In this study, a 900-1700nm hyperspectral camera was applied, and FS-15, the product of Hangzhou Color Spectrum Technology Co., LTD., could be used for related research. Short-wave near-infrared hyperspectral camera, the acquisition speed of the full spectrum up to 200FPS, is widely used in the composition identification, substance identification, machine vision, agricultural product quality, screen detection and other fields.
Tomato is a berry crop with a unique flavor and rich in a variety of nutrients, including glutathione, vitamins, lycopene, beta-carotene and other bioactive ingredients, and has high food value. With the rapid development of the global economy, the demand for tomatoes and tomato processing products in the consumer market is increasing. The tomato has also become one of the most widely cultivated and consumed vegetable and fruit crops in the world. In addition, with the general improvement of people's living standards, the internal quality, appearance quality, storage and transportation quality and excellent flavor and taste of tomatoes have become more and more important to consumers, and China's tomato industry is also facing new challenges and opportunities. According to the survey, the maturity and storage quality of tomatoes are very important for the tomato industry, and the internal quality of cherry tomatoes, as well as excellent flavor and taste are more concerned by consumers. Based on the development and application of big data, automatic planting, mechanized picking and intelligent classification of tomatoes are realized to achieve increased production and efficiency of tomatoes. At present, there have been some researches on tomato quality detection based on spectrum at home and abroad, but in the existing tomato quality detection models, the extraction of effective spectral information is still a research difficulty, and the detection of tomato internal quality through appropriate non-destructive testing methods remains to be studied.
In the study of non-destructive detection of soluble solid content of cherry tomatoes based on hyperspectral imaging technology, 191 cherry tomatoes were selected as research objects, hyperspectral image data in the range of 865.11~1711.71 nm were collected, and the region of interest of cherry tomatoes hyperspectral image was segmsegmed by K-means algorithm. The average spectrum of this region was extracted as the original spectral data of cherry tomato. MA and MSC were used to preprocess the original spectral data, and the cherry tomato samples were divided into training sets and test sets based on KS algorithm. In order to improve the effectiveness of the information contained in the feature band, SPA algorithm and PCA algorithm were combined to perform principal component analysis on the spectral data, and then compared with PCA and miRF algorithms, a PLSR-based SSC detection model of cherry tomato was established, and the model was verified by the test set data. The results show that the detection accuracy of the model based on the principal component extracted by SPA-PCA is obviously optimized. From the detection results of the models, among the three models, SPA-PCA-PLSR model has the best detection effect, R, 0.9039. The detection effect of miRF-PLSR model was the second, RF was 0.8878. The fitting effect of PCA-PLSR model is the worst.