|
|
|
|
|
|
Multi-Parameter Potato Quality Non-Destructive Rapid Detection by Visible/Near-Infrared Spectra |
WANG Fan1, LI Yong-yu1*, PENG Yan-kun1, YANG Bing-nan2, LI Long1, LIU Ya-chao1 |
1. College of Engineering, China Agricultural University, National Research and Development Center for Agro-processing Equipment, Beijing 100083, China
2. Chinese Acadey of Agricultural Mechanization Sciences, Beijing 100083,China |
|
|
Abstract Potato is the fourth important grain crop coordinated with wheat, rice and corn. At present, China is actively promoting the development of potato staple foods, but the uneven quality of potatoes has seriously hampered the process of the main food industry of the potatoes. Therefore, rapid non-destructive testing of potato quality is of great significance to the industrialization of processing. Domestic and foreign scholars have conducted a number of related researches on the detection of potato internal quality based on the visible/near-infrared diffuse reflectance principle. This method is commonly used, but the rough skin of the potato has a great impact on the detection. Another detection method is the transmission spectrum. This method can better reflect the internal quality information of the sample. However, the total transmission spectrum of the potato varies with the size of the sample and results in a large change in spectral intensity. Considering the above two reasons and average quality of potato, this study uses partial transmission spectrum as the detection method. This method can not only avoid the influence of the potato epidermis, but also obtain the internal information of the sample while maintaining the same path length. The spectral acquisition system consists of spectral acquisition units (spectroscopes and coupling lenses) and light source units (halogen lamps and lamp cups) which are arranged side by side. During testing, the two parts are attached to the sample surface to ensure that the spectral acquisition unit does not receive reflected light from the potato surface. Based on this system, partial transmission spectra of 120 potatoes are collected ranging from 650 to 1 100 nm. The prediction model of dry matter, starch and reducing sugar content was established using partial least squares regression after pretreat by detrend, multivariate scattering correction (MSC), standard normal variable transformation (SNV) and first-order derivative (FD). The result shows that the prediction models of dry matter and starch content using multiple scatter correction pretreatment are effective. The determination coefficients of validation set are 0.854 0 and 0.851 0, respectively, and the root mean square errors are 0.521 9% and 0.484 8%, respectively. The reducing sugar prediction model using first-order derivative pretreatment has the best result. The determination coefficients of validation set is 0.768 6 and the root mean square error is 0.025 1%. In order to optimize the model, three methods such as competitive adaptive reweighted sampling (CARS) are used to filter the characteristic wavelengths, and an optimized partial least-square prediction model is established. The result shows that the prediction effect of potato quality parameters has been greatly improved. The determination coefficient of validation sets for dry matter, starch, and reducing sugar prediction models after CARS screening are 0.877 6, 0.865 3 and 0.887 7, respectively. And the root mean square errors of the validation set are 0.449 2%, 0.930 2% and 0.016 7%, respectively. The use of CARS feature wavelength extraction can simplify the model and remove irrelevant variables and collinearity variables. This will improve the accuracy and stability of the model, especially for low-component content parameters such as reducing sugars. Finally, in order to verify the robust of the potato quality parameters prediction model, 30 potato samples are selected for external validation of the prediction model. The determination coefficients between model predicted values and standard physicochemical values of potato dry matter, starch, and reducing sugar are 0.849 9, 0.867 1, and 0.877 6, respectively. The root mean square errors are 0.660 9, 0.480 9, and 0.016 9, respectively. The average relative errors are 2.03%, 1.77% and 7.58%, respectively. The present study shows that the partial transmission spectrum carries the internal information of the potato and it is significantly related to the contents of dry matter, starch, and reducing sugar. The visible/near-infrared partial transmission detection system can achieve rapid and non-destructive prediction of multi-parameters of potatoes, especially good prediction results of dry matter content and starch content, but there is a large relative error in the prediction of individual samples with very low levels of reducing sugars. The next step of the study needs further optimization and improvement.
|
Received: 2018-03-10
Accepted: 2018-06-27
|
|
Corresponding Authors:
LI Yong-yu
E-mail: yyli@cau.edu.cn
|
|
[1] XIE Cong-hua(谢从华). Journal of Huazhong Agricultural University·Social Sciences Edition(华中农业大学学报·社会科学版),2012,(1):1.
[2] 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(01):29.
[3] CHEN Meng-shan,WANG Xiao-hu(陈萌山, 王小虎). Issues in Agricultural Economy(农业经济问题),2015,36(12):4.
