2011
DOI: 10.1590/s0102-695x2011005000127
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Scent analysis of Rosa laevigata through metal oxide sensor array electronic nose

Abstract: Abstract:The scent fingerprint of Rosa laevigata Michx., Rosaceae, samples harvested at different periods was investigated. Principal component analysis (PCA) and discriminant factor analysis (DFA) were done on the scent response values measured by an electronic nose (EN) sensor. Statistical quality control (SQC) analysis was also conducted. The R. laevigata samples were clustered into two categories after being analyzed by PCA and DFA. One cluster consisted of samples No. 1 to No. 6, and the other consisted o… Show more

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Cited by 3 publications
(2 citation statements)
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“…DFA is a classification technique that optimizes the distinguishing ability of variables by recombining sensor data 18 . With mathematical manipulation, it minimizes the differences between data of the same type and broadens the disparities between data from different categories to establish a data recognition model 19 .…”
Section: Resultsmentioning
confidence: 99%
“…DFA is a classification technique that optimizes the distinguishing ability of variables by recombining sensor data 18 . With mathematical manipulation, it minimizes the differences between data of the same type and broadens the disparities between data from different categories to establish a data recognition model 19 .…”
Section: Resultsmentioning
confidence: 99%
“… Chen et al (2011) investigated the variation of odour fingerprints of R. laevigata at different time-periods, and then conducted principal component analysis (PCA) and discriminant factor analysis (DFA) on the odour response values measured by using electronic nose (EN) sensors. They also performed statistical quality control (SQC) analysis.…”
Section: Quality Controlmentioning
confidence: 99%