Three-dimensional imaging of morphological changes of potato slices during drying

Cai Jianrong; Lu Yue; Bai Junwen; Sun Li; Xiao Hongwei

doi:10.11975/j.issn.1002-6819.2019.01.034

Volume 35 Issue 1

Dec. 2018

Turn off MathJax

Article Contents

Abstract

References

Transactions of the Chinese Society of Agricultural Engineering (Transactions of the CSAE) > 2019 > 35(1): 278-284. > DOI: 10.11975/j.issn.1002-6819.2019.01.034

Cai Jianrong, Lu Yue, Bai Junwen, Sun Li, Xiao Hongwei. Three-dimensional imaging of morphological changes of potato slices during drying[J]. Transactions of the Chinese Society of Agricultural Engineering (Transactions of the CSAE), 2019, 35(1): 278-284. DOI: 10.11975/j.issn.1002-6819.2019.01.034

Citation:

PDF (1611 KB)

Three-dimensional imaging of morphological changes of potato slices during drying

1.
School of Food and Biological Engineering, Jiangsu University, Zhenjiang 212013, China
2.
College of Engineering, China Agricultural University, Beijing 100083, China

More Information

Received Date: July 15, 2018
Revised Date: October 25, 2018
Published Date: December 31, 2018

Graphical Abstract

Abstract

Abstract

Abstract: Drying is an important method for agricultural products processing. It can reduce the moisture content of agricultural products to a certain extent and extend the shelf life. But irregular deformation resulting from drying process will cause inconvenience for subsequent processing. In order to study the regularity of deformation during the drying process of potato slices, we built an image acquisition platform based on the Kinect sensor. Firstly, we verified the accuracy of the depth detection of the Kinect image acquisition platform by cubes with 5, 8, and 10 mm sides. The results showed that the accuracy can reach 2 mm. Secondly, we selected potato as the research object and dried them using a tunnel hot air dryer. Controlled potato slice thickness was 1 mm, drying room humidity was 15%, hot wind speed was 3 m/s to study the deformation regularity of potato slice at different temperatures (50, 60, 70, 80 ℃). After the drying process began, the potato slices were taken out of the drying chamber and put on the Kinect image acquisition platform to acquire depth and color images, then weighed every 10 minutes. We used the Kinect SDK function to achieve a one-to-one correspondence between color images and depth images. According to the position of the material in the color image, the region where the potato slices were located of interest was established, and the potato slices were located at the same coordinate position in the depth image. Gray value stretching, threshold segmentation, and edge denoising were performed on the corresponding region of depth images. Then feature extraction was used to distinguish every potato slice and calculate its shrinkage rate, mean depth values and standard deviation. The mean depth value can reflect the curling of the potato slices during the drying process. The shrinkage rate could reflect the shrinkage characteristics of the potato slices in the drying process. And the standard deviation of the depth value could reflect the surface flatness of potato slices in drying process. Then we drew the curve of dry basis moisture content and the three parameters under different temperature conditions, which could be more directly to observe the effect of temperature on the deformation of potato slices during drying. The results showed that temperature had a significant effect on the shrinkage, surface curl, and surface flatness of the potato slices in the drying process (P<0.05). With the increase of temperature, the shrinkage of potato slices increased gradually. At 50 ℃, the shrinkage rate was 54.97%. When the temperature rose to 80 ℃, the shrinkage rate increased to 64.55%. With the increase of temperature, the variation of curl and flatness of the potato chips was small. The mean depth value of the potato slices was 27.81 mm at 60 ℃, and it decreased to 18.86 mm at 80 ℃. At 50 ℃, the standard deviation of potato slices depth was 7.99 mm, which decreased to 5.71 mm at 80 ℃. Finally, using MATLAB software to display three-dimensional graphics of potato slices in five different time periods, the surface deformation of potato slices could be clearly observed. It was illustrated that the Kinect image acquisition platform could be applied to the study of deformation regularity in the drying process of potato slices, and provide technical basis for intelligent control of the drying process.
- drying,
- sensor,
- image processing,
- potato slices,
- deformation regularity

FullText(HTML)

References (32)