[4] PANG Shao-jin,GUO An-qiang,YANG Jian-zhong,et al(庞昭进, 郭安强, 杨建忠, 等). Journal of Hebei Agricultural Sciences(河北农业科学),2018,1:1.
[5] Subedi P P,Walsh K B. Potato Research,2009,52 (1):67.
[6] Trygve Helgerud, Jens P Wold, Morten B Pedersen, et al. Talanta,2015,143(1):138.
[7] ZHANG Jing-ting,WU Jian-hu,CAI Ya-qin(张婧婷, 吴建虎, 蔡亚琴). Food Safety and Quality Detection Technology(食品安全质量检测学报),2015,6(8):3014.
[8] Sun Xudong, Dong Xiaoling. Journal of Food Measurement and Characterization,2015,9(1):95.
[9] MEGN Qing-yan,HE Jian-guo,LIU Gui-shan,et al(孟庆琰,何建国,刘贵珊,等). Food Science and Technology(食品科技),2015,40(3):287.
[10] Fraser D G, Jordan R B, Künnemeyer R, et al. Postharvest Biol. Technol.,2003,27 (2):185.
[11] FraserD G, Kunnemeyer R, McGlone V A, et al. Postharvest Biol. Technol.,2001,22(3):191.
[12] Lammertyn J, Peirs A, De Baerdemaeker J, et al. Biol. Technol.,2000,18(2) :121.
[13] WANG Jia-hua,CHEN Zhuo,LI Zhen-ru,et al(王加华, 陈 卓, 李振茹,等). Tran. Chin. Soc. Agric. Mach.(农业机械学报),2010,41(11):129.
[14] DAI Fen,CAI Bo-kun,HONG Tian-sheng, et al(代 芬,蔡博昆,洪添胜,等). Transactions of the Chinese Society of Agricultural Engineering(农业工程学报),2012,28(15): 287.
[15] XU Wen-li,SUN Tong,WU Wen-qiang,et al(许文丽,孙 通,吴文强,等). Acta Photonica Sinica(光子学报),2013,42(12):1486.
[16] XU Chang-jie,CHEN Wen-jun,CHEN Kun-song,et al(徐昌杰,陈文峻,陈昆松,等). Biotechnology(生物技术),1998,8(2):41.
[17] Kennard R W,Stone L A. Technometrics,1969,11:137.
[18] Galvao R K H,Araujo M C U,José G E,et al. Talanta,2005,67(4):736.
[19] Wold S, Sjostrom M, Eriksson L. Chemometrics and Intelligent Laboratory Systems,2001,58(2):109.
[20] Nicolaie Bart M, Beullens Katrien, Bobelyn Els, et al. Postharvest Biology and Technology,2007,46(2):99.
[21] Noha M,Da-Wen S. Meat Science,2013,93(2):292.
[22] YANG Bing-nan,ZHANG Xiao-yan,ZHAO Feng-min,et al(杨炳南,张小燕,赵凤敏,等). Transactions of the Chinese Society of Agricultural Engineering(农业工程学报),2015,31(20):301.
[23] Habib A T,Brown H D. Food Technology,1957,11(2):85.
[24] HU Xiao-nan,PENG Yun-fa,LUO Hua-ping,et al(胡晓男,彭云发,罗华平,等). The Food Industry(食品工业),2015,36(5):232.
[25] ZENG Yi-fan,LIU Chun-sheng,SUN Xu-dong,et al(曾一凡,刘春生,孙旭东,等). Transactions of the Chinese Society of Agricultural Engineering(农业工程学报),2008,24(5):250.
[26] LUO Xia,HONG Tian-sheng,LUO Kuo,et al(罗 霞,洪添胜,罗 阔,等). Spectroscopy and Spectral Analysis(光谱学与光谱分析),2016,36(5):1345.
[27] Ye Shengfeng, Wang Dong, Min Shungeng. Chemometrics and Intelligent Laboratory Systems,2008,91:194.
[28] Polanski J,Gieleciak R. Journal of Chemical Information and Computer Sciences,2003,43(2):656.
[29] Centner V,Massart D L,Denoord O E,et al. Analytical Chemistry,1996,68(21):3851.
