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Non-Destructive Prediction of Moisture Content and Freezable Water Content of Purple-Fleshed Sweet Potato Slices during Drying Process Using Hyperspectral Imaging Technique

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Abstract

The aim of this study was to investigate the feasibility of hyperspectral imaging in measuring moisture content and freezable water content during drying process. Hyperspectral images were acquired for purple-fleshed sweet potato (PFSP) slices during contact ultrasound assisted hot drying (CUHAD) process, and the corresponding mean reflectance spectra from regions of interest in visible and near infrared (371–1023 nm) regions were extracted. Moving average, Savitzky-Golay smoothing filter (S_G filter) and multiplicative scatter correction (MSC) were investigated to preprocess the raw spectra and partial least square regression (PLSR) calibration model was established to analyze the relationship between the extracted spectral data and measured quality attributes. Comparing the performance of model based on different preprocessing methods, the PLSR model with MSC pre-treatment presented better results with coefficients of determination for prediction (\( {R}_P^2 \)) of 0.9837 and 0.9323 for moisture content and freezable water content, respectively. Instead of selecting full range spectra data, optimal wavelengths were identified based on the regression coefficients (RC) method. Then two linear calibration algorithms named PLSR and multiple linear regression (MLR), and a non-linear calibration algorithm named backpropagation (BP) neural network were used to establish models to predict quality attributes of samples simultaneously. The results showed that the RC-MLR with \( {R}_P^2 \) of 0.9359 and 0.8592 was considered as the best for determining moisture content and freezable water content of PFSP slices. Therefore, the study demonstrates the potential of using hyperspectral imaging in tandem with chemometrics analysis as an objective, fast and non-destructive method for predicting the moisture content and freezable water content at different dehydrated times.

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Acknowledgments

The authors express their sincere appreciation to the National Natural Science Foundation of China (No. U1404334), the College Young Teachers Development Program of Henan province (No.2015GGJS-048) and the Science and Technology Project of Henan Province (No. 172102310618) for support this study financially.

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Authors and Affiliations

  1. College of Food and Bio-engineering, Henan University of Science and Technology, 263 Kaiyuan Road, Luolong District, Luoyang, 471003, China

    Yue Sun, Yunhong Liu, Huichun Yu, Anguo Xie, Xin Li, Yong Yin & Xu Duan

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  1. Yue Sun
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  2. Yunhong Liu
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Correspondence to Yunhong Liu.

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Funding

This study was funded by the National Natural Science Foundation of China (No. U1404334), the College Young Teachers Development Program of Henan province (No.2015GGJS-048) and the Science and Technology Project of Henan Province (No. 172102310618).

Conflict of Interest

Yue Sun has no conflict of interest. Yunhong Liu declares that he has no conflict of interest. Huichun Yu declares that she has no conflict of interest. Anguo Xie declares that he has no conflict of interest. Xin Li declares that she has no conflict of interest. Yong Yin declares that he has no conflict of interest. Xu Duan declares that he has no conflict of interest. This article does not contain any studies with human or animal subjects.

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Sun, Y., Liu, Y., Yu, H. et al. Non-Destructive Prediction of Moisture Content and Freezable Water Content of Purple-Fleshed Sweet Potato Slices during Drying Process Using Hyperspectral Imaging Technique. Food Anal. Methods 10, 1535–1546 (2017). https://doi.org/10.1007/s12161-016-0722-0

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