This study aimed to investigate the capabilities of hyperspectral scattering imaging in tandem with Gaussian function,Exponential function and Lorentzian function for rapid and nondestructive determination of total vi...This study aimed to investigate the capabilities of hyperspectral scattering imaging in tandem with Gaussian function,Exponential function and Lorentzian function for rapid and nondestructive determination of total viable count(TVC)in pork meat.Two batches of fresh pork meat was purchased from a local market and stored at 10°C for 1-9 d.Totally 60 samples were used,and several samples were taken out randomly for hyperspectral scattering imaging and conventional microbiological tests on each day of the experiments.The functions of Gaussian,Exponential and Lorentzian were employed to model the hyperspectral scattering profiles of pork meat,and good fitting results were obtained by all three functions between 455 nm and 1000 nm.The Lorentzian function performed best for fitting the hyperspectral scattering profiles of pork meat compared with other functions.Both principal component regression(PCR)and partial least squares regression(PLSR)methods were performed to establish the prediction models.Among all the developed models,the models developed using parameters CE(scattering width parameter of Exponential function)and CL(scattering width parameter of Lorentzian function)by PLSR method gave superior results for predicting pork meat TVC,with RV and RMSEV of 0.92,0.59 log CFU/g,and 0.91,0.61 log CFU/g,respectively.In addition,based on the improved hyperspectral scattering system,parameter c which represented the scattering widths in all three functions gave more accurate prediction results,regardless of the modeling methods(PCR or PLSR).The obtained results demonstrated that hyperspectral scattering imaging combined with the presented data analysis algorithm can be a powerful tool for evaluating the microbial safety of meat in the future.展开更多
Background: Due to their delicious taste, high nutritional content, and health benefits, fruit juices are well-known drinks in many countries and are now an essential component of the modern diet. Objective: Determini...Background: Due to their delicious taste, high nutritional content, and health benefits, fruit juices are well-known drinks in many countries and are now an essential component of the modern diet. Objective: Determining the microbiological quality of both packaged and freshly made fruit and milk juices. Method: The spread-plate approach was employed to isolate and count the bacteria. 90 ml of sterile peptone water were blended with 10 ml of well-mixed, packed, and freshly made fruit juices. The samples were sequentially diluted (101 - 105) in accordance with the Indian Manual of Food Microbiological Testing Methods. Results: From eight samples of imported packaged fruit and milk juice, the average of total coliform, staphylococci, and viable bacterial counts were zero, 1.39 × 102, and 2 × 102 CFU/ml, respectively. In contrast, from three samples of locally produced fruit and milk juice, the average of total coliform, staphylococci, and viable bacterial counts were zero, 5.83 × 102, and 2.73 × 103 CFU/ml, respectively. Four samples of handmade prepared fruit and milk juices had a mean of total coliform, staphylococci, and viable bacterial count of 1.441 × 104, 4.1 × 103, and 2.35 × 105 CFU/ml, respectively. Conclusion: 33.3% of the results from microbiological analysis of freshly made fruit and milk juices met the permissible range of the Revised Microbiological Standards for Fruit and Vegetables and Their Products, which were published in 2018 and as well as the Hong Kong Center for Food Safety, whereas 66.7% of the microbiological analyses of freshly prepared fruit and milk juices were above the permissible reference range of GSO standard 2000. 