基于天然色素嗅觉可视化技术的牛肉新鲜度的快速检测
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摘要
以托盘包装冷鲜牛腱为研究对象,选择蓝莓、玫瑰、菠菜、黑豆、黑米、柑橘皮、紫葡萄、姜黄和玫瑰茄共9种天然色素原材料制作3×3矩阵的嗅觉可视化阵列,利用色素气体可视化传感器来提取牛肉储藏期间的挥发性气味信息,同时测定挥发性盐基氮(total volatile base nitrogen,TVB-N)和细菌总数(total viable count,TVC),将天然色素气体可视化传感器阵列与挥发性气体成分反应前后产生的红绿蓝(RGB)颜色变化值作为响应信号,利用偏最小二乘法(partial Least Square,PLS)和最小二乘支持向量机(least-squares support vector machine,LS-SVM)来建立气体可视化传感器阵列响应信号与牛肉TVB-N含量和TVC值的相关回归模型,定量预测牛肉的新鲜度。结果表明,LS-SVM的定量预测模型最佳,TVB-N和TVC含量的LS-SVM模型的预测集均方差RMSEP分别为65.66 mg·kg~(-1)和0.383 log(cfu·g~(-1)),校正集预测值与实测值的相关系数R_p分别为0.863 4和0.854 7。
Taking tray-packaged chilled burdock as the research object, 9 raw materials of natural pigments such as blueberry, rose, spinach, black rice, black bean, citrus, purple grape, turmeric and rose eggplant were selected to make 3×3 matrix olfactory visualization arrays. The natural pigment gas visualization sensor was used to extract the volatile odor information during beef storage, and the volatile salt nitrogen(TVB-N) and the total number of bacteria(TVC) were measured simultaneously. The generated RGB color change value before and after the reaction of the natural pigment gas visual sensor array with the volatile gas component was used as a response signal, and the regression model of beef TVB-N content and TVC value was established by using a partial least squares(PLS) and a least-squares support vector machine(LS-SVM) to quantitatively predict the freshness of beef. The results show that the quantitative prediction model of LS-SVM is the best. The prediction set RMSEP of TVB-N content and TVC LS-SVM model was 65.66 mg·kg~(-1) and 0.383 log(cfu·g~(-1)), respectively. The correlation coefficients R_p between the predicted and measured values of the calibration set were 0.863 4 and 0.854 7, respectively.
Taking tray-packaged chilled burdock as the research object, 9 raw materials of natural pigments such as blueberry, rose, spinach, black rice, black bean, citrus, purple grape, turmeric and rose eggplant were selected to make 3×3 matrix olfactory visualization arrays. The natural pigment gas visualization sensor was used to extract the volatile odor information during beef storage, and the volatile salt nitrogen(TVB-N) and the total number of bacteria(TVC) were measured simultaneously. The generated RGB color change value before and after the reaction of the natural pigment gas visual sensor array with the volatile gas component was used as a response signal, and the regression model of beef TVB-N content and TVC value was established by using a partial least squares(PLS) and a least-squares support vector machine(LS-SVM) to quantitatively predict the freshness of beef. The results show that the quantitative prediction model of LS-SVM is the best. The prediction set RMSEP of TVB-N content and TVC LS-SVM model was 65.66 mg·kg~(-1) and 0.383 log(cfu·g~(-1)), respectively. The correlation coefficients R_p between the predicted and measured values of the calibration set were 0.863 4 and 0.854 7, respectively.
引文
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[25] 侯志强,李苗云,赵改名,等.肉品微生物预报模型系统构建[J].河南农业大学学报,2015,49(1):107-111.
[26] 赵杰文,黄晓玮,邹小波,等.基于嗅觉可视化技术的猪肉新鲜度检测[J].食品科学技术学报,2013,31(1):9-13.
[2] 罗天林,沈慧,郭兆斌,等.真空包装牛肉品质主成分分析[J].甘肃农业大学学报,2017,52(1):126-131.
