Growth Simulation and Discrimination of Botrytis cinerea, Rhizopus stolonifer and Colletotrichum acutatum Using Hyperspectral Reflectance Imaging

Rhizopus stolonifer and related control strategies in postharvest fruit: A review

Rhizopus stolonifer is one of the main pathogens in postharvest storage logistics of more than 100 kinds of fruit, such as strawberries, tomatoes and melons. In this paper, the research on the morphology and detection, pathogenicity and infection mechanism of Rhizopus stolonifer was reviewed. The control methods of Rhizopus stolonifer in recent years was summarized from three dimensions of physics, chemistry and biology, including the nanomaterials, biological metabolites, light control bacteria, etc. Future direction of postharvest Rhizopus stolonifer infection control was analyzed from two aspects of pathogenic mechanism research and new composite technology. The information provided in this review will help researchers and technicians to deepen their understanding of the pathogenicity of Rhizopus stolonifer, and develop more effective control methods in the future.

Detection of Rice Fungal Spores Based on Micro- Hyperspectral and Microfluidic Techniques

As rice is one of the world's most important food crops, protecting it from fungal diseases is very important for agricultural production. At present, it is difficult to diagnose rice fungal diseases at an early stage using relevant technologies, and there are a lack of rapid detection methods. This study proposes a microfluidic chip-based method combined with microscopic hyperspectral detection of rice fungal disease spores. First, a microfluidic chip with a dual inlet and three-stage structure was designed to separate and enrich Magnaporthe grisea spores and Ustilaginoidea virens spores in air. Then, the microscopic hyperspectral instrument was used to collect the hyperspectral data of the fungal disease spores in the enrichment area, and the competitive adaptive reweighting algorithm (CARS) was used to screen the characteristic bands of the spectral data collected from the spores of the two fungal diseases. Finally, the support vector machine (SVM) and convolutional neural network (CNN) were used to build the full-band classification model and the CARS filtered characteristic wavelength classification model, respectively. The results showed that the actual enrichment efficiency of the microfluidic chip designed in this study on Magnaporthe grisea spores and Ustilaginoidea virens spores was 82.67% and 80.70%, respectively. In the established model, the CARS-CNN classification model is the best for the classification of Magnaporthe grisea spores and Ustilaginoidea virens spores, and its F1-core index can reach 0.960 and 0.949, respectively. This study can effectively isolate and enrich Magnaporthe grisea spores and Ustilaginoidea virens spores, providing new methods and ideas for early detection of rice fungal disease spores.

Fungi Classification in Various Growth Stages Using Shortwave Infrared (SWIR) Spectroscopy and Machine Learning

Dark septate endophytes (DSEs) fungi are beneficial to host plants with regard to abiotic stress. Here, we examined the capability of SWIR spectroscopy to classify fungus types and detected the growth stages of DSEs fungi in a timely, non-destructive and time-saving manner. The SWIR spectral data of five DSEs fungi in six growth stages were collected, and three pre-processing methods and sensitivity analysis (SA) variable selection methods were performed using a machine learning model. The results showed that the De-trending + first Derivative (DET_FST) processing spectra combined with the support vector machine (SVM) model yielded the best classification accuracy for fungi classification at different growth stages and growth stage detection on different fungus types. The mean accuracy of generic model for fungi classification and growth stage detection are 0.92 and 0.99 on the calibration set, respectively. Seven important bands, 1164, 1456, 2081, 2272, 2278, 2448 and 2481 nm, were found to be related to the SVM fungi classification. This study provides a rapid and efficient method for the classification of fungi in different growth stages and the detection of fungi growth stage of various types of fungi and could serve as a tool for fungi study.

Detection and identification of fungal growth on freeze-dried Agaricus bisporus using spectra and olfactory sensors

BACKGROUND

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.

RESULTS

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_c ² ) = 0.786, root mean-square error of calibration (RMSEC) = 0.125 log CFU g^-1 . The A. flavus group had the highest prediction performance: coefficient of prediction (R_p ² ) = 0.821, root mean-square error of prediction (RMSEP) = 0.083 log CFU g^-1 . 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.

CONCLUSION

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.

Early discrimination and growth tracking of Aspergillus spp. contamination in rice kernels using electronic nose

Early detection of Aspergillus spp. contamination in rice was investigated by electronic nose (E-nose) in this study. Sterilized rice artificially inoculated with three Aspergillus strains were subjected to GC-MS and E-nose analyses. Principle Component Analysis (PCA), Partial Least Squares Regression (PLSR), Back-propagation neural network (BPNN), Support Vector Machine (SVM) and Learning Vector Quantization (LVQ) were employed for qualitative classification and quantitative regression. GC-MS analysis revealed a significant correlation between the volatile compounds and total amounts/species of fungi. While X-axis barycenters of PC1 scores were significantly correlated with fungal counts, logistic model could be employed to simulate the growth of individual fungus (R² = 0.978-0.996). Fungal species and counts in rice could be classified and predicted by BPNN (96.4%) and PLSR (R² = 0.886-0.917), respectively. The results demonstrated that E-nose combined with BPNN might offer the feasibility for early detection of Aspergillus spp. contamination in rice.

