小麦是我国战略性储藏粮食品种, 但小麦易受霉菌感染而发生霉变, 影响其食用安全。 加强小麦有害霉菌侵染程度的早期检测是控制其危害的需要措施。 然而, 现有的平板计数和荧光染色等检测方法, 普遍存在前处理繁杂、 时效性差等不足。 故此, 拟运用阵列式光纤光谱仪结合化学计量学方法, 建立霉变小麦的实时在线检测方法, 并为进一步开发粮食品质与安全在线检测装备提供参考。 小麦样品经辐照灭菌后分别接种五种谷物中常见有害霉菌: 串珠镰刀菌83227、 层出镰刀菌195647、 雪腐镰刀菌3.503、 寄生曲霉3.3950和赭曲霉3.3486, 并置于28 ℃和85%相对湿度环境中储藏7 d以加速霉变。 在样品储藏的第0, 1, 3, 5和7 d, 运用阵列式光纤光谱仪和漫反射探头在线采集样品的漫反射特征光谱, 并依据国标平板计数法测定样品菌落总数水平。 光谱采集步骤为: 在线检测平台上, 设置样品运动速度0.15 m·s-1和光谱仪积分时间20 ms, 采集波段为600~1 600 nm, 重复测量3次。 然后, 首先对小麦原始光谱进行平滑、 多元散射校正和导数变换等预处理以消除光谱噪音; 随后, 运用主成分分析(PCA)对受不同霉变程度(储藏阶段)的小麦样品进行区分; 最后, 利用线性判别分析(LDA)和偏最小二乘回归分析(PLSR)建立小麦有害霉菌侵染程度的定性定量分析模型。 小麦在储藏期内经历未霉变、 开始霉变和严重霉变三个阶段。 原始和二阶微分光谱显示霉菌侵染引起小麦光谱特征发生显著改变, PCA结果表明不同储藏阶段(霉变程度)小麦样品存在一定分离趋势。 利用前十个主成分得分建立的LDA判别模型, 对不同霉变程度小麦样品的识别率达90.0%以上。 结果表明: 小麦样品菌落总数的PLSR定量预测模型的预测决定系数(R2p)为0.859 2, 预测均方根误差(RMSEP)为0.401 Log CFU·g-1, 相对分析偏差(RPD)达2.65。 应用阵列式光纤光谱仪结合计量学方法在线评估小麦霉变具有一定可行性。 下一步研究中, 应扩大样品量, 补充自然霉变及受更多代表性霉菌侵染的小麦样品, 以不断增强模型的鲁棒性和方法的适用性。

Wheat is one of the main strategic stored grain varieties in China. But wheat is susceptible to fungal infection, which affects its safety as food. Early detection of harmful fungal infection in wheat is the precondition to control its hazard. However, current methods for mold detection, such as plate counting and fluorescence staining, are usually laboratory-intensive and time-consuming, and can not fulfill the need for on-site testing. Concerning this issue, this work intends to apply array fiber spectrometer series and chemometrics to establish an on-line method for detection of wheat mildew, and to provide a reference for further development of on-line sensing instruments for grain quality and safety. Sterile wheat kernels were inoculated with 5 different spore suspensions of fungal strains respectively, which were F. moniliforme 83227, F. proliferatum 195647, F. nivale 3.503, A. parasiticus 3.3950 and A. ochraceus 3.3486. Wheat samples were then stored at 28 ℃ and 85% relative humidity after inoculation to accelerate mildew process. At storage stage of 0, 1, 3, 5 and 7 d, Vis/NIR spectra of samples were collected by an on-line sensing system which was mainly conposed of an array fiber spectrometer and a diffuse reflectance probe. Total colony count of samples was also determined according to plate count method. Spectra of samples were measured at moving speed of 0.15 m·s-1 with integral time of 20 ms. Each sample was collected three times and the acquisition band ranged from 600 to 1 600 nm. Then original spectra of samples were firstly pre-processed by smoothing, multivariate scatter correction and derivative transformation to eliminate spectral noise. Subsequently, principal component analysis (PCA) was used to discriminate wheat samples with different mildew degrees (storage stage). Finally, liner discriminant analysis (LDA) and partial least squares regression (PLSR) were employed to develop qualitative and quantitative analysis models for fungal infection in wheat. Wheat samples undergone three stages during the storage period according to colony counts, which were not mildew, mildew and serious mildew. Analysis of original and second derivative spectra indicated that fungal infection resulted in significant changes in wheat spectra. PCA results showed that there was a certain trend of separation between wheat with different mildew degrees. The overall recognition rate obtained by LDA for the classification of samples with different mildew degrees was more than 90.0%. Colony counts in wheat samples was predicted by PLSR and coefficient of determination for the prediction set (R2p), root mean square error of prediction (RMSEP) and residual predictive deviation (RPD) value obtained were 0.859 2, 0.401 Log CFU·g-1 and 2.65, respectively. The combination of array fiber spectrometer series and chemometrics is feasible for on-line detection of wheat mildew. In further studies, natural infected wheat samples and samples contaminated with more representative fungal strains should be incorporated to enhance the robustness and applicability of the calibration model.