灵武长枣蔗糖含量的高光谱无损检测
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摘要
利用高效液相色谱法检测蔗糖含量,同时运用高光谱成像技术结合化学计量方法建立蔗糖预测模型;通过竞争性自适应加权(competitive adaptive reweighted sampling,CARS)算法、连续投影算法(successive projection algorithm,SPA)和无信息消除变量(uninformative variable elimination,UVE)降维处理,建立特征波段和全波段的主成分回归(principal component regression,PCR)、偏最小二乘回归(partial least squares regression,PLSR)和多元线性回归(multivariable linear regression,MLR)模型。结果表明,采用蒙特卡洛方法剔除异常样本后,相关系数由0.611增大到0.846;正交信号校正法预处理效果最佳,RC和RP分别为0.853和0.794;利用SPA、UVE、CARS、CARS+SPA和CARS+UVE五种方法提取了5、21、17、10、18个特征变量,其中CARS-PCR模型最好,校正集、预测集的相关系数为0.861、0.843,校正集、预测集的均方根误差为0.013 mg/g和0.014 mg/g。综上,高光谱成像技术可以实现长枣蔗糖含量的预测,为更深一步探讨枣的内部品质提供参考。
In the present study, we focused on developing a rapid non-destructive method for rapid detection of the sugar content in Lingwu Changzao jujubes in order to predict the distribution of sugar content. High performance liquid chromatography(HPLC) was used to detect the sucrose content. Simultaneously, hyperspectral imaging combined with chemometrics method was used to establish a predictive model for sucrose content. The predictive models were built based on the full spectra and the feature wavelengths using partial least squares regression(PLSR), principle component regression(PCR) and multi-variable linear regression(MLR) through dimensionality reduction using competitive adaptive reweighed sampling(CARS), successive projections algorithm(SPA) and uninformative variable elimination(UVE). It was found that the correlation coefficient was increased from 0.611 to 0.846 by removing the abnormal samples using the Monte Carlo method; orthogonal signal correction(OSC) was the optimal preprocessing approach, and the correlation coefficients of calibration and prediction sets(RC and RP) of the PLS model were 0.853 and 0.794, respectively; SPA, UVE, CARS, CARS +SPA and CARS + UVE methods were used to select 5, 21, 17, 10, and 18 characteristic wavelengths, respectively. The CARS-PCR model was the best among the models developed, and its RC, RP, root mean square error of calibration(RMSEC)and root mean square error of prediction(RMSEP) values were 0.861, 0.843, 0.013 mg/g and 0.014 mg/g, respectively.According to the results of this study, hyperspectral imaging can be used to predict the sucrose content of jujube, laying the foundation for insights into the internal quality of jujubes.
In the present study, we focused on developing a rapid non-destructive method for rapid detection of the sugar content in Lingwu Changzao jujubes in order to predict the distribution of sugar content. High performance liquid chromatography(HPLC) was used to detect the sucrose content. Simultaneously, hyperspectral imaging combined with chemometrics method was used to establish a predictive model for sucrose content. The predictive models were built based on the full spectra and the feature wavelengths using partial least squares regression(PLSR), principle component regression(PCR) and multi-variable linear regression(MLR) through dimensionality reduction using competitive adaptive reweighed sampling(CARS), successive projections algorithm(SPA) and uninformative variable elimination(UVE). It was found that the correlation coefficient was increased from 0.611 to 0.846 by removing the abnormal samples using the Monte Carlo method; orthogonal signal correction(OSC) was the optimal preprocessing approach, and the correlation coefficients of calibration and prediction sets(RC and RP) of the PLS model were 0.853 and 0.794, respectively; SPA, UVE, CARS, CARS +SPA and CARS + UVE methods were used to select 5, 21, 17, 10, and 18 characteristic wavelengths, respectively. The CARS-PCR model was the best among the models developed, and its RC, RP, root mean square error of calibration(RMSEC)and root mean square error of prediction(RMSEP) values were 0.861, 0.843, 0.013 mg/g and 0.014 mg/g, respectively.According to the results of this study, hyperspectral imaging can be used to predict the sucrose content of jujube, laying the foundation for insights into the internal quality of jujubes.
引文
[1]吴龙国,王松磊,康宁波,等.基于高光谱成像技术的灵武长枣缺陷识别[J].农业工程学报,2015,31(20):281-286.DOI:10.11975/j.issn.1002-6819.2015.20.039.
