-
摘要: 本文探索了基于光谱指数的蜜橘成熟度快速无损评价方法及模型。以2016年9~11月份6个不同采收期的300个蜜橘作为实验样品，采集重量、横纵径、叶绿素、色差、可溶性固形物（SSC）、酸度（TA）、近红外光谱等数据。通过对比分析上述各参数的平均值和偏差，筛选出叶绿素、叶绿素/SSC、叶绿素/固酸比作为蜜橘成熟度评价指标。利用光谱变异系数分析光谱的特征，筛选出649、724、672、1 100 nm 4个特征波长，通过特征波长线性组合方法以及相关性分析，得出最佳光谱指数。接着，以225个样品为建模集、75个样品为预测集，在成熟度评价指标与光谱指数间进行多元线性回归（MLR）分析。对比发现，以叶绿素为成熟度评价指标的评价模型的预测结果最准确，建模和预测相关系数分别达到0.98和0.96，建模均方根误差（RMSEC）和预测均方根误差（RMSEP）分别为0.49和0.59，建模和预测偏差分别为-6.1×10-8和-0.014。实验结果表明，利用光谱指数能便捷、准确地评定蜜橘成熟度，为后续开发低成本测量成熟度的仪器提供了理论依据。Abstract: Based on the spectral analysis, we explored a rapid nondestructive evaluation method for maturity of tangerine and estabilizhed a evaluation model. The data of weight, transverse and longitudinal diameter, chlorophyll, color difference, soluble solids(SSC), acidity(TA) and near infrared spectra were collected from 300 tangerines as experimental samples of 6 different picking time from September to November in 2016. The chlorophyll, chlorophyll/SSC and chlorophyll/solid acid ratio were screened out as the evaluation index of maturity of tangerine by comparing and analyzing the average value and deviation of the above parameters. By using spectral coefficients of variation to analyze the characteristics of the spectra, four characteristic wavelengths of 649, 724, 672, and 1 100 nm were selected. The optimal spectral indexes were obtained by linear combination and correlation analysis of these wavelengths. Then, taking 225 samples as model sets and 75 samples as prediction sets, multiple linear regression(MLR) analysis was conducted for both maturity index and spectral index. By comparison, the prediction model based on chlorophyll as the maturity index was the most accurate, and the correlation coefficient between modeling and prediction was 0.98 and 0.96 respectively. The root mean square error of modeling(RMSEC) and root mean square error of prediction(RMSEP) were 0.49 and 0.59, and the modeling and forecast deviations were -6.1×10-8 and -0.014, respectively. The results showed that the spectral index can be used to conveniently and accurately evaluate the maturity of tangerine, which provided a theoretical basis for the subsequent development of low-cost maturity-measuring instruments.
-
Key words:
- tangerine /
- maturity index /
- spectral index /
- evaluation model
-
图 10 不同采收期的叶绿素与糖度的比值
Figure 10. Ratio of chlorophyll to SSC in different harvest periods
图 11 不同采收期的叶绿素与固酸比的比值
Figure 11. Ratios of chlorophyll to solid-acid in different harvest periods
图 12 不同采收期的江西蜜橘近红外光谱特性
Figure 12. Near infrared spectrum characteristics of Jiangxi orange in different harvest periods
图 14 成熟度光谱指数随采收期的变化
Figure 14. Maturity spectral index changes with the harvest period
表 1 不同采收期的参数范围
Table 1. Parameter ranges of different harvest periods
批次 采摘时间 重量/g 横径/mm 纵径/mm 叶绿素 色差 糖度(Bix°) 酸度/% 1 9月1日 94~116.7 60~63 47~54 5.8~35 -50~-27.9 9.1~11.6 0.53~2.88 2 9月11日 94.43~137.82 60~68 46~54 3.9~40 55.7~66.8 9.2~11.5 0.3~2.41 3 10月9日 110.4~156.1 66~74 46~56 0.1~23.5 57.4~74.4 9.8~12.2 0.47~2.22 4 10月24日 109.87~141.89 62~71 50~59 0.1~13.2 56.6~75.7 9.4~12.8 0.84~1.66 5 11月8日 87.44~136.11 62~71 48~57 0.1~3.2 65.1~76.3 9.5~13.1 0.4~1.4 6 11月28日 108.36~151.73 60~75 46~58 0.1~1.8 58.6~75.7 10.7~13.3 0.41~0.97 
下载: 导出CSV
