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The internal parameters of kiwifruit are mostly detected using traditional destructive physical–chemical methods, which are not only labor and time consuming but also inconvenient in operation. The hyperspectral imaging technique is now considered a new non-destructive method for detecting the quality parameters of kiwifruits. However, most studies focused on detecting the soluble solid content, hardness, and ripeness of this fruit. Thus, the detection precision of this imaging technique needs to be improved. Moreover, few of these techniques are involved in the detection of the dry matter content. A non-destructive detection method based on the hyperspectral imaging technique is proposed in this study to detect the dry matter content of kiwifruit online rapidly and precisely. First, the hyperspectral images of kiwifruit were analyzed, the interested regions therein were extracted, and denoising was preprocessed using the multiplicative scatter correction. Second, the redundancy of the 217 pieces of full-band spectral information was researched, and 66 characteristic spectral bands were initially screened out through uninformed variable elimination (UVE). The collinearity among these bands was eliminated using successive projection algorithm (SPA), and five characteristic spectral bands were extracted. Finally, the dry matter content of the kiwifruit was detected by taking least squares support vector (LSSVM) as the detector, by employing particle swarm optimization (PSO) to optimize LSSVM’s parameters, and by entering the five bands into the LSSVM later. Test results show that: (1) the redundancy and the collinearity of the full spectral bands can be eliminated effectively by combining SPA with UVE so that the extracted low-dimensional characteristic spectral bands can reflect the dry matter content of kiwifruit better. (2) The detection indicators of UVE+SPA+LSSVM to the training set is that the coefficient of correlation (R) = 0.91, root-mean-square error (RMSE) = 0.28, and the detection indicators to the prediction set is that R = 0.89, RMSE = 0.31, indicating that the detection precision is higher than the other methods. This study shows that the non-destructive detection method proposed in this paper can detect the dry matter content of kiwifruit rapidly and efficiently. This method serves as a theoretical basis for the industrialized classification of kiwifruit that is based on the internal parameters. Keywords: Dry matter content, Uninformed variable elimination (UVE), Successive projection algorithm (SPA), Least squares support vector machine (LSSVM)

No abstract

Flesh hardness is an important index to measure the quality and storage character of fresh fruit. At present, destructive test method is usually used to test kiwifruit hardness by sampling, while a new method based on hyperspectral technique is proposed in this study. Firstly, hyperspectral images of kiwifruit samples are collected in the spectral range of [400nm, 1000nm], then the hyperspectral images are pre-processed by standard normal transformation, and three sub-ranges of the best combination are selected through synergy interval Partial Least Square (siPLS). Secondly, kernel principal component analysis is adopted for dimension reduction of spectral bands of the best combination sub-intervals, and the first two principal components are input into the trained partial least square (PLS) as characteristic spectral bands. Finally, the test results are compared with physicochemical measurement values. The experimental results show that the Cross-validation Root Mean Square Errors (RMSEC) and the correlation coefficients of the training set and the testing set obtained by the proposed method are 0.2698/0.9315, and 0.3573/0.8738 respectively, indicating that it can effectively test kiwifruit hardness.