高光谱技术融合平板菌落法同步计数酸奶中益生菌
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摘要
采用高光谱技术结合平板菌落法快速表征益生菌酸奶中常见的发酵菌种(保加利亚乳杆菌、嗜热链球菌)和益生菌种(干酪乳杆菌、嗜酸乳杆菌、植物乳杆菌)的光谱差异,并结合不同模式识别方法对混合菌种发酵酸奶中每种益生菌的数量进行同步计数,最后与传统计数法得到的菌落数量结果进行对比,通过2种计数结果的差异性分析验证高光谱菌落计数的可行性。结果表明,当主成分数为9时,最小二乘支持向量机模型对不同种类菌落识别效果均优于K-最近邻法和误差反向传播神经网络模型,其中校正集识别率为99.20%,预测集识别率为93.33%,为最佳计数模型,与传统计数法并无显著差异(P>0.05),验证高光谱技术应用在混合菌种发酵酸奶中对每种益生菌同步计数的可行性,解决传统计数法无法同步计数混合菌种酸奶中各益生菌数量的缺陷问题,为快速无损检测酸奶品质与保健活性提供检测依据。
This study aimed to simultaneously and rapidly identify and count the common fermentation strains(Lactobacillus bulgaricus and Streptococcus thermophiles) and probiotic strains(Lactobacillu caseii, Lactobacillus acidophilus and Lactobacillus plantarum) in probiotic yogurt by hyperspectral imaging technology combined with plate colony method using pattern recognition. The obtained results were compared with those obtained by the traditional counting method to evaluate the feasibility of colony counting using hyperspectral imaging technology. It was shown that when nine principal components were used, the least square support vector machine(LS-SVM) model was superior to the K-nearest neighbor(KNN) and back-propagation artificial neural network(BP-ANN) models in identifying the different types of colonies. The recognition rate of the LS-SVM model was 99.20% and 93.33% for calibration and prediction sets, respectively. Thus, the LS-SVM model was the best counting model. No significant difference was observed between the two counting methods(P > 0.05), confirming the feasibility of using hyperspectral imaging technology to simultaneously count the number of each probiotic species in yogurt with mixed strains. The method overcame the defect of the traditional counting method which could not simultaneously count the number of each probiotic species in yogurt mixed strains, and therefore could provide the basis for rapid and non-destructive detection of the quality and health benefits of yoghurt.
This study aimed to simultaneously and rapidly identify and count the common fermentation strains(Lactobacillus bulgaricus and Streptococcus thermophiles) and probiotic strains(Lactobacillu caseii, Lactobacillus acidophilus and Lactobacillus plantarum) in probiotic yogurt by hyperspectral imaging technology combined with plate colony method using pattern recognition. The obtained results were compared with those obtained by the traditional counting method to evaluate the feasibility of colony counting using hyperspectral imaging technology. It was shown that when nine principal components were used, the least square support vector machine(LS-SVM) model was superior to the K-nearest neighbor(KNN) and back-propagation artificial neural network(BP-ANN) models in identifying the different types of colonies. The recognition rate of the LS-SVM model was 99.20% and 93.33% for calibration and prediction sets, respectively. Thus, the LS-SVM model was the best counting model. No significant difference was observed between the two counting methods(P > 0.05), confirming the feasibility of using hyperspectral imaging technology to simultaneously count the number of each probiotic species in yogurt with mixed strains. The method overcame the defect of the traditional counting method which could not simultaneously count the number of each probiotic species in yogurt mixed strains, and therefore could provide the basis for rapid and non-destructive detection of the quality and health benefits of yoghurt.
引文
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[6]赵鑫,赵洪双,姜国龙.不同比例的嗜热链球菌与保加利亚乳杆菌对酸奶品质的影响[J].农产品加工(学刊),2009(3):177-180.DOI:10.3969/j.issn.1671-9646-B.2009.03.047.