12.5% of the investigated imported and packed fruits and milk juices had one failed test (TSC), which was above the acceptable limit, 87.5% of the tested samples of fruit and milk juices fulfilled the necessary standards of TCC, TVBC, and TSC. 100% of the tested locally manufactured fruit and milk juices complied with TSC, TCC, and TVBC requirements. All investigations showed that freshly made fruit and milk juices were heavily contaminated (Total viable bacterial count, total coliform count, and total staphylococcus count). .展开更多
利用可见/近红外光谱技术对冷却肉菌落总数和颜色进行快速、无损检测。采用400~1 100 nm可见/近红外光谱成像系统,获取54个冷却肉样本表面的光谱图像,采用主成分分析结合马氏距离方法对异常光谱进行判别及剔除。通过Gompertz分布函数对...利用可见/近红外光谱技术对冷却肉菌落总数和颜色进行快速、无损检测。采用400~1 100 nm可见/近红外光谱成像系统,获取54个冷却肉样本表面的光谱图像,采用主成分分析结合马氏距离方法对异常光谱进行判别及剔除。通过Gompertz分布函数对散射特征曲线进行拟合,得到表征光谱信息的Gompertz参数,结合支持向量机算法建立冷却肉菌落总数和肉色L*的预测模型。α、β、θ、δ组合和α、β、δ组合建模对细菌总数预测效果最好,预测相关系数分别为0.937和0.935,预测标准差为0.600 lg CFU/g和0.702 lg CFU/g。β、δ组合建模对肉色L*预测效果较好,预测相关系数达到0.930,预测标准差为1.515。研究结果表明利用Vis/NIR光谱散射特征结合支持向量机可以实现冷却肉品质的快速、高效、无损伤检测。展开更多
目的通过近红外光谱技术对不同贮藏时间下冰鲜大黄鱼的鲜度进行评价。方法以菌落总数为鲜度评价指标,基于均值中心化、标准正态变量变换、趋近归一化法(Normalization by Closure,Ncl)、多元散射校正、一阶导数和二阶导数等预处理方法,...目的通过近红外光谱技术对不同贮藏时间下冰鲜大黄鱼的鲜度进行评价。方法以菌落总数为鲜度评价指标,基于均值中心化、标准正态变量变换、趋近归一化法(Normalization by Closure,Ncl)、多元散射校正、一阶导数和二阶导数等预处理方法,运用偏最小二乘法(Partial Least Squares,PLS)建模,比较所建模型的定标集与验证集间的相关系数和标准偏差,构建大黄鱼冰藏期间菌落总数的定量模型,以期快速预测其新鲜度。结果Ncl比其它预处理方法可以更好地消除光谱噪音,提高模型的预测能力。经Ncl光谱预处理,利用PLS建模,可达到最佳的建模效果,其定标集相关系数为0.9095,校正标准偏差相关系数为0.5872,验证集相关系数为0.8858,预测标准偏差为0.6615。模型相关系数>0.9;结论表明该模型预测精度较好,在大黄鱼新鲜度检测和品质评价方面应用前景良好。展开更多
基金The authors gratefully acknowledge the China Postdoctoral Science Foundation(Project No.2014M561096)the Special Fund for Agro-scientific Research in the Public Interest Program(Project No.201003008)the National Science and Technology Support Program(Project No.2012BAH04B00)for supporting this research.
文摘This study aimed to investigate the capabilities of hyperspectral scattering imaging in tandem with Gaussian function,Exponential function and Lorentzian function for rapid and nondestructive determination of total viable count(TVC)in pork meat.Two batches of fresh pork meat was purchased from a local market and stored at 10°C for 1-9 d.Totally 60 samples were used,and several samples were taken out randomly for hyperspectral scattering imaging and conventional microbiological tests on each day of the experiments.The functions of Gaussian,Exponential and Lorentzian were employed to model the hyperspectral scattering profiles of pork meat,and good fitting results were obtained by all three functions between 455 nm and 1000 nm.The Lorentzian function performed best for fitting the hyperspectral scattering profiles of pork meat compared with other functions.Both principal component regression(PCR)and partial least squares regression(PLSR)methods were performed to establish the prediction models.Among all the developed models,the models developed using parameters CE(scattering width parameter of Exponential function)and CL(scattering width parameter of Lorentzian function)by PLSR method gave superior results for predicting pork meat TVC,with RV and RMSEV of 0.92,0.59 log CFU/g,and 0.91,0.61 log CFU/g,respectively.In addition,based on the improved hyperspectral scattering system,parameter c which represented the scattering widths in all three functions gave more accurate prediction results,regardless of the modeling methods(PCR or PLSR).The obtained results demonstrated that hyperspectral scattering imaging combined with the presented data analysis algorithm can be a powerful tool for evaluating the microbial safety of meat in the future.
