基于四方对称光源透射光谱的脐橙可溶性固形物检测
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摘要
提高利用可见-近红外(Vis-NIR)透射光谱检测脐橙内部物质含量的准确性在生产实际中具有重要意义。该研究利用特制的可见-近红外透射光谱测量装置采集了199个福本脐橙果蒂向上、水平、向下3种位置的透射光谱,比较了多元散射校正(multivariate scattering correction, MSC)、标准正态变量变换(standard normal variate transformation, SNV)、一阶导数和二阶导数预处理的效果,并采用效果最好的一阶导数对透射光谱进行预处理。在此基础上,结合后向区间偏最小二乘法(backward interval partial least squares, BiPLS)优选特征波段,竞争性自适应重加权采样(competitive adaptive re-weightedsampling,CARS)挑选特征变量建立了基于果蒂向上、水平、向下3种位置各自的透射光谱以及3种位置的平均光谱和加权光谱的可溶性固形物(soluble solid content, SSC)的偏最小二乘(partial least squares, PLS)模型。在果蒂向上、水平、向下3种位置各自的透射光谱建立的PLS模型中,基于果蒂水平位置透射光谱的PLS模型最优,校正相关系数为0.914,校正均方根误差为0.380,预测相关系数为0.924,预测均方根误差为0.404。基于果蒂向上、水平、向下3种位置平均透射光谱和加权透射光谱建立的PLS模型均取得了较好的预测结果,预测相关系数均大于0.91,预测均方根误差均小于0.43。该研究可以为脐橙内部物质含量在线检测装备的研制提供参考。
Navel orange is a very popular fruit in China, which is mainly cultivated along the Yangtze River. Navel oranges are classified into different grades based on external quality and internal quality before they are sold. Soluble solids content is one of the main indices for evaluating the internal quality of navel orange. Therefore, it is very important to improve the detection accuracy of soluble solids content in production. So far, visible and near infrared spectroscopy(Vis-NIR) is one of the most widely used and effective techniques in internal quality assessment of fruits. In this study, 199 Fukumoto navel oranges were taken as experimental samples. The transmission spectra of navel oranges of three positions including pedicle upwards(P1), pedicle horizontal(P2) and pedicle downward(P3) were acquired by using a special visible and near infrared transmission spectrum measurement system designed by ourselves. The average spectra(P4) and weighted spectra(P5) of P1, P2 and P3 were calculated. The transmission spectra, including P1, P2, P3, P4 and P5 were preprocessed by multivariate scattering correction, standard normal variate transformation, first derivative and second derivative respectively. The best pretreatment results were obtained based on first derivative after comparative study. Then the spectra data preprocessed by first derivative were divided into 30 to 50 intervals with step length of 5, and backward interval partial least squares was used to select the optimal band combination. Good results observed when P1, P2, P3, P4 and P5 were divided into 35, 40, 30, 35 and 40 intervals, in which 161, 180, 114, 308 and 170 variables were retained. On this basis, competitive adaptive re-weighted sampling(CARS) was used to select feature variables. After running CARS for 20 times in each selection, 24, 23, 18, 39 and 22 variables were kept respectively. Finally, Five PLS models were established, including P1-PLS, P2-PLS, P3-PLS, P4-PLS and P5-PLS. Among the P1-PLS, P2-PLS and P3-PLS models, P2-PLS model was the best one, as the value of correlation coefficients of prediction was 0.924 and the value of root mean square error of prediction was 0.404. This model can be realized by adjusting the navel oranges to pedicle horizontal in modeling. P4-PLS model and P5-PLS model had achieved good prediction results, as the value of correlation coefficients of prediction was higher than 0.91 and the value of root mean square error of prediction was lower than 0.43. P4-PLS model was based on the average spectra of P1, P2 and P3, and had potential to be realized by rolling the navel oranges in actual application. However, P5-PLS model was based on weighted spectra of P1, P2 and P3, which was difficult to realize in on-line detection. This study can provide a reference for the development of on-line detection equipment for the assessment of internal content of substances in navel orange.
