| Citation: | LI Bin, WAN Xia, LIU Ai-lun, ZOU Ji-ping, LU Ying-jun, YAO Chi, LIU Yan-de. Optimal position for suger content detection of Yongquan honey oranges based on hyperspectral imaging technology[J]. Chinese Optics, 2024, 17(1): 128-139. doi: 10.37188/CO.2023-0057
|
The objective of this study is to explore the optimal detection location and the best prediction model of the suger level of Yongquan honey oranges, which can provide a theoretical basis for the brix measurement and classification of honey oranges. With the wavelength range of 390.2−981.3 nm hyperspectral imaging system was used to study the best position for detecting the sugar content of Yongquan honey oranges, and the spectral information of the calyx, fruit stem, equator and global of Yongquan honey oranges were combined with their sugar content of corresponding parts to establish its prediction model. The original spectra from the different locations were pre-processed by Standard Normal Variance (SNV) transformation, Multiple Scattering Correction (MSC), baseline calibration (Baseline) and SG smoothing, respectively, and the Partial Least Squares Regression (PLSR) and Least Squares Support Vector Machine (LSSVM) models were established based on the pre-processed spectral data. The best pre-processing methods for different parts of the honey oranges were found, and the optimal spectral data obtained by the best pre-processing methods were conducted to identify characteristic wavelengths using the Competitive Adaptive Re-weighting Sampling algorithm (CARS) and Uninformative Variable Elimination (UVE). Finally, the PLSR and LSSVM models were established and compared based on the selected spectral data. The results show that the global MSC-CARS-LSSVM model demonstrates the most accurate prediction performance, with a correlation coefficient of
| [1] |
王玲娇, 蒋守渭. 临海涌泉蜜桔网络营销策略研究[J]. 现代商业,2015(35):36-37.
WANG L J, JIANG SH W. Research on network marketing strategy of Linhai Yongquan honey tangerine[J]. Modern Business, 2015(35): 36-37. (in Chinese)
|
| [2] |
介邓飞, 杨杰, 彭雅欣, 等. 基于高光谱技术的柑橘不同部位糖度预测模型研究[J]. 食品与机械,2017,33(3):51-54.
JIE D F, YANG J, PENG Y X, et al. Research on the detection model of sugar content in different position of citrus based on the hyperspectral technology[J]. Food &Machinery, 2017, 33(3): 51-54. (in Chinese)
|
| [3] |
王玥, 樊柳荫. 猪肉可视化新鲜度智能指示薄膜研究[J]. 分析化学,2023,51(1):139-145.
WANG Y, FAN L Y. Intelligent indicator film for visual meat freshness monitoring[J]. Chinese Journal of Analytical Chemistry, 2023, 51(1): 139-145. (in Chinese)
|
| [4] |
田喜, 陈立平, 王庆艳, 等. 全透射近红外光谱的苹果整果糖度在线检测模型优化[J]. 光谱学与光谱分析,2022,42(6):1907-1914.
TIAN X, CHEN L P, WANG Q Y, et al. Optimization of online determination model for sugar in a whole apple using full transmittance spectrum[J]. Spectroscopy and Spectral Analysis, 2022, 42(6): 1907-1914. (in Chinese)
|
| [5] |
陈玥瑶, 夏静静, 韦芸, 等. 近红外光谱法无损检测平谷产大桃品质方法研究[J]. 分析化学,2023,51(3):454-462.
CHEN Y Y, XIA J J, WEI Y, et al. Research on nondestructive quality test of Pinggu peach by near-infrared spectroscopy[J]. Chinese Journal of Analytical Chemistry, 2023, 51(3): 454-462. (in Chinese)
|
| [6] |
LU Y ZH, HUANG Y P, LU R F. Innovative hyperspectral imaging-based techniques for quality evaluation of fruits and vegetables: A review[J]. Applied Sciences, 2017, 7(2): 189. doi: 10.3390/app7020189
|
| [7] |
MESA A R, CHIANG J Y. Multi-input deep learning model with RGB and hyperspectral imaging for banana grading[J]. Agriculture, 2021, 11(8): 687. doi: 10.3390/agriculture11080687
|
| [8] |
徐婧, 郑红, 谢丽芳, 等. 鸡肉样品中痕量喹诺酮类抗生素的表面增强拉曼光谱检测研究[J]. 分析化学,2023,51(3):397-404.
XU J, ZHENG H, XIE L F, et al. Fast detection of trace enrofloxacin and ciprofloxacin in chicken meat by surface-enhanced Raman spectroscopy[J]. Chinese Journal of Analytical Chemistry, 2023, 51(3): 397-404. (in Chinese)
|
| [9] |
沈彦龙, 程立业, 孟祥茹, 等. 人参连作土壤对不同生育期人参生长发育及抗氧化系统的影响[J]. 应用化学,2023,40(1):109-115.
SHEN Y L, CHENG L Y, MENG X R, et al. Effects of ginseng continuous soil crop on growth development and antioxidant system of ginseng at different fertility stages[J]. Chinese Journal of Applied Chemistry, 2023, 40(1): 109-115. (in Chinese)
|
| [10] |
黄蕊, 叶长青, 李亚军, 等. 线粒体靶向的近红外HClO/ClO-荧光探针的研究进展[J]. 应用化学,2022,39(3):407-424.
HUANG R, YE CH Q, LI Y J, et al. Progress of mitochondria-targeted near-infrared HClO/ClO- fluorescent probes[J]. Chinese Journal of Applied Chemistry, 2022, 39(3): 407-424. (in Chinese)
|
| [11] |
王瑞, 孟祥茹, 李琼, 等. 人参属中药腐解化感作用研究进展[J]. 应用化学,2023,40(1):1-8.
