CAJ | 학술논문

目的：通过对近红外广谱数据进行神经网络系统训练,讨论近红外广谱技术对冷藏三文鱼菌落总数快速预测的可行性。方法针对三文鱼在4℃贮藏过程中的微生物变化,利用手持式近红外光谱仪,通过小波分析对于光谱进行预处理,之后结合遗传算法和BP神经网络系统方法建立预测和检测模型。结果该模型与传统平板计数方法的相关系数为0.981,均方根误差为0.097,验证模型的相关系数为0.960,均方根误差为0.098,具有良好精确度、准确度。结论该方法能够用于冷藏三文鱼菌落总数的无损、现场检测。
목적：통과대근홍외엄보수거진행신경망락계통훈련,토론근홍외엄보기술대랭장삼문어균락총수쾌속예측적가행성。방법침대삼문어재4℃저장과정중적미생물변화,이용수지식근홍외광보의,통과소파분석대우광보진행예처리,지후결합유전산법화BP신경망락계통방법건립예측화검측모형。결과해모형여전통평판계수방법적상관계수위0.981,균방근오차위0.097,험증모형적상관계수위0.960,균방근오차위0.098,구유량호정학도、준학도。결론해방법능구용우랭장삼문어균락총수적무손、현장검측。
Objective To develop a new method by using artificial neural network for discussing the feasibility of predicting the aerobic plate count of salmon. Methods After spectral pretreatment by wavelet analysis, a new prediction and validation model was established by using a combined tactic of genetic algorithm (GA) and back-propagation artificial neural network (BP-ANN) to predict the aerobic palate count of salmon based on the change of microbe during the storage at 4℃, and portable near infrared spectrometer was used. Results The model had high accuracy and precision, the calibration curve coefficient of correlation (R) of the model and the traditional plate count method was 0.981, and root mean square error (RMSE) was 0.097. Correlation coefficient of validation model was 0.960 and root mean square error (RMSE) was 0.098. Conclusion This model could be used for non-destructive and on-site detection of the total bacteria colonies in frozen salmon.