References

[1]	苏丹，李树君，赵凤敏，等. 农产品联合干燥技术的研究进展[J]. 农机化研究，2014，36(11)：236－240.Su Dan, Li Shujun, Zhao Fengmin, et al. Advances in the combined drying technology of agricultural products[J] . Journal of Agricultural Mechanization Research, 2014, 36(11): 236－240. (in Chinese with English abstract)
[2]	吴海虹，朱道正，卞欢，等. 农产品干燥技术发展现状[J]. 现代农业科技，2016(14)：279－281.
[3]	白竣文，田潇瑜，刘宇婧，等. 大野芋薄层干燥特性及收缩动力学模型研究[J]. 中国食品学报，2018，18(4)： 124－130.
[4]	Nadian M H, Rafiee S, Aghbashlo M, et al. Continuous real-time monitoring and neural network modeling of apple slices color changes during hot air drying[J]. Food & Bioproducts Processing, 2015, 94: 263－274.
[5]	Sampson D J, Chang Y K, Rupasinghe H P V, et al. A dual-view computer-vision system for volume and image texture analysis in multiple apple slices drying[J]. Journal of Food Engineering, 2014, 127(4): 49－57.
[6]	Ortiz-García-Carrasco B, Ya?ez-Mota E, Pacheco-Aguirre F M, et al. Drying of shrinkable food products: Appraisal of deformation behavior and moisture diffusivity estimation under isotropic shrinkage[J]. Journal of Food Engineering, 2015, 144: 138－147.
[7]	Onwude D I, Hashim N, Abdan K, et al. Combination of computer vision and backscattering imaging for predicting the moisture content and colour changes of sweet potato (Ipomoea batatas, L.) during drying[J]. Computers & Electronics in Agriculture, 2018, 150: 178－187.
[8]	沈跃，徐慧，刘慧，等. 基于K-means和近邻回归算法的Kinect植株深度图像修复[J]. 农业工程学报，2016，32(19)：188－194.Shen Yue, Xu Hui, Liu Hui, et, al. Kinect scanning plant depth image restoration based on K-means and K-nearest neighbor algorithms[J]. Transactions of the Chinese Society of Agricultural Engineering (Transactions of the CSAE), 2016, 32(19): 188－194. (in Chinese with English abstract)
[9]	邹广群. 基于TOF相机的深度图增强算法研究[D]. 青岛: 青岛大学，2016.
[10]	赵志平，陈雷月. 基于深度传感器图像分割技术的研究[J]. 信息技术，2014 (1)：109－112.Zhao Zhiping, Chen Leiyue. Research on image segment technique based on Kinect[J]. Information Technology, 2014(1): 109－112. (in Chinese with English abstract)
[11]	许常蕾，王庆，陈洪，等. 基于体感交互的仿上肢采摘机器人系统设计与仿真[J]. 农业工程学报，2017，33(增刊1)：49－55.Xu Changlei, Wang Qing, Chen Hong, et al. Design and simulation of artificial limb picking robot based on somatosensory interaction[J]. Transactions of the Chinese Society of Agricultural Engineering (Transactions of the CSAE), 2017, 33(Supp.1): 49－55.(in Chinese with English abstract)
[12]	Meng Ming, Yang Fangbo, She Qingshan, et al. Human motion detection based on the depth image of Kinect[J]. Chinese Journal of Scientific Instrument, 2015, 36(2): 386－393.
[13]	Harte J M, Golby C K, Acosta J, et al. Chest wall motion analysis in healthy volunteers and adults with cystic fibrosis using a novel Kinect-based motion tracking system[J]. Medical & Biological Engineering & Computing, 2016, 54(11): 1631－1640.
[14]	Sun Y, Li C, Li G, et al. Gesture recognition based on Kinect and sEMG signal fusion[J]. Mobile Networks & Applications, 2018(1): 1－9.
[15]	Rosa-Pujazón A, Barbancho I, Tardón L J, et al. Fast-gesture recognition and classification using Kinect: An application for a virtual reality drumkit[J]. Multimedia Tools & Applications, 2016, 75(14): 8137－8164.
[16]	Gianaria E, Grangetto M, Balossino N. Kinect-based gait analysis for people recognition over time[C]// International Conference on Image Analysis and Processing. Catania: Springer, 2017: 648－658.
[17]	付代昌，徐立鸿，李大威，等. 基于Kinect的温室番茄盆栽茎干检测与分割[J]. 现代农业科技，2014(3)：336－338.
[18]	沈跃，徐慧，刘慧，等. 基于Kinect传感器的温室植株绿色与深度检测方法[J]. 中国农机化学报，2016，37(8)： 155－161.
[19]	沈跃，朱嘉慧，刘慧，等. 基于深度和彩色双信息特征源的Kinect植物图像拼接[J]. 农业工程学报，2018，34(5)：176－182.Shen Yue, Zhu Jiahui, Liu Hui, et, al. Plant image mosaic based on depth and color dual information feature source from Kinect[J]. Transactions of the Chinese Society of Agricultural Engineering (Transactions of the CSAE), 2018, 34(5): 176－182. (in Chinese with English abstract)
[20]	肖珂，高冠东，马跃进. 基于Kinect视频技术的葡萄园农药喷施路径规划算法[J]. 农业工程学报，2017，33(24)：192－199.Xiao Ke, Gao Guandong, Ma Yuejin. Pesticide spraying route planning algorithm for grapery based on Kinect video technique[J]. Transactions of the Chinese Society of Agricultural Engineering (Transactions of the CSAE), 2017, 33(24): 192－199. (in Chinese with English abstract)
[21]	闻陶，朱跃钊，孙庆梅，等. 马铃薯吸附干燥特性及模型拟合[J]. 生物加工过程，2006，4(3)：56－61.
[22]	Hafezi N，Sheikhdavoodi M J，Sajadiye S M，et al. Shrinkage characteristic of potato slices based on computer vision[J]. Agricultural Engineering International Cigr Journal, 2015, 17(3): 287－295.
[23]	夏春华，施滢，尹文庆. 基于TOF深度传感的植物三维点云数据获取与去噪方法[J]. 农业工程学报，2018，34(6)：168－174.Xia Chunhua, Shi Ying, Yin Wenqing. Obtaining and denoising method of three-dimensional point cloud data of plants based on TOF depth sensor[J]. Transactions of the Chinese Society of Agricultural Engineering (Transactions of the CSAE), 2018, 34 (6): 168－174.(in Chinese with English abstract)
[24]	Takenaka H, Wada T, Oike H, et al. Bilateral filtering based depth image correction consistent with color image captured by Kinect[J]. Technical Report of Ieice Prmu, 2012, 112: 311－316.
[25]	徐胜勇，黄伟军，周俊，等. 使用Kinect传感器的油菜叶片面积测量方法[J]. 中国油料作物学报，2017，39(1)：55－59.
[26]	刘小丹，张淑娟，贺虎兰，等. 红枣微波-热风联合干燥特性及对其品质的影响[J]. 农业工程学报，2012，28(24)：280－286.Liu Xiaodan, Zhang Shujuan, He Hulan, et al. Drying characteristics and its effects on quality of jujube treated by combined microwave-hot-air drying[J]. Transactions of the Chinese Society of Agricultural Engineering (Transactions of the CSAE), 2012, 28(24): 280－286.(in Chinese with English abstract)
[27]	朱文魁，徐德龙，周雅宁，等. 基于数字图像分析法的片烟干燥收缩特性研究[J]. 河南农业科学，2014，43(7)：160－164.
[28]	徐娓，丁静，赵义，等. 多孔物料干燥中物料体积的收缩特性[J]. 华南理工大学学报：自然科学版，2006，34(8)：61－65.
[29]	Wang J, Law C L, Nema P K, et al. Pulsed vacuum drying enhances drying kinetics and quality of lemon slices[J]. Journal of Food Engineering, 2018, 224: 129－138.
[30]	王丽红，高振江，张茜，等. 加工番茄收缩特性的试验研究[J]. 食品科技，2011(8)：102－105.
[31]	Mayor L, Sereno A M. Modelling shrinkage during convective drying of food materials: A review[J]. Journal of Food Engineering, 2004, 61(3): 373－386.
[32]	沈文超. 不规则扁平粒状物表面平整度识别与分选方法的研究[D]. 苏州：苏州大学，2010.

