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The rapid and convenient detection of maturity is of great significance to determine the harvest time and postharvest storage conditions of apples. In this study, a portable visible and near-infrared (VIS/NIR) analysis device prototype was developed based on a multispectral sensor and applied to ‘Fuji’ apple maturity detection. The multispectral data of apples with maturity variation was measured, and the prediction model was established by a least-square support vector machine and linear discriminant analysis. Due to the low resolution of the multispectral data, regular preprocessing methods cannot improve the prediction accuracy. Instead, the spectral shape features (spectral ratio, spectral difference, and normalized spectral intensity difference) were used for preprocessing and model establishment, and the combination of the three features effectively improved the model performance with a prediction accuracy of 88.46%. In addition, the validation accuracy of the optimal model was 84.72%, and the area under curve (AUC) value of each maturity level was higher than 0.8972. The results show that the multispectral sensor is an appliable choice for the development of the portable detection device of apple maturity, and the data processing method proposed in this study provides a potential solution to improve the detection accuracy for multispectral sensors.

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Actin-like MreB paralogs play important roles in cell shape maintenance, cell wall synthesis and the regulation of the D,L-endopeptidases, CwlO and LytE. The gram-positive bacteria, Bacillus amyloliquefaciens LL3, is a poly-γ-glutamic acid (γ-PGA) producing strain that contains three MreB paralogs: MreB, Mbl and MreBH. In B. amyloliquefaciens, CwlO and LytE can degrade γ-PGA. In this study, we aimed to test the hypothesis that modulating transcript levels of MreB paralogs would alter the synthesis and degradation of γ-PGA. The results showed that overexpression or inhibition of MreB, Mbl or MreBH had distinct effects on cell morphology and the molecular weight of the γ-PGA products. In fermentation medium, cells of mreB inhibition mutant were 50.2% longer than LL3, and the γ-PGA titer increased by 55.7%. However, changing the expression level of mbl showed only slight effects on the morphology, γ-PGA molecular weight and titer. In the mreBH inhibition mutant, γ-PGA production and its molecular weight increased by 56.7% and 19.4%, respectively. These results confirmed our hypothesis that suppressing the expression of MreB paralogs might reduce γ-PGA degradation, and that improving the cell size could strengthen γ-PGA synthesis. This is the first report of enhanced γ-PGA production via suppression of actin-like MreB paralogs.

Due to the imperfect development of the photosynthetic apparatus of the newborn leaves of the canopy, the photosynthesis ability is insufficient, and the photosynthesis intensity is not only related to the external environmental factors, but also significantly related to the internal mechanism characteristics of the leaves. Light suppression and even light destruction are likely to occur when there is too much external light. Therefore, focus on the newborn leaves of the canopy, the accurate construction of photosynthetic rate prediction model based on environmental factor analysis and fluorescence mechanism characteristic analysis has become a key problem to be solved in facility agriculture. According to the above problems, a photosynthetic rate prediction model of newborn leaves in canopy of cucumber was proposed. The multi-factorial experiment was designed to obtain the multi-slice large-sample data of photosynthetic and fluorescence of newborn leaves. The correlation analysis method was used to obtain the main environmental impact factors as model inputs, and core chlorophyll fluorescence parameters was used for auxiliary verification. The best modeling method PSO-BP neural network was used to construct the newborn leaf photosynthetic rate prediction model. The validation results show that the net photosynthetic rate under different environmental factors of cucumber canopy leaves can be accurately predicted. The coefficient of determination between the measured values and the predicted values of photosynthetic rate was 0.9947 and the root mean square error was 0.8787. Meanwhile, combined with the core fluorescence parameters to assist the verification, it was found that the fluorescence parameters can accurately characterize crop photosynthesis. Therefore, this study is of great significance for improving the precision of light environment regulation for new leaf of facility crops.

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