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1. 华中农业大学工学院,湖北 武汉,430070
2. 福建农林大学机电工程学院,福建 福州,350002
纸质出版日期:2017-5-5,
收稿日期:2016-11-5,
修回日期:2017-1-24,
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介邓飞, 李泽海, 赵竣威等. 基于高光谱漫透射成像可视化检测脐橙可溶性固形物[J]. 发光学报, 2017,38(5): 685-691
JIE Deng-fei, LI Ze-hai, ZHAO Jun-wei etc. Visualized Detection of Soluble Solid Content Distribution of Navel Orange Based on Hyperspectral Diffuse Transmittance Imaging[J]. Chinese Journal of Luminescence, 2017,38(5): 685-691
介邓飞, 李泽海, 赵竣威等. 基于高光谱漫透射成像可视化检测脐橙可溶性固形物[J]. 发光学报, 2017,38(5): 685-691 DOI: 10.3788/fgxb20173805.0685.
JIE Deng-fei, LI Ze-hai, ZHAO Jun-wei etc. Visualized Detection of Soluble Solid Content Distribution of Navel Orange Based on Hyperspectral Diffuse Transmittance Imaging[J]. Chinese Journal of Luminescence, 2017,38(5): 685-691 DOI: 10.3788/fgxb20173805.0685.
厚皮类瓜果内部品质的无损检测是目前水果产业的检测技术瓶颈。本文采用高光谱漫透射技术对脐橙可溶性固形物(SSC)含量进行可视化分析研究。通过基线校正(Baseline)预处理结合连续投影算法(SPA)优选9个特征波长,建立SSC偏最小二乘回归(PLSR)模型,校正集相关系数
r
cal
为0.891,校正集均方根误差RSMEC为0.612 Brix,预测集相关系数
r
pre
为0.889,预测集均方根误差RMSEP为0.630 Brix。最后,计算各个像素点的SSC值结合图像处理技术得出SSC的可视化分布图,直观判断脐橙SSC含量高低。
Compared with the fruit with thin skin
it is more difficult to acquire the internal quality information of fruits with thick skin. In this study
the hyperspectral diffuse transmission technique was used to visually analyze the soluble solids content (SSC) of navel orange. By comparison of the results
the model using the spectra pretreated by baseline correction as the input was the best one. Based on the baseline corrected spectra
successive projections algorithm (SPA) was applied to select feature wavelengths and finally 9 bands were remained. The results of the partial least squares regression (PLSR) model for SSC prediction indicate that the correlation coefficient of calibration (
r
cal
) is 0.891
the root mean square error of calibration (RSMEC) is 0.612
the correlation coefficient of prediction (
r
pre
) is 0.889
and the root mean square error of prediction (RMSEP) is 0.630
respectively. Using the spectra of feature wavelengths as the input
the multiple linear regression (MLR) models for SSC prediction were calibrated. Based on the MLR model
each pixel value of the images was calculated. Combined with the image processing
the distribution maps of SSC in navel orange were drawn. So
the SSC of navel orange can be intuitive judged.
脐橙可溶性固形物高光谱成像可视化无损检测
navel orangesoluble solids contenthyperspectral imagingvisualizationnondestructive detection
WU L G, HE J G, LIU G S, et al.. Detection of common defects on jujube using Vis-NIR and NIR hyperspectral imaging [J]. Postharv. Biol. Technol., 2016, 112:134-142.
