|
|
|
|
|
|
Digital Non-Destructive Testing of Internal Color of Potatoes and Rapid
Identification of Yellow and White Core Potatoes |
WANG Wei, NIE Sen*, LI Yong-yu, PENG Yan-kun, MA Shao-jin, ZHANG Yue-xiang |
College of Engineering, China Agricultural University, National Research and Development Center for Agro-processing Equipment, Beijing 100083, China
|
|
|
Abstract Color is an important quality indicator for agricultural products. The internal color of potatoes directly affects the sensory quality of their processed products. The rapid and non-destructive digital detection of the internal color of potatoes, as well as the quick classification of yellow-core and white-core potatoes, is of significant importance for advancing China's staple potato industry and the rapid rise of the prepared food industry. This study aims to achieve rapid, non-destructive digital characterization of the internal color of potatoes. It utilizes two self-developed, portable, multi-quality non-destructive detection devices based on a mini-spectrometer and a discrete spectral sensor: one is a laboratory-based, portable multi-quality non-destructive detection device for potatoes, and the other is a handheld, multi-quality non-destructive detection device for potatoes. A total of 209 potato samples from 26 different varieties, grown in various regions, were selected. Continuous spectral and discrete spectral data were collected, and the internal color parameters L*,a* and b* of the potato samples were measured using a colorimeter. First, based on the L*,a* and b* values measured by the colorimeter, an SVM classification model was established to distinguish between yellow-core and white-core potatoes. The threshold plane for distinguishing between yellow- and white-core potatoes was determined, which will serve as the basis for the next step in the non-destructive and rapid identification of yellow- and white-core potatoes. Secondly, based on the continuous spectral data collected by the portable continuous spectral device from the 209 potato samples, SNV preprocessing combined with the Random Frog Jump (RF) algorithm was used to select 200 characteristic wavelengths to establish a PLSR (Partial Least Squares Regression) prediction model for the L*,a* and b* parameters of potatoes. The root mean square errors (RMSE) for the validation set of L*,a* and b* were 1.278 8, 0.081 6, and 1.407 1, respectively. Similarly, for the discrete spectral data collected by the handheld spectral device, after SNV preprocessing, PLSR prediction models for the L*,a* and b* parameters of potatoes were also established. The RMSE for the validation set of L*,a* and b* were 1.278 8, 0.081 6, and 1.407 1, respectively. The results showed that the prediction models for the internal color parameters of potatoes established with both devices can meet the demand for rapid, non-destructive, digital detection of potato internal color in the field. Finally, 52 potato samples from 26 varieties, which were not involved in model training, were selected for external validation of the L*,a* and b* predicted values using both devices. For the portable continuous spectral device, the maximum absolute residuals for L*,a* and b* were 2.617 3, 0.141 3, and 2.779 1, respectively, and the mean residuals were 0.857 7, 0.049 0, and 0.697 2, respectively. For the handheld discrete spectral device, the maximum absolute residuals for L*,a* and b* were 3.262 8, 0.203 4, and 3.519 5, respectively, and the mean residuals were 1.093 0, 0.066 7, and 1.268 8, respectively. Based on the L*,a* and b* predicted values from both devices, rapid non-destructive identification of yellow and white-core potatoes was performed using the previously established classification threshold plane. The classification accuracy for yellow and white-core potatoes was 92.31% for the portable continuous spectral device and 86.54% for the handheld discrete spectral device. This technology enables the rapid, non-destructive, and real-time digital detection of potato internal color, as well as the quick classification of yellow- and white-core potatoes in the field. It provides technical support for the entire potato industry chain, including planting, processing, and sales.
|
Received: 2025-01-17
Accepted: 2025-04-22
|
|
Corresponding Authors:
NIE Sen
E-mail: niesencau@cau.edu.cn
|
|
[1] Zhang Zhong, Zhou Dao, Li Shalan, et al. Journal of Agriculturaland Food Chemistry, 2023, 71(43):16402.
[2] XU Ying-chao,WANG Xiang-you,YIN Xiang, et al(许英超, 王相友, 印 祥, 等). Transactions of the Chinese Society for Agricultural Machinery(农业机械学报), 2018, 49(2): 339.
[3] López-Maestresalas Ainara, Keresztes Janos C, Goodarzi Mohammad, et al. Food Control, 2016, 70: 229.
[4] XU Ying-chao,WANG Xiang-you,YIN Xiang, et al(许英超, 王相友, 印 祥, 等). Transactions of the Chinese Society for Agricultural Machinery(农业机械学报), 2018, 49(4): 366.
[5] López-Maestrealas Ainara, Arazuri Silvia, Jarén Carmen, et al. Procedia Technology, 2013, 8: 488.