Navel orange is a very popular fruit in China, which is mainly cultivated along the Yangtze River. Navel oranges are classified into different grades based on external quality and internal quality before they are sold. Soluble solids content is one of the main indices for evaluating the internal quality of navel orange. Therefore, it is very important to improve the detection accuracy of soluble solids content in production. So far, visible and near infrared spectroscopy(Vis-NIR) is one of the most widely used and effective techniques in internal quality assessment of fruits. In this study, 199 Fukumoto navel oranges were taken as experimental samples. The transmission spectra of navel oranges of three positions including pedicle upwards(P1), pedicle horizontal(P2) and pedicle downward(P3) were acquired by using a special visible and near infrared transmission spectrum measurement system designed by ourselves. The average spectra(P4) and weighted spectra(P5) of P1, P2 and P3 were calculated. The transmission spectra, including P1, P2, P3, P4 and P5 were preprocessed by multivariate scattering correction, standard normal variate transformation, first derivative and second derivative respectively. The best pretreatment results were obtained based on first derivative after comparative study. Then the spectra data preprocessed by first derivative were divided into 30 to 50 intervals with step length of 5, and backward interval partial least squares was used to select the optimal band combination. Good results observed when P1, P2, P3, P4 and P5 were divided into 35, 40, 30, 35 and 40 intervals, in which 161, 180, 114, 308 and 170 variables were retained. On this basis, competitive adaptive re-weighted sampling(CARS) was used to select feature variables. After running CARS for 20 times in each selection, 24, 23, 18, 39 and 22 variables were kept respectively. Finally, Five PLS models were established, including P1-PLS, P2-PLS, P3-PLS, P4-PLS and P5-PLS. Among the P1-PLS, P2-PLS and P3-PLS models, P2-PLS model was the best one, as the value of correlation coefficients of prediction was 0.924 and the value of root mean square error of prediction was 0.404. This model can be realized by adjusting the navel oranges to pedicle horizontal in modeling. P4-PLS model and P5-PLS model had achieved good prediction results, as the value of correlation coefficients of prediction was higher than 0.91 and the value of root mean square error of prediction was lower than 0.43. P4-PLS model was based on the average spectra of P1, P2 and P3, and had potential to be realized by rolling the navel oranges in actual application. However, P5-PLS model was based on weighted spectra of P1, P2 and P3, which was difficult to realize in on-line detection. This study can provide a reference for the development of on-line detection equipment for the assessment of internal content of substances in navel orange.
引文
[1]Magwaza L S,Opara U L,Nieuwoudt H,et al.NIRspectroscopy applications for internal and external quality analysis of citrus fruit:A review[J].Food and Bioprocess Technology,2012,5:425-444.
[2]Xie L J,Wang A C,Xu H R,et al.Applications of near-infrared systems for quality evaluation of fruits:A-review[J].Transactions of the ASABE,2016,59(2):399-419.
[3]Nicolai B M,Defraeye T,Ketelaere B D,et al.Nondestructive measurement of fruit and vegetable quality[J]Annual Review of Food Science and Technology,2014,5:285-312.
[4]Magwaza L S,Opara U L,Nieuwoudt H,et al.NIRspectroscopy applications for internal and external quality analysis of citrus fruit-A review[J].Food and Bioprocess Technology,2012,5:425-444.
[5]Wang H L,Peng J Y,Xie C Q,et al.Fruit quality evaluation using spectroscopy technology:A review[J].Sensors,2015,15:11889-11927.
[6]Yuan L M,Cai J R,Sun L,et al.Nondestructive measurement of soluble solids content in apples by a portable fruit analyzer[J].Food Analytical Methods,2016,9:785-794.
[7]Fan S X,Guo Z M,Zhang B H,et al.Using Vis/NIR diffuse transmittance spectroscopy and multivariate analysis to predicate soluble solids content of apple[J].Food Analytical Methods,2016,9:1333-1343.
[8]Luo X,Ye Z Z,Xu H R,et al.Robustness improvement of NIR-based determination of soluble solids in apple fruit by local calibration[J].Postharvest Biology and Technology2018,139:82-90.
