Quantitative study on impact damage of honey peaches based on reflection, absorbance, and Kubelka‐Munk spectrum combined with color characteristics

• Published in: Journal of chemometrics Vol. 38; no. 2
• Main Authors: Li, Bin, Zou, Jiping, Su, Chengtao, Zhang, Feng, Liu, Yande, Wu, Jian, Chen, Nan, Xiao, Yihua
• Format: Journal Article
• Language: English
• Published: Chichester Wiley Subscription Services, Inc 01-02-2024
• Subjects: Absorbance; absorbance spectra; Adaptive sampling; Algorithms; Biomechanics; Color; Fruits; Hyperspectral imaging; hyperspectral imaging technology; Impact damage; Kubelka‐Munk spectra; Least squares method; mechanical parameters; Mechanical properties; Packaging; Parameters; partial least squares regression; Peaches; Performance prediction; Quantitative analysis; Quantitative research; reflection spectra; Regression models; Spectra; Wavelengths
• ISSN: 0886-9383; 1099-128X
• DOI: 10.1002/cem.3532

Impact damage is one of the key factors affecting the quality of honey peaches. Quantitative study of impact damage is of great significance for the sorting of postharvest quality of honey peaches. In order to realize the quantitative prediction of impact damage of honey peaches, the impact damage of honey peaches was quantitatively studied based on the fusion of color characteristics with reflection spectra (R), absorbance spectra (A), and Kubelka‐Munk spectra (K‐M). The mechanical parameters of honey peaches during collision were obtained using a single pendulum collision device. Reflectance spectra and color characteristics of damaged honey peaches were obtained by a hyperspectral imaging system. The R spectra were converted into A and K‐M spectra, and the partial least squares regression (PLSR) model was built based on the three spectra and the three spectra combined with color characteristics for quantitative prediction of mechanical parameters. The results show that the prediction performance of the PLSR model is improved by combining color features with spectral information. In order to eliminate the redundant information in the spectral data, the competitive adaptive reweighted sampling (CARS) algorithm was used to select the characteristic wavelengths of the three spectra, and the selected characteristic wavelengths were fused with the color features to establish the PLSR model. The results show that the PLSR model built by the characteristic wavelengths of the A spectrum combined with the color features has the best prediction performance for the mechanical parameters. The RP value for maximum force is 0.862, and the RP value for damage depth is 0.894. The results of this study not only provide the theoretical support for the quality sorting, packaging, and transportation of honey peaches but also provide the reference for the biomechanical properties of various agricultural products.
In order to reduce impact damage during postharvest handling of honey peaches. In this study, the prediction model of mechanical parameters during honey peaches collision was built by using color features fused with reflectance, absorption, and K‐M spectra, respectively. The results show that spectral and image information fusion can improve the prediction performance of the model. The model built by fusing the color features with the characteristic wavelengths of absorption spectrum has the best performance.