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SIMCA Discrimination of Ring Rot Potatoes Based on Near Infrared Spectroscopy |
ZHANG Xiao-yan, YANG Bing-nan, CAO You-fu, LI Shao-ping, ZHAO Qing-liang, XING Li* |
Chinese Academy of Agricultural Mechanization Sciences, Beijing 100083, China |
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Abstract China is one of the world’s largest countries in potato production and consumption. In 2015, the Chinese government put forward a staple-potato development strategy aimed to change the Chinese traditional diet habit of vegetable-potato and promote potato’s status in safeguarding food security. Potato ring rot is a common disease which has restricted the development of potato industry. With the ring rot potato as the seed, it would cause unhealthy plants; with the ring rot potato as the raw materials for processing, it would cause lower efficiency and worse product quality. Visual inspection, machinevisiontechnology and hyperspectral imaging are the traditional methodsto detect potato diseases. However, it is destructive testing when visual inspection and machinevisiontechnology are used to detect ring rot potatoes; and hyperspectral imaging is at a significant cost. There are some limitations of application on these traditional methods. Internal quality changesof potatoes is caused by ring rot diseases. Near infrared spectroscopy (NIRS) could be used to reflect the quality change of the whole potato. Therefore, NIRS can be used to distinguishring rot potatoes from healthy potatoes. It’s feasible and practical to detect potato ring rot nondestructively with near infrared spectroscopy. Combined with NIRS and soft independent modeling of class analogy (SIMCA), this experiment was aimed to identify ring rot potatoes from healthy potatoes. The results showed that, SIMCA mode based on principal component analysis (PCA) was effective to identify ring rot potatoes. In calibration set, the recognition rate and rejection rate of ring rot potatoes and healthy potatoes were both 100%. In validation set, the recognition rate and rejection rate of ring rot potatoes were 99.00% and 100%. The recognition rate and rejection rate of healthy potatoes were 94.12% and 100%. For external validation, the recognition rate of ring rot potatoes and healthy potatoes were 87.50% and 80.00% respectively without misjudge. The SIMCA model was accurate in prediction and suitable to practical application, but the precision would be improved in further research. The pathogenic site of ring rot potatoes was close to epidermis for about 0.5 cm; and there was transmission and diffuse reflection when NIRS Penetrating potatoes. So that it is possible to collect the NIRS information of potato tuber flesh near to potato epidermis. Combined with pathogenic mechanism of potato ring rot disease and characteristics of near-infrared diffuse reflectance spectra, it is innovative and practical to use NIRS to distinguish ring rot potatoes from healthy potatoes.
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Received: 2017-08-18
Accepted: 2018-01-06
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Corresponding Authors:
XING Li
E-mail: xinglifan@163.com
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[1] XU Hai-quan,SUN Jun-mao,WANG Xiao-hu,et al(徐海泉,孙君茂,王小虎,等). Food and Nutrition in China(中国食物与营养),2016,22(5): 13.
[2] LU Xiao-ping(卢肖平). Journal of Huazhong Agricultural University·Social Sciences Edition(华中农业大学学报·社会科学版),2015,(3): 1.
[3] YANG Ya-lun,GUO Yan-zhi,SUN Jun-mao(杨雅伦,郭燕枝,孙君茂). Journal of Agricultural Science and Technology(中国农业科技导报),2017,19(1): 29.
[4] Mcm P. Plant Pathology,2015,51(1): 1.
[5] CHEN Yun,YUE Xin-li,WANG Yu-chun(陈 云,岳新丽,王玉春). Journal of Shanxi Agricultural Sciences(山西农业科学),2010,38(7): 140.
[6] Gamard P,Boer S H D. European Journal of Plant Pathology,1995,101(5): 519.
[7] Qu J H,Liu D,Cheng J H,et al. Critical Reviews in Food Science & Nutrition,2015,55(13): 1939.
[8] ZHANG Xiao-yan,YANG Bing-nan,LIU Wei,et al(张小燕,杨炳南,刘 威,等). Food Science(食品科学),2013,34(2): 165.
[9] ZHOU Zhu,LI Xiao-yu,GAO Hai-long,et al(周 竹,李小昱,高海龙,等). Transactions of the Chinese Society of Agricultural Engineering(农业工程学报),2012,28(11): 237.
[10] SU Wen-hao,HE Jian-guo,LIU Gui-shan,et al(苏文浩,何建国,刘贵珊,等). Food and Machinery(食品与机械),2013,(5): 127.
[11] Howard R J,Harding M W,Daniels G C,et al. Canadian Journal of Plant Pathology,2015,37(3): 273.
[12] XINASHUNCHAOKETU,YU Zhi-hong,ZHANG Bao-chao,et al(席那顺朝克图,郁志宏,张宝超,等). Mechanization Rural & Pastoral Areas(农村牧区机械化),2013,(3): 19.
[13] Branden K V,Hubert M. Chemometrics & Intelligent Laboratory Systems,2005,79(1-2): 10.
[14] Sgarbossa A,Costa C,Menesatti P,et al. Renewable Energy,2015,76: 258. |
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