手持式马铃薯干物质含量无损检测装置设计与试验

doi:10.3969/j.issn.1004-1524.20230381

浙江农业学报 ›› 2024, Vol. 36 ›› Issue (4): 943-951.DOI: 10.3969/j.issn.1004-1524.20230381

手持式马铃薯干物质含量无损检测装置设计与试验

丛杰¹^,²(), 张悦如², 李禧龙², 潘宇轩², 吕黄珍², 吕程序²^,^*()

1.青岛农业大学机电工程学院,山东青岛 266109
2.中国农业机械化科学研究院集团有限公司农业装备技术全国重点实验室,北京 100083

收稿日期:2023-03-20 出版日期:2024-04-25 发布日期:2024-04-29
作者简介:丛杰(1998—),男,山东威海人,硕士研究生,研究方向为农产品无损检测。E-mail:conglaishun@163.com
通讯作者: *吕程序,E-mail:lvchengxu1026@163.com
基金资助:
现代农业产业技术体系建设专项资金项目(CARS-10)

Design and experiment of a handheld non-destructive detection device for potato dry matter content

CONG Jie¹^,²(), ZHANG Yueru², LI Xilong², PAN Yuxuan², LYU Huangzhen², LYU Chengxu²^,^*()

1. School of Mechanical and Electrical Engineering, Qingdao Agricultural University, Qingdao 266109, Shandong, China
2. National Key Laboratory of Agricultural Equipment Technology, China Academy of Agricultural Mechanization Sciences Group Co., Ltd., Beijing 100083, China

Received:2023-03-20 Online:2024-04-25 Published:2024-04-29
Contact: LYU Chengxu

摘要/Abstract

摘要：

面向马铃薯品质抽检的需求,研发了手持式马铃薯干物质无损检测装置。装置硬件部分包括光谱采集模块、电路控制模块、控制与显示模块、外壳模块,装置设计为枪形,尺寸为180 mm×85 mm×210 mm。利用装置采集可见-近红外漫反射光谱,比较Savitzky-Golay卷积平滑(SM)、一阶导数(first-order derivative, FD)、多元散射校正(multiple scattering correction, MSC)和标准正态变量变换(standard normal variate transformation, SNV)的预处理方式,SM结果较优。采用竞争性自适应重加权采样(competitive adapative reweighted sampling, CARS)筛选27个特征波长,建立马铃薯干物质含量的支持向量回归(support vector regression, SVR)预测模型,结果显示,验证集决定系数和均方根误差分别为0.802和0.98%。基于QT开发工具编写装置软件,包括黑白校正与测量模块、电量显示模块、光谱数据显示模块、保存数据模块、光谱数据刷新模块、检测结果显示模块。开展装置验证,预测均方根误差为1.01%,单次测量平均耗时为0.62 s。结果表明,手持式马铃薯干物质无损检测装置可快速、准确检测干物质含量,具有在马铃薯生产源头与加工现场应用的潜力。

关键词: 马铃薯, 干物质含量, 手持式装置, 可见-近红外漫反射光谱

Abstract:

In response to the demand for quality inspection of potatoes, a handheld non-destructive detection device for potato dry matter was developed. The hardware components of the device included a spectrum acquisition module, a circuit control module, a control and display module, device housing module. The design of the device was gun-shaped with dimensions of 180 mm×85 mm×210 mm. The device utilized visible-near infrared diffuse reflectance spectroscopy, comparing pre-processing methods such as Savitzky-Golay convolution smoothing (SM), first-order derivative (FD), multiple scattering correction (MSC), and standard normal variate transformation (SNV), with SM yielding better results. Competitive adaptive reweighted sampling (CARS) was used to select 27 feature wavelengths to establish a support vector regression (SVR) prediction model for potato dry matter content, with validation set coefficient of determination and root mean square error of 0.802 and 0.98%, respectively. The device software was developed using the QT development tool, including modules for black and white calibration and measurement, power display, spectral data display, data storage, spectral data refresh, and detection result display. Device verification was conducted, showing a prediction root mean square error of 1.01% and an average time consumption of 0.62 s per measurement. The results indicated that the handheld non-destructive detection device for potato dry matter could rapidly and accurately detect dry matter content, demonstrating potential for application at the source and processing sites of potato production.

Key words: potato, dry matter content, hand-held device, visible/near infrared diffuse reflectance spectroscopy

中图分类号:

TH79

丛杰, 张悦如, 李禧龙, 潘宇轩, 吕黄珍, 吕程序. 手持式马铃薯干物质含量无损检测装置设计与试验[J]. 浙江农业学报, 2024, 36(4): 943-951.