[2]FU L.Simulation study of vibratory harvesting of Chinese winter jujube(Zizyphus jujuba Mill.cv.Dongzao)[J].Computers&Electronics in Agriculture,2017,143:57-65.DOI:10.1016/j.compag.2017.09.036.
[3]LAM C T,CHAN P H,LEE P S,et al.Chemical and biological assessment of Jujube(Ziziphus jujuba)-containing herbal decoctions:induction of erythropoietin expression in cultures[J].Journal of Chromatography B,2016,1026:254-262.DOI:10.1016/j.jchromb.2015.09.021.
[4]张春梅.枣糖酸代谢及其驯化的分子机制研究[D].杨凌:西北农林科技大学,2016:13-17.
[5]王斌,尹丽华,张淑娟.梨枣糖度无损检测建模分析:基于高光谱成像技术[J].农机化研究,2014,36(10):50-53;57.DOI:10.13427/j.cnki.njyi.2014.10.012.
[6]刘燕德,马奎荣,孙旭东,等.梨和苹果糖度在线检测通用数学模型研究[J].光谱学与光谱分析,2017,37(7):2177-2183.DOI:10.3964/j.issn.1000-0593(2017)07-2177-07.
[7]DAS B,SAHOO R N,PARGAL S,et al.Quantitative monitoring of sucrose,reducing sugar and total sugar dynamics for phenotyping of water-deficit stress tolerance in rice through spectroscopy and chemometrics[J].Spectrochimica Acta Part A:Molecular and Biomolecular Spectroscopy,2017,192:41-51.DOI:10.1016/j.saa.2017.10.076.
[8]吴静珠,刘倩,陈岩,等.高光谱技术检测单籽粒小麦粗蛋白含量探索[J].红外与激光工程,2016,45(增刊1):134-138.DOI:10.3788/IRLA201645.S123002.
[9]房盟盟,刘贵珊,何建国,等.红葡萄酒中白藜芦醇含量的高光谱快速检测算法优化[J].食品科学,2017,38(24):87-93.DOI:10.7506/spkx1002-6630-201724014.
[10]WU L,HE J,LIU G,et al.Detection of common defects on jujube using Vis-NIR and NIR hyperspectral imaging[J].Postharvest Biology&Technology,2016,112:134-142.DOI:10.1016/j.postharvbio.2015.09.003.
[11]杨一,张淑娟,何勇.基于ELM和可见/近红外光谱的鲜枣动态分类检测[J].光谱学与光谱分析,2015,35(7):1870-1874.DOI:10.3964/j.issn.1000-0593(2015)07-1870-05.
[12]SU W H,SUN D W,HE J G,et al.Variation analysis in spectral indices of volatile chlorpyrifos and non-volatile imidacloprid in jujube(Ziziphus jujuba,Mill.)using near-infrared hyperspectral imaging(NIR-HSI)and gas chromatograph-mass spectrometry(GC-MS)[J].Computers&Electronics in Agriculture,2017,139:41-55.DOI:10.1016/j.compag.2017.04.017.
[13]CHEN Q,SONG J,BI J,et al.Characterization of volatile profile from ten different varieties of Chinese jujubes by HS-SPME/GC-MS coupled with E-nose[J].Food Research International,2018,105:605-615.DOI:10.1016/j.foodres.2017.11.054.
[14]NAJAFABADI N S,SAHARI M A,BARZEGAR M,et al.Effect of gamma irradiation on some physicochemical properties and bioactive compounds of jujube(Ziziphus jujuba,var vulgaris)fruit[J].Radiation Physics&Chemistry,2017,130:62-68.DOI:10.1016/j.radphyschem.2016.07.002.
[15]GUO Y,NI Y,KOKOT S.Evaluation of chemical components and properties of the jujube fruit using near infrared spectroscopy and chemometrics[J].Spectrochimica Acta Part A:Molecular&Biomolecular Spectroscopy,2016,153:79-86.DOI:10.1016/j.saa.2015.08.006.
[16]MA T,LI X,INAGAKI T,et al.Noncontact evaluation of soluble solids content in apples by near-infrared hyperspectral imaging[J].Journal of Food Engineering,2017,224:53-61.DOI:10.1016/j.jfoodeng.2017.12.028.
[17]HU W H,SUN D W,BLASCO J.Rapid monitoring 1-MCP-induced modulation of sugars accumulation in ripening‘Hayward’kiwifruit by Vis/NIR hyperspectral imaging[J].Postharvest Biology&Technology,2017,125:168-180.DOI:10.1016/j.postharvbio.2016.11.001.
