高光谱成像技术无损测定马铃薯面条硬度*

doi:10.12398/j.issn.2096-7217.2020.01.006

智能化农业装备学报（中英文） ›› 2020, Vol. 1 ›› Issue (1): 44-48.DOI: 10.12398/j.issn.2096-7217.2020.01.006

高光谱成像技术无损测定马铃薯面条硬度^*

任志尚¹, 张光耀¹, 杜娟¹, 印祥¹, 金诚谦^1,2, 马成业¹

1.山东理工大学农业工程与食品科学学院,山东淄博,255000;
2.农业农村部南京农业机械化研究所,南京市,210014

收稿日期:2019-12-15 出版日期:2020-08-15 发布日期:2021-11-30
通讯作者: 马成业,男,1978年生,副教授,博士;研究方向为农产品贮藏与加工。E-mail: mcycn2002@163.com
作者简介:任志尚,男,1994年生,硕士研究生;研究方向为食品科学与工程。E-mail:1525169572@qq.com
基金资助:
山东省重点研发计划项目(2019JZZY010734);山东省高等学校优势学科人才团队培育计划项目(2016—2020) ;国家重点研发计划项目(2016YFD040130301)

Nondestructive determination of potato noodle hardness using hyperspectral imaging technology Nondestructive determination of potato noodle hardness using hyperspectral imaging technology^*

Zhishang Ren¹, Guangyao Zhang¹, Juan Du¹, Xiang Yin¹, Chengqian Jin^1,2, Chengye Ma¹

1. School of Agricultural Engineeringand Food Science, Shandong University of Technology, Zibo, 255000, China;
2. Nanjing Institute of Agricultural Mechanization, Ministry of Agriculture and Rural Affairs, Nanjing, 210014, China

Received:2019-12-15 Online:2020-08-15 Published:2021-11-30
Contact: Chengye Ma, Associate professor, research direction: storage and processing of agricultural products. E-mail: mcycn2002@sdut.edu.cn
About author:Zhishang Ren, Master, Researcher, research direction: food science and engineering. E-mail: 1525169572@qq.com
Supported by:
Shandong key research and development projects (2019JZZY010734); Talent team cultivation program of superior disciplines in Shandong colleges and Universities (2016—2020);National key research and development projects (2016YFD040130301)

摘要/Abstract

摘要： 面条的质地是衡量面条品质的重要指标。本研究以108份不同马铃薯添加量的面条为样本,研究了马铃薯面条硬度与质地之间的关系。利用高光谱成像技术获取面条图像,并在此过程中分析纹理。建立了基于最小二乘法的校准和交叉验证模型。以72个面条为训练样本建立模型,以36个样本验证校准模型。校准模型和交叉验证模型的测定系数(R²_C和R²_cv)分别为0.877和0.842,交叉验证模型的校准均方根误差(RMSEC)和交叉验证均方根误差(RMSECV)分别为11.405和13.166。预测集的确定系数(R²_P)为0.988,预测均方根误差(RMSEP)为3.585。结果表明,该方法具有较强的鲁棒性和稳定性,可作为检测面条硬度的无损检测技术。

关键词: 无损检测, 高光谱成像, 马铃薯面条硬度, 偏最小二乘回归

Abstract: The texture is an important index that indicates the noodle quality. In this research, the relationship between hardness and texture with respect to potato noodles was demonstrated by using 108 noodle samples with different amounts of potatoes. Noodle images would be captured by hyperspectral imaging technology and textures would be analyzed during that process. A calibration and cross-validation model were established based on the least squares regression method. 72 noodles as training samples were used to establish the model and the other 36 samples were used to verify the calibration model. The coefficient of determination (R²_C and R²_cv) values of the calibration model and cross-validation model were 0.877 and 0.842 while the root mean square error of calibration (RMSEC) and root mean square error of cross-validation (RMSECV) values were 11.405 and 13.166. The coefficient of determination (R²_P) of the prediction set was 0.988 and the root mean square error of prediction (RMSEP) was 3.585. Results showed that the proposed determination method of potato noodle hardness was of high robustness and stability and could be used as a non-destructive detection technology for detecting the noodle hardness.

Key words: nondestructive determination, hyperspectral imaging, potato noodle hardness, partial least squares regression

任志尚, 张光耀, 杜娟, 印祥, 金诚谦, 马成业. 高光谱成像技术无损测定马铃薯面条硬度^*[J]. 智能化农业装备学报（中英文）, 2020, 1(1): 44-48.

Zhishang Ren, Guangyao Zhang, Juan Du, Xiang Yin, Chengqian Jin, Chengye Ma. Nondestructive determination of potato noodle hardness using hyperspectral imaging technology Nondestructive determination of potato noodle hardness using hyperspectral imaging technology^*[J]. Journal of Intelligent Agricultural Mechanization, 2020, 1(1): 44-48.

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高光谱成像技术无损测定马铃薯面条硬度^*

Nondestructive determination of potato noodle hardness using hyperspectral imaging technology Nondestructive determination of potato noodle hardness using hyperspectral imaging technology^*

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高光谱成像技术无损测定马铃薯面条硬度^*

Nondestructive determination of potato noodle hardness using hyperspectral imaging technology Nondestructive determination of potato noodle hardness using hyperspectral imaging technology^*