Discrimination and growth tracking of fungi contamination in peaches using electronic nose

A non-destructive method for detection of fungal contamination in peaches using an electronic nose (E-nose) is presented. Peaches were inoculated with three common spoilage fungi, Botrytis cinerea, Monilinia fructicola and Rhizopus stolonifer and then stored for various periods. E-nose was then used to analyze volatile compounds generated in the fungi-inoculated peaches, which was then compared with the growth data (colony counts) of the fungi. The results showed that changes in volatile compounds in fungi-inoculated peaches were correlated with total amounts and species of fungi. Terpenes and aromatic compounds were the main contributors to E-nose responses. While principle component analysis (PC1) scores were highly correlated with fungal colony counts, Partial Least Squares Regression (PLSR) could effectively be used to predict fungal colony counts in peach samples. The results also showed that the E-nose had high discrimination accuracy, demonstrating the potential use of E-nose to discriminate among fungal contamination in peaches.

Classification and Discrimination of Different Fungal Diseases of Three Infection Levels on Peaches Using Hyperspectral Reflectance Imaging Analysis

Peaches are susceptible to infection from several postharvest diseases. In order to control disease and avoid potential health risks, it is important to identify suitable treatments for each disease type. In this study, the spectral and imaging information from hyperspectral reflectance (400~1000 nm) was used to evaluate and classify three kinds of common peach disease. To reduce the large dimensionality of the hyperspectral imaging, principal component analysis (PCA) was applied to analyse each wavelength image as a whole, and the first principal component was selected to extract the imaging features. A total of 54 parameters were extracted as imaging features for one sample. Three decayed stages (slight, moderate and severe decayed peaches) were considered for classification by deep belief network (DBN) and partial least squares discriminant analysis (PLSDA) in this study. The results showed that the DBN model has better classification results than the classification accuracy of the PLSDA model. The DBN model based on integrated information (494 features) showed the highest classification results for the three diseases, with accuracies of 82.5%, 92.5%, and 100% for slightly-decayed, moderately-decayed and severely-decayed samples, respectively. The successive projections algorithm (SPA) was used to select the optimal features from the integrated information; then, six optimal features were selected from a total of 494 features to establish the simple model. The SPA-PLSDA model showed better results which were more feasible for industrial application. The results showed that the hyperspectral reflectance imaging technique is feasible for detecting different kinds of diseased peaches, especially at the moderately- and severely-decayed levels.

Evaluation of lipid oxidation of Chinese-style sausage during processing and storage based on electronic nose

A portable electronic nose was used for extracting flavour fingerprint map of Chinese-style sausage during processing and storage, in parallel with detection of acid value (AV) and peroxide value (POV) for evaluating lipid oxidation. Sausage samples during processing and storage were divided into three and five quality phases, respectively. After comparison of sensors response to lipid oxidation, optimal sensor array was determined. Several classification and regression models were developed to classify samples into their respective quality phase and predict lipid oxidation using full and optimal sensor array. Results indicated classification accuracy for sausage samples were, respectively, above 95% and 82% during the processing and storage. For support vector machine (SVM) and artificial neural networks (ANN) regression models, good performance in predicting AV and POV were obtained, with the coefficients of determination (R²s) >0.914 and 0.814 during processing and storage, respectively. Thus, E-nose demonstrated acceptable feasibility in evaluating the degree of lipid oxidation of Chinese-style sausage during processing and storage.

Predicting the growth situation of Pseudomonas aeruginosa on agar plates and meat stuffs using gas sensors

A rapid method of predicting the growing situation of Pseudomonas aeruginosa is presented. Gas sensors were used to acquire volatile compounds generated by P. aeruginosa on agar plates and meat stuffs. Then, optimal sensors were selected to simulate P. aeruginosa growth using modified Logistic and Gompertz equations by odor changes. The results showed that the responses of S₈ or S₁₀ yielded high coefficients of determination (R²) of 0.89-0.99 and low root mean square errors (RMSE) of 0.06-0.17 for P. aeruginosa growth, fitting the models on the agar plate. The responses of S₉, S₄ and the first principal component of 10 sensors fit well with the growth of P. aeruginosa inoculated in meat stored at 4 °C and 20 °C, with R² of 0.73-0.96 and RMSE of 0.25-1.38. The correlation coefficients between the fitting models, as measured by electronic nose responses, and the colony counts of P. aeruginosa were high, ranging from 0.882 to 0.996 for both plate and meat samples. Also, gas chromatography-mass spectrometry results indicated the presence of specific volatiles of P. aeruginosa on agar plates. This work demonstrated an acceptable feasibility of using gas sensors-a rapid, easy and nondestructive method for predicting P. aeruginosa growth.