[18]GOMES V M,FERNANDESA M,FAIA A,et al.Comparison of different approaches for the prediction of sugar content in new vintages of whole Port wine grape berries using hyperspectral imaging[J].Computers&Electronics in Agriculture,2017,140:244-254.DOI:10.1016/j.compag.2017.06.009.
[19]于慧春,王润博,殷勇,等.基于不同波段的枸杞多糖及总糖高光谱成像检测[J].食品科学,2017,38(8):191-197.DOI:10.7506/spkx1002-6630-201708030.
[20]管晓梅,杜军,张立人,等.基于高光谱技术的果糖检测优化算法和可视化方法[J].光电子·激光,2018,29(2):173-180.DOI:10.16136/j.joel.2018.02.0178.
[21]冯迪,纪建伟,张莉,等.基于高光谱成像提取苹果糖度与硬度最佳波长[J].发光学报,2017,38(6):799-806.DOI:10.3788/fgxb20173806.0799.
[22]李瑞,傅隆生.基于高光谱图像的蓝莓糖度和硬度无损测量[J].农业工程学报,2017,33(增刊1):362-366.DOI:10.11975/j.issn.1002-6819.2017.z1.054.
[23]刘燕德,吴明明,李轶凡,等.苹果可溶性固形物和糖酸比可见/近红外漫反射与漫透射在线检测对比研究[J].光谱学与光谱分析,2017,37(8):2424-2429.DOI:10.3964/j.issn.1000-0593(2017)08-2424-06.
[24]郭文川,董金磊.高光谱成像结合人工神经网络无损检测桃的硬度[J].光学精密工程,2015,23(6):1530-1537.DOI:10.3788/OPE.20152306.1530.
[25]王巧华,周凯,吴兰兰,等.基于高光谱的鸡蛋新鲜度检测[J].光谱学与光谱分析,2016,36(8):2596-2600.DOI:10.3964/j.is sn.1000-0593(2016)08-2596-05.
[26]宁井铭,李姝寰,王玉洁,等.基于高光谱成像技术的工夫红茶数字化拼配研究[J].食品科学,2019,40(4):318-323.DOI:10.7506/spkx1002-6630-20171120-247.
[27]何嘉琳,乔春燕,李冬冬,等.可见-近红外高光谱成像技术对灵武长枣VC含量的无损检测方法[J].食品科学,2018,39(6):194-199.DOI:10.7506/spkx1002-6630-201806031.
[28]丁佳兴,吴龙国,何建国,等.高光谱成像技术对灵武长枣果皮强度的无损检测[J].食品工业科技,2016,37(24):58-62;68.DOI:10.13386/j.issn1002-0306.2016.24.003.
[29]RAJKUMAR P,WANG N,EIMASRY G,et al.Studies on banana fruit quality and maturity stages using hyperspectral imaging[J].Journal of Food Engineering,2012,108(1):194-200.DOI:10.1016/j.jfoodeng.2011.05.002.
[30]刘善梅.基于高光谱成像技术的冷鲜猪肉品质无损检测方法研究[D].武汉:华中农业大学,2015.
[31]GALVAO R K,ARAUJO M C,JOSE G E,et al.A method for calibration and validation subset partitioning[J].Talanta,2005,67(4):736-740.DOI:10.1016/j.talanta.2005.03.025.
[32]张海东,赵杰文,刘木华.基于正交信号校正和偏最小二乘(OSC/PLS)的苹果糖度近红外检测[J].食品科学,2005,26(6):189-192.DOI:10.3321/j.issn:1002-6630.2005.06.041.
[33]于慧春,娄楠,殷勇,等.基于高光谱技术及SPXY和SPA算法的玉米毒素检测模型研究[J].食品科学,2018,39(16):328-335.DOI:10.7506/spkx1002-6630-201816048.
[34]刘燕德,肖怀春,孙旭东,等.柑桔叶片黄龙病光谱特征选择及检测模型[J].农业工程学报,2018,34(3):180-187.DOI:10.11975/j.issn.1002-6819.2018.03.024.
[35]王海龙,杨国国,张瑜,等.竞争性自适应重加权算法和相关系数法提取特征波长检测番茄叶片真菌病害[J].光谱学与光谱分析,2017,37(7):2115-2119.DOI:10.3964/j.issn.1000-0593(2017)07-2115-05.