[6] Rady Ahmed M, Guyer Daniel E. Postharvest Biology and Technology, 2015, 103: 17.
[7] ZHOU Zhu, LI Xiao-yu, GAO Hao-long, et al(周 竹,李小昱, 高海龙, 等). Transactions of the Chinese Society for Agricultural Engineering(农业工程学报), 2012, 28(11): 237.
[8] Trygve Helgerud, Jens P Wold, Morten B Pedersen, et al. Talanta, 2015, 143(1): 138.
[9] WANG Fan, LI Yong-yu, PENG Yan-kun, et al(王 凡,李永玉,彭彦昆,等). Transactions of the Chinese Society for Agricultural Machinery(农业机械学报), 2018, 49(7): 348.
[10] WANG Wei, LI Yong-yu, PENG Yan-kun, et al(王 威, 李永玉, 彭彦昆, 等). Spectroscopy and Spectral Analysis(光谱学与光谱分析), 2022, 42(12): 3889.
[11] Di Song, Lang Qiao, Dehua Gao, et al. Computers and Electronics in Agriculture, 2021, 187: 106260.
[12] Yang Biao, Huang Xiaolan, Yan Xin, et al. Computers and Electronics in Agriculture, 2020, 179: 105823.
[13] Yang Biao, Guo Wenchuan, Huang Xiaolan, et al. Computers and Electronics in Agriculture, 2020, 179: 105831.
[14] YAN Yan-lu(严衍禄). Principle, Technology and Application of NIR Spectra Analysis(近红外光谱分析的原理、技术与应用). Beijing:China Light Industry Press(北京:中国轻工业出版社), 2013.
[15] Jamshidi B, Minaei S, Mohajerani E, et al. Computers and Electronics in Agriculture, 2012, 85: 64.
|
[1] |
LI Wen, LI De-jian*, MA Yong-yue, TIAN Wang, CHEN Yin-yin, WANG Li-min, LÜ He, LI Jie, LUO Zi-yun. Study on the Multi-Parameter Detection Method With Variable Optical Path Length for Water Quality Based on Ultrasound and Micro-Nano
Bubble[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(07): 2037-2044. |
[2] |
LI Wen, CHEN Yin-yin*, LUO Xue-ke, HE Na. Research on Testing NH3-N and COD in Water Quality Based on
Continuous Spectroscopy Method[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(01): 254-259. |
[3] |
CHEN Xiao-li1, WANG Li-chun1, LI You-li1, GUO Wen-zhong1, 2*. Effects of Alternating Light Spectrum on the Mineral Element Level of Lettuce[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2022, 42(09): 2813-2817. |
[4] |
WU Yuan-jie1,2, YE Hui-qi1,2, HAN Jian1,2, XIAO Dong1,2*. Supercontinuum Generation Degradation of 1 040 nm Laser Pumped Photonic Crystal Fibers[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2021, 41(11): 3588-3594. |
[5] |
LI Wen, CHENG Li*, WANG Li-min, XU Ming-gang, ZHANG Peng. Study on New Method of Online Detection of Total Alkalinity of Trace In-Situ Water Quality[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2021, 41(05): 1477-1482. |
[6] |
WANG Hong-peng2,3, FANG Pei-pei1,6, MA Huan-zhen1,6, WAN Xiong1,2,3*, JIA Jian-jun2,3,4*, HE Zhi-ping2,3,4*, LING Zong-cheng5. Research on Nondestructive and Noninvasive Detection Technology of Cells Based on Supercontinuum Spectrum[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2021, 41(04): 1011-1015. |
[7] |
LI Wen, WANG Li-min*, CHENG Li, CHEN Hai-qi. Sequential Injection-Continuous Spectroscopy Based Multi-Parameter Method for Water Quality Analysis[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2021, 41(02): 612-617. |
[8] |
WANG Hong-peng, WAN Xiong*, YUAN Ru-jun. Rapid Detection of Extra Virgin Olive Oil Based on Supercontinuum Spectroscopy[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2020, 40(04): 1251-1256. |
[9] |
ZHANG Lei, DAI Jing-min . Calibration Method for the Monochromator Based on Continuous Spectrum Light Source[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2015, 35(08): 2348-2351. |
[10] |
ZHOU Lan1, 2, MO Zhi-hong1, 2*, WEN Zhi-yu1, WEI Wen-jing1, 2 . Simultaneous Detection of Phenol and Anionic Surfactant in Water Based on Continuous Spectrum [J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2012, 32(05): 1316-1319. |
[11] |
HAN Yong1,2,RAO Rui-zhong2,WANG Ying-jian2. Multi-Wavelength Spectral Aerosol Scale Height in Inshore in Contrast with that in Inland[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2009, 29(01): 33-37. |
|
|
|
|