[7]秦南冰,李妍,袁珠妮,等.保加利亚乳杆菌与嗜热链球菌的比例变化对酸奶品质的影响[J].中国乳品工业,2011,39(2):37-40.DOI:10.3969/j.issn.1001-2230.2011.02.011.
[8]辛若竹,丁梅,孟宪志,等.活性酸乳中每种益生菌总数的同时计数法[J].食品科学,2008,29(4):244-248.DOI:10.3321/j.issn:1002-6630.2008.04.051.
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[10]周晓红,徐佩华,孙明华,等.实时荧光定量PCR技术在食源性致病菌快速检测中的应用[J].上海预防医学,2014,26(6):313-317.DOI:10.19428/j.cnki.sjpm.2014.06.012.
[11]李亚茹,周冬根,夏杏洲,等.免疫磁珠分离-实时荧光PCR快速检测虾中沙门氏菌[J].现代食品科技,2017,33(11):235-242.DOI:10.13982/j.mfst.1673-9078.2017.11.034.
[12]SHELL W S,SAYED M L,SAMY A A,et al.Using real-time polymerase chain reaction as an alternative rapid method for enumeration of colony count in live Brucella vaccines[J].Veterinary World,2017,10(6):610-615.DOI:10.14202/vetworld.2017.610-615.
[13]司秀娟,王红强,宋良图.基于图像处理的菌落自动计数系统[J].北京生物医学工程,2017,36(6):633-638.DOI:10.3969/j.issn.1002-3208.2017.06.014.
[14]周莹莉,曾立波,刘均堂,等.基于图像处理的菌落自动计数方法及其实现[J].数据采集与处理,2003,18(4):460-464.DOI:10.3969/j.issn.1004-9037.2003.04.020.
[15]DAHLE J,KAKAR M,STEEN H B,et al.Automated counting of mammalian cell colonies by means of a flat bed scanner and image processing[J].Cytometry,2004,60A(2):182-188.DOI:10.1016/j.eupc.2005.05.002.
[16]BEWES J M,SUCHOWERSKA N,MCKENZIE D R.Automated cell colony counting and analysis using the circular hough image transform algorithm(CHiTA)[J].Physics in Medicine&Biology,2008,53(21):5991-6008.DOI:10.1088/0031-9155/53/21/007.
[17]成军虎.基于高光谱成像鱼肉新鲜度无损快速检测方法研究[D].武汉:华南理工大学,2016.
[18]HUANG L,ZHAO J,CHEN Q,et al.Rapid detection of total viable count(TVC)in pork meat by hyperspectral imaging[J].Food Research International,2013,54(1):821-828.DOI:10.1016/j.foodres.2013.08.011.
[19]邹小波,申婷婷,石吉勇,等.基于高光谱图像技术的固态发酵中芽孢杆菌的快速识别[J].现代食品科技,2016,32(4):235-240.DOI:10.13982/j.mfst.1673-9078.2016.4.037.
[20]刘爱萍,蒋菁莉,任发政,等.发酵乳中益生菌选择性计数培养基的研究[C]//中国奶业协会2008年年会论文集.北京:中国奶业协会,2008.
[21]赵建新,田丰伟,陈卫,等.酸奶中嗜酸乳杆菌和植物乳杆菌检测方法的研究[J].中国乳品工业,2005,33(2):54-57.DOI:10.3969/j.issn.1001-2230.2005.02.016.
[22]蒋曙光.酸奶中嗜酸乳杆菌计数检测方法的研究[J].乳业科学与技术,2011,34(3):131-134.DOI:10.3969/j.issn.1671-5187.2011.03.009.
[23]ZOU X,ZHAO J,HOLMES M,et al.Independent component analysis in information extraction from visible/near-infrared hyperspectral imaging data of cucumber leaves[J].Chemometrics&Intelligent Laboratory Systems,2010,104(2):265-270.DOI:10.1016/j.chemolab.2010.08.019.
[24]ZHANG C,LIU F,KONG W,et al.Application of visible and nearinfrared hyperspectral imaging to determine soluble protein content in oilseed rape leaves[J].Sensors,2015,15(7):16576-16588.DOI:10.3390/s150716576.
