高光谱快速预测冷鲜鸡胸肉中乳酸菌  

Rapid Prediction of Lactic Acid Bacteria in Chilled Chicken Breast by Hyperspectral Imaging

作　　者：何鸿举[1,2,3] 蒋圣启 王魏 王玉玲 马汉军[1,2] 陈复生[2,3] 朱明明 赵圣明 周浩宇[1] HE Hong-ju;JIANG Sheng-qi;WANG Wei;WANG Yu-ling;MA Han-jun;CHEN Fu-sheng;ZHU Ming-ming;ZHAO Sheng-ming;ZHOU Hao-yu(School of Food Science,Henan Institute of Science and Technology,Xinxiang 453003,China;Postdoctoral Research and Development Base,Henan Institute of Science and Technology,Xinxiang 453003,China;College of Grain,Oil and Food,Henan University of Technology,Zhengzhou 450001,China;School of Life Science and Technology,Henan Institute of Science and Technology,Xinxiang 453003,Chona)

机构地区：[1]河南科技学院食品学院,河南新乡453003 [2]河南科技学院博士后研发基地,河南新乡453003 [3]河南工业大学粮油食品学院,河南郑州450001 [4]河南科技学院生命科技学院,河南新乡453003

出　　处：《食品工业科技》2020年第12期227-233,共7页Science and Technology of Food Industry

基　　金：河南省重大科技专项项目(161100110600);中国博士后科学基金(2018M632767);河南省科技攻关项目(182102310060);河南省青年人才托举工程项目(2018HYTP008);河南省博士后科研项目(001801021);河南科技学院高层次人才引进项目(2015015);河南科技学院重大科研培育项目(2016ZD03)。

摘　　要：乳酸菌含量是评价冷鲜鸡胸肉品质的重要指标。随着储藏天数的增加,当乳酸菌含量超过10~6 CFU/g,冷鲜鸡胸肉黏度增加,开始腐败变味。本研究通过化学计量学算法挖掘高光谱数据快速预测鸡胸肉中乳酸菌含量。首先,采集119个冷鲜鸡胸肉样品900~1700 nm的高光谱图像,提取肉样图像感兴趣区域(Region of interest,ROI)内的光谱信息,经多元散射校正(Multiplicative Scatter Correction,MSC)等8种方法预处理原始光谱,采用偏最小二乘(Partial Least Squares,PLS)算法挖掘光谱信息,构建全波段PLS预测模型(F-PLS)。然后,选用回归系数法(Regression Coefficient,RC)、逐步回归法(Stepwise)和连续投影算法(Successive Projections Algorithm,SPA)筛选最优波长优化F-PLS模型。结果显示,基于SPA法从基线校正(Baseline Correction,BC)预处理光谱中筛选出21个最优波长(903.8、905.5、912.1、915.4、917.0、920.3、923.6、931.8、941.7、1107.0、1135.9、1157.3、1269.2、1303.7、1320.2、1348.2、1551.1、1676.9、1686.9、1695.1和1698.4 nm)构建的SPA-PLS模型预测最好(r_P=0.949,RMSEP=0.439lg CFU/g,RPD=2.787)。本试验表明,采用近红外高光谱技术快速预测冷鲜鸡胸肉中乳酸菌含量是可行的。The contents of lactic acid bacteria is an important indicator to evaluate the quality of chilled chicken breast. With the increasing of storage days the contents of lactic acid bacteria exceeds 10~6 CFU/g,the chilled chicken breast becomes sticky and begins to rot. In order to predict the contents of lactic acid bacteria in chicken breast in his paper,the hyperspectral data were analyzed through chemometric algorithms. First of all,119 samples of hyperspectral images of cold fresh chicken breast in range of 900~1700 nm were obtained,and the spectral information within the region of interests(ROIs)of the images were extracted. The original spectral data were pretreated by eight pretreatment methods,and partial least squares(PLS)algorithm was used for mining the spectral information to build F-PLS model in the full wavelength range. Then,regression coefficient method(RC),stepwise and successive projections algorithm(SPA)were applied for screening optimal wavelengths to optimize the F-PLS model. The results showed that the SPA-PLS model based on 21 optimal wavelengths(903.8,905.5,912.1,915.4,917.0,920.3,923.6,931.8,941.7,1107.0,1135.9,1157.3,1269.2,1303.7,1320.2,1348.2,1551.1,1676.9,1686.9,1695.1 and 1698.4 nm)selected by SPA from baseline correction(BC)pretreatment spectra had best performance(r_P=0.949,RMSEP=0.439 lg CFU/g,RPD=2.787). The results show that it would be feasible to predict the content of lactic acid bacteria in chilled chicken breast based on near-infrared hyperspectral imaging technology.

关 键 词：高光谱 化学计量学算法 快速预测 冷鲜鸡胸肉 乳酸菌 

分 类 号：TS251.7[轻工技术与工程—农产品加工及贮藏工程]