Rapid Detection of Stored Grain Pests in Flour Based on GC-MS and E-nose
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摘要: 储粮害虫会降低粮食及其产品的重量、品质和营养健康指数，并且我国粮虫检测方式仍然以人工检测为主。为满足储粮害虫快速检测的需求，采用气相色谱-质谱联用仪(GC-MS)获得了赤拟谷盗(Tribolium castaneum(Herbst))的主要特定挥发性有机化合物(VOCs),根据这些化合物的性质筛选出多个金属氧化物气敏传感器，并以传感器阵列为核心开发了储粮害虫电子鼻检测装置。该装置采集了赤拟谷盗、被赤拟谷盗侵染的面粉、被长头谷盗(Latheticus oryzae Waterhouse)侵染的面粉3种实验对象的气味信息，提取每条响应曲线的相对变化值和相对积分值作为原始特征矩阵(10×2),使用主成分分析(PCA)和偏最小二乘回归算法(PLSR)对原始特征矩阵进行分析，并通过建立回归预测模型，实现了对面粉中赤拟谷盗和长头谷盗虫口密度的预测。优化后的传感器数量由10个减少至8个，赤拟谷盗样品的两个主成分累计的贡献率为79.4%。基于PLSR的预测模型对面粉中赤拟谷盗的数量有很好的预测效果(校正集：相关系数r=0.88,均方根误差为8.09;验证集：r=0.89,均方根误差为7.75);该预测模型对面粉中长头谷盗的数量也有很好的预测效果(校正集：r=0.94,均方根误差为5.85;验证集：r=0.94,均方根误差为6.08)。研究结果表明：该装置能够满足判别储粮中不同虫口密度样本的基本需要，并且具有可靠的稳定性。Abstract: Grain storage pests can reduce the weight, quality and nutritional health index of grain and its products, and the way of grain pest detection in China is still dominated by manual detection. To meet the needs of modern detection of grain storage pests, gas chromatography-mass spectrometry（GC-MS） was used to obtain the main specific volatile organic compounds（VOCs） of Tribolium castaneum（Herbst）, screened multiple metal oxide gas sensors with the obtained compounds as reference. Then the air chamber for sensor response was optimally designed and an electronic nose detection device for grain storage pests was developed based on the composed sensor array. The device collected odor information from three experimental subjects, including T.castaneum, the flour during infestation of T.castaneum and the flour during infestation of Latheticus oryzae Waterhouse. Relative change values and relative integration values of the response curve corresponding to each sensor as the original feature matrix（10×2）. Principal component analysis（PCA） and partial least squares regression algorithm（PLSR） were used to optimize the original feature matrix. Finally, a predictive regression model was built to forecast the population density of T.castaneum and Latheticus oryzae Waterhouse in flour. Two GC-MS studies were carried out for the purpose to collect 12 distinct volatile chemical compounds of T.castaneum that were not found in other grain insects or stored grains. The number of sensors was reduced from 10 to 8, and the contribution of the two principal components of the T.castaneum samples was increased to 79.4%. The odor of the flour itself would be a great interference to the electronic nose detection of T.castaneum, and under the condition of no flour interference, the electronic nose device can discriminate between samples with varying insect population densities. The PLSR-based prediction model was highly effective in predicting the number of T.castaneum in flour（correction set: correlation coefficient r=0.88, root mean square error（RMSE） was 8.09; validation set: correlation coefficient r=0.89,RMSE was 7.75）; the prediction model was also highly effective in predicting the number of Latheticus oryzae Waterhouse in flour（correction set: correlation coefficient r=0.94, RMSE was 5.85; validation set: r=0.94, RMSE was 6.08）. The research results indicated that the device can meet the needs of distinguishing samples with different insect densities in stored grains and had reliable stability. This method also provided a method reference for detecting other pests in stored grain.
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