WANG R, MENG X R, LI Q, et al. Research progress on the decomposed Allelopathy of Panax genus[J]. Chinese Journal of Applied Chemistry, 2023, 40(1): 1-8. (in Chinese)
|
| [12] |
KIM D, BURKS T F, RITENOUR M A, et al. Citrus black spot detection using hyperspectral imaging[J]. International Journal of Agricultural and Biological Engineering, 2014, 7(6): 20-27.
|
| [13] |
孟田源, 王转卫, 迟茜, 等. 基于高光谱成像技术生长发育后期苹果糖度的无损检测[J]. 西北农林科技大学学报(自然科学版),2016,44(6):228-234.
MENG T Y, WANG ZH W, CHI X, et al. Hyperspectral imaging based non-destructive prediction of soluble solids content in apples at late development period[J]. Journal of Northwest A&F University (Natural Science Edition), 2016, 44(6): 228-234. (in Chinese)
|
| [14] |
许丽佳, 陈铭, 王玉超, 等. 高光谱成像的猕猴桃糖度无损检测方法[J]. 光谱学与光谱分析,2021,41(7):2188-2195.
XU L J, CHEN M, WANG Y CH, et al. Study on non-destructive detection method of kiwifruit sugar content based on hyperspectral imaging technology[J]. Spectroscopy and Spectral Analysis, 2021, 41(7): 2188-2195. (in Chinese)
|
| [15] |
杨杰, 马本学, 王运祥, 等. 葡萄可溶性固形物的高光谱无损检测技术[J]. 江苏农业科学,2016,44(6):401-403.
YANG J, MA B X, WANG Y X, et al. Hyperspectral nondestructive detection technique of soluble solids in grapes[J]. Jiangsu Agricultural Science, 2016, 44(6): 401-403. (in Chinese)
|
| [16] |
SUN X D, XU C, LUO CH G, et al. Non-destructive detection of tea stalk and insect foreign bodies based on THz-TDS combination of electromagnetic vibration feeder[J]. Food Quality and Safety, 2023, 7: fyad004. doi: 10.1093/fqsafe/fyad004
|
| [17] |
LIU Y D, SUN X D, OUYANG A G. Nondestructive measurement of soluble solid content of navel orange fruit by visible-NIR spectrometric technique with PLSR and PCA-BPNN[J]. LWT - Food Science and Technology, 2010, 43(4): 602-607. doi: 10.1016/j.lwt.2009.10.008
|
| [18] |
YANG Y, ZHAO CH J, HUANG W Q, et al. Optimization and compensation of models on tomato soluble solids content assessment with online Vis/NIRS diffuse transmission system[J]. Infrared Physics &Technology, 2022, 121: 104050.
|
| [19] |
袁琳, 徐怀德, 李钰金. 近红外漫反射光谱检测网纹瓜可溶性固形物含量的研究[J]. 中国食品学报,2010,10(4):272-277.
YUAN L, XU H D, LI Y J. Studies on the rapid measurements of soluble solids content in nutmeg melon by near infrared diffuse reflectance spectroscopy[J]. Journal of Chinese Institute of Food Science and Technology, 2010, 10(4): 272-277. (in Chinese)
|
| [20] |
ZHANG D Y, XU L, WANG Q Y, et al. The optimal local model selection for robust and fast evaluation of soluble solid content in melon with thick peel and large size by Vis-NIR spectroscopy[J]. Food Analytical Methods, 2019, 12(1): 136-147. doi: 10.1007/s12161-018-1346-3
|
| [21] |
孙博康, 刘贵珊. 基于高光谱技术检测香水梨硬度的研究[J]. 食品安全导刊,2021(19):118-120.
SUN B K, LIU G SH. Study on the detection of hardness of perfumed pears based on hyperspectral technology[J]. Journal of Food Safety, 2021(19): 118-120. (in Chinese)
|
| [22] |
ZHANG F, LI B, YIN H, et al. Study on the quantitative assessment of impact damage of yellow peaches using the combined hyperspectral technology and mechanical parameters[J]. Journal of Spectroscopy, 2022, 2022: 7526826.
|
| [23] |
WANG ZH L, CHEN J, FAN Y F, et al. Evaluating photosynthetic pigment contents of maize using UVE-PLS based on continuous wavelet transform[J]. Computers and Electronics in Agriculture, 2020, 169: 105160. doi: 10.1016/j.compag.2019.105160
|
| [24] |
廉小亲, 汤燊淼, 吴静珠, 等. 基于近红外的兰州百合品质定量建模方法研究[J]. 食品科技,2020,45(7):298-302.
LIAN X Q, TANG S M, WU J ZH, et al. Research on quantitative model of Lilium Lanzhou quality by near infrared spectroscopy[J]. Food Science and Technology, 2020, 45(7): 298-302. (in Chinese)
|
| [25] |
YANG X Y, LIU G SH, HE J G, et al. Determination of sugar content in Lingwu jujube by NIR–hyperspectral imaging[J]. Journal of Food Science, 2021, 86(4): 1201-1214. doi: 10.1111/1750-3841.15674
|
| [26] |
李斌, 邹吉平, 张烽, 等. 基于高光谱成像技术和力学参数对贡梨冲击损伤的定量研究[J]. 中国农业大学学报,2023,28(2):186-197.
LI B, ZOU J P, ZHANG F, et al. Quantitative study on impact damage of Gongli based on hyperspectral imaging technology and mechanical parameters[J]. Journal of China Agricultural University, 2023, 28(2): 186-197. (in Chinese)
|
Figures(10) / Tables(5)
DownLoad:
