Rapid and Non-Destructive Detection of Compression Damage of Yellow Peach Using an Electronic Nose and Chemometrics

被引:44
作者
Yang, Xiangzheng [1 ]
Chen, Jiahui [2 ]
Jia, Lianwen [1 ]
Yu, Wangqing [1 ]
Wang, Da [1 ]
Wei, Wenwen [1 ]
Li, Shaojia [2 ]
Tian, Shiyi [3 ]
Wu, Di [2 ]
机构
[1] All China Federat Supply & Mkt Cooperat, Jinan Fruit Res Inst, Jinan 250014, Peoples R China
[2] Zhejiang Univ, Coll Agr & Biotechnol, Zijingang Campus, Hangzhou 310058, Peoples R China
[3] Zhejiang GongShang Univ, Sch Food Sci & Biotechnol, Hangzhou 310018, Peoples R China
基金
中国国家自然科学基金; 国家重点研发计划;
关键词
yellow peach; electronic nose; compression damage; non-destructive; GC-MS; SUCCESSIVE PROJECTIONS ALGORITHM; GAS-CHROMATOGRAPHY; QUALITY DETERMINATION; VOLATILE COMPOUNDS; CHEMICAL SENSORS; SPACE-SHUTTLE; FRUIT; CLASSIFICATION; IDENTIFICATION; DISCRIMINATION;
D O I
10.3390/s20071866
中图分类号
O65 [分析化学];
学科分类号
070302 ; 081704 ;
摘要
The rapid and non-destructive detection of mechanical damage to fruit during postharvest supply chains is important for monitoring fruit deterioration in time and optimizing freshness preservation and packaging strategies. As fruit is usually packed during supply chain operations, it is difficult to detect whether it has suffered mechanical damage by visual observation and spectral imaging technologies. In this study, based on the volatile substances (VOCs) in yellow peaches, the electronic nose (e-nose) technology was applied to non-destructively predict the levels of compression damage in yellow peaches, discriminate the damaged fruit and predict the time after the damage. A comparison of the models, established based on the samples at different times after damage, was also carried out. The results show that, at 24 h after damage, the correct answer rate for identifying the damaged fruit was 93.33%, and the residual predictive deviation in predicting the levels of compression damage and the time after the damage, was 2.139 and 2.114, respectively. The results of e-nose and gas chromatography-mass spectrophotometry (GC-MS) showed that the VOCs changed after being compressed-this was the basis of the e-nose detection. Therefore, the e-nose is a promising candidate for the detection of compression damage in yellow peach.
引用
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页数:17
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共 63 条
  • [1] Measurement of Single Soybean Seed Attributes by Near-Infrared Technologies. A Comparative Study
    Agelet, Lidia Esteve
    Armstrong, Paul R.
    Romagosa Clariana, Ignacio
    Hurburgh, Charles R.
    [J]. JOURNAL OF AGRICULTURAL AND FOOD CHEMISTRY, 2012, 60 (34) : 8314 - 8322
  • [2] The successive projections algorithm for variable selection in spectroscopic multicomponent analysis
    Araújo, MCU
    Saldanha, TCB
    Galvao, RKH
    Yoneyama, T
    Chame, HC
    Visani, V
    [J]. CHEMOMETRICS AND INTELLIGENT LABORATORY SYSTEMS, 2001, 57 (02) : 65 - 73
  • [3] Fluctuation enhanced sensing (FES) with a nanostructured, semiconducting metal oxide film for gas detection and classification
    Ayhan, Bulent
    Kwan, Chiman
    Zhou, Jin
    Kish, Laszlo B.
    Benkstein, Kurt D.
    Rogers, Phillip H.
    Semancik, Steve
    [J]. SENSORS AND ACTUATORS B-CHEMICAL, 2013, 188 : 651 - 660
  • [4] Electronic-Nose Applications for Fruit Identification, Ripeness and Quality Grading
    Baietto, Manuela
    Wilson, Alphus D.
    [J]. SENSORS, 2015, 15 (01) : 899 - 931
  • [5] A combination of physiological and chemometrics analyses reveals the main associations between quality and ripening traits and volatiles in two loquat cultivars
    Besada, Cristina
    Salvador, Alejandra
    Sdiri, Sawsen
    Gil, Rebeca
    Granell, Antonio
    [J]. METABOLOMICS, 2013, 9 (02) : 324 - 336
  • [6] BINWANG, 2019, POSTHARVEST BIOL TEC, V158
  • [7] Study of 'Redhaven' peach and its white-fleshed mutant suggests a key role of CCD4 carotenoid dioxygenase in carotenoid and norisoprenoid volatile metabolism
    Brandi, Federica
    Bar, Einat
    Mourgues, Fabienne
    Horvath, Gyoergyi
    Turcsi, Erika
    Giuliano, Giovanni
    Liverani, Alessandro
    Tartarini, Stefano
    Lewinsohn, Efraim
    Rosati, Carlo
    [J]. BMC PLANT BIOLOGY, 2011, 11
  • [8] Byrne DH, 2012, HANDB PLANT BREED, V8, P505, DOI 10.1007/978-1-4419-0763-9_14
  • [9] A feature extraction method for chemical sensors in electronic noses
    Carmel, L
    Levy, S
    Lancet, D
    Harel, D
    [J]. SENSORS AND ACTUATORS B-CHEMICAL, 2003, 93 (1-3) : 67 - 76
  • [10] Elimination of uninformative variables for multivariate calibration
    Centner, V
    Massart, DL
    deNoord, OE
    deJong, S
    Vandeginste, BM
    Sterna, C
    [J]. ANALYTICAL CHEMISTRY, 1996, 68 (21) : 3851 - 3858