CAJ | 학술논문

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R2p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R2p为0.787，均方根误差RMSEP为6.186 mg/100g；而融合模型的决定系数R2p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。
위료실현어류신선도적쾌속무손검측，해연구상시이용후각가시화여근홍외광보융합기술대어적휘발성염기담함량진행예측，종이평개기신선도。시험대상선용해로어，4℃랭장대측。용주성분분석법대종가시화전감기진렬제취도적특정변량진행강유，용유전산법결합편최소이승법대예처리후적근홍외광보특정변량진행사선，장강유화사선후적변량진행특정층융합。용지지향량회귀산법분별건립기우후각가시화、근홍외광보화다전감기신식융합기술적휘발성염기담함량예측모형。기우후각가시화기술적모형적예측집결정계수R2p위0.757，균방근오차RMSEP위6.755 mg/100g；기우근홍외광보기술적모형적결정계수R2p위0.787，균방근오차RMSEP위6.186 mg/100g；이융합모형적결정계수R2p위0.882，균방근오차RMSEP위4.585 mg/100g，여전량개모형상비，예측경준학。연구결과표명，이용후각가시화화근홍외광보융합기술평개해로어신선도시가행적。
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 spectrum. After preprocessing, genetic algorithm (GA) combined with partial least squares (PLS) method was carried out on the near-infrared spectrum data to remove irrelevant information as well as simplify the prediction model. The results showed that characteristic variables of near-infrared spectrum reduced from 1557 to 79 after the optimization of GA-PLS. Meanwhile, the root mean square error of cross validation (RMSECV) reduced from 12.763 to 6.585 mg/100 g, which indicated that the remaining variables had higher correlation with TVB-N content than the original variables. Based on the informative near-infrared spectrum data, another TVB-N prediction model was established by SVR algorithm. The R2c and R2p of the model were 0.810 and 0.787 respectively, while the RMSEC and RMSEP were 5.385 and 6.186 mg/100 g respectively. Because of the insufficiency in getting freshness information, performance of the two single-sensor models above was unsatisfactory. To improve the predictive accuracy of TVB-N content, the colorimetric sensor array data after dimension reducing and the informative near-infrared spectrum data were fused, and a multi-sensor information fusion model was established based on the fused data. The R2c and R2p of the model were 0.893 and 0.882 respectively, while the RMSEC and RMSEP were 4.032 and 4.585 mg/100g respectively. Compared with other two single-sensor models, the R2p of fusion model increased by 0.095 and the RMSEP reduced by 1.601 mg/100g, which proved the superiority of fusion model. This study shows that multi-sensor information fusion based on olfactory visualization and near-infrared spectroscopy technique can be a feasible method for the evaluation of sea bass freshness.