表 2 光谱评价指数相关性分析
Table 2. Correlation analysis of spectral evaluation indices
光谱评价指数 相关系数(r) 
0.967 8 
0.955 9 
0.991 6 
-0.975 3 
0.991 2 
-0.964 3
下载: 导出CSV
表 3 成熟度指标与光谱指数建模结果
Table 3. Modeling results of maturity index and spectral indices
成熟度指标 rc RMSEC Bias rp RMSEP Bias 叶绿素 0.98 0.49 -6.1×10-8 0.96 0.59 -0.014 叶绿素/SSC 0.95 0.71 3.7×10-7 0.94 0.82 -0.041 叶绿素/固酸比 0.92 0.97 -4.1×10-8 0.89 1.02 0.084
下载: 导出CSV
-
[1] GB/T 12947-2008《鲜柑橘》[S]. GB/T 12947-2008(Fresh citrus)[S]. (in Chinese) [2] 应义斌, 饶秀勤, 马俊福, 等.柑橘成熟度机器视觉无损检测方法研究[J].农业工程学报, 2004, 20(2):144-147. http://industry.wanfangdata.com.cn/dl/Detail/Periodical?id=Periodical_nygcxb200402034YING Y B, RAO X Q, MA J F, et al.. Methodology for nondestructive inspection of citrus maturity with machine vision[J]. Transactions of the CSAE, 2004, 20(2):144-147.(in Chinese) http://industry.wanfangdata.com.cn/dl/Detail/Periodical?id=Periodical_nygcxb200402034 [3] BURDON J, PIDAKALA P, MARTIN P, et al. Postharvest performance of the yellow-fleshed 'Hort16A' kiwifruit in relation to fruit maturation[J]. Postharvest Biology and Technology, 2014, 92:98-106. doi: 10.1016/j.postharvbio.2014.01.004 [4] NAVARRO G N, MARTINEZ R D, PEREZT O. Assessment of the impact of ethylene and ethylene modulators in citrus limon organogenesis[J]. Plant Cell, Tissue and Organ Culture, 2016, 127(2):405-415. doi: 10.1007/s11240-016-1062-x [5] 王乐妍, 张冬仙, 章海军, 等.基于激光光致发光光谱的果实成熟度测试方法研究[J].光谱学与光谱分析, 2008, 28(12):2772-2776. doi: 10.3964/j.issn.1000-0593(2008)12-2772-05WANG L Y, ZHANG D X, ZHANG H J, et al.. Measurement of fruit maturity based on laser-induced photoluminescence spectrum[J]. Spectroscopy and Spectral Analysis, 2008, 28(12):2772-2776.(in Chinese) doi: 10.3964/j.issn.1000-0593(2008)12-2772-05 [6] JONATHAN V B, LAURENT T, BEN S, et al.. Stem water potential monitoring in pear orchards through worldview-2 multispectral imagery[J]. Remote Sensing, 2015, 7(8):9886-9903. http://www.academia.edu/23188991/Stem_Water_Potential_Monitoring_in_Pear_Orchards_through_WorldView-2_Multispectral_Imagery [7] ZHAO CH J, LI H L, GU X H, et al.. Effect of vertical distribution of crop structure and biochemical parameters of winter wheat on canopy reflectance characteristics and spectral indices[J]. IEEE Transactions on Geoscience & Remote Sensing, 2017, 55(1):236-247. https://www.researchgate.net/publication/309047816_Effect_of_Vertical_Distribution_of_Crop_Structure_and_Biochemical_Parameters_of_Winter_Wheat_on_Canopy_Reflectance_Characteristics_and_Spectral_Indices [8] RAYMOND E H, CRAIG S T D, LI L. Feasibility of estimating leaf water content using spectral indices from WorldView-3's near-infrared and shortwave infrared bands[J]. International Journal of Remote Sensing, 2016, 37(2):388-402. doi: 10.1080/01431161.2015.1128575 [9] 罗丹, 常庆瑞, 齐雁冰, 等.基于光谱指数的冬小麦冠层叶绿素含量估算模型研究[J].麦类作物学报, 2016, 36(9):1225-1233. http://www.cjae.net/EN/article/downloadArticleFile.do?attachType=PDF&id=18898LUO D, CHANG Q R, QI Y B, et al.. Estimation model for chlorophyll content in winter wheat canopy based on spectral indices[J]. Journal of Triticeae Crops, 2016, 36(9):1225-1233.