Cited By

Cited by

Periodical cited type(8)

1.	刘永杰，陶乐仁，冯东昱. 冷风干燥鲫鱼干燥特性及品质分析. 食品与发酵工业. 2024(19): 223-228 .
2.	唐诗，陆涛，黄志昌. 基于红外检测仪的三维激光扫描成像输入系统. 激光杂志. 2022(12): 216-220 .
3.	刘海波，王佳倩，李耀，金雪冻，朱静，王辉，刘雄. 马铃薯片热泵干燥动力学研究及其干燥工艺优化. 中国粮油学报. 2022(10): 106-115 .
4.	曲文娟，凡威，熊婷，郭甜甜，师俊玲，马海乐，潘忠礼. 核桃干燥过程的低场核磁共振横向驰豫分析. 现代食品科技. 2021(09): 145-154+324 .
5.	刘德成，郑霞，肖红伟，姚雪东，单春会，常安太，李义璨，李祥雨. 红枣片冷冻-红外分段组合干燥工艺优化. 农业工程学报. 2021(17): 293-302 . 本站查看
6.	李雅琪，张鹏起，蔡健荣，白竣文，孙力. 马铃薯薄片干燥过程热变形量分析. 食品科学. 2021(23): 123-128 .
7.	效碧亮，彭丹，冉学贝，甘瑞，刘晓风. 百合真空旋蒸干燥工艺优化. 真空科学与技术学报. 2020(05): 405-415 .
8.	渠琛玲，汪紫薇，王雪珂，王殿轩. 基于低场核磁共振的热风干燥过程花生仁含水率预测模型. 农业工程学报. 2019(12): 290-296 . 本站查看