[9]李龙,彭彦昆,李永玉,等.苹果内外品质在线无损检测分级系统设计与试验[J].农业工程学报,2018,34(9):267-275.Li Long,Peng Yankun,Li Yongyu,et al.Design and experiment on grading system for online non-destructive detection of internal and external quality of apple[J].Transactions of the Chinese Society of Agricultural Engineering(Transactions of the CSAE),2018,34(9):267-275.(in Chinese with English abstract)
[10]刘燕德,吴明明,孙旭东,等.黄桃表面缺陷和可溶性固形物光谱同时在线检测[J].农业工程学报,2016,32(6):289-295.Liu Yande,Wu Mingming,Sun Xudong,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(Transactions of the CSAE),2016,32(6):289-295.(in Chinese with English abstract)
[11]Nascimento P M,Carvalho L C,Junior L C,et al.Robust PLS models for soluble solids content and firmness determination in low chilling peach using near-infrared spectroscopy(NIR)[J].Postharvest Biology and Technology,2016,111:345-351.
[12]介邓飞,陈猛,谢丽娟,等.适宜西瓜检测部位提高近红外光谱糖度预测模型精度[J].农业工程学报,2014,30(9):229-234.Jie Dengfei,Chen Meng,Xie Lijuan,et al.Improving precision of soluble solid content predictive model by adopting suitable detective position of watermelon based on near infrared spectroscopy[J].Transactions of the Chinese Society of Agricultural Engineering(Transactions of the CSAE),2014,30(9):229-234.(in Chinese with English abstract)
[13]钱曼,黄文倩,王庆艳,等.西瓜检测部位差异对近红外光谱可溶性固形物预测模型的影响[J].光谱学与光谱分析,2016,36(6):1700-1705.Qian Man,Huang Wenqian,Wang Qingyan,et al.Assessment of influence detective position variability on precision of near infrared models for soluble solid content of watermelon[J].Spectroscopy and Spectral Analysis,2016,36(6):1700-1705.(in Chinese with English abstract)
[14]樊书祥,黄文倩,李江波,等.LS-SVM的梨可溶性固形物近红外光谱检测的特征波长筛选[J].光谱学与光谱分析,2014,34(8):2089-2093.Fan Shuxiang,Huang Wenqian,Li Jiangbo,et al.Characteristic wavelengths selection of soluble solids content of pear based on NIR spectral and LS-SVM[J].Spectroscopy and Spectral Analysis,2014,34(8):2089-2093.(in Chinese with English abstract)
[15]Sun X D,Liu Y D,Li Y F,et al.Simultaneous measurement of brown core and soluble solids content in pear by on-line visible and near infrared spectroscopy[J].Postharvest Biology and Technology,2016,116:80-87.
[16]Jamshidi B,Minaei S,Mohajerani E,et al.Reflectance Vis/NIR spectroscopy for nondestructive taste characterization of valencia oranges[J].Computers and Electronics in Agriculture,2012,85:64-69.
[17]Ncama K,Opara U L,Tesfay S Z,et al.Application of Vis/NIR spectroscopy for predicting sweetness and flavour parameters of‘valencia’orange(citrus sinensis)and‘star ruby’grapefruit(citrus x paradisi macfad)[J].Journal of Food Engineering,2017,193:86-94.
[18]Peiris K,Kays S.Spatial variability of soluble solids or dry-matter content within individual fruits,bulbs,or tubers implications for the development and use of NIRspectrometric techniques[J].HortScience,1999,34(1):114-117.
