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Correlation of analyses of odor prof...

Xiong, Li.

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Correlation of analyses of odor profiles of HDPE films coated with different adhesives using electronic nose, sensory evaluation, and GC-MS.

紀錄類型:	書目-語言資料,印刷品 : Monograph/item
正題名/作者:	Correlation of analyses of odor profiles of HDPE films coated with different adhesives using electronic nose, sensory evaluation, and GC-MS./
作者:	Xiong, Li.
面頁冊數:	287 p.
附註:	Adviser: Susan Selke.
Contained By:	Dissertation Abstracts International68-01B.
標題:	Agriculture, Food Science and Technology. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3248632

Correlation of analyses of odor profiles of HDPE films coated with different adhesives using electronic nose, sensory evaluation, and GC-MS.
Xiong, Li.

Correlation of analyses of odor profiles of HDPE films coated with different adhesives using electronic nose, sensory evaluation, and GC-MS. - 287 p.

Adviser: Susan Selke.

Thesis (Ph.D.)--Michigan State University, 2006.

The two dimensional PCA (Principle Component Analysis) module of the E-nose system differentiated successfully five groups of HDPE films (one uncoated film and four films coated with different adhesives) and a DI (Discrimination Index) of 82 was obtained. A DFA (Discriminative Function Analysis) model with a POR (Percentage of Recognition) of 94 was built and proved effective in identifying unknown samples as one of the training samples.Subjects--Topical Terms:

1017813
Agriculture, Food Science and Technology.

Correlation of analyses of odor profiles of HDPE films coated with different adhesives using electronic nose, sensory evaluation, and GC-MS.
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245 1 0 $a Correlation of analyses of odor profiles of HDPE films coated with different adhesives using electronic nose, sensory evaluation, and GC-MS.
300 $a 287 p.
500 $a Adviser: Susan Selke.
500 $a Source: Dissertation Abstracts International, Volume: 68-01, Section: B, page: 0601.
502 $a Thesis (Ph.D.)--Michigan State University, 2006.
520 $a The two dimensional PCA (Principle Component Analysis) module of the E-nose system differentiated successfully five groups of HDPE films (one uncoated film and four films coated with different adhesives) and a DI (Discrimination Index) of 82 was obtained. A DFA (Discriminative Function Analysis) model with a POR (Percentage of Recognition) of 94 was built and proved effective in identifying unknown samples as one of the training samples.
520 $a Both the pairwise ranking test and the quantitative affective consumer test indicated the control sample (HDPE base film) had the weakest odor while one type of coated film had the strongest odor among the five groups of samples. In addition, there was no significant difference in terms of the intensity of the perceived odor among the other three types of coated films. The "Acceptability" and "Intensity" sensory scores of the odor profiles were also obtained.
520 $a Two different sample preparation techniques, DH-TD (Dynamic Headspace-Thermal Desorption) and SPME (Solid Phase Microextraction), were used to concentrate volatile compounds released from the HDPE film samples before the compounds were analyzed by GC-MS (Gas Chromatography-Mass Spectrometry). The DH-TD technique seemed to collect more volatile compounds from the sample matrix but its repeatability was affected by the connection between the transfer line of the thermal desorption unit and the gas chromatograph. In comparison, the SPME technique proved to be easier, faster, and cheaper to use. The same groups of potential volatile compounds were identified in the odor profiles of all five HDPE films, including ketones, aldehydes, aromatics, and hydrocarbons, among which were acetone, hexanal, benzaldehyde, nonanal, and 1,3-di-tert-butylbenzene. An SGE ODO II olfactory detector was used in the sniffing tests to describe the odor profiles of two coated HDPE films. Sniffing tests proved 1,3-di-tert-butylbenzene was not the crucial compound responsible for the stronger odor of one of the coated films.
520 $a PLS (Partial Least Square) models with correlation coefficients of 0.92-0.99 in the E-nose system were proved effective and robust in predicting the "Acceptability" sensory scores when the models were built for pairs of samples. On the other hand, the effectiveness and robustness of PLS models for predicting the amounts of acetone and nonanal in the odor profiles were affected by which pair of samples and what volatile compound were being investigated. Nevertheless, the E-nose system showed its potential as a quality control tool in packaging systems if the correlation between the E-nose system, sensory evaluation, and GC/MS was established. Moreover, the study showed the importance of complementing the three analytical techniques with one another.
590 $a School code: 0128.
650 4 $a Agriculture, Food Science and Technology. $3 1017813
650 4 $a Engineering, Packaging. $3 1025152
690 $a 0359
690 $a 0549
710 2 $a Michigan State University. $3 676168
773 0 $t Dissertation Abstracts International $g 68-01B.
790 $a 0128
790 1 0 $a Selke, Susan, $e advisor
791 $a Ph.D.
792 $a 2006
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3248632