[2]FU L.Simulation study of vibratory harvesting of Chinese winter jujube(Zizyphus jujuba Mill.cv.Dongzao)[J].Computers&Electronics in Agriculture,2017,143:57-65.DOI:10.1016/j.compag.2017.09.036.
[3]LAM C T,CHAN P H,LEE P S,et al.Chemical and biological assessment of Jujube(Ziziphus jujuba)-containing herbal decoctions:induction of erythropoietin expression in cultures[J].Journal of Chromatography B,2016,1026:254-262.DOI:10.1016/j.jchromb.2015.09.021.
[4]张春梅.枣糖酸代谢及其驯化的分子机制研究[D].杨凌:西北农林科技大学,2016:13-17.
[5]王斌,尹丽华,张淑娟.梨枣糖度无损检测建模分析:基于高光谱成像技术[J].农机化研究,2014,36(10):50-53;57.DOI:10.13427/j.cnki.njyi.2014.10.012.
[6]刘燕德,马奎荣,孙旭东,等.梨和苹果糖度在线检测通用数学模型研究[J].光谱学与光谱分析,2017,37(7):2177-2183.DOI:10.3964/j.issn.1000-0593(2017)07-2177-07.
[7]DAS B,SAHOO R N,PARGAL S,et al.Quantitative monitoring of sucrose,reducing sugar and total sugar dynamics for phenotyping of water-deficit stress tolerance in rice through spectroscopy and chemometrics[J].Spectrochimica Acta Part A:Molecular and Biomolecular Spectroscopy,2017,192:41-51.DOI:10.1016/j.saa.2017.10.076.
[8]吴静珠,刘倩,陈岩,等.高光谱技术检测单籽粒小麦粗蛋白含量探索[J].红外与激光工程,2016,45(增刊1):134-138.DOI:10.3788/IRLA201645.S123002.
[9]房盟盟,刘贵珊,何建国,等.红葡萄酒中白藜芦醇含量的高光谱快速检测算法优化[J].食品科学,2017,38(24):87-93.DOI:10.7506/spkx1002-6630-201724014.
[10]WU L,HE J,LIU G,et al.Detection of common defects on jujube using Vis-NIR and NIR hyperspectral imaging[J].Postharvest Biology&Technology,2016,112:134-142.DOI:10.1016/j.postharvbio.2015.09.003.
[11]杨一,张淑娟,何勇.基于ELM和可见/近红外光谱的鲜枣动态分类检测[J].光谱学与光谱分析,2015,35(7):1870-1874.DOI:10.3964/j.issn.1000-0593(2015)07-1870-05.
[12]SU W H,SUN D W,HE J G,et al.Variation analysis in spectral indices of volatile chlorpyrifos and non-volatile imidacloprid in jujube(Ziziphus jujuba,Mill.)using near-infrared hyperspectral imaging(NIR-HSI)and gas chromatograph-mass spectrometry(GC-MS)[J].Computers&Electronics in Agriculture,2017,139:41-55.DOI:10.1016/j.compag.2017.04.017.
[13]CHEN Q,SONG J,BI J,et al.Characterization of volatile profile from ten different varieties of Chinese jujubes by HS-SPME/GC-MS coupled with E-nose[J].Food Research International,2018,105:605-615.DOI:10.1016/j.foodres.2017.11.054.
[14]NAJAFABADI N S,SAHARI M A,BARZEGAR M,et al.Effect of gamma irradiation on some physicochemical properties and bioactive compounds of jujube(Ziziphus jujuba,var vulgaris)fruit[J].Radiation Physics&Chemistry,2017,130:62-68.DOI:10.1016/j.radphyschem.2016.07.002.
[15]GUO Y,NI Y,KOKOT S.Evaluation of chemical components and properties of the jujube fruit using near infrared spectroscopy and chemometrics[J].Spectrochimica Acta Part A:Molecular&Biomolecular Spectroscopy,2016,153:79-86.DOI:10.1016/j.saa.2015.08.006.
[16]MA T,LI X,INAGAKI T,et al.Noncontact evaluation of soluble solids content in apples by near-infrared hyperspectral imaging[J].Journal of Food Engineering,2017,224:53-61.DOI:10.1016/j.jfoodeng.2017.12.028.
[17]HU W H,SUN D W,BLASCO J.Rapid monitoring 1-MCP-induced modulation of sugars accumulation in ripening‘Hayward’kiwifruit by Vis/NIR hyperspectral imaging[J].Postharvest Biology&Technology,2017,125:168-180.DOI:10.1016/j.postharvbio.2016.11.001.