[19]刘燕德,施宇,蔡丽君,等.基于CARS算法的脐橙可溶性固形物近红外在线检测[J].农业机械学报,2013,44(9):138-144.Liu Yande,Shi Yu,Cai Lijun,et al.On-line NIR detection model optimization of soluble solids content in navel orange based on CARS[J].Transactions of Chinese Society for Agricultural Machinery,2013,44(9):138-144.(in Chinese with English abstract)
[20]刘燕德,胡军,欧阳玉平,等.赣南脐橙可溶性固形物近红外光谱在线无损检测[J].广东农业科学,2016,43(9):105-111.Liu Yande,Hu Jun,Ouyang Yuping,et al.Online detection of soluble solids content for gannan navel by visible-near infrared diffuse transmission spectroscopy[J].Guangdong Agricultural Sciences,2016,43(9):105-111.(in Chinese with English abstract)
[21]Fraser D G,Jordan R B,Kunnemeyer R,et al.Light distribution inside mandarin fruit during internal quality assessment by NIR spectroscopy[J].Postharvest Biology and Technology,2003,27:185-196.
[22]孙通,莫欣欣,刘木华.果皮对脐橙可溶性固形物可见/近红外检测精度的影响[J].光谱学与光谱分析,2018,38(5):1406-1411.Sun Tong,Mo Xinxin,Liu Muhua.Effect of pericarp on prediction accuracy of soluble solid content in navel oranges by visible/near infrared spectroscopy[J].Spectroscopy and Spectral Analysis,2018,38(5):1406-1411.(in Chinese with English abstract)
[23]许文丽,孙通,吴文强,等.脐橙放置位置对近红外光谱检测结果的影响[J].光谱学与光谱分析,2012,32(11):3002-3005.Xu Wenli,Sun Tong,Wu Wenqiang,et al.Near-infrared spectrum detection result influenced by navel oranges placement position[J].Spectroscopy and Spectral Analysis,2012,32(11):3002-3005.(in Chinese with English abstract)
[24]Gomez A H,He Y,Pereira A G.Non-destructive measurement of acidity,soluble solids and firmness of satsuma mandarin using Vis/NIR spectroscopy techniques[J].Journal of Food Engineering,2006,77:313-319.
[25]Golic M,Walsh K,Lawson P.Short-wavelength near-infrared spectra of sucrose,glucose,and fructose with respect to sugar concentration and temperature[J].Applied Spectroscopy,2003,57:139-145.
[26]Magwaza L S,Opara U L,Terry L A,et al.Evaluation of fourier transform-NIR spectroscopy for integrated external and internal quality assessment of valencia oranges[J].Journal of Food Composition and Analysis,2013,31:144-154.
[27]Leardi R,Norgaard L.Sequential application of backward interval partial least squares and genetic of relevant spectral regions[J].Journal of Chemometrics,2004,18:486-497.
[28]Zhou X B,Zhao J W,Li Y X.Selection of the efficient wavelength regions in FT-NIR spectroscopy for determination of SSC of‘fuji’apple based on BiPLS and FiPLS models[J].Vibrational Spectroscopy,2007,44:220-227.
[29]Fan S X,Zhang B H,Li J B,et al.Effect of spectrum measurement position variation on the robustness of NIRspectroscopy models for soluble solids content of apple[J].Biosystems Engineering,2016,143:9-19.
[30]Li H D,Liang Y Z,Xu Q S,et al.Key wavelengths screening using competitive adaptive reweighted sampling method for multivariate calibration[J].Analytica Chimica Acta,2009,648:77-84.
[31]Dhanoa M S,Lister S J,Sanderson R,et al.The link between multiplicative scatter correction(MSC)and standard normal variate(SNV)transformations of NIR spectra[J].Journal of Near Infrared Spectroscopy,1994,2:43-47.
[2]Xie L J,Wang A C,Xu H R,et al.Applications of near-infrared systems for quality evaluation of fruits:A-review[J].Transactions of the ASABE,2016,59(2):399-419.
[3]Nicolai B M,Defraeye T,Ketelaere B D,et al.Nondestructive measurement of fruit and vegetable quality[J]Annual Review of Food Science and Technology,2014,5:285-312.
[4]Magwaza L S,Opara U L,Nieuwoudt H,et al.NIRspectroscopy applications for internal and external quality analysis of citrus fruit-A review[J].Food and Bioprocess Technology,2012,5:425-444.