[30] Jiang Hui,Zhang Hang,Chen Quansheng,et al. Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy,2015,149:1.
|
[1] |
LI Xin-ting, ZHANG Feng, FENG Jie*. Convolutional Neural Network Combined With Improved Spectral
Processing Method for Potato Disease Detection[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(01): 215-224. |
[2] |
LI Yang1, LI Xiao-qi1, YANG Jia-ying1, SUN Li-juan2, CHEN Yuan-yuan1, YU Le1, WU Jing-zhu1*. Visualisation of Starch Distribution in Corn Seeds Based on Terahertz Time-Domain Spectral Reflection Imaging Technology[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(09): 2722-2728. |
[3] |
FENG Hai-kuan1, 2, YUE Ji-bo3, FAN Yi-guang2, YANG Gui-jun2, ZHAO Chun-jiang1, 2*. Estimation of Potato Above-Ground Biomass Based on VGC-AGB Model and Hyperspectral Remote Sensing[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(09): 2876-2884. |
[4] |
FAN Yi-guang1, 3, 5, FENG Hai-kuan1, 2, 3*, LIU Yang1, 3, 4, LONG Hui-ling1, 3, YANG Gui-jun1, 3, QIAN Jian-guo5. Estimation of Potato Plant Nitrogen Content Based on UAV Hyperspectral Imaging[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(05): 1524-1531. |
[5] |
FAN Yi-guang1, 3, 5, FENG Hai-kuan1, 2, 3*, LIU Yang1, 3, 4, BIAN Ming-bo1, 3, ZHAO Yu1, 3, YANG Gui-jun1, 3, QIAN Jian-guo5. Estimation of Nitrogen Content in Potato Plants Based on Spectral Spatial Characteristics[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(05): 1532-1540. |
[6] |
YANG Ping1, LI Xue2, WANG Hui1, LIU Guang-xian2*. Analysis of the Effect of Different Reducing Sugars on Ara h2 Glycation Based on Spectral Technology[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(04): 1291-1297. |
[7] |
HAN Min-jie, WANG Xiang-you, XU Ying-chao*, CUI Ying-jun, LÜ Dan-yang. Research on the Factors Influencing the Non-Destructive Detection of
Potatoes by Near-Infrared Spectroscopy[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(01): 37-42. |
[8] |
AN Huan-jiong1, ZHAI Chen2, MA Qian-yun1, ZHANG Fan1, WANG Shu-ya2, SUN Jian-feng1, WANG Wen-xiu1*. Quantitative Characterization of Wheat Starch Retrogradation by
Combining 2D-COS and Spectral Fusion[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(01): 162-168. |
[9] |
WANG Wei, LI Yong-yu*, PENG Yan-kun, YANG Yan-ming, YAN Shuai, MA Shao-jin. Design and Experiment of a Handheld Multi-Channel Discrete Spectrum Detection Device for Potato Processing Quality[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2022, 42(12): 3889-3895. |
[10] |
LI Hong-qiang1, SUN Hong2, LI Min-zan2*. Study on Identification of Common Diseases in Potato Storage Period Based on Spectral Structure[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2022, 42(08): 2471-2476. |
[11] |
HOU Bing-ru1, LIU Peng-hui1, ZHANG Yang1, HU Yao-hua1, 2, 3*. Prediction of the Degree of Late Blight Disease Based on Optical Fiber Spectral Information of Potato Leaves[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2022, 42(05): 1426-1432. |
[12] |
LIU Yang1, 4, 5, ZHANG Han2, FENG Hai-kuan1, 3, 5*, SUN Qian1, 5, HUANG Jue4, WANG Jiao-jiao1, 5, YANG Gui-jun1, 5. Estimation of Potato Above Ground Biomass Based on Hyperspectral Images of UAV[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2021, 41(09): 2657-2664. |
[13] |
LIU Yang1, 2, 4, SUN Qian1, 4, HUANG Jue2, FENG Hai-kuan1, 3, 4*, WANG Jiao-jiao1, 4, YANG Gui-jun1, 4. Estimation of Potato Above Ground Biomass Based on UAV Multispectral Images[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2021, 41(08): 2549-2555. |
[14] |
LIU Yang 1, 2, 3, 4, FENG Hai-kuan1, 3, 4*, SUN Qian1, 3, 4, YANG Fu-qin5, YANG Gui-jun1, 3, 4. Estimation Study of Above Ground Biomass in Potato Based on UAV Digital Images With Different Resolutions[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2021, 41(05): 1470-1476. |
[15] |
LIU Yang1, 2, 3, SUN Qian1, 3, FENG Hai-kuan1, 3*, YANG Fu-qin4. Estimation of Above-Ground Biomass of Potato Based on Wavelet Analysis[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2021, 41(04): 1205-1212. |
|
|
|
|