文摘Background: Due to their delicious taste, high nutritional content, and health benefits, fruit juices are well-known drinks in many countries and are now an essential component of the modern diet. Objective: Determining the microbiological quality of both packaged and freshly made fruit and milk juices. Method: The spread-plate approach was employed to isolate and count the bacteria. 90 ml of sterile peptone water were blended with 10 ml of well-mixed, packed, and freshly made fruit juices. The samples were sequentially diluted (101 - 105) in accordance with the Indian Manual of Food Microbiological Testing Methods. Results: From eight samples of imported packaged fruit and milk juice, the average of total coliform, staphylococci, and viable bacterial counts were zero, 1.39 × 102, and 2 × 102 CFU/ml, respectively. In contrast, from three samples of locally produced fruit and milk juice, the average of total coliform, staphylococci, and viable bacterial counts were zero, 5.83 × 102, and 2.73 × 103 CFU/ml, respectively. Four samples of handmade prepared fruit and milk juices had a mean of total coliform, staphylococci, and viable bacterial count of 1.441 × 104, 4.1 × 103, and 2.35 × 105 CFU/ml, respectively. Conclusion: 33.3% of the results from microbiological analysis of freshly made fruit and milk juices met the permissible range of the Revised Microbiological Standards for Fruit and Vegetables and Their Products, which were published in 2018 and as well as the Hong Kong Center for Food Safety, whereas 66.7% of the microbiological analyses of freshly prepared fruit and milk juices were above the permissible reference range of GSO standard 2000. 12.5% of the investigated imported and packed fruits and milk juices had one failed test (TSC), which was above the acceptable limit, 87.5% of the tested samples of fruit and milk juices fulfilled the necessary standards of TCC, TVBC, and TSC. 100% of the tested locally manufactured fruit and milk juices complied with TSC, TCC, and TVBC requirements. All investigations showed that freshly made fruit and milk juices were heavily contaminated (Total viable bacterial count, total coliform count, and total staphylococcus count). .
文摘利用可见/近红外光谱技术对冷却肉菌落总数和颜色进行快速、无损检测。采用400~1 100 nm可见/近红外光谱成像系统,获取54个冷却肉样本表面的光谱图像,采用主成分分析结合马氏距离方法对异常光谱进行判别及剔除。通过Gompertz分布函数对散射特征曲线进行拟合,得到表征光谱信息的Gompertz参数,结合支持向量机算法建立冷却肉菌落总数和肉色L*的预测模型。α、β、θ、δ组合和α、β、δ组合建模对细菌总数预测效果最好,预测相关系数分别为0.937和0.935,预测标准差为0.600 lg CFU/g和0.702 lg CFU/g。β、δ组合建模对肉色L*预测效果较好,预测相关系数达到0.930,预测标准差为1.515。研究结果表明利用Vis/NIR光谱散射特征结合支持向量机可以实现冷却肉品质的快速、高效、无损伤检测。
文摘以整块鸡胸肉为研究对象,利用在线近红外光谱系统采集其900~1650 nm波长范围内的光谱信息,探究光谱信息与细菌菌落总数(Total Viable Count,TVC)之间的定量关系。对采集的原始光谱信息进行高斯滤波平滑(Gaussian Filter Smoothing,GFS)等五种预处理后,建立全波段偏最小二乘(Partial Least Squares,PLS)回归模型。采用回归系数法(Regression Coefficient,RC)和连续投影算法(Successive Projections Algorithm,SPA)筛选最优波长,构建优化的PLS模型和多元线性回归(Multiple Linear Regression,MLR)模型。结果表明,基于全波段GFS光谱构建的GFS-PLS模型预测鸡胸肉TVC效果最佳(rP=0.964,RMSEP=0.806 lg CFU/g)。基于SPA法从GFS光谱中筛选出的25个最优波长(907.0、913.7、923.8、927.2、937.2、947.3、974.0、987.3、997.3、1007.3、1040.4、1080.1、1099.9、1132.9、1155.9、1185.5、1215.0、1241.2、1270.6、1358.2、1380.8、1403.3、1419.3、1578.9和1615.2 nm),建立的SPA-GFS-MLR模型预测性能(rP=0.944,RMSEP=1.022 lg CFU/g)最接近GFS-PLS模型。基于在线近红外光谱系统可实现对大批量整块鸡胸肉细菌总数含量的快速无接触检测。
文摘目的通过近红外光谱技术对不同贮藏时间下冰鲜大黄鱼的鲜度进行评价。方法以菌落总数为鲜度评价指标,基于均值中心化、标准正态变量变换、趋近归一化法(Normalization by Closure,Ncl)、多元散射校正、一阶导数和二阶导数等预处理方法,运用偏最小二乘法(Partial Least Squares,PLS)建模,比较所建模型的定标集与验证集间的相关系数和标准偏差,构建大黄鱼冰藏期间菌落总数的定量模型,以期快速预测其新鲜度。结果Ncl比其它预处理方法可以更好地消除光谱噪音,提高模型的预测能力。经Ncl光谱预处理,利用PLS建模,可达到最佳的建模效果,其定标集相关系数为0.9095,校正标准偏差相关系数为0.5872,验证集相关系数为0.8858,预测标准偏差为0.6615。模型相关系数>0.9;结论表明该模型预测精度较好,在大黄鱼新鲜度检测和品质评价方面应用前景良好。
