Key Laboratory of Grains and Oils Quality Control and Processing, Collaborative Innovation Center for Modern Grain Circulation and Safety, College of Food Science and Engineering, Nanjing University of Finance and Economics, Nanjing, China.
Faculty of Agriculture and Food Science, Meru University of Science and Technology, Meru, Kenya.
J Sci Food Agric. 2020 May;100(7):3136-3146. doi: 10.1002/jsfa.10348. Epub 2020 Mar 13.
Fungal contamination in food products leads to mustiness, biochemical changes, and undesirable odors, which result in lower food quality and lower market value. To develop a rapid method for detecting fungi, hyperspectral imaging (HSI) was applied to identify five fungi inoculated on plates (Aspergillus niger, Aspergillus flavus, Penicillium chrysogenum, Aspergillus fumigatus, and Aspergillus ochraceus). Near-infrared (NIR) spectroscopy, mid-infrared (MIR) spectroscopy, and an electronic nose (E-nose) were applied to detect and identify freeze-dried Agaricus bisporus infected with the five fungi.
Partial least squares regression (PLSR) models were used to distinguish the HSI spectra of the five fungi on the plates. The A. ochraceus group had the highest calibration performance: coefficient of calibration (R ) = 0.786, root mean-square error of calibration (RMSEC) = 0.125 log CFU g . The A. flavus group had the highest prediction performance: coefficient of prediction (R ) = 0.821, root mean-square error of prediction (RMSEP) = 0.083 log CFU g . The ratio of performance deviation (RPD) values of all of the models was higher than 2.0 for the NIR, MIR, and E-nose results for freeze-dried A. bisporus infected with different fungi. The fungal species and degree of infection can be distinguished by principal component analysis (PCA) and partial least squares discriminant analysis (PLS-DA) using NIR, MIR, and E-nose, as the discrimination accuracy was more than 90%. The NIR methods had a higher recognition rate than the MIR and E-nose methods.
Principal component analysis (PCA) and PLSR models based on full spectra of HSI can achieve good discrimination results for these five fungi on plates. Moreover, NIR, MIR, and the E-nose were proven to be effective in monitoring fungal contamination on freeze-dried A. bisporus. However, NIR could be a more accurate method. © 2020 Society of Chemical Industry.
食品中真菌的污染会导致食物出现霉味、生化变化和不良气味，从而降低食品质量和市场价值。为了开发一种快速检测真菌的方法，应用高光谱成像（HSI）来识别平板上接种的五种真菌（黑曲霉、黄曲霉、产黄青霉、烟曲霉和赭曲霉）。近红外（NIR）光谱、中红外（MIR）光谱和电子鼻（E-nose）用于检测和识别受五种真菌感染的冻干双孢蘑菇。
偏最小二乘法回归（PLSR）模型用于区分平板上五种真菌的 HSI 光谱。A. ochraceus 组具有最高的校准性能：校准系数（R ）= 0.786，校准均方根误差（RMSEC）= 0.125 log CFU g 。A. flavus 组具有最高的预测性能：预测系数（R ）= 0.821，预测均方根误差（RMSEP）= 0.083 log CFU g 。对于不同真菌感染的冻干双孢蘑菇，NIR、MIR 和 E-nose 的所有模型的性能偏差（RPD）值均高于 2.0。通过主成分分析（PCA）和偏最小二乘判别分析（PLS-DA），可以使用 NIR、MIR 和 E-nose 区分真菌种类和感染程度，因为判别准确率超过 90%。NIR 方法的识别率高于 MIR 和 E-nose 方法。
基于 HSI 全谱的主成分分析（PCA）和偏最小二乘法（PLSR）模型可以对平板上的这五种真菌进行良好的区分。此外，NIR、MIR 和 E-nose 被证明可有效监测冻干双孢蘑菇的真菌污染。然而，NIR 可能是一种更准确的方法。 © 2020 化学工业协会。
