Evaluation of storage bags for odour sampling from intensive pig production measured by proton-transfer-reaction mass-spectrometry

Man, Zun; Dai, Xiaorong; Rong, Li; Kong, Xianwang; Ying, Shihao; Xin, Yicong; Liu, Dezhao

doi:10.1016/j.biosystemseng.2019.11.007

Cited by 12 publications

(2 citation statements)

References 35 publications

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“…Rincón et al (2019) [6] conducted a study on estimating odor concentration using a statistical method called partial least squares regression (PLSR) by converting the concentration of odor substances from composting plants into Odor Activity Values (OAV). Man et al (2020) [7] reported that the estimation performance was excellent during their study to estimate the concentration of odor substances using on-site odor concentration evaluation and laboratory evaluations of odor samples. Similarly, Jonassen et al (2016) [8] used principal component analysis (PCA) to analyze odor and develop a multivariate statistical prediction method.…”

Section: Introductionmentioning

confidence: 99%

Evaluation of Odor Prediction Model Performance and Variable Importance according to Various Missing Imputation Methods

et al. 2022

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The aim of this study is to ascertain the most suitable model for predicting complex odors using odor substance data that has a small number of data and a large number of missing data. First, we compared the data removal and imputation methods, and the method of imputing missing data was found to be more effective. Then, in order to recommend a suitable model, we created a total of 126 models (missing imputation: single imputation, multiple imputations, K-nearest neighbor imputation; data preprocessing: standardization, principal component analysis, partial least square; and predictive method: multiple regression, machine learning, deep learning) and compared them using R2 and mean absolute error (MAE) values. Finally, we investigated variable importance using the best prediction model. The results identified the best model as a combination of multivariate imputation using Bayesian ridge as the missing imputation method, standardization for data preprocessing, and an extremely randomized tree as the predictive method. Among the odor compounds, Methyl mercaptan, acetic acid, and dimethyl sulfide were identified as the most important odor compounds in predicting complex odors.

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Section: Introductionmentioning

confidence: 99%

Evaluation of Odor Prediction Model Performance and Variable Importance according to Various Missing Imputation Methods

et al. 2022

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“…  ′ the th standardized independent variable,   the th independent variable,    the mean of the th independent variable,    the standard deviation of the th independent variable,   the th principal component,   the coeffient of principal component matrix (the matrix consists of   and   )(Liu et al, 2003) Koziel et al, 2005;Man et al, 2020…”

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Odor concentration estimation using the principal component regression ; in the grow to finish swine pen

Woo

Jang

Jung

et al. 2021

Journal of Animal Environmental Science

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In this study, a odor concentration estimation model in the grow to finish swine pen was derived using principal component analysis and multiple regression models. 14 kinds of odor compounds were used. Prior to the statistical analysis, the missing value (less than MDL)for each compounds was imputed with MDL/2, and the concentration of the odor compounds and the odor unit were log-transformed to follow a normal distribution. The three principal components extracted through principal component analysis explained for 77.03% of the initial variables. The correlation between Principal components and odor compounds was evaluated by the loading value. PC1 was correlated with VOC (10 Type), PC2 with sulfur compounds (3 Type), and PC3 with ammonia and hydrogen sulfide. A odor concentration estimation model was derived using PC1, PC2, PC3 as independent variables. As a result, the model and the regression coefficient were statistically significant (p < 0.05), but adjusted R 2 (0.25) was low.

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Methods of Testing in Odor Analysis

Wypych¹

2023

Handbook of Odors in Plastic Materials

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Evaluation of storage bags for odour sampling from intensive pig production measured by proton-transfer-reaction mass-spectrometry

Cited by 12 publications

References 35 publications

Evaluation of Odor Prediction Model Performance and Variable Importance according to Various Missing Imputation Methods

Evaluation of Odor Prediction Model Performance and Variable Importance according to Various Missing Imputation Methods

Odor concentration estimation using the principal component regression ; in the grow to finish swine pen

Methods of Testing in Odor Analysis

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