scholarly journals Characterization and Discrimination of Key Aroma Compounds in Pre- and Postrigor Roasted Mutton by GC-O-MS, GC E-Nose and Aroma Recombination Experiments

Foods ◽  
2021 ◽  
Vol 10 (10) ◽  
pp. 2387
Author(s):  
Huan Liu ◽  
Teng Hui ◽  
Fei Fang ◽  
Qianli Ma ◽  
Shaobo Li ◽  
...  
Keyword(s):  
Principal Component Analysis ◽  
Least Squares ◽  
Partial Least Squares ◽  
Principal Component ◽  
Component Analysis ◽  
Aroma Compounds ◽  
Discrimination Analysis ◽  
Key Odorants ◽  
Recombination Experiments ◽  
Aroma Recombination

The key aroma compounds in the pre- and postrigor roasted mutton were studied in this study. The results showed that 33 and 30 odorants were detected in the pre- and postrigor roasted mutton, respectively. Eight aroma compounds, including 3-methylbutanal, pentanal, hexanal, heptanal, octanal, nonanal, 1-octen-3-ol, and 2-pentylfuran, were confirmed as key odorants by aroma recombination and omission experiments. The aroma profiles of pre- and postrigor roasted mutton both presented great fatty, roasty, meaty, grassy, and sweet odors. Particularly, the concentrations of hexanal, heptanal, octanal, nonanal, 1-octen-3-ol, and 2-pentylfuran in postrigor roasted mutton were significantly higher (p < 0.05) than those in the prerigor roasted mutton. The postrigor back strap was more suitable for roasting than the prerigor back strap. The pre- and postrigor roasted mutton could be obviously discriminated based on the aroma compounds by orthogonal partial least squares discrimination analysis (OPLS-DA) and principal component analysis (PCA). Hexanal and 1-octen-3-ol might potential markers for the discrimination of the pre- and postrigor roasted mutton.

scholarly journals Modelos PCA a partir de conjuntos de datos con información faltante. ¿Se afectan sus propiedades?

SaberEs ◽  
2010 ◽  
Author(s):  
María Susana Vitelleschi ◽  
Directora: Marta Beatriz Quaglino
Keyword(s):  
Principal Component Analysis ◽  
Least Squares ◽  
Partial Least Squares ◽  
Expectation Maximization ◽  
Principal Component ◽  
Component Analysis

En este trabajo se aborda la problemática de la construcción de modelos PCA (Principal Component Analysis) a partir de conjuntos de datos con información faltante. Se trabaja sobre tres situaciones diferentes con relación a la matriz de datos originales. En cada situación se generaron pérdidas a través de mecanismos aleatorios y no aleatorios, en diferentes porcentajes en una sola variable por vez, seleccionada mediante dos criterios: la que más contribuye y menos contribuye en la formación de la primera componente principal. A partir de cada conjunto de datos incompletos se construye el modelo PCA utilizando: Casos Completos, Nonlinear Iterative Partial Least Squares (NIPALS) y Expectation Maximization (EM). Se comparan los resultados con los obtenidos a través del conjunto de datos originales. Se definen una serie de medidas para estudiar cómo se afectan los resultados según la dimensión de la matriz de datos, el porcentaje y el mecanismo de pérdida, con relación a: bondad del ajuste, bondad de predicción, vectores cargas, ortonormalidad de la matriz de cargas y ortogonalidad de la matriz de “scores”.

Comparison and Discrimination of Artemisia argyi and Artemisia lavandulifolia by Gas Chromatography-Mass Spectrometry-Based Metabolomic Approach

2019 ◽  
Vol 102 (6) ◽  
pp. 1814-1821 ◽  
Author(s):  
Long Guo ◽  
Dan Zhang ◽  
Lei Wang ◽  
Zijing Xue ◽  
Mei Guo ◽  
...  
Keyword(s):  
Principal Component Analysis ◽  
Least Squares ◽  
Volatile Compounds ◽  
Partial Least Squares ◽  
Herbal Medicines ◽  
Principal Component ◽  
Component Analysis ◽  
Chemical Markers ◽  
Artemisia Argyi ◽  
Metabolomic Approach

Abstract Background: Artemisia argyi and A. lavandulifolia are two morphologically similar herbal medicines derived from the Artemisia genus. Although the two Artemisia herbs have been used as herbal medicines for a long time, studies on their phytochemicals and bioactive compositions are still limited, and no research has been devoted to compare the volatile compounds in A. argyi and A. lavandulifolia. Objective: To compare the volatile constituents in A. argyi and A. lavandulifolia and to explore chemical markers for discrimination and quality evaluation of the two Artemisia herbal medicines. Methods: A GC-MS-based metabolomic approach was employed to compare and discriminate A. argyi and A. lavandulifolia from the aspect of volatile compounds. Multivariate statistical methods, including principal component analysis and orthogonal partial least-squares discriminate analysis, were applied to explore chemical markers for discrimination of the two Artemisia herbal medicines. Results: Thirty volatile compounds were identified, and the chemical profiles of volatile compounds in A. argyi and A. lavandulifolia were quite similar. Principal component analysis and orthogonal partial least-squares discrimination analysis results indicated that the two Artemisia herbal medicines could be distinguished effectively from each other. Ten volatile compounds were selected as potential chemical markers for discrimination of the two Artemisia herbal medicines. Conclusions: The GC-MS-based metabolomics could be an acceptable strategy for comparison and discrimination of A. argyi and A. lavandulifolia as well as authentication of herbal medicines derived from other closely related species. Highlights: GC-MS based metabolomic approach was firstly applied to compare and discriminate Artemisia argyi and Artemisia lavandulifolia.

Study of the Fouling Characteristics of Arc Pipe Based on Principal Component Analysis and Partial Least Squares

2012 ◽  
Vol 443-444 ◽  
pp. 731-737 ◽  
Author(s):  
Li Jing ◽  
Xiao Qiang Wen
Keyword(s):  
Principal Component Analysis ◽  
Least Squares ◽  
Partial Least Squares ◽  
Principal Component ◽  
Component Analysis ◽  
Fouling Resistance ◽  
Experimental Platform ◽  
Input And Output ◽  
Input Variables ◽  
Fouling Characteristics

An experimental platform of arc pipe was built to measure the corresponding parameters of fouling and the equation of fouling characteristics was built based on principal component analysis and partial least squares, in which there are several input variables such as input and output temperature, wall temperature, flow rate and so on, and there is only one output variables—fouling resistance. In order to compare with the five-input-variables equation, the six-input-variables equation was also built. The prediction results show the model built in this paper is reasonable and feasible.

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