[25]邹小波,陈正伟,石吉勇,等.基于近红外高光谱图像的黄瓜叶片色素含量快速检测[J].农业机械学报,2012,43(5):152-156.DOI:10.6041/j.issn.1000-1298.2012.05.026.
[26]夏俊芳,李培武,李小昱,等.不同预处理对近红外光谱检测脐橙VC含量的影响[J].农业机械学报,2007,38(6):107-111.DOI:10.3969/j.issn.1000-1298.2007.06.027.
[27]金航峰.基于光谱和高光谱图像技术的蚕茧品质无损检测研究[D].杭州:浙江大学,2013.
[28]徐明珠.基于高光谱成像技术的马铃薯早疫病无损检测研究[D].杨凌:西北农林科技大学,2016.
[29]杨小玲.高光谱图像技术检测玉米种子品质研究[D].杭州:浙江大学,2016.
[30]翁诗甫.傅里叶变换红外光谱分析[M].北京:化学工业出版社,2010.
[31]ZOU X,ZHAO J,POVEY M J W,et al.Variables selection methods in near-infrared spectroscopy[J].Analytica Chimica Acta,2010,667(1/2):14-32.DOI:10.1016/j.aca.2010.03.048.
[2]杨晓燕,杨洋.益生菌保健作用及发展动态[J].现代食品,2015(23):29-34.DOI:10.3969/j.issn.1007-3582.2015.23.011.
[3]肖平,吕嘉枥,沈文.益生菌的保健功能及其在食品中的应用概述[J].食品科技,2009,34(10):23-26.DOI:10.13684/j.cnki.spkj.2009.10.013.
[4]张兴昌,孙婷,陈世贤,等.对酸奶中益生菌应用种类和含量的分析[J].饮料工业,2014,17(2):35-38.
[5]ADOLFSSON O,MEYDANI S N,RUSSELL R M.Yogurt and gut function[J].American Journal of Clinical Nutrition,2004,80(2):245-256.DOI:10.1093/ajcn/80.2.245.
[6]赵鑫,赵洪双,姜国龙.不同比例的嗜热链球菌与保加利亚乳杆菌对酸奶品质的影响[J].农产品加工(学刊),2009(3):177-180.DOI:10.3969/j.issn.1671-9646-B.2009.03.047.
[7]秦南冰,李妍,袁珠妮,等.保加利亚乳杆菌与嗜热链球菌的比例变化对酸奶品质的影响[J].中国乳品工业,2011,39(2):37-40.DOI:10.3969/j.issn.1001-2230.2011.02.011.
[8]辛若竹,丁梅,孟宪志,等.活性酸乳中每种益生菌总数的同时计数法[J].食品科学,2008,29(4):244-248.DOI:10.3321/j.issn:1002-6630.2008.04.051.
[9]辛若竹,丁梅.功能性酸乳中每种益生菌的单独计数方法及其混合发酵工艺的研究[J].食品与发酵工业,2006,32(7):151-156.DOI:10.13995/j.cnki.11-1802/ts.2006.07.039.
[10]周晓红,徐佩华,孙明华,等.实时荧光定量PCR技术在食源性致病菌快速检测中的应用[J].上海预防医学,2014,26(6):313-317.DOI:10.19428/j.cnki.sjpm.2014.06.012.
[11]李亚茹,周冬根,夏杏洲,等.免疫磁珠分离-实时荧光PCR快速检测虾中沙门氏菌[J].现代食品科技,2017,33(11):235-242.DOI:10.13982/j.mfst.1673-9078.2017.11.034.
[12]SHELL W S,SAYED M L,SAMY A A,et al.Using real-time polymerase chain reaction as an alternative rapid method for enumeration of colony count in live Brucella vaccines[J].Veterinary World,2017,10(6):610-615.DOI:10.14202/vetworld.2017.610-615.
[13]司秀娟,王红强,宋良图.基于图像处理的菌落自动计数系统[J].北京生物医学工程,2017,36(6):633-638.DOI:10.3969/j.issn.1002-3208.2017.06.014.
