西红柿成熟度的近红外漫透射光谱无损检测
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摘要
为了证实以近红外漫透射光谱技术结合化学计量学方法能有效实现西红柿成熟度的检测,采用美国海洋光学公司的QE65000光谱仪取得了西红柿的漫透射光谱数据,采用日本柯尼卡美能达CR-10反射式色差计取得了亮度、红绿色相、黄蓝色相和总色差颜色数据;通过标准数学建模方法建立了颜色模型,并通过偏最小二乘回归校正算法建立了近红外光谱模型。结果表明,两组模型都是以红绿色相指标建模时效果最佳,在99个未知样品数据中,预测模型的颜色模型误判情况分别为未成熟0%、半成熟33.33%、成熟0%;而近红外预测模型的误判情况分别为未成熟32.14%、半成熟50%、成熟0%。该研究验证了近红外光谱技术对西红柿成熟度进行无损检测的可行性,这对实现西红柿的快速、批量分选具有一定的实际意义。
In order to prove that near-infrared diffuse transmission spectroscopy combining with chemometrics can effectively detect the maturity of tomatoes,diffuse transmission spectroscopy data of tomatoes were obtained by using QE65000 spectrometer of Ocean Optics Company of America,and the color datum of brightness,red and green,yellow blue color and total chromatic aberration were obtained by using CR-10 reflectance colorimeter of Konica Minolta of Japan.The color model was established by the standard mathematical modeling method,and the near infrared spectroscopy model was established by the partial least squares regression correction algorithm.The results show that,the red-green phase index was the best for both models.Among the 99 unknown samples,the misjudgments of the color model were immature 0%,semi-mature 33.33% and mature 0%,while the misjudgments of the near-infrared spectroscopy prediction model were immature 32.14%,semi-mature 50% and mature 0%respectively.This study verifies the feasibility of non-destructive detection of tomato maturity by near infrared spectroscopy,which has a certain practical significance for rapid and batch sorting of tomatoes.
In order to prove that near-infrared diffuse transmission spectroscopy combining with chemometrics can effectively detect the maturity of tomatoes,diffuse transmission spectroscopy data of tomatoes were obtained by using QE65000 spectrometer of Ocean Optics Company of America,and the color datum of brightness,red and green,yellow blue color and total chromatic aberration were obtained by using CR-10 reflectance colorimeter of Konica Minolta of Japan.The color model was established by the standard mathematical modeling method,and the near infrared spectroscopy model was established by the partial least squares regression correction algorithm.The results show that,the red-green phase index was the best for both models.Among the 99 unknown samples,the misjudgments of the color model were immature 0%,semi-mature 33.33% and mature 0%,while the misjudgments of the near-infrared spectroscopy prediction model were immature 32.14%,semi-mature 50% and mature 0%respectively.This study verifies the feasibility of non-destructive detection of tomato maturity by near infrared spectroscopy,which has a certain practical significance for rapid and batch sorting of tomatoes.
引文
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[19]OUYANG A G,XIE X Q,ZHOU Y R,et al.Partial least squares regression variable screening studies on apple soluble solids NIRspectral detection[J].Spectroscopy and Spectral Analysis,2012,32(10):2680-2684(in Chinese).
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[2]CAMPS C,SIMONE C,GILLI C.Assessment of tomato quality using portable NIR spectroscopy and PLSR with wavelengths selection[DB/OL].(2014-12-01)[2018-04-08].https://www.researchgate.net/publication/290241088.
[3]ZHANG B F,YUAN L B,ZHANG B X.Study on predicting total acid content and soluble sugar of tomato juice by near infrared optical fiber spectrometer technique[J].Spectroscopy and Spectral Analysis,2014,34(2):350-353(in Chinese).
[4]SUN T,XU H R,YING Y B.Progress in application of near infrared spectroscopy to nondestructive on-line detection of products/food quality[J].Spectroscopy and Spectral Analysis,2009,29(1):122-126(in Chinese).
[5]LIU Y D,WU M M,SUN X D,et al.Simultaneous detection of surface deficiency and soluble solids content for amygdalus persica by online visible-near infrared transmittance spectroscopy[J].Transactions of the Chinese Society of Agricultural Engineering,2016,32(6):289-295(in Chinese).
[6]PAN Y Y,LIU Y D,SUN X D.Research progress o f on-line detection of fruit quality using near infrared spectroscopy[J].Laser&Infrared,2010,40(8):805-810(in Chinese).
[7]CHU X L,LIU H Y,YAN Z Ch.Near-infrared spectroscopy technology practical manual[M].Beijing:Machinery Industry Press,2016:114-133(in Chinese).
[8]RAO X Q,YING Y B.Grading a fruit by it’s surface color[J].Journal of Zhejiang University(Engineering Science Edition),2009,43(5):869-871(in Chinese).
[9]LIU Y D,CHEN X M,OUYANG A G.Non-destructive measurement of surface color of pear by visible/near-infrared diffuse reflectance spectra[J].Journal of Infrared and Millimeter Waves,2008,27(4):266-268(in Chinese).
[10]ZHANG Y P,LI Ch,ZHAO M,et al.Analysis of color difference of cigarette packaging material based spectroscopy on near-infrared[J].Tobacco Science&Technology,2016,49(2):75-81(in Chinese).
[11]QIN J,ZHANG M M,ZHAO G J,et al.Characterization of wood surface treated with electroless copper plating by near infrared spectroscopy technology[J].Spectroscopy and Spectral Analysis,2015,35(5):1253-1257(in Chinese).
[12]PANG X M,YAN Z J,KAN J M.Color image segmentation based on HSI and LAB color space[J].Journal of Guangxi University(Natural Science Edition),2011,36(6):976-980(in Chinese).
[13]JI Sh J,LUO Y,LI D H,et al.Effect of honey color on determination model establishment of near infrared spectrum[J].Science and Technology of Food Industry,2010,31(8):345-347(in Chinese).
[14]YAN Y L.Fundamentals and applications of near infrared spectroscopy[M].Beijing:China Light Industry Press,2005:8-39(in Chinese).
[15]CHU X L.Chemometrics and molecular spectroscopy[M].Beijing:Chemical Industry Press,2011:41-53(in Chinese).
[16]MA C H,CUI J L.Quantitative analysis of composition in molten steel by LIBS based on improved partial least squares[J].Laser Technology,2016,40(6):876-881(in Chinese).
[17]LIU Y D,XIE Q H,WANG H Y,et al.Quantitative study on phosmet residues in navel oranges based on surface enhanced Raman spectra[J].Laser Technology,2017,41(4):545-548(in Chinese).
[18]FU Y,ZHANG Y J,CHEN H C,et al.Establishment of NIR models for components determination in fresh chestnut[J].Food Science and Technology,2012,37(5):42-45(in Chinese).
[19]OUYANG A G,XIE X Q,ZHOU Y R,et al.Partial least squares regression variable screening studies on apple soluble solids NIRspectral detection[J].Spectroscopy and Spectral Analysis,2012,32(10):2680-2684(in Chinese).
[20]XU L,SHAO X G.Methods of chemometrics[M].2nd ed.Beijing:Science Press,2004:163-169(in Chinese).