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  

Freshness evaluation of sea bass using multi-sensor information fusion based on olfactory visualization and NIR spectroscopy technique

作　　者：黄星奕[1] 管超[1] 丁然[1] 吕日琴[1] 

机构地区：[1]江苏大学食品与生物工程学院,镇江212013

出　　处：《农业工程学报》2015年第8期277-282,共6页Transactions of the Chinese Society of Agricultural Engineering

基　　金：江苏省高校自然科学研究重大项目(14KJA550001);公益性行业(农业)科研专项(201003008);国家自然科学基金项目(31071549);江苏省高校优势学科建设工程资助项目

摘　　要：为了实现鱼类新鲜度的快速无损检测,该研究尝试利用嗅觉可视化与近红外光谱融合技术对鱼的挥发性盐基氮含量进行预测,从而评价其新鲜度。试验对象选用海鲈鱼,4℃冷藏待测。用主成分分析法对从可视化传感器阵列提取到的特征变量进行降维,用遗传算法结合偏最小二乘法对预处理后的近红外光谱特征变量进行筛选,将降维和筛选后的变量进行特征层融合。用支持向量回归算法分别建立基于嗅觉可视化、近红外光谱和多传感器信息融合技术的挥发性盐基氮含量预测模型。基于嗅觉可视化技术的模型的预测集决定系数R2 p为0.757,均方根误差RMSEP为6.755 mg/100g;基于近红外光谱技术的模型的决定系数R2 p为0.787,均方根误差RMSEP为6.186 mg/100g;而融合模型的决定系数R2 p为0.882,均方根误差RMSEP为4.585 mg/100g,与前两个模型相比,预测更准确。研究结果表明,利用嗅觉可视化和近红外光谱融合技术评价海鲈鱼新鲜度是可行的。Total volatile basic nitrogen （TVB-N） is an important reference index of fish freshness. This study attempted to measure TVB-N content of sea bass using multi-sensor information fusion based on olfactory visualization and near-infrared spectroscopy technique. Sea bass samples were stored under the condition of 4℃ in refrigerator. The total number of samples was 270, among them 18 random samples every day were firstly detected by olfactory visualization detecting instrument, and then by near-infrared spectrometer. TVB-N content of these samples was measured according to the kjeldahl method. The experiment finished after 15 days because of the serious corruption of samples. Two-thirds of total samples were chosen as calibration set and the remaining samples were taken as prediction set using SPXY （sample set partitioning based on joint x–y distances） algorithm. So the sample sizes of calibration set and prediction set were 180 and 90 respectively. After sample division, a TVB-N prediction model was established based on the fused multi-sensor information. Other two models were also established based on the single-sensor information for comparing. Principal component analysis （PCA） method was used to reduce the dimension of colorimetric sensor array data. The results of PCA showed that the cumulative contribution rate of the first three principal components was 85.441%, which indicated that the first three principal components had been able to explain the vast majority of the overall information about original samples. Based on the first three principal components, a TVB-N prediction model was established by support vector regression （SVR） algorithm. The determination coefficients of calibration set （R2c ） and prediction set （R2p ） of the model were 0.762 and 0.757 respectively, while the root mean square errors of calibration set （RMSEC） and prediction set （RMSEP） were 6.012 and 6.755 mg/100g respectively. Mean centering （MC） method was used to preprocess the raw near-infrared

关 键 词：近红外光谱 模型 可视化 多信息融合 海鲈鱼 挥发性盐基氮 

分 类 号：S126[农业科学—农业基础科学] S983