Structure and Ecological Function of Fungal Endophytes from Stems of Different Mulberry Cultivars.

To explore the microbial community structure and ecological function of mulberry and their potential relationship with the resistance of mulberry, the community structure and function of endophytic fungi in 18 mulberry cultivars were analyzed and predicted by using high-throughput sequencing technology and the FUNGuild database. A total of 352 operational taxonomic units of fungi were observed at a 97% similarity level, representing six phyla of fungi, Fungi_unclassified, Ascomycota, Basidiomycota, Zygomycota, Rozellomycota, and Chytridiomycota. Fungi_unclassified was dominant, and Ascomycota was relatively dominant in all cultivars. At the genus level, Ascomycota_unclassified was dominant, and Ampelomyces was relatively dominant, with a richness in TAIWANCHANGGUOSANG 16.47-8975.69 times that in the other cultivars. Classified Ascomycota_unclassified was 4.75-296.65 times more common in NANYUANSIJI than in the other cultivars. Based on the FUNGuild analysis method, we successfully annotated six nutrient types, namely, pathotroph, pathotroph-saprotroph, pathotroph-saprotroph-symbiotroph, saprotroph, saprotroph-symbiotroph, and symbiotroph, among which saprophytic-symbiotic accounted for the largest proportion and was absolutely dominant in TWC. This research suggests that community composition differs among cultivars and that the diversity and richness of endophytic fungi in resistant cultivars are higher than those in susceptible cultivars. The ecological functions of cultivars with different resistances are quite different.

Non-destructive detection of protein content in mulberry leaves by using hyperspectral imaging.

Protein content is one of the most important indicators for assessing the quality of mulberry leaves. This work is carried out for the rapid and non-destructive detection of protein content of mulberry leaves using hyperspectral imaging (HSI) (Specim FX10 and FX17, Spectral Imaging Ltd., Oulu, Finland). The spectral range of the HSI acquisition system and data processing methods (pretreatment, feature extraction, and modeling) is compared. Hyperspectral images of three spectral ranges in 400-1,000 nm (Spectral Range I), 900-1,700 nm (Spectral Range II), and 400-1,700 nm (Spectral Range III) were considered. With standard normal variate (SNV), Savitzky-Golay first-order derivation, and multiplicative scatter correction used to preprocess the spectral data, and successive projections algorithm (SPA), competitive adaptive reweighted sampling, and random frog used to extract the characteristic wavelengths, regression models are constructed by using partial least square and least squares-support vector machine (LS-SVM). The protein content distribution of mulberry leaves is visualized based on the best model. The results show that the best results are obtained with the application of the model constructed by combining SNV with SPA and LS-SVM, showing an R 2 of up to 0.93, an RMSE of just 0.71 g/100 g, and an RPD of up to 3.83 based on the HSI acquisition system of 900-1700 nm. The protein content distribution map of mulberry leaves shows that the protein of healthy mulberry leaves distributes evenly among the mesophyll, with less protein content in the vein of the leaves. The above results show that rapid, non-destructive, and high-precision detection of protein content of mulberry leaves can be achieved by applying the SWIR HSI acquisition system combined with the SNV-SPA-LS-SVM algorithm.

Non-destructive prediction and visualization of anthocyanin content in mulberry fruits using hyperspectral imaging.

Being rich in anthocyanin is one of the most important physiological traits of mulberry fruits. Efficient and non-destructive detection of anthocyanin content and distribution in fruits is important for the breeding, cultivation, harvesting and selling of them. This study aims at building a fast, non-destructive, and high-precision method for detecting and visualizing anthocyanin content of mulberry fruit by using hyperspectral imaging. Visible near-infrared hyperspectral images of the fruits of two varieties at three maturity stages are collected. Successive projections algorithm (SPA), competitive adaptive reweighted sampling (CARS) and stacked auto-encoder (SAE) are used to reduce the dimension of high-dimensional hyperspectral data. The least squares-support vector machine and extreme learning machine (ELM) are used to build models for predicting the anthocyanin content of mulberry fruit. And genetic algorithm (GA) is used to optimize the major parameters of models. The results show that the higher the anthocyanin content is, the lower the spectral reflectance is. 15, 7 and 13 characteristic variables are extracted by applying CARS, SPA and SAE respectively. The model based on SAE-GA-ELM achieved the best performance with R2 of 0.97 and the RMSE of 0.22 mg/g in both the training set and testing set, and it is applied to retrieve the distribution of anthocyanin content in mulberry fruits. By applying SAE-GA-ELM model to each pixel of the mulberry fruit images, distribution maps are created to visualize the changes in anthocyanin content of mulberry fruits at three maturity stages. The overall results indicate that hyperspectral imaging, in combination with SAE-GA-ELM, can help achieve rapid, non-destructive and high-precision detection and visualization of anthocyanin content in mulberry fruits.

