生鲜紫薯花青素等多品质参数的可见-近红外快速无损检测
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摘要
基于实验室自行搭建的可见-近红外光谱系统,以市售生鲜紫薯为研究对象,探讨其花青素、可溶性固形物(soluble solid contents,SSC)以及总糖(total sugars,TS)的同时快速无损检测方法。对紫薯原始光谱进行SG(Savitzky-Golay)平滑、标准正态变量变换以及一阶求导预处理,然后用偏最小二乘回归法进行建模分析。对于花青素和TS,经SG平滑结合一阶求导预处理的模型预测效果最佳;对于SSC,经SNV预处理的模型预测效果最好。针对紫薯各参数最佳预处理光谱采用竞争性自适应加权算法进行波长筛选,再次建立模型。花青素模型预测集的相关系数为0.942 1,预测均方根误差(root mean square error of prediction,RMSEP)为0.225 9 mg/g;SSC模型预测集相关系数为0.943 1,RMSEP为0.878 7°Brix;TS模型预测集的相关系数为0.925 3,RMSEP为0.244 3%。结果显示,利用可见-近红外光谱可以实现对生鲜紫薯的花青素、SSC以及TS的同时快速无损检测,对生鲜紫薯品质的快速无损检测分选有着重要的实用意义。
This study aimed to simultaneously, rapidly and nondestructively detect anthocyanins, soluble solid content(SSC) and total sugar(TS) in commercial fresh purple sweet potato by visible and near infrared(VIS/NIR) spectroscopy. The infrared spectra of 52 purple potato cultivars were recorded with the VIS/NIR spectroscopy system set up in our laboratory and Savitzky-Golay(SG) smoothing, standard normal variate(SNV) transformation and first derivative(FD) were separately used to preprocess the original spectra. Partial least squares regression(PLSR) models were established. The best predictive model for anthocyanins and TS was obtained with SG smoothing combined with FD, whereas SNV transformation gave the best predictive model for SSC. Furthermore, new models were developed with the selected optimal pretreatments by competitive adaptive reweighed sampling(CARS) algorithm. For anthocyanins, the correlation coefficient of prediction was 0.942 1, and the root mean square error of prediction was 0.225 9 mg/g; for SSC, the correlation coefficient of prediction was 0.943 1, and the root mean square error of prediction was 0.878 7 °Brix; for TS, the correlation coefficient of prediction was 0.925 3, and the root mean square error of prediction was 0.244 3%. These results showed that using visible/near infrared spectroscopy could achieve the simultaneous, rapid and nondestructive detection of anthocyanins, SSC and TS in fresh purple sweet potato, which is of great practical importance for rapid non-destructive quality testing of fresh purple sweet potato.
This study aimed to simultaneously, rapidly and nondestructively detect anthocyanins, soluble solid content(SSC) and total sugar(TS) in commercial fresh purple sweet potato by visible and near infrared(VIS/NIR) spectroscopy. The infrared spectra of 52 purple potato cultivars were recorded with the VIS/NIR spectroscopy system set up in our laboratory and Savitzky-Golay(SG) smoothing, standard normal variate(SNV) transformation and first derivative(FD) were separately used to preprocess the original spectra. Partial least squares regression(PLSR) models were established. The best predictive model for anthocyanins and TS was obtained with SG smoothing combined with FD, whereas SNV transformation gave the best predictive model for SSC. Furthermore, new models were developed with the selected optimal pretreatments by competitive adaptive reweighed sampling(CARS) algorithm. For anthocyanins, the correlation coefficient of prediction was 0.942 1, and the root mean square error of prediction was 0.225 9 mg/g; for SSC, the correlation coefficient of prediction was 0.943 1, and the root mean square error of prediction was 0.878 7 °Brix; for TS, the correlation coefficient of prediction was 0.925 3, and the root mean square error of prediction was 0.244 3%. These results showed that using visible/near infrared spectroscopy could achieve the simultaneous, rapid and nondestructive detection of anthocyanins, SSC and TS in fresh purple sweet potato, which is of great practical importance for rapid non-destructive quality testing of fresh purple sweet potato.