[3] 包应时,蒋焕煜,吴晓莲.近红外光谱分析技术在农业与食品无损检测上的应用[J].农机化研究,2006(10):167-168.
[4] 李洁.食品感官检验技术及其应用探讨[J].食品安全导刊,2017(32):73-74.
[5] 陈德兰,杜书楠.影响食品理化检测准确度因素[J].现代食品,2016,5(9):29-30.
[6] 邹小波,赵杰文,殷晓平,等.嗅觉可视化技术在白酒识别中的应用[J].农业机械学报,2009,40(1):110-113.
[7] 邹小波,张建,赵杰文.嗅觉可视化技术及其对四种食醋的识别[J].农业工程学报,2008,24(6):165-168.
[8] 周芳.嗅觉可视化技术检测猪肉新鲜度的研究[D].镇江:江苏大学,2011.
[9] 赵杰文,黄晓玮,邹小波,等.基于嗅觉可视化技术的猪肉新鲜度检测[J].食品科学技术学报,2013,31(1):9-13.
[10] RAKOW N A,SUSLICK K S.A colorimetric sensor array for odour visualization[J].Nature,2000,406(6797):710-713.
[11] DICKINSON T A,WHITE J,KAUER J S,et al.A chemical-detecting system based on a cross-reactive optical sensor array[J].Nature,1996,382(6593):697-700.
[12] VAUGHAN A A,BARON M G,NARAYANASWAMY R.Optical ammonia sensing films based on an immobilized metalloporphyrin[J].Analytical Communications,1996,33(11):393-396.
[13] 黄星奕,辛君伟,邹小波,等.用于食品气味快速检测的可视传感器特性研究[J].传感技术学报,2009,22(7):913-917.
[14] 邓祥元,王淑军,李富超,等.天然色素的资源和应用[J].中国调味品,2006,10(10):49-53.
[15] 高爱红,童华荣.天然食用色素: 花青素研究进展[J].保鲜与加工,2001,1(3):25-27.
[16] 中华人民共和国国家卫生和计划生育委员会.食品安全国家标准食品中挥发性盐基氮的测定: GB 5009.228—2016 [S].北京:中国标准出版社,2016:9.
[17] 中华人民共和国国家卫生和计划生育委员会.食品安全国家标准食品微生物学检验菌落总数测定: GB 4789.2—2016 [S].北京:中国标准出版社,2017:6.
[18] CORTES C,VAPNIK V.Support-vector networks[J].Machine Learning,1995,20(3):273-297.
[19] 张琳,张黎明,李燕,等.偏最小二乘法在傅里叶变换红外光谱中的应用及进展[J].光谱学与光谱分析,2005,25(10):1610-1613.
[20] 张浩然,汪晓东.回归最小二乘支持向量机的增量和在线式学习算法[J].计算机学报,2006,29(3):400-406.
[21] 陈琳,叶阳,董春旺,等.基于嗅觉可视化技术的工夫红茶发酵程度判定方法[J].茶叶科学,2017,37(3):258-265.
[22] 管彬彬,赵杰文,金鸿娟,等.基于嗅觉可视化技术的醋酸发酵过程中酒精度的检测[J].食品与发酵工业,2015,41(12):191-195.
[23] 黄星奕,管超,丁然,等.基于嗅觉可视化和近红外光谱融合技术的海鲈鱼新鲜度评价[J].农业工程学报,2015,31(8):277-282.
[24] 孙晓敏,赵改名,李苗云,等.真空包装冷鲜牛肉中腐败菌与腐败品质相关性分析[J].河南农业大学学报,2012,46(3):328-331.
[25] 侯志强,李苗云,赵改名,等.肉品微生物预报模型系统构建[J].河南农业大学学报,2015,49(1):107-111.
[26] 赵杰文,黄晓玮,邹小波,等.基于嗅觉可视化技术的猪肉新鲜度检测[J].食品科学技术学报,2013,31(1):9-13.