In this study, a 400-1000nm hyperspectral camera was applied, and FS13, a product of Hangzhou Color Spectrum Technology Co., LTD., could be used for related research. The spectral range is 400-1000nm, the wavelength resolution is better than 2.5nm, and up to 1200 spectral channels can be reached. The acquisition speed can reach 128FPS in the full spectrum, and the maximum after band selection is 3300Hz (support multi-region band selection).
Blueberries, also known as blueberries, dark blue fruits, berries, so also known as blue berries, is one of the emerging small berries in China. Due to its unique health and nutritional value, it has many nutrients needed by the human body, excellent processing properties, etc., and has been paid attention to." The internal quality of blueberries has a great impact on the taste of blueberries, and is also one of the important indicators to evaluate the quality of blueberries. The traditional test method generally uses measuring device to detect the sugar content and hardness of blueberry. Because of the single detection index, long time consuming and destructive, these detection methods are difficult to be applied to the industrial detection of sugar content and hardness of fruit. Therefore, it is of great significance to develop a non-destructive and efficient method to detect the sugar content and hardness of blueberry based on internal quality.
Throughout the domestic and foreign research on fruit sugar content and hardness detection, it can be seen that the use of characteristic wavelength selection method can effectively reduce the dimension of hyperspectral image data, reduce the redundancy of spectral data, improve the calibration performance and detection efficiency of the model, and obtain good prediction results. It shows that these characteristic wavelength selection methods can be beneficial to realize online fruit detection. However, these studies are mainly aimed at the detection of single indicators, and multiple models need to be established to detect multiple indicators of fruit, which increases the complexity of data processing. Therefore, it is necessary to establish a model for multi-index detection to save time and improve the efficiency of online detection. In this study, hyperspectral imaging technology was used to propose a multi-stage feature wavelength selection method for detecting both sugar content and hardness of blueberries in hyperspectral images. Feature wavelength selection methods such as continuous projection algorithm or stepwise multiple linear regression were used successively to select the feature wavelengths that could reflect both sugar content and hardness of blueberries, and BP neural network model was used as the detection model. The sugar content and hardness of blueberry were predicted in order to realize rapid and non-destructive testing of the internal quality of blueberry, and to provide theoretical basis for the construction of online quality testing of blueberry.