Nondestructive Prediction And Visualization Of Total Flavonoids Content In Cerasus Humilis Fruit During Storage Periods Based On Hyperspectral Imaging Technique

In this study, the changes of total flavonoids content in Cerasus Humilis fruits during storage periods were detected by using hyperspectral imaging (HSI) technique over the spectral region of 895-1,700 nm, and the spatial distribution of flavonoids was visualized during 24 days storage periods. The hyperspectral images of 240 samples of Cerasus Humilis were collected at different storage times (0, 8, 16, and 24 days). The Monte-Carlo outlier detection method was applied to identify four abnormal samples, three spectral preprocessing methods were used to preprocess the original spectral data, including savitzky-golay, standard normal variate, and baseline correction (BC). The x-loading weights, competitive adaptive reweighed sampling (CARS), uninformative variable elimination (UVE), UVE-CARS and the combination of UVE and successive projections algorithm (UVE-SPA) were used to reduce the dimension of spectral data and extract the characteristic wavelength, respectively. Then multiple linear regression and nonlinear least squared support vector machine (LS-SVM) regression models were developed based on full-spectrum data and selected characteristic wavelength. The results showed that BC was the best preprocessing approach, the LS-SVM models based on the nine effective wavelengths selected by UVE-CARS achieved the best results with correlation coefficient of prediction of .9357, root mean square error of prediction of 2.0107, and residual prediction deviation of 2.2809, respectively. The overall results demonstrated that the HSI technology coupled with chemometric algorithms is feasible to determine total flavonoids content, UVE-CARS-LS-SVM model was the optimal model. Visualization of the total flavonoids content during storage periods distribution map was performed, which clearly showed that the total flavonoids content kept decreasing with storage time. Practical Applications Cerasus Humilis is a fruit tree resource endemic to China. The content of total flavonoids is one of the key factors that affect the edible quality of Cerasus Humilis fruit. With the increase of storage time, the active components of flavonoids are easy to be degraded by oxidation, the quality and commercial value of Cerasus Humilis fruit were negatively affected. Thus, the detection of total flavonoids content during the storage of Cerasus Humilis fruit is of great significance. Traditional methods for determining total flavonoids content (such as the spectrophotometric techniques method) are mostly destructive, inefficient, time-consuming, and environmental pollution, which cannot meet the requirements of online monitoring of total flavonoids content. Hyperspectral imaging (HSI) technology has the advantages of rapid, nondestructive, accurate, and nonpollution. The results indicated that the HSI technology coupled with chemometric algorithms could be used to predict the changes of the total flavonoids content in Cerasus Humilis fruit during storage periods, which provided a theoretical basis for online and real-time monitoring of the quality of Cerasus Humilis fruit during storage.