Volatile composition changes in lemon during fruit maturation by HS-SPME-GC-MS.

BACKGROUND: Volatiles are determinants of fruit aroma and flavor characteristics and also provide valuable information for lemon as ingredient for the food and drinks industry. Volatiles in 'Eureka' lemon and 'Xiangshui' lemon pulps from 130 to 186 days after flowering were enriched by headspace-solid-phase microextraction (HS-SPME), and analyzed by gas chromatography-mass spectrometry (GC-MS). RESULTS: Seventy-seven volatiles of two lemon cultivars at the different ripening stages were identified and divided into six categories. Varieties and ripening stages had significant effects on individual volatiles in each category. The proportion of monoterpenes was found to be higher in 'Eureka' lemon, while 'Xiangshui' lemon had a higher proportion of sesquiterpenes, aldehydes and alcohols. The proportion of monoterpene fluctuation decreased during fruit ripening, while fluctuation of sesquiterpenes, alcohols, aldehydes and esters increased. Among the hydrocarbons, monoterpenes decreased their relative abundance from 91.67% to 81.04% in 'Eureka' lemon, and from 83.01% to 60.04% in 'Xiangshui' lemon; conversely, sesquiterpenes increased from 0.73% to 2.89% in 'Eureka' lemon, and from 3.21% to 8.48% in 'Xiangshui' lemon. Among the oxygenated volatiles, the proportions of alcohols, aldehydes and esters were higher at 186 days after flowering in both two cultivars. CONCLUSION: The volatile organic compounds during fruit ripening of lemon varieties with different resistance were elucidated. The proportion of oxygenated volatiles increased during fruit ripening, and disease-resistant varieties had a higher proportion. These results provided important theoretical support for the utilization of lemon fruits and the innovation of disease-resistant germplasm resources. © 2021 Society of Chemical Industry.

[Identification of Pummelo Cultivars Based on Hyperspectral Imaging Technology].

Existing methods for the identification of pummelo cultivars are usually time-consuming and costly, and are therefore inconvenient to be used in cases that a rapid identification is needed. This research was aimed at identifying different pummelo cultivars by hyperspectral imaging technology which can achieve a rapid and highly sensitive measurement. A total of 240 leaf samples, 60 for each of the four cultivars were investigated. Samples were divided into two groups such as calibration set (48 samples of each cultivar) and validation set (12 samples of each cultivar) by a Kennard-Stone-based algorithm. Hyperspectral images of both adaxial and abaxial surfaces of each leaf were obtained, and were segmented into a region of interest (ROI) using a simple threshold. Spectra of leaf samples were extracted from ROI. To remove the absolute noises of the spectra, only the date of spectral range 400~1000 nm was used for analysis. Multiplicative scatter correction (MSC) and standard normal variable (SNV) were utilized for data preprocessing. Principal component analysis (PCA) was used to extract the best principal components, and successive projections algorithm (SPA) was used to extract the effective wavelengths. Least squares support vector machine (LS-SVM) was used to obtain the discrimination model of the four different pummelo cultivars. To find out the optimal values of σ2 and γ which were important parameters in LS-SVM modeling, Grid-search technique and Cross-Validation were applied. The first 10 and 11 principal components were extracted by PCA for the hyperspectral data of adaxial surface and abaxial surface, respectively. There were 31 and 21 effective wavelengths selected by SPA based on the hyperspectral data of adaxial surface and abaxial surface, respectively. The best principal components and the effective wavelengths were used as inputs of LS-SVM models, and then the PCA-LS-SVM model and the SPA-LS-SVM model were built. The results showed that 99.46% and 98.44% of identification accuracy was achieved in the calibration set for the PCA-LS-SVM model and the SPA-LS-SVM model, respectively, and a 95.83% of identification accuracy was achieved in the validation set for both the PCA-LS-SVM and the SPA- LS-SVM models, which were built based on the hyperspectral data of adaxial surface. Comparatively, the results of the PCA-LS-SVM and the SPA-LS-SVM models built based on the hyperspectral data of abaxial surface both achieved identification accuracies of 100% for both calibration set and validation set. The overall results demonstrated that use of hyperspectral data of adaxial and abaxial leaf surfaces coupled with the use of PCA-LS-SVM and the SPA-LS-SVM could achieve an accurate identification of pummelo cultivars. It was feasible to use hyperspectral imaging technology to identify different pummelo cultivars, and hyperspectral imaging technology provided an alternate way of rapid identification of pummelo cultivars. Moreover, the results in this paper demonstrated that the data from the abaxial surface of leaf was more sensitive in identifying pummelo cultivars. This study provided a new method for to the fast discrimination of pummelo cultivars.