Sökning: onr:"swepub:oai:DiVA.org:uu-415251" >
Classification for ...
-
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
Förlag, utgivningsår, omfång ...
-
2020-04-09
-
MDPI AG,2020
-
electronicrdacarrier
Nummerbeteckningar
-
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
Kompletterande språkuppgifter
-
Språk:engelska
-
Sammanfattning på:engelska
Ingår i deldatabas
Klassifikation
-
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
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
Internetlänk
Hitta via bibliotek
-
Sensors
(Sök värdpublikationen i LIBRIS)
Till lärosätets databas