[5]Wang H L,Peng J Y,Xie C Q,et al.Fruit quality evaluation using spectroscopy technology:A review[J].Sensors,2015,15:11889-11927.
[6]Yuan L M,Cai J R,Sun L,et al.Nondestructive measurement of soluble solids content in apples by a portable fruit analyzer[J].Food Analytical Methods,2016,9:785-794.
[7]Fan S X,Guo Z M,Zhang B H,et al.Using Vis/NIR diffuse transmittance spectroscopy and multivariate analysis to predicate soluble solids content of apple[J].Food Analytical Methods,2016,9:1333-1343.
[8]Luo X,Ye Z Z,Xu H R,et al.Robustness improvement of NIR-based determination of soluble solids in apple fruit by local calibration[J].Postharvest Biology and Technology2018,139:82-90.
[9]李龙,彭彦昆,李永玉,等.苹果内外品质在线无损检测分级系统设计与试验[J].农业工程学报,2018,34(9):267-275.Li Long,Peng Yankun,Li Yongyu,et al.Design and experiment on grading system for online non-destructive detection of internal and external quality of apple[J].Transactions of the Chinese Society of Agricultural Engineering(Transactions of the CSAE),2018,34(9):267-275.(in Chinese with English abstract)
[10]刘燕德,吴明明,孙旭东,等.黄桃表面缺陷和可溶性固形物光谱同时在线检测[J].农业工程学报,2016,32(6):289-295.Liu Yande,Wu Mingming,Sun Xudong,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(Transactions of the CSAE),2016,32(6):289-295.(in Chinese with English abstract)
[11]Nascimento P M,Carvalho L C,Junior L C,et al.Robust PLS models for soluble solids content and firmness determination in low chilling peach using near-infrared spectroscopy(NIR)[J].Postharvest Biology and Technology,2016,111:345-351.
[12]介邓飞,陈猛,谢丽娟,等.适宜西瓜检测部位提高近红外光谱糖度预测模型精度[J].农业工程学报,2014,30(9):229-234.Jie Dengfei,Chen Meng,Xie Lijuan,et al.Improving precision of soluble solid content predictive model by adopting suitable detective position of watermelon based on near infrared spectroscopy[J].Transactions of the Chinese Society of Agricultural Engineering(Transactions of the CSAE),2014,30(9):229-234.(in Chinese with English abstract)
[13]钱曼,黄文倩,王庆艳,等.西瓜检测部位差异对近红外光谱可溶性固形物预测模型的影响[J].光谱学与光谱分析,2016,36(6):1700-1705.Qian Man,Huang Wenqian,Wang Qingyan,et al.Assessment of influence detective position variability on precision of near infrared models for soluble solid content of watermelon[J].Spectroscopy and Spectral Analysis,2016,36(6):1700-1705.(in Chinese with English abstract)
[14]樊书祥,黄文倩,李江波,等.LS-SVM的梨可溶性固形物近红外光谱检测的特征波长筛选[J].光谱学与光谱分析,2014,34(8):2089-2093.Fan Shuxiang,Huang Wenqian,Li Jiangbo,et al.Characteristic wavelengths selection of soluble solids content of pear based on NIR spectral and LS-SVM[J].Spectroscopy and Spectral Analysis,2014,34(8):2089-2093.(in Chinese with English abstract)
[15]Sun X D,Liu Y D,Li Y F,et al.Simultaneous measurement of brown core and soluble solids content in pear by on-line visible and near infrared spectroscopy[J].Postharvest Biology and Technology,2016,116:80-87.
[16]Jamshidi B,Minaei S,Mohajerani E,et al.Reflectance Vis/NIR spectroscopy for nondestructive taste characterization of valencia oranges[J].Computers and Electronics in Agriculture,2012,85:64-69.
[17]Ncama K,Opara U L,Tesfay S Z,et al.Application of Vis/NIR spectroscopy for predicting sweetness and flavour parameters of‘valencia’orange(citrus sinensis)and‘star ruby’grapefruit(citrus x paradisi macfad)[J].Journal of Food Engineering,2017,193:86-94.