引文
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[10]彭彦昆,张雷蕾.农畜产品品质安全光学无损检测技术的进展和趋势[J].食品安全质量检测学报,2012,3(6):561-568.
[11]王文秀,彭彦昆.基于近红外光谱的冷鲜肉-解冻肉的判别研究[J].食品安全质量检测学报,2014,5(3):754-760.
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[13]NOHA M,SUN D W.Robust linear and non-linear models of NIR spectroscopy for detection and quantification of adulterants in fresh and frozen-thawed minced beer[J].Meat Science,2013,93(2):292-302.DOI:10.1016/j.meatsci.2012.09.005.
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[15]褚小立,陆婉珍.近五年我国近红外光谱分析技术研究与应用进展[J].光谱学与光谱分析,2014,34(10):2595-2605.DOI:10.3964/j.issn.1000-0593(2014)10-2595-11.
[16]毛建霏,周虹,雷绍荣,等.高效液相色谱法测定紫甘薯花青素含量[J].西南农业学报,2012,25(1):123-127.DOI:10.16213/j.cnki.scjas.2012.01.043.
[17]毛建霏,付成平,郭灵安,等.可见分光光度法测定紫甘薯总花青素含量[J].食品与发酵科技,2010,46(2):101-104.DOI:10.3969/j.issn.1674-506X.2010.02-026.
[18]桑戈,赵力,谭婷婷,等.pH示差法测定紫薯酒中花青素的含量[J].酿酒科技,2015(6):88-91.DOI:10.13746/j,njkj.2014449.
[19]曹海燕,潘王盈,施永清,等.近红外光谱技术快速检测紫薯半干面中菌落总数及新鲜度[J].中国食品学报,2016,16(10):160-166.DOI:10.16429/j.1009-7848.2016.10.022.
[20]杨哲萱,周立红,章顺楠,等.苯酚硫酸一步法测定白芍提取物中总糖含量[J].中国实验方剂学杂志,2013,19(18):139-142.DOI:10.11653/syfj2013180139.
[21]NATALIS S,ELEUTERIO A,SANTIAGO A B,et al.Visible/near infrared-partial least squares analysis of Brix in sugar cane juice a test field for variable selection methods[J].Chemometrics and Intelligent Laboratory Systems,2010,102(2):100-109.DOI:10.1016/j.chemolab.2010.04.009.
[22]李桂峰,赵国建,王向东,等.苹果质地品质近红外无损检测和指纹分析[J].农业工程学报,2008,24(6):169-173.
[23]李晓丽,程术希,何勇.基于漫反射光谱的初制绿茶含水率无损检测方法[J].农业工程学报,2010,26(5):195-201.DOI:10.3969/j.issn.1002-6819.2010.05.034.
[24]吕强,何绍兰,刘斌,等.班菲尔脐橙可溶性固形物近红外光谱特征谱区选择[J].农业机械学报,2012,43(增刊1):211-214.DOI:10.6041/j.issn.1000-1298.2012.S0.042.
[25]马世榜,汤修映,徐杨,等.可见/近红外光谱结合遗传算法无损检测牛肉pH值[J].农业工程学报,2012,28(18):263-268.DOI:10.3969/j.issn.1002-6819.2012.18.037.
[26]李庆波,黄彦文,张广军,等.基于可见-近红外光谱的植物叶绿素含量无损检测方法研究[J].光谱学与光谱分析,2009,29(12):3275-3278.DOI:10.3964/j.issn.1000-0593(2009)12-3275-04.
[27]高彦祥,许正虹.紫甘薯色素研究进展[J].中国食品添加剂,2005(1):1-6.
[28]侯瑞丽,程玉来.蜂蜜主要成分的近红外光谱检测技术的研究[D].沈阳:沈阳农业大学,2007:8.
[29]惠光艳,孙来军,王佳楠,等.可见-近红外光谱的小麦硬度预测模型预处理方法的研究[J].光谱学与光谱分析,2016,36(7):2111-2116.DOI:10.3964/j.issn.1000-0593(2016)07-2111-06.
