Evolution of Volatile Compounds and Spoilage Bacteria in Smoked Bacon during Refrigeration Using an E-Nose and GC-MS Combined with Partial Least Squares Regression

The changes in the concentration of volatile organic compounds (VOCs) and biogenic amines (BAs) in smoked bacon during 45-day refrigerated storage is investigated using solid-phase micro-extraction coupled with gas chromatography-mass spectrometry and high-performance liquid chromatography. In total, 56 VOCs and 6 BAs were identified and quantified. The possible pathways leading to their formation are analyzed and considered as the potential signs of microbial activity, especially by specific spoilage microorganisms (SSOs). Leuconostoc and Lactobacillus, which levels increased markedly with the extension of storage time, were recognized as SSOs. An electronic nose (e-nose) was employed to determine the changes in concentration of the odor components per sample present within half an hour. Partial least squares regression was then carried out to analyze the correlation between SSO growth, metabolite concentration, BA accumulation, and e-nose response. The results show that ten VOCs (ethanol, 2-furanmethanol, 1-hexanol, 1-propanol, phenol, 2-methoxyphenol, acetic acid, 3-ethyl-2-cyclopenten-1-one, furfural, and ethyl hexanoate) and three BAs (putrescine, cadaverine, and tyramine) can be associated with the growth of SSOs. Thus, they can be adopted as potential indicators to evaluate and monitor the quality of the bacon and develop appropriate detection methods. E-noses can used to recognize odors and diagnose quality of bacon.

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Effects of sterilization methods on pomegranate juice evaluated by descriptive sensory analysis and gas chromatography‐mass spectrometry through partial least‐squares regression

Flavour and Fragrance Journal ◽

10.1002/ffj.3606 ◽

2020 ◽

Vol 35 (6) ◽

pp. 674-685

Author(s):

Honglei Tian ◽

Cong Lu ◽

Ping Zhan ◽

Peng Wang ◽

Yu Zhao ◽

...

Keyword(s):

Mass Spectrometry ◽

Gas Chromatography ◽

Least Squares ◽

Partial Least Squares ◽

Sensory Analysis ◽

Partial Least Squares Regression ◽

Pomegranate Juice ◽

Gas Chromatography Mass Spectrometry ◽

Least Squares Regression ◽

Descriptive Sensory Analysis

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Multivariate Methods Based Soft Measurement for Wine Quality Evaluation

Abstract and Applied Analysis ◽

10.1155/2014/740754 ◽

2014 ◽

Vol 2014 ◽

pp. 1-7 ◽

Cited By ~ 1

Author(s):

Shen Yin ◽

Lei Liu ◽

Xin Gao ◽

Hamid Reza Karimi

Keyword(s):

Least Squares ◽

Partial Least Squares ◽

Partial Least Squares Regression ◽

Principal Component ◽

Ordinary Least Squares ◽

Multivariate Methods ◽

Least Squares Regression ◽

Wine Quality ◽

Soft Measurement

Soft measurement is a new, developing, and promising industry technology and has been widely used in the industry nowadays. This technology plays a significant role especially in the case where some key variables are difficult to be measured by traditional measurement methods. In this paper, the quality of the wine is evaluated given the wine physicochemical indexes according to multivariate methods based soft measurement. The multivariate methods used in this paper include ordinary least squares regression (OLSR), principal component regression (PCR), partial least squares regression (PLSR), and modified partial least squares regression (MPLSR). By comparing the performance of the four methods, the MPLSR prediction model shows superior results than the others. In general, to determine the quality of the wine, experienced wine tasters are hired to taste the wine and make a decision. However, since the physicochemical indexes of wine can to some extent reflect the quality of wine, the multivariate statistical methods based soft measure can help the oenologist in wine evaluation.

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