A novel strategy of “pick the best of the best” for the nondestructive identification of Poria cocos based on near‐infrared spectroscopy

Li, Jiayi; Yu, Mei; Li, Shangke; Jiang, Liwen; Yu, Zheng; Li, Pao

doi:10.1002/fsn3.2383

Cited by 3 publications

(1 citation statement)

References 19 publications

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“…In addition, the results with different combination orders are different. The results with the first‐SNV method are better than the SNV‐first method, which are consistent with the conclusions of the literature (Li et al, 2021). The results of six sampling points are also worse than those of four sampling points.…”

Section: Resultssupporting

confidence: 92%

Accurate nondestructive prediction of soluble solids content in citrus by near‐infrared diffuse reflectance spectroscopy with characteristic variable selection

Zhang

Shan

et al. 2022

Food Processing Preservation

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This study employs a near‐infrared diffuse reflectance spectroscopy (NIRDRS) system to accurate nondestructive determine citrus soluble solids content (SSC). The penetration experiment results showed that the interference of thick peel is large and NIRDRS light has the ability to penetrate the peel to a certain extent. Partial least squares with different characteristic variable selection methods were used to establish the quantitative model of SSC. The results demonstrated that characteristic variable selection methods can select targeted characteristic variables and improve the accuracy with few variables. Monte Carlo‐uninformative variable elimination method was selected as the optimal prediction performance. In the best prediction model, the correlation coefficient and root mean square error of prediction of the prediction set are 0.854 and 0.7%Brix, respectively, while the variable number decreases to 440 from 1557. Furthermore, the models using the average spectra of four points on the equator are the most appropriate. Novelty impact statement The penetrating ability of near‐infrared diffuse reflectance spectroscopy (NIRDRS) light to thick peel and the prediction of internal quality of citrus is still unsatisfactory with traditional partial least squares (PLS) algorithm due to the interference of thick peel. In this study, a nondestructive method for the analysis of soluble solids content (SSC) in citrus was established by NIRDRS with characteristic variable selection algorithms. The results demonstrated that NIRDRS light has the ability to penetrate the peel to a certain extent, while characteristic variable selection methods can select targeted characteristic variables and improve the accuracy of quantitative analysis models with fewer variables.

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Section: Resultssupporting

confidence: 92%

Accurate nondestructive prediction of soluble solids content in citrus by near‐infrared diffuse reflectance spectroscopy with characteristic variable selection

Zhang

Shan

et al. 2022

Food Processing Preservation

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Characterization of volatile components of microwave dried perilla leaves using GC–MS and E-nose

Jin,

Zhu,

et al. 2023

Food Bioscience

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Origin Identification of Poria cocos Based on Terahertz Time-Domain Spectroscopic Techniques

Xiang,

Jie-Guo,

Ying-Zhang

et al. 2024

Journal of Nanoelectronics and Optoelectronics

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Poria cocos is an important traditional Chinese medicinal material. The quality and efficacy of Poria cocos grown in different origin places are significantly different. The purpose of this study is to accurately identify the origin of Poria cocos using terahertz time-domain spectroscopy. The refractive index and the absorption coefficient spectra in the range of 0.4˜1.5 THz of Poria cocos samples from twelve provinces across China had similar change trends, with a little difference from the intensity. Combined with principal component analysis (PCA), the absorption coefficient spectrum was established to identify the origin of Poria cocos based on random forest (RF), radial basis function neural network (RBFNN) and convolutional neural network (CNN). The experimental results show that the RBFNN algorithm has the best identification effect among the 12 different origins of Poria, and the correct rate of the test set can be up to 98.2609%, which is higher than that of the RF algorithm (97.3913%) and the CNN algorithm (93.913%). Accurate identification of the origin of Poria cocos was realized with terahertz time-domain spectroscopy, providing a new idea and technical solution for the origin identification of Poria cocos.

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A novel strategy of “pick the best of the best” for the nondestructive identification of Poria cocos based on near‐infrared spectroscopy

Cited by 3 publications

References 19 publications

Accurate nondestructive prediction of soluble solids content in citrus by near‐infrared diffuse reflectance spectroscopy with characteristic variable selection

Accurate nondestructive prediction of soluble solids content in citrus by near‐infrared diffuse reflectance spectroscopy with characteristic variable selection

Characterization of volatile components of microwave dried perilla leaves using GC–MS and E-nose

Origin Identification of Poria cocos Based on Terahertz Time-Domain Spectroscopic Techniques

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