[30]刘炜,常庆瑞,郭曼,等.不同尺度的微分窗口下土壤有机质的一阶导数光谱响应特征分析[J].红外与毫米波学报,2011,30(4):316-321.
[31]TOLEDO-MARTIN E M,GARCIA-GARCIA M,FONT R,et al.Application of visible/near-infrared reflectance spectroscopy for predicting internal and external quality in pepper[J].Journal of the Science of Food and Agricultural,2016,96(9):3114-3125.DOI:10.1002/jsfa.7488.
[2]SUDA I,OKI T,MASUDA M,et al.Direct absorption of acylated anthocyanin in purple-fleshed sweet potato into rats[J].Journal of Agriculture and Food Chemistry,2002,50(6):1672-1676.DOI:10.1021/jf011162x.
[3]ZHANG Z F,FAN S H,ZHANG Y L,et al.Purple sweet potato color attenuates oxidative stress and inflammatory response induced by D-galactose in mouse liver[J].Food and Chemical Toxicology,2009,47(2):496-501.DOI:10.1016/j.fct.2008.12.005.
[4]YONG P H,JAE H C,JUN M C,et al.Protective mechanisms of anthocyanins from purple sweet potato against tert-butyl hydroperoxide-induced hepatotoxicity[J].Food and Chemical Toxicology,2011,49(9):2081-2089.DOI:10.1016/j.fct.2011.05.021.
[5]YOSHIMOTO M,OKUNO S,YAMAGUCHI M,et al.Antimutagenicity of deacylated anthocyanins in purple-fleshed sweet potato[J].Bioscience-Biotechnology and Biochemistry,2001,65(7):1652-1655.DOI:10.1271/bbb.65.1652.
[6]MATSIU T,EBUCHI S,KOBAYASHI M,et al.Anti-hyperglycemic effect of diacylated anthocyanin derived from Ipomoea batatascultivar ayamurasaki can be achieved through the alpha-glucosidase inhibitory action[J].Journal of Agricultural and Food Chemistry,2002,50(25):7244-7248.DOI:10.1021/jf025913m.
[7]TRUONG V,DEIGHTON N,THOMPSONR T,et al.Characterization of anthocyanins and anthocyanidins in purple-fleshed sweetpotatoes by HPLC-DAD/ESI-MS/MS[J].Journal of Agricultural and Food Chemistry,2010,58(2):404-410.DOI:10.1021/jf902799a.
[8]张慢,潘丽军,姜绍通,等.响应面法优化酶-超声波辅助同步提取紫薯花青素工艺[J].食品科学,2014,35(10):23-28.DOI:10.7506/spkx1002-6630-201410005.
[9]张慢,潘丽军,姜绍通,等.紫薯花青素降血脂及抗氧化效果[J].食品科学,2014,35(19):246-250.DOI:10.7506/spkx1002-6630-201419049.
[10]彭彦昆,张雷蕾.农畜产品品质安全光学无损检测技术的进展和趋势[J].食品安全质量检测学报,2012,3(6):561-568.
[11]王文秀,彭彦昆.基于近红外光谱的冷鲜肉-解冻肉的判别研究[J].食品安全质量检测学报,2014,5(3):754-760.
[12]JAN U P,DIETMAR R K,REINHOLD C.On-line application of near infrared(NIR)spectroscopy in food production[J].Trends in Food Science&Technology,2015,46(2):211-230.DOI:10.1016/j.tifs.2015.10.002.
[13]NOHA M,SUN D W.Robust linear and non-linear models of NIR spectroscopy for detection and quantification of adulterants in fresh and frozen-thawed minced beer[J].Meat Science,2013,93(2):292-302.DOI:10.1016/j.meatsci.2012.09.005.
[14]WU D,HE Y,SHI J H,et al.Exploring near and mid-infrared spectroscopy to predict trace iron and zinc contents in powdered milk[J].Journal of Agricultural and Food Chemistry,2009,57(5):1697-1704.DOI:10.1021/jf8030343.