(in Chinese) http://www.cjae.net/EN/article/downloadArticleFile.do?attachType=PDF&id=18898 [10] NAGY A, PETER R, JANOS T. Spectral evaluation of apple fruit ripening and pigment content alteration[J]. Scientia Horticulturae, 2016, 201:256-264. doi: 10.1016/j.scienta.2016.02.016 [11] ALEJANDRA R F, MASSIMO N, EMILIO J F, et al.. Assessment of technological maturity parameters and anthocyanins in berries of cv. Sangiovese(Vitis vinifera L.) by a portable vis/NIR device[J]. Scientia Horticulturae, 2016, 209:229-235. doi: 10.1016/j.scienta.2016.06.004 [12] 刘燕德, 肖怀春, 孙旭东, 等.基于高光谱成像的柑橘黄龙病无损检测[J].农业机械学报, 2016, 47(11):231-238, 277. doi: 10.6041/j.issn.1000-1298.2016.11.032LIU Y D, XIAO H CH, SUN X D, et al.. Non-destructive detection of citrus huanglong disease using hyperspectral image technique[J]. Transactions of the Chinese Society for Agricultural Machinery, 2016, 47(11):231-238, 277.(in Chinese) doi: 10.6041/j.issn.1000-1298.2016.11.032 [13] LI M, LV W B, ZHAO R, et al.. Non-destructive assessment of quality parameters in Friar' plums during low temperature storage using visible/near infrared spectroscopy[J]. Food Control, 2017, 73(B):1334-1341. https://www.sciencedirect.com/science/article/pii/S0956713516306107 [14] 刘凯, 张立福, 杨杭, 等.面向对象分析的非结构化背景目标高光谱探测方法研究[J].光谱学与光谱分析, 2013, 33(6):1653-1657. http://kns.cnki.net/KCMS/detail/detail.aspx?filename=guan201306047&dbname=CJFD&dbcode=CJFQLIU K, ZHANG L F, YANG H, et al.. Hyperspectral unstructured background target detection approach based on object-oriented analysis[J]. Spectroscopy and Spectral Analysis, 2013, 33(6):1653-1657.(in Chinese) http://kns.cnki.net/KCMS/detail/detail.aspx?filename=guan201306047&dbname=CJFD&dbcode=CJFQ [15] 田青, 罗金平, 刘晓红, 等.生物发光法细菌快速检测仪的研制及应用[J].光学精密工程, 2010, 18(4):771-778. http://www.eope.net/fileup/PDF/2009-0565.pdfTIAN Q, LUO J P, LIU X H, et al.. Development and application of rapid detecting instrument for bacteria based on bioluminescence[J]. Opt. Precision Eng., 2010, 18(4):771-778.(in Chinese) http://www.eope.net/fileup/PDF/2009-0565.pdf [16] TAMBURINI E, FERRARI G, MARCHETT M G, et al.. Development of FT-NIR models for the simultaneous estimation of chlorophyll and nitrogen content in fresh apple(Malus Domestica) leaves[J]. Sensors(Basel), 2015, 15(2):2662-2679. https://www.researchgate.net/profile/Sergio_Ferro/publication/271597970_Development_of_FT-NIR_Models_for_the_Simultaneous_Estimation_of_Chlorophyll_and_Nitrogen_Content_in_Fresh_Apple_Malus_Domestica_Leaves/links/54da02f30cf25013d043a655/Development-of-FT-NIR-Models-for-the-Simultaneous-Estimation-of-Chlorophyll-and-Nitrogen-Content-in-Fresh-Apple-Malus-Domestica-Leaves.pdf [17] GITELSON AA, MERZLYAK M N. Signature analysis of leaf reflectance spectra-algorithm development for remote sensing of chlorophyll[J]. Journal of Plant Physiology, 1996, 148(3-4):494-500. doi: 10.1016/S0176-1617(96)80284-7 [18] 王智宏, 张福东, 滕飞, 等.基于近红外波长组合快速检测油页岩含油率[J].光学精密工程, 2015, 23(2):371-377. http://cdmd.cnki.com.cn/Article/CDMD-10496-1017006928.htmWANG ZH H, ZHANG F D, TENG F, et al.. Rapid detection of oil yield of oil shale by combination of wavelengths in near infrared spectroscopy[J]. Opt. Precision Eng., 2015, 23(2):371-377.(in Chinese) http://cdmd.cnki.com.cn/Article/CDMD-10496-1017006928.htm -
下载:
图(15) / 表(3)
计量
- 文章访问数: 1992
- HTML全文浏览量: 755
- PDF下载量: 491
- 被引次数: 0
