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2016 | OriginalPaper | Buchkapitel

Non-destructive Detection of the pH Value of Cold Fresh Pork Using Hyperspectral Imaging Technique

verfasst von : Shanmei Liu, Ruifang Zhai, Hui Peng

Erschienen in: Computer and Computing Technologies in Agriculture IX

Verlag: Springer International Publishing

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Abstract

In this paper, the pH value of cold fresh pork was non-destructively detected based on hyperspectral imaging (HSI) technique, and some useful data processing methods were discussed. After some sample set partition methods, some spectral pretreatment methods, and some optimum wavelength selection methods were compared respectively, the most suitable data processing method was chosen and the robust hyperspectral model for predicting the pH value of cold fresh pork was established. The results indicated that the pH value hyperspectral model of cold fresh pork established by using the whole wavelengths after the sample set was divided by using concentration gradient (CG) algorithm, and the spectral data was pretreated by using normalization combined with mean center(MC) had the best prediction abilities, with the determination coefficients \( \text{R}_{{\text{cv}}}^{\text{2}} \) equaled to 0.768, \( \text{R}_{\text{p}}^{\text{2}} \) equaled to 0.694, RMSECV equaled to 0.1113, and RMSEP equaled to 0.1204. The results also indicated that the model established by using the characteristic wavelengths which were selected by using CARS algorithm had better prediction abilities, with \( \text{R}_{{\text{cv}}}^{\text{2}} \) equaled to 0.8581, \( \text{R}_{\text{p}}^{\text{2}} \) equaled to 0.8668, RMSECV equaled to 0.0858, and RMSEP equaled to 0.0772. All the results showed that suitable data processing methods was advantageous to the prediction ability of the model, and that HSI technique can be utilized to measure the pH value of cold fresh pork in a rapid and non-destructive way.

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Metadaten
Titel
Non-destructive Detection of the pH Value of Cold Fresh Pork Using Hyperspectral Imaging Technique
verfasst von
Shanmei Liu
Ruifang Zhai
Hui Peng
Copyright-Jahr
2016
DOI
https://doi.org/10.1007/978-3-319-48357-3_26