ResNet incorporating the fusion data of RGB & hyperspectral images improves classification accuracy of vegetable soybean freshness.

The freshness of vegetable soybean (VS) is an important indicator for quality evaluation. Currently, deep learning-based image recognition technology provides a fast, efficient, and low-cost method for analyzing the freshness of food. The RGB (red, green, and blue) image recognition technology is widely used in the study of food appearance evaluation. In addition, the hyperspectral image has outstanding performance in predicting the nutrient content of samples. However, there are few reports on the research of classification models based on the fusion data of these two sources of images. We collected RGB and hyperspectral images at four different storage times of VS. The ENVI software was adopted to extract the hyperspectral information, and the RGB images were reconstructed based on the downsampling technology. Then, the one-dimensional hyperspectral data was transformed into a two-dimensional space, which allows it to be overlaid and concatenated with the RGB image data in the channel direction, thereby generating fused data. Compared with four commonly used machine learning models, the deep learning model ResNet18 has higher classification accuracy and computational efficiency. Based on the above results, a novel classification model named ResNet-R &H, which is based on the residual networks (ResNet) structure and incorporates the fusion data of RGB and hyperspectral images, was proposed. The ResNet-R &H can achieve a testing accuracy of 97.6%, which demonstrates a significant enhancement of 4.0% and 7.2% compared to the distinct utilization of hyperspectral data and RGB data, respectively. Overall, this research is significant in providing a unique, efficient, and more accurate classification approach in evaluating the freshness of vegetable soybean. The method proposed in this study can provide a theoretical reference for classifying the freshness of fruits and vegetables to improve classification accuracy and reduce human error and variability.

Isolation and identification of high-yielding alkaline phosphatase strain: a novel mutagenesis technique and optimization of fermentation conditions.

Isolating and screening enzyme-producing strains from microorganisms and the commercial production of ALPs from microorganisms are of increasing interest. In this work, isolation and identification of high-yielding alkaline phosphatase strain were carried out using atmospheric and room temperature plasma mutagenesis (ARTP) for optimization of fermentation conditions. A strain of alkaline phosphatase-producing bacteria was screened from soil and identified by 16S rRNA gene sequencing as Bacillus amyloliquefaciens and named S-1. This strain had an alkaline phosphatase activity of 2594.73 U/L. Later, mutagenesis breeding of the alkaline phosphatase-producing S-1 strain was conducted using (ARTP), from which a higher alkaline phosphatase-producing positive mutant strain S-52 was screened. A central combination of five factors, including corn starch, yeast extract, metal ions, fermentation temperature and inoculum ratio, was then used to influence the activity of alkaline phosphatase. Results from the response surface methodology showed that the maximum enzyme activity of alkaline phosphatase was 12,110.6 U/L at corn starch, yeast extract and magnesium ions concentrations of 17.48 g/L, 18.052 g/L and 0.744 g/L, respectively; fermentation temperature of 37.192 °C; and inoculation ratio of 5.59%. This study is important for further exploring ARTP mutagenesis in B. amyloliquefaciens and the commercialization of ALPs.

Deep Learning and Hyperspectral Images Based Tomato Soluble Solids Content and Firmness Estimation.

Cherry tomato (Solanum lycopersicum) is popular with consumers over the world due to its special flavor. Soluble solids content (SSC) and firmness are two key metrics for evaluating the product qualities. In this work, we develop non-destructive testing techniques for SSC and fruit firmness based on hyperspectral images and the corresponding deep learning regression model. Hyperspectral reflectance images of over 200 tomato fruits are derived with the spectrum ranging from 400 to 1,000 nm. The acquired hyperspectral images are corrected and the spectral information are extracted. A novel one-dimensional (1D) convolutional ResNet (Con1dResNet) based regression model is proposed and compared with the state of art techniques. Experimental results show that, with a relatively large number of samples our technique is 26.4% better than state of art technique for SSC and 33.7% for firmness. The results of this study indicate the application potential of hyperspectral imaging technique in the SSC and firmness detection, which provides a new option for non-destructive testing of cherry tomato fruit quality in the future.