CONG Jie, ZHANG Yueru, LI Xilong, PAN Yuxuan, LYU Huangzhen, LYU Chengxu. Design and experiment of a handheld non-destructive detection device for potato dry matter content[J]. Acta Agriculturae Zhejiangensis, 2024, 36(4): 943-951.

图/表 13

图1 装置硬件系统框架图

Fig.1 Frame diagram of the device hardware system

图2 光谱采集模块结构示意图

Fig.2 Structure of the spectral acquisition module

图3 漫反射光路支架工作示意图

Fig.3 Working schematic diagram of diffuse reflection light path bracket

图4 手持式马铃薯干物质含量无损检测装置实物照片

Fig.4 Physical picture of handheld non-destructive detection device for potato dry matter content

图5 原始光谱

Fig.5 Original spectra

表1 校正集和验证集理化值分布情况

Table 1 Statistic of calibration and prediction sets of DMC

数据集 Data set	样本数 Sample size	最小值 Minimum/%	最大值 Maximum/%	平均值 Average/%	标准差 Standard deviation/%	变异系数 Coefficient of variation/%
校正集Correction set	102	11.18	23.52	15.69	2.30	0.15
验证集Validation set	30	11.78	21.67	15.82	2.17	0.14

图6 不同预处理后的光谱图像 SM,Savitzky-Golay卷积平滑;MSC,多元散射校正;FD,一阶导数;SNV,标准正态变量变换。

Fig.6 Spectral images after different pretreatments SM, Savitzky-Golay convolution smoothing; MSC, Multiple scattering correction; FD, First-order derivative; SNV, Standard normal variate transformation.

表2 不同预处理方法建模结果

Table 2 Modeling results of different pretreatment methods

预处理方式 Preprocessing method	R²_c	RMSEC/%	R²_p	RMSEP/%	RPD
无None	0.676	1.31	0.604	1.39	1.563
MSC	0.680	1.30	0.638	1.32	1.636
SNV	0.706	1.25	0.633	1.33	1.623
FD	0.551	1.43	0.574	1.44	1.500
SM	0.715	1.23	0.675	1.26	1.724