李江波, 饶秀勤, 应义斌, 等. 基于高光谱成像技术检测脐橙溃疡 [J]. 农业工程学报, 2010, 26(8):222-228. LI J B, RAO X Q, YING Y B, et al.. Detection of navel oranges canker based on hyperspectral imaging technology [J]. Trans. CSAE, 2010, 26(8):222-228. (in Chinese)
田有文, 程怡, 王小奇, 等. 基于高光谱成像的苹果虫害检测特征向量的选取 [J]. 农业工程学报, 2014, 30(12):132-139. TIAN Y W, CHENG Y, WANG X Q, et al.. Feature vectors determination for pest detection on apples based on hyperspectral imaging [J]. Trans. Chin. Soc. Agric. Eng., 2014, 30(12):132-139. (in Chinese)
章海亮, 高俊峰, 何勇. 基于高光谱成像技术的柑橘缺陷无损检测 [J]. 农业机械学报, 2013, 44(9):177-181. ZHANG H L, GAO J F, HE Y. Nondestructive detection of Citrus defection using hyper-spectra imaging technology [J]. Trans. Chin. Soc. Agric. Mech., 2013, 44(9):177-181. (in Chinese)
LI J B, CHEN L P, HUANG W Q, et al.. Multispectral detection of skin defects of bi-colored peaches based on Vis-NIR hyperspectral imaging [J]. Postharv. Biol. Technol., 2016, 112:121-133.
YU K Q, ZHAO Y R, LI X L, et al.. Identification of crack features in fresh jujube using Vis/NIR hyperspectral imaging combined with image processing [J]. Comput. Electron. Agric., 2014,103:1-10.
李丹, 何建国, 刘贵珊, 等. 基于高光谱成像技术的小黄瓜水分无损检测 [J]. 红外与激光工程, 2014, 43(7):2393-2397. LI D, HE J G, LIU G S, et al.. Non-destructive detection of moisture content in gherkin using hyperspectral imaging [J]. Infrared Laser Eng., 2014, 43(7):2393-2397. (in Chinese)
LI B C, HOU B L, ZHANG D W, et al.. Pears characteristics (soluble solids content and firmness prediction, varieties) testing methods based on visible-near infrared hyperspectral imaging [J]. Optik-Int. J. Light Electron. Opt., 2016, 127(5):2624-2630.
詹白勺, 倪君辉, 李军. 高光谱技术结合CARS算法的库尔勒香梨可溶性固形物定量测定 [J]. 光谱学与光谱分析, 2014, 34(10):2752-2757. ZHAN B S, NI J H, LI J. Hyperspectral technology combined with CARS algorithm to quantitatively determine the SSC in Korla Fragrant pear [J]. Spectrosc. Spect. Anal., 2014, 34(10):2752-2757. (in Chinese)
LEIVA-VALENZUELA G A, LU R F, AGUILERA J M. Prediction of firmness and soluble solids content of blueberries using hyperspectral reflectance imaging [J]. J. Food Eng., 2013, 115(1):91-98.
马本学, 肖文东, 祁想想, 等. 基于漫反射高光谱成像技术的哈密瓜糖度无损检测研究 [J]. 光谱学与光谱分析, 2012, 32(11):3093-3097. MA B X, XIAO W D, QI X X, et al.. Nondestructive measurement of sugar content of Hami melon based on diffuse reflectance hyperspectral imaging technique [J]. Spectrosc. Spect. Anal., 2012, 32(11):3093-3097. (in Chinese)
董金磊, 郭文川. 采后猕猴桃可溶性固形物含量的高光谱无损检测 [J]. 食品科学, 2015, 36(16):101-106. DONG J L, GUO W C. Nondestructive detection of soluble solid content of postharvest kiwifruits based on hyperspectral imaging technology [J]. Food Sci., 2015, 36(16):101-106. (in Chinese)
BAIANO A, TERRACONE C, PERI G, et al.. Application of hyperspectral imaging for prediction of physico-chemical and sensory characteristics of table grapes [J]. Comput. Electron. Agric., 2012, 87:142-151.
ELMASRY G, WANG N, ELSAYED A, et al.. Hyperspectral imaging for nondestructive determination of some quality attributes for strawberry [J]. J. Food Eng., 2007, 81(1):98-107.
SIVAKUMAR S S, QIAO J, WANG N, et al. Detecting maturity parameters of mango using hyperspectral imaging technique [C]. American Society of Agricultural and Biological Engineers Annual Meeting, Portland, Oregon, 2006.
