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Fermentation process monitoring of broad bean paste quality by NIR combined with chemometrics

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A Correction to this article was published on 27 May 2022

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Abstract

Near-infrared (NIR) spectroscopy coupled with chemometric methods were employed for quantitatively analyzing broad bean paste quality during fermentation. The quality parameters including the contents of moisture, total acid (TA), and amino acid nitrogen (AAN) were determined by traditional chemical methods as reference. Savitzky–Golay first derivative (FD), Savitzky–Golay second derivative, Savitzky–Golay smoothing, standard normal variate, and multiplicative scatter correction (MSC) were employed to preprocess the raw spectrums. Furthermore, the pretreated spectrums were selected by successive projections algorithm for dimension reducing. To investigate the quantitative relationship between quality parameters and spectrums, linear models of partial least squares regression and multiple linear regression, as well as non-linear models of random forest, gradient boosting decision tree, and adaptive boosting (AdaBoost) were introduced for regression modeling. The results revealed that the MSC was the best preprocessing method for the NIR spectrums of broad bean paste. Meanwhile, compared with linear modeling, non-linear models had better performances for the prediction of the parameters. In addition, among all the three non-linear methods, AdaBoost provided the optimal regression results with satisfactory prediction statistics for moisture (Rp2 = 0.963, RMSEP = 0.416), TA (Rp2 = 0.917, RMSEP = 0.030) and AAN (Rp2 = 0.908, RMSEP = 0.011) contents. This study demonstrated that NIR spectroscopy could be applied as a rapid and effective tool for quantitatively analyzing the quality parameters of broad bean paste during the fermentation process.

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Acknowledgements

This research was funded by the key R&D project of the Sichuan Provincial Department of Science and Technology (2020YFN0151, 2019YFN0173), and the Talent introduction project of Xihua University (RZ2021143). In addition, the authors would like to thank Haifeng Chen and Jiaquan Huang in the Sichuan Pixian Douban Co., Ltd. for their support in the sample collection.

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Authors and Affiliations

  1. School of Food Science and Bioengineering, Xihua University, Chengdu, 610039, China

    Min Xu, Yao Wang, Xingbin Wang, Wenwu Ding, Zhenming Che & Ping Liu

  2. School of Electrical Engineering, Guangxi University, Nanning, 53004, China

    Pengfei Jia

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  1. Min Xu
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Xu, M., Wang, Y., Wang, X. et al. Fermentation process monitoring of broad bean paste quality by NIR combined with chemometrics. Food Measure 16, 2929–2938 (2022). https://doi.org/10.1007/s11694-022-01392-4

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