黄瓜贮藏中微生物信息三维荧光判别及其数量监控模型构建

为实现黄瓜贮藏过程中基于微生物内源荧光信息的微生物数量变化情况的快速实时监控，并为腐败预判提供依据，在不同贮藏时间对黄瓜表面进行三维荧光信息采集。运用多项式插值方法去除原始光谱中的瑞利散射，并进行Savitzky-Golay多项式平滑降噪处理。采用核心一致诊断（core consistency diagnostic，CORCONDIA）法对组件数进行估计，以避免黄瓜在贮藏过程中表面微生物自身的代谢作用对样本荧光信号的分析造成干扰。运用交替三线性分解（alternating trilinear decomposition，ATLD）算法，按该组件数对三维荧光矩阵进行分解，获得不同组件的相对激发强度光谱、相对发射强度光谱和相对浓度阵。解析结果表明：通过CORCONDIA法在函数值大于60%的前提下，确定组件数为4。以ATLD算法分解出的4 个组件中，组件1和组件3呈现特殊双峰结构，其特征激发-发射光谱与微生物主要内源荧光物质，即类色氨酸和类酪氨酸的荧光指纹图谱吻合，且组件1所代表的类色氨酸具有较高的荧光量子产率。运用荧光区域积分法对组件1的特征光谱中高激发类色氨酸和低激发类色氨酸区域的荧光总量进行定量分析，并用多元逐步回归方法构建了标准化区域积分值与微生物数量间的函数关系。回归分析结果显示，采用二元四次逐步回归方法构建的回归模型决定系数R2可达98.309 8%。采用1 个未参与模型建立的样本对预测模型进行检测，获得微生物数量的相对误差为1.037 1%。结论：在对黄瓜表面荧光光谱中微生物信息判别的基础上，可以实现黄瓜贮藏过程中基于微生物三维荧光信息的微生物数量变化监控模型的构建，为实时监控腐败进程提供依据。

Three-Dimensional Fluorescence Discrimination and Quantitative Modelling of Microorganisms on Cucumbers during Storage

In order to realize the rapid and real-time monitoring of the changes in the number of microorganisms on cucumbers during storage based on the endogenous fluorescence information of microorganisms on them,and to provide a basis for predicting microbial spoilage,three-dimensional fluorescence information was collected on the surface of cucumbers at different storage times.The polynomial interpolation method was used to remove the Rayleigh scattering in the original spectra,and Savitzky-Golay polynomial smoothing and denoising were performed.Meanwhile,in order to avoid the metabolism of microorganisms on cucumbers from interfering with the fluorescence signal analysis,the core consistency diagnostic(CORCONDIA)method was used to estimate the number of components.By using the alternating trilinear decomposition(ATLD)algorithm,the three-dimensional fluorescence array was decomposed according to the number of components,yielding the relative excitation intensity spectrum,relative emission intensity spectrum and relative concentration matrix of each component.The results showed that the number of components was 4 when the function value was more than 60%.Among the four components obtained by the ATLD algorithm,components 1 and 3 had a special bimodal structure,and their characteristic excitation and emission spectra were consistent with the fluorescence fingerprints of tryptophan and tyrosine as the major endogenous fluorescent substances in microorganisms,and the tryptophan represented by component 1 had a high fluorescence quantum yield.The fluorescence region integration method was used to quantitatively analyze the total fluorescence of tryptophan-like regions with high and low excitation in the characteristic spectrum of component 1,and the functional relationship between the integral value of standardized region and the number of microorganisms was constructed by multiple stepwise regression analysis.The regression results showed that the determination coefficient of the regression model constructed by two-element four-power stepwise regression analysis was 98.3098%.The prediction model was validated using samples not involved in its establishment,and the relative error of the obtained number of microorganisms was 1.0371%.These results indicated that the model for monitoring changes in the microbial load on the surface of cucumbers during storage could provide basis for monitoring vegetable spoilage in real time.