不同产地苹果糖度可见近红外光谱在线检测

刘燕德; 徐海; 孙旭东; 姜小刚; 饶宇; 徐佳; 王军政

doi:10.3788/CO.2019-0128

Volume 13 Issue 3

Jun. 2020

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Article Contents

Chinese Optics > 2020 > 13(3): 482-491.

LIU Yan-de, XU Hai, SUN Xu-dong, JIANG Xiao-gang, RAO Yu, XU Jia, WANG Jun-zheng. On-line detection of soluble solids content of apples from different origins by visible and near-infrared spectroscopy[J]. Chinese Optics, 2020, 13(3): 482-491. doi: 10.3788/CO.2019-0128

Citation:

LIU Yan-de, XU Hai, SUN Xu-dong, JIANG Xiao-gang, RAO Yu, XU Jia, WANG Jun-zheng. On-line detection of soluble solids content of apples from different origins by visible and near-infrared spectroscopy[J]. Chinese Optics, 2020, 13(3): 482-491. doi: 10.3788/CO.2019-0128

Citation:

LIU Yan-de, XU Hai, SUN Xu-dong, JIANG Xiao-gang, RAO Yu, XU Jia, WANG Jun-zheng. On-line detection of soluble solids content of apples from different origins by visible and near-infrared spectroscopy[J]. Chinese Optics, 2020, 13(3): 482-491. doi: 10.3788/CO.2019-0128

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On-line detection of soluble solids content of apples from different origins by visible and near-infrared spectroscopy

doi: 10.3788/CO.2019-0128

LIU Yan-de^{1, 2
,
,},
XU Hai^{1, 2},
SUN Xu-dong^{1, 2},
JIANG Xiao-gang^{1, 2},
RAO Yu^{1, 2},
XU Jia^{1, 2},
WANG Jun-zheng^{1, 2}

1.
School of Mechatronics & Vehicle Engineering, East China Jiaotong University, Nanchang 330013, China
2.
National and Local Joint Engineering Research Center of Fruit Intelligent Photoelectric Detection Technology and Equipment, Nanchang 330013, China

Funds: Supported by National Natural Science Foundation of China(No.31760344); Jiangxi Provincial Project for Innovation Capacity Construction(No.S2016-90)

More Information

Corresponding author: jxliuyd@163.com
Received Date: 21 Jun 2019
Rev Recd Date: 20 Aug 2019
Publish Date: 01 Jun 2020

Abstract

Abstract

In order to realize fast, on-line, non-destructive testing of the Soluble Solids Content (SSC) of apples from different origins, and to reduce the effect of origin variability on NIR models, a universal model for predicting the SSC of apples from different origins is established. Firstly, the diffuse transmission spectra of Fuji apples from Qixia, Luochuan and Huining are collected with fruit dynamic online detection equipment. Then, 58 characteristic variables are selected and a UVE-PLS universal model for predicting the SSC of apples is established using the Partial Least Squares (PLS) algorithm combined with Uninformative Variable Elimination (UVE). The root mean square errors of single-origin prediction sets and the total-origin prediction set are 0.50~0.74° Brix and 0.63° Brix, respectively, which increase by 23.2%~44.4% and 35.7% respectively compared to the original individual model. Finally, a new external sample set is used to assess the performance of the model, showing a residual prediction deviation of 2.33 and ratios of the predicted values within the error range of ±1.0° Brix and ±1.5° Brix of 85% and 100%, respectively. Experimental results indicate that the establishment of a universal model for on-line detection of the SSC of apples, including those from multiple origins can improve the robustness of predicting the SSC of the samples from other origins. The results also show that an appropriate wavelength screening method can simplify the model. The development of a common model for the internal quality of fruit from different origins has strong potential for applications in wavelength-limited spectroscopy equipment.
- online detection,
- near infrared spectroscopy,
- soluble solids,
- partial least squares

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References(20)

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