RAJKUMAR P, WANG N, EIMASRY G, et al.. Studies on banana fruit quality and maturity stages using hyperspectral imaging [J]. J. Food Eng., 2012, 108(1):194-200.
薛建新, 张淑娟, 张晶晶. 基于高光谱成像技术的沙金杏成熟度判别 [J]. 农业工程学报, 2015, 31(11):300-307. XUE J X, ZHANG S J, ZHANG J J. Ripeness classification of Shajin apricot using hyperspectral imaging technique [J]. Trans. Chin. Soc. Agric. Eng., 2015, 31(11):300-307. (in Chinese)
ZHANG C, GUO C T, LIU F, et al.. Hyperspectral imaging analysis for ripeness evaluation of strawberry with support vector machine [J]. J. Food Eng., 2016, 179:11-18.
PU Y Y, SUN D W. Prediction of moisture content uniformity of microwave-vacuum dried mangoes as affected by different shapes using NIR hyperspectral imaging [J]. Innov. Food Sci. Emerg. Technol., 2016, 33:348-356.
郭志明, 赵春江, 黄文倩, 等. 苹果糖度高光谱图像可视化预测的光强度校正方法 [J]. 农业机械学报, 2015, 46(7):227-232. GUO Z M, ZHAO C J, HUANG W Q, et al.. Intensity correction of visualized prediction for sugar content in apple using hyperspectral imaging [J]. Trans. Chin. Soc. Agric. Mech., 2015, 46(7):227-232. (in Chinese)
SUN M J, ZHANG D, LIU L, et al.. How to predict the sugariness and hardness of melons: a near-infrared hyperspectral imaging method [J]. Food Chem., 2017, 218:413-421.
郭恩有, 刘木华, 赵杰文, 等. 脐橙糖度的高光谱图像无损检测技术 [J]. 农业机械学报, 2008, 39(5):91-93. GUO E Y, LIU M H, ZHAO J W, et al.. Nondestructive detection of sugar content on navel orange with hyperspectral imaging [J]. Trans. Chin. Soc. Agric. Mech., 2008, 39(5):91-93. (in Chinese)
许文丽, 孙通, 吴文强, 等. 可见/近红外漫反射与漫透射检测脐橙可溶性固形物的对比研究 [J]. 光子学报, 2013, 42(12):1486-1490. XU W L, SUN T, WU W Q, et al.. Contrast of Vis/NIR reflection and diffuse transmission in detecting the SSC of Ganan navel oranges [J]. Acta Photon. Sinica, 2013, 42(12):1486-1490. (in Chinese)
贾志成, 薛允艳, 陈雷, 等. 基于去噪降维和蝙蝠优化的高光谱图像盲解混算法 [J]. 光子学报, 2016, 45(5):0511001-1-10. JIA Z C, XUE Y Y, CHEN L, et al.. Blind separation algorithm for hyperspectral image based on the denoising reduction and the bat optimization [J]. Acta Photon. Sinica, 2016, 45(5):0511001-1-10. (in Chinese)
介邓飞, 谢丽娟, 饶秀勤, 等. 近红外光谱变量筛选提高西瓜糖度预测模型精度 [J]. 农业工程学报, 2013, 29(12):264-270. JIE D F, XIE L J, RAO X Q, et al.. Improving accuracy of prediction model for soluble solids content of watermelon by variable selection based on near-infrared spectroscopy [J]. Trans. Chin. Soc. Agric. Eng., 2013, 29(12):264-270. (in Chinese)
顾建芹. 暗柳橙及其突变体红暗柳橙果实发育过程中糖酸组分的变化 [D]. 武汉: 华中农业大学, 2007. GUJ Q. Changing of Sugar and Acid Contents of Anliu Sweet Orange and Its Mutant Red Anliu Sweet Orange During Fruit Developments[D]. Wuhan: Huazhong Agricultural University, 2007. (in Chinese)
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