[15]褚小立,陆婉珍.近五年我国近红外光谱分析技术研究与应用进展[J].光谱学与光谱分析,2014,34(10):2595-2605.DOI:10.3964/j.issn.1000-0593(2014)10-2595-11.
[16]毛建霏,周虹,雷绍荣,等.高效液相色谱法测定紫甘薯花青素含量[J].西南农业学报,2012,25(1):123-127.DOI:10.16213/j.cnki.scjas.2012.01.043.
[17]毛建霏,付成平,郭灵安,等.可见分光光度法测定紫甘薯总花青素含量[J].食品与发酵科技,2010,46(2):101-104.DOI:10.3969/j.issn.1674-506X.2010.02-026.
[18]桑戈,赵力,谭婷婷,等.pH示差法测定紫薯酒中花青素的含量[J].酿酒科技,2015(6):88-91.DOI:10.13746/j,njkj.2014449.
[19]曹海燕,潘王盈,施永清,等.近红外光谱技术快速检测紫薯半干面中菌落总数及新鲜度[J].中国食品学报,2016,16(10):160-166.DOI:10.16429/j.1009-7848.2016.10.022.
[20]杨哲萱,周立红,章顺楠,等.苯酚硫酸一步法测定白芍提取物中总糖含量[J].中国实验方剂学杂志,2013,19(18):139-142.DOI:10.11653/syfj2013180139.
[21]NATALIS S,ELEUTERIO A,SANTIAGO A B,et al.Visible/near infrared-partial least squares analysis of Brix in sugar cane juice a test field for variable selection methods[J].Chemometrics and Intelligent Laboratory Systems,2010,102(2):100-109.DOI:10.1016/j.chemolab.2010.04.009.
[22]李桂峰,赵国建,王向东,等.苹果质地品质近红外无损检测和指纹分析[J].农业工程学报,2008,24(6):169-173.
[23]李晓丽,程术希,何勇.基于漫反射光谱的初制绿茶含水率无损检测方法[J].农业工程学报,2010,26(5):195-201.DOI:10.3969/j.issn.1002-6819.2010.05.034.
[24]吕强,何绍兰,刘斌,等.班菲尔脐橙可溶性固形物近红外光谱特征谱区选择[J].农业机械学报,2012,43(增刊1):211-214.DOI:10.6041/j.issn.1000-1298.2012.S0.042.
[25]马世榜,汤修映,徐杨,等.可见/近红外光谱结合遗传算法无损检测牛肉pH值[J].农业工程学报,2012,28(18):263-268.DOI:10.3969/j.issn.1002-6819.2012.18.037.
[26]李庆波,黄彦文,张广军,等.基于可见-近红外光谱的植物叶绿素含量无损检测方法研究[J].光谱学与光谱分析,2009,29(12):3275-3278.DOI:10.3964/j.issn.1000-0593(2009)12-3275-04.
[27]高彦祥,许正虹.紫甘薯色素研究进展[J].中国食品添加剂,2005(1):1-6.
[28]侯瑞丽,程玉来.蜂蜜主要成分的近红外光谱检测技术的研究[D].沈阳:沈阳农业大学,2007:8.
[29]惠光艳,孙来军,王佳楠,等.可见-近红外光谱的小麦硬度预测模型预处理方法的研究[J].光谱学与光谱分析,2016,36(7):2111-2116.DOI:10.3964/j.issn.1000-0593(2016)07-2111-06.
[30]刘炜,常庆瑞,郭曼,等.不同尺度的微分窗口下土壤有机质的一阶导数光谱响应特征分析[J].红外与毫米波学报,2011,30(4):316-321.
[31]TOLEDO-MARTIN E M,GARCIA-GARCIA M,FONT R,et al.Application of visible/near-infrared reflectance spectroscopy for predicting internal and external quality in pepper[J].Journal of the Science of Food and Agricultural,2016,96(9):3114-3125.DOI:10.1002/jsfa.7488.