[14]周莹莉,曾立波,刘均堂,等.基于图像处理的菌落自动计数方法及其实现[J].数据采集与处理,2003,18(4):460-464.DOI:10.3969/j.issn.1004-9037.2003.04.020.
[15]DAHLE J,KAKAR M,STEEN H B,et al.Automated counting of mammalian cell colonies by means of a flat bed scanner and image processing[J].Cytometry,2004,60A(2):182-188.DOI:10.1016/j.eupc.2005.05.002.
[16]BEWES J M,SUCHOWERSKA N,MCKENZIE D R.Automated cell colony counting and analysis using the circular hough image transform algorithm(CHiTA)[J].Physics in Medicine&Biology,2008,53(21):5991-6008.DOI:10.1088/0031-9155/53/21/007.
[17]成军虎.基于高光谱成像鱼肉新鲜度无损快速检测方法研究[D].武汉:华南理工大学,2016.
[18]HUANG L,ZHAO J,CHEN Q,et al.Rapid detection of total viable count(TVC)in pork meat by hyperspectral imaging[J].Food Research International,2013,54(1):821-828.DOI:10.1016/j.foodres.2013.08.011.
[19]邹小波,申婷婷,石吉勇,等.基于高光谱图像技术的固态发酵中芽孢杆菌的快速识别[J].现代食品科技,2016,32(4):235-240.DOI:10.13982/j.mfst.1673-9078.2016.4.037.
[20]刘爱萍,蒋菁莉,任发政,等.发酵乳中益生菌选择性计数培养基的研究[C]//中国奶业协会2008年年会论文集.北京:中国奶业协会,2008.
[21]赵建新,田丰伟,陈卫,等.酸奶中嗜酸乳杆菌和植物乳杆菌检测方法的研究[J].中国乳品工业,2005,33(2):54-57.DOI:10.3969/j.issn.1001-2230.2005.02.016.
[22]蒋曙光.酸奶中嗜酸乳杆菌计数检测方法的研究[J].乳业科学与技术,2011,34(3):131-134.DOI:10.3969/j.issn.1671-5187.2011.03.009.
[23]ZOU X,ZHAO J,HOLMES M,et al.Independent component analysis in information extraction from visible/near-infrared hyperspectral imaging data of cucumber leaves[J].Chemometrics&Intelligent Laboratory Systems,2010,104(2):265-270.DOI:10.1016/j.chemolab.2010.08.019.
[24]ZHANG C,LIU F,KONG W,et al.Application of visible and nearinfrared hyperspectral imaging to determine soluble protein content in oilseed rape leaves[J].Sensors,2015,15(7):16576-16588.DOI:10.3390/s150716576.
[25]邹小波,陈正伟,石吉勇,等.基于近红外高光谱图像的黄瓜叶片色素含量快速检测[J].农业机械学报,2012,43(5):152-156.DOI:10.6041/j.issn.1000-1298.2012.05.026.
[26]夏俊芳,李培武,李小昱,等.不同预处理对近红外光谱检测脐橙VC含量的影响[J].农业机械学报,2007,38(6):107-111.DOI:10.3969/j.issn.1000-1298.2007.06.027.
[27]金航峰.基于光谱和高光谱图像技术的蚕茧品质无损检测研究[D].杭州:浙江大学,2013.
[28]徐明珠.基于高光谱成像技术的马铃薯早疫病无损检测研究[D].杨凌:西北农林科技大学,2016.
[29]杨小玲.高光谱图像技术检测玉米种子品质研究[D].杭州:浙江大学,2016.
[30]翁诗甫.傅里叶变换红外光谱分析[M].北京:化学工业出版社,2010.
[31]ZOU X,ZHAO J,POVEY M J W,et al.Variables selection methods in near-infrared spectroscopy[J].Analytica Chimica Acta,2010,667(1/2):14-32.DOI:10.1016/j.aca.2010.03.048.