图7 特征波长

Fig.7 Characteristic wavelength

图8 模型结果散点图

Fig.8 Scatterplot of model results

图9 软件界面

Fig.9 Software interface

图10 操作流程图

Fig.10 Operational flow chart

图11 测量结果

Fig.11 Measurement result

参考文献 19

[1]	Food and Agriculture Orgsnization. Crops and livestock products[DB/OL].[2023-03-15]. https://www.fao.org/faostat/zh/#data/QCL.
[2]	罗其友, 高明杰, 张烁, 等. 中国马铃薯产业国际比较分析[J]. 中国农业资源与区划, 2021, 42(7): 1-8.
	LUO Q Y, GAO M J, ZHANG S, et al. Comparative analysis on potato industry between China and other countries[J]. Chinese Journal of Agricultural Resources and Regional Planning, 2021, 42(7): 1-8. (in Chinese)
[3]	沈辰, 孙家波, 吴建寨, 等. 世界马铃薯生产、消费与贸易格局及演化分析[J]. 山东农业科学, 2021, 53(2): 127-132.
	SHEN C, SUN J B, WU J Z, et al. Analysis on pattern and evolution of world potato production, consumption and trade[J]. Shandong Agricultural Sciences, 2021, 53(2): 127-132. (in Chinese)
[4]	杨雅伦, 郭燕枝, 孙君茂. 我国马铃薯产业发展现状及未来展望[J]. 中国农业科技导报, 2017, 19(1): 29-36.
	YANG Y L, GUO Y Z, SUN J M. Present status and future prospect for potato industry in China[J]. Journal of Agricultural Science and Technology, 2017, 19(1): 29-36. (in Chinese with English abstract)
[5]	王金华, 秦礼康, 叶春. 乌洋芋主要营养成分分析与评价[J]. 食品与机械, 2007, 23(6): 79-82.
	WANG J H, QIN L K, YE C. Main nutrition ingredients analysis and evaluation of purple potato[J]. Food & Machinery, 2007, 23(6): 79-82. (in Chinese with English abstract)
[6]	张玉胜, 刘洋, 高明杰, 等. 中国马铃薯产品国际竞争力比较分析[J]. 农业展望, 2020, 16(9): 101-106.
	ZHANG Y S, LIU Y, GAO M J, et al. Comparative analysis on international competitiveness of China’s potato products[J]. Agricultural Outlook, 2020, 16(9): 101-106. (in Chinese)
[7]	GIOVENZANA V, CIVELLI R, BEGHI R, et al. Testing of a simplified LED based vis/NIR system for rapid ripeness evaluation of white grape (Vitis vinifera L.) for Franciacorta wine[J]. Talanta, 2015, 144: 584-591.
[8]	SINGH H, SRIDHAR A, SAINI S S. Ultra-low-cost self-referencing multispectral detector for non-destructive measurement of fruit quality[J]. Food Analytical Methods, 2020, 13(10): 1879-1893.
[9]	王凡, 李永玉, 彭彦昆, 等. 便携式番茄多品质参数可见/近红外检测装置研发[J]. 农业工程学报, 2017, 33(19): 295-300.
	WANG F, LI Y Y, PENG Y K, et al. Development of portable device for simultaneous detection on multi-quality attributes of tomato by visible and near-infrared[J]. Transactions of the Chinese Society of Agricultural Engineering, 2017, 33(19): 295-300. (in Chinese with English abstract)
[10]	李伟强. 便携式猕猴桃糖度无损检测仪的研发[D]. 杨凌: 西北农林科技大学, 2017.
	LI W Q. Development on nondestructive sugar content detector for kiwi fruits[D]. Yangling: Northwest A & F University, 2017. (in Chinese with English abstract)
[11]	刘燕德, 朱丹宁, 孙旭东, 等. 苹果可溶性固形物便携式检测实验研究[J]. 光谱学与光谱分析, 2017, 37(10): 3260.
	LIU Y D, ZHU D N, SUN X D, et al. Study on detecting soluble solids in fruits based on portable near infrared spectrometer[J]. Spectroscopy and Spectral Analysis, 2017, 37(10): 3260. (in Chinese with English abstract)
[12]	王凡, 李永玉, 彭彦昆, 等. 便携式马铃薯多品质参数局部透射光谱无损检测装置[J]. 农业机械学报, 2018, 49(7): 348-354.
	WANG F, LI Y Y, PENG Y K, et al. Hand-held device for non-destructive detection of potato quality parameters[J]. Transactions of the Chinese Society for Agricultural Machinery, 2018, 49(7): 348-354. (in Chinese with English abstract)
[13]	王凡. 番茄和马铃薯品质光学无损检测技术及装备研发[D]. 北京: 中国农业大学, 2019.
	WANG F. Research and development of optical non-destructive testing technology and equipment for tomato and potato quality[D]. Beijing: China Agricultural University, 2019. (in Chinese with English abstract)
[14]	何平. 便携式产品设计分析[J]. 数位时尚(新视觉艺术), 2010(5): 63-64.
	HE P. Portable product design analysis[J]. Digital Fashion, 2010(5): 63-64. (in Chinese)
[15]	GALVÃO R K H, ARAUJO M C U, JOSÉ G E, et al. A method for calibration and validation subset partitioning[J]. Talanta, 2005, 67(4): 736-740.
[16]	褚小立, 袁洪福, 陆婉珍. 近红外分析中光谱预处理及波长选择方法进展与应用[J]. 化学进展, 2004, 16(4): 528-542.
	CHU X L, YUAN H F, LU W Z. Progress and application of spectral data pretreatment and wavelength selection methods in NIR analytical technique[J]. Progress in Chemistry, 2004, 16(4): 528-542. (in Chinese)
[17]	王凡, 李永玉, 彭彦昆, 等. 基于可见/近红外透射光谱的番茄红素含量无损检测方法研究[J]. 分析化学, 2018, 46(9): 1424-1431.
	WANG F, LI Y Y, PENG Y K, et al. Nondestructive determination of lycopene content based on visible/near infrared transmission spectrum[J]. Chinese Journal of Analytical Chemistry, 2018, 46(9): 1424-1431. (in Chinese)
[18]	杜晨朝, 赵安邦, 吴志生, 等. 近红外光谱结合不同变量筛选方法用于金银花提取过程中绿原酸量的在线监测[J]. 中草药, 2017, 48(16): 3317-3321.
	DU C C, ZHAO A B, WU Z S, et al. Online control of chlorogenic acid in Lonicerae Japonicae Flos by near infrared spectroscopy combined with different variable selections[J]. Chinese Traditional and Herbal Drugs, 2017, 48(16): 3317-3321. (in Chinese)
[19]	WU D, SUN D W. Advanced applications of hyperspectral imaging technology for food quality and safety analysis and assessment: a review: Part II: applications[J]. Innovative Food Science & Emerging Technologies, 2013, 19: 15-28.

手持式马铃薯干物质含量无损检测装置设计与试验

Design and experiment of a handheld non-destructive detection device for potato dry matter content

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