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Estimation model of soluble solids content in bagged and non-bagged apple fruits based on spectral data

文献类型： 外文期刊

作者： Wang, Fengyun 1 ; Zhao, Yimin 2 ; Zheng, Jiye 1 ; Qi, Kangkang 1 ; Fan, Yangyang 1 ; Yuan, Xulin 3 ; Ruan, Huaijun 1 ;

作者机构： 1.Shandong Acad Agr Sci, Informat & Econ Inst, Jinan, Peoples R China

2.Shandong Stand Test Technol Co Ltd, Jinan, Peoples R China

3.Shandong Univ, Control Sci & Engn Sch, Jinan, Peoples R China

关键词： Bagged apple fruit; Non-bagged apple fruit; Soluble solids content; Spectral data

期刊名称：COMPUTERS AND ELECTRONICS IN AGRICULTURE （ 影响因子：5.565； 五年影响因子：5.494 ）

ISSN： 0168-1699

年卷期： 2021 年 191 卷

页码：

收录情况： SCI

摘要： Response of bagged and non-bagged apple to light is different. Meanwhile, for the same variety, average sugar content of the non-bagged apple is higher than that of the bagged apple. Spectral data have been used to rapidly and quantitatively determine soluble solids content (SSC) of the bagged and non-bagged apples. First, the spectral data were acquired from four circular areas evenly distributed around the equatorial circumference of each apple, which would be used to determine the SSC value. Standard normal variable (SNV) transformation was used to pre-process the spectral data. Second, Monte Carlo cross validation (MCCV) algorithm was employed to reject abnormal samples. Sample set was divided into a training set and a test set by uniform sampling. Third, Characteristic wavelengths were extracted by principal component analysis (PCA) and ant colony optimization (ACO). Finally, regression models between the spectra and SSCs were established by back-propagation neural network (BP-ANN) and partial least squares (PLS). In this study, six non-destructive SSC estimation models were established for the bagged and non-bagged apple fruits, where optimal models were established respectively by comparing estimation accuracy. Results showed that ACO-PLS model achieved the highest SSC estimation accuracy for both bagged and non-bagged apples. The root mean square error (RMSE) and correlation coefficient (R) for the training set of the non-bagged apple fruits were 0.35 degrees Bx and 0.93, while that for the corresponding test set were 0.32 degrees Bx and 0.93, respectively. The RMSE and R for the training set of the bagged apple fruits were 0.38 degrees Bx and 0.94, and that for the corresponding test set were 0.31 degrees Bx and 0.96, respectively. It indicated the ACO-PLS estimation model is promising for grading interior apple quality because it provides a quick and nondestructive method for measuring the SSC.

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