sensors showed high sensitivity (>6.55 mV/ppm), low limit of detection (LD <0.31 ppm), limit of quantification (LQ <5.43 ppm), short response time (<63 s) and recovery time (<10.15 s) for different artificial strawberry aromas. The results demonstrate that the electronic nose could differentiate among the artificial strawberry aromas. The volatile profile of strawberry aromas was distinguished into distinct groups using both LDA and PCA analysis. The use of IDMAP permitted the identification of the major contributors to the distinguishing ability of gas sensors with a high silhouette coefficient (0.91). The sensor array was efficient at aroma discrimination, demonstrating excellent potential for food analysis in industrial applications.Practical applicationsThe distinction between natural and artificial flavors has significantly shrunk as volatile chemical technology has advanced. Historically, it was difficult to industrially replicate natural aromas. Identification and analysis of aromas are challenging tasks. Targeted aroma compounds are frequently present in low concentrations (parts per million), which makes it very challenging to identify them. The electronic nose system is a promising new analytical method that can quickly and accurately, without pre‐treating the sample, detect and identify the full information of aroma compounds in real time. It is generally accepted as a nondestructive, affordable, and portable method with high feasibility, speed, and ease of use. For qualitative differentiation and distinguishing, PCA, LDA, and IDMAP methods were also used.

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“…In recent years, researchers have mainly used hyperspectral imaging [3][4][5][6][7][8][9], machine vision [10,11], thermal imaging [12], nuclear magnetic resonance [13], electronic nose [14], multispectral reflection, and fluorescence imaging [9] to detect mechanical damage, bruise, and the internal quality of fruit. However, these technologies are limited in practical application due to the high equipment costs, long data acquisition and processing time, and possible health and safety issues [15].…”

Section: Introductionmentioning

confidence: 99%

Discrimination of Inner Injury of Korla Fragrant Pear Based on Multi-Electrical Parameters

Luo

Xiong

et al. 2023

Foods

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Inner-injury fragrant pears are easily prone to rot during storage. Discriminating inner injury in the Korla fragrant pear from the normal pear is difficult as the flesh may be injured while the peel of the fruit remains intact. This study demonstrated the recognition of inner-injury pears based on their electric characteristics to pick out the inner-injury pears before storage. The electrical parameters parallel equivalent capacitance, quality factor, parallel equivalent inductance, parallel equivalent resistance, complex impedance, and phase angle were measured using the fruit electrical characteristic detection instrument. Principal component analysis and correlation analysis were used to determine the characteristic parameters, connected with the qualitative value of the fragrant pear to establish three discrimination models. When the measurement frequency was 100 kHz, compared with the Naïve Bayes and K-nearest neighbor models, the Support Vector Machine model with the characteristic parameters of quality factor, parallel equivalent resistance, and phase angle performed best. The recognition accuracy of the test set was 92.00%, the precision was 92.41%, the recall was 97.33%, and the F1 score was 0.95. Therefore, the electrical characteristic technique effectively detected the inner injury of fragrant pears and provided a new way to distinguish the inner injury of fruits.

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Non-Destructive Detection of Damaged Strawberries after Impact Based on Analyzing Volatile Organic Compounds

Cited by 12 publications

References 45 publications

A bibliometric and visual analysis of fruit quality detection research

A bibliometric and visual analysis of fruit quality detection research

Discrimination of artificial strawberry aroma by electronic nose based on nanocomposites

Discrimination of Inner Injury of Korla Fragrant Pear Based on Multi-Electrical Parameters

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