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

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Guo, ZhimingJiangsu Univ, Sch Food & Biol Engn, Zhenjiang 212013, Jiangsu, Peoples R China (författare)

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

Artikel/kapitelEngelska2020

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2020-04-09
MDPI AG,2020
electronicrdacarrier

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LIBRIS-ID:oai:DiVA.org:uu-415251
https://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-415251URI
https://doi.org/10.3390/s20072130DOI

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Språk:engelska
Sammanfattning på:engelska

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Ämneskategori:ref swepub-contenttype
Ämneskategori:art swepub-publicationtype

Anmärkningar

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.

Ämnesord och genrebeteckningar

LANTBRUKSVETENSKAPER Lantbruksvetenskap, skogsbruk och fiske Livsmedelsvetenskap hsv//swe
AGRICULTURAL SCIENCES Agriculture, Forestry and Fisheries Food Science hsv//eng
NATURVETENSKAP Kemi Analytisk kemi hsv//swe
NATURAL SCIENCES Chemical Sciences Analytical Chemistry hsv//eng
gas sensors
electronic nose
apple
pattern recognition
variable selection

Biuppslag (personer, institutioner, konferenser, titlar ...)

Guo, ChuangJiangsu Univ, Sch Food & Biol Engn, Zhenjiang 212013, Jiangsu, Peoples R China (författare)
Chen, QuanshengJiangsu Univ, Sch Food & Biol Engn, Zhenjiang 212013, Jiangsu, Peoples R China (författare)
Ouyang, QinJiangsu Univ, Sch Food & Biol Engn, Zhenjiang 212013, Jiangsu, Peoples R China (författare)
Shi, JiyongJiangsu Univ, Sch Food & Biol Engn, Zhenjiang 212013, Jiangsu, Peoples R China (författare)
El-Seedi, HeshamUppsala universitet,Farmakognosi,Jiangsu Univ, Sch Food & Biol Engn, Zhenjiang 212013, Jiangsu, Peoples R China(Swepub:uu)heels173 (författare)
Zou, XiaoboJiangsu Univ, Sch Food & Biol Engn, Zhenjiang 212013, Jiangsu, Peoples R China (författare)
Jiangsu Univ, Sch Food & Biol Engn, Zhenjiang 212013, Jiangsu, Peoples R ChinaFarmakognosi (creator_code:org_t)

Sammanhörande titlar

Ingår i:Sensors: MDPI AG20:71424-8220

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Av författaren/redakt...: Guo, Zhiming; Guo, Chuang; Chen, Quansheng; Ouyang, Qin; Shi, Jiyong; El-Seedi, Hesham; visa fler...; Zou, Xiaobo; visa färre...

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LANTBRUKSVETENSKAPER: LANTBRUKSVETENSK ...; och Lantbruksvetensk ...; och Livsmedelsvetens ...

NATURVETENSKAP: NATURVETENSKAP; och Kemi; och Analytisk kemi

Artiklar i publikationen: Sensors

Av lärosätet: Uppsala universitet

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