[18]Peiris K,Kays S.Spatial variability of soluble solids or dry-matter content within individual fruits,bulbs,or tubers implications for the development and use of NIRspectrometric techniques[J].HortScience,1999,34(1):114-117.
[19]刘燕德,施宇,蔡丽君,等.基于CARS算法的脐橙可溶性固形物近红外在线检测[J].农业机械学报,2013,44(9):138-144.Liu Yande,Shi Yu,Cai Lijun,et al.On-line NIR detection model optimization of soluble solids content in navel orange based on CARS[J].Transactions of Chinese Society for Agricultural Machinery,2013,44(9):138-144.(in Chinese with English abstract)
[20]刘燕德,胡军,欧阳玉平,等.赣南脐橙可溶性固形物近红外光谱在线无损检测[J].广东农业科学,2016,43(9):105-111.Liu Yande,Hu Jun,Ouyang Yuping,et al.Online detection of soluble solids content for gannan navel by visible-near infrared diffuse transmission spectroscopy[J].Guangdong Agricultural Sciences,2016,43(9):105-111.(in Chinese with English abstract)
[21]Fraser D G,Jordan R B,Kunnemeyer R,et al.Light distribution inside mandarin fruit during internal quality assessment by NIR spectroscopy[J].Postharvest Biology and Technology,2003,27:185-196.
[22]孙通,莫欣欣,刘木华.果皮对脐橙可溶性固形物可见/近红外检测精度的影响[J].光谱学与光谱分析,2018,38(5):1406-1411.Sun Tong,Mo Xinxin,Liu Muhua.Effect of pericarp on prediction accuracy of soluble solid content in navel oranges by visible/near infrared spectroscopy[J].Spectroscopy and Spectral Analysis,2018,38(5):1406-1411.(in Chinese with English abstract)
[23]许文丽,孙通,吴文强,等.脐橙放置位置对近红外光谱检测结果的影响[J].光谱学与光谱分析,2012,32(11):3002-3005.Xu Wenli,Sun Tong,Wu Wenqiang,et al.Near-infrared spectrum detection result influenced by navel oranges placement position[J].Spectroscopy and Spectral Analysis,2012,32(11):3002-3005.(in Chinese with English abstract)
[24]Gomez A H,He Y,Pereira A G.Non-destructive measurement of acidity,soluble solids and firmness of satsuma mandarin using Vis/NIR spectroscopy techniques[J].Journal of Food Engineering,2006,77:313-319.
[25]Golic M,Walsh K,Lawson P.Short-wavelength near-infrared spectra of sucrose,glucose,and fructose with respect to sugar concentration and temperature[J].Applied Spectroscopy,2003,57:139-145.
[26]Magwaza L S,Opara U L,Terry L A,et al.Evaluation of fourier transform-NIR spectroscopy for integrated external and internal quality assessment of valencia oranges[J].Journal of Food Composition and Analysis,2013,31:144-154.
[27]Leardi R,Norgaard L.Sequential application of backward interval partial least squares and genetic of relevant spectral regions[J].Journal of Chemometrics,2004,18:486-497.
[28]Zhou X B,Zhao J W,Li Y X.Selection of the efficient wavelength regions in FT-NIR spectroscopy for determination of SSC of‘fuji’apple based on BiPLS and FiPLS models[J].Vibrational Spectroscopy,2007,44:220-227.
[29]Fan S X,Zhang B H,Li J B,et al.Effect of spectrum measurement position variation on the robustness of NIRspectroscopy models for soluble solids content of apple[J].Biosystems Engineering,2016,143:9-19.
[30]Li H D,Liang Y Z,Xu Q S,et al.Key wavelengths screening using competitive adaptive reweighted sampling method for multivariate calibration[J].Analytica Chimica Acta,2009,648:77-84.
[31]Dhanoa M S,Lister S J,Sanderson R,et al.The link between multiplicative scatter correction(MSC)and standard normal variate(SNV)transformations of NIR spectra[J].Journal of Near Infrared Spectroscopy,1994,2:43-47.