[18]GOMES V M,FERNANDESA M,FAIA A,et al.Comparison of different approaches for the prediction of sugar content in new vintages of whole Port wine grape berries using hyperspectral imaging[J].Computers&Electronics in Agriculture,2017,140:244-254.DOI:10.1016/j.compag.2017.06.009.
[19]于慧春,王润博,殷勇,等.基于不同波段的枸杞多糖及总糖高光谱成像检测[J].食品科学,2017,38(8):191-197.DOI:10.7506/spkx1002-6630-201708030.
[20]管晓梅,杜军,张立人,等.基于高光谱技术的果糖检测优化算法和可视化方法[J].光电子·激光,2018,29(2):173-180.DOI:10.16136/j.joel.2018.02.0178.
[21]冯迪,纪建伟,张莉,等.基于高光谱成像提取苹果糖度与硬度最佳波长[J].发光学报,2017,38(6):799-806.DOI:10.3788/fgxb20173806.0799.
[22]李瑞,傅隆生.基于高光谱图像的蓝莓糖度和硬度无损测量[J].农业工程学报,2017,33(增刊1):362-366.DOI:10.11975/j.issn.1002-6819.2017.z1.054.
[23]刘燕德,吴明明,李轶凡,等.苹果可溶性固形物和糖酸比可见/近红外漫反射与漫透射在线检测对比研究[J].光谱学与光谱分析,2017,37(8):2424-2429.DOI:10.3964/j.issn.1000-0593(2017)08-2424-06.
[24]郭文川,董金磊.高光谱成像结合人工神经网络无损检测桃的硬度[J].光学精密工程,2015,23(6):1530-1537.DOI:10.3788/OPE.20152306.1530.
[25]王巧华,周凯,吴兰兰,等.基于高光谱的鸡蛋新鲜度检测[J].光谱学与光谱分析,2016,36(8):2596-2600.DOI:10.3964/j.is sn.1000-0593(2016)08-2596-05.
[26]宁井铭,李姝寰,王玉洁,等.基于高光谱成像技术的工夫红茶数字化拼配研究[J].食品科学,2019,40(4):318-323.DOI:10.7506/spkx1002-6630-20171120-247.
[27]何嘉琳,乔春燕,李冬冬,等.可见-近红外高光谱成像技术对灵武长枣VC含量的无损检测方法[J].食品科学,2018,39(6):194-199.DOI:10.7506/spkx1002-6630-201806031.
[28]丁佳兴,吴龙国,何建国,等.高光谱成像技术对灵武长枣果皮强度的无损检测[J].食品工业科技,2016,37(24):58-62;68.DOI:10.13386/j.issn1002-0306.2016.24.003.
[29]RAJKUMAR P,WANG N,EIMASRY G,et al.Studies on banana fruit quality and maturity stages using hyperspectral imaging[J].Journal of Food Engineering,2012,108(1):194-200.DOI:10.1016/j.jfoodeng.2011.05.002.
[30]刘善梅.基于高光谱成像技术的冷鲜猪肉品质无损检测方法研究[D].武汉:华中农业大学,2015.
[31]GALVAO R K,ARAUJO M C,JOSE G E,et al.A method for calibration and validation subset partitioning[J].Talanta,2005,67(4):736-740.DOI:10.1016/j.talanta.2005.03.025.
[32]张海东,赵杰文,刘木华.基于正交信号校正和偏最小二乘(OSC/PLS)的苹果糖度近红外检测[J].食品科学,2005,26(6):189-192.DOI:10.3321/j.issn:1002-6630.2005.06.041.
[33]于慧春,娄楠,殷勇,等.基于高光谱技术及SPXY和SPA算法的玉米毒素检测模型研究[J].食品科学,2018,39(16):328-335.DOI:10.7506/spkx1002-6630-201816048.
[34]刘燕德,肖怀春,孙旭东,等.柑桔叶片黄龙病光谱特征选择及检测模型[J].农业工程学报,2018,34(3):180-187.DOI:10.11975/j.issn.1002-6819.2018.03.024.
[35]王海龙,杨国国,张瑜,等.竞争性自适应重加权算法和相关系数法提取特征波长检测番茄叶片真菌病害[J].光谱学与光谱分析,2017,37(7):2115-2119.DOI:10.3964/j.issn.1000-0593(2017)07-2115-05.