Impact damage of apple was quantitatively investigated by hyperspectral imaging (HSI) technology within the wavelength region of 900–1700 nm. The pressure-sensitive film technique was used to measure damaged area. Statistical analysis shows a significant linear correlation between absorbed energy and damaged area, contact load, and damaged area with coefficients of determination (R2) of 0.93 and 0.92. Then, the quantitative relationships between damaged area, absorbed energy, contact load, undamaged firmness, and spectral data were established by partial least square regression (PLS). The best prediction performance yielded by the PLS model measured by coefficient of determination (RP2) and root mean square errors of prediction (RMSEP) values were 0.8 and 116.73 mm2 for damaged area, 0.89 and 0.075 J for absorbed energy, 0.53 and 67.38 N for contact load, and 0.65 and 19.99 N for undamaged firmness, respectively. The overall results demonstrate the potential of HSI for rapid and nondestructive prediction of impact damage to apples.
Apple Hyperspectral imaging Impact damage Partial least square regression Mechanical parameters Quantitative evaluation
This is a preview of subscription content, log in to check access.
The support by the National Natural Science Foundation of China (Grant No. 11572223, No. 11772225) is greatly acknowledged.
Compliance with Ethical Standards
Conflict of Interest
Duohua Xu declares that he has no conflict of interest. Huaiwen Wang declares that he has no conflict of interest. Hongwei Ji declares that he has no conflict of interest. Xiaochuan Zhang declares that he has no conflict of interest. Camelia Cerbu declares that she has no conflict of interest. Eric Hu declares that he has no conflict of interest. Fuyuan Dong declares that he has no conflict of interest.
Informed consent was obtained from all individual participants included in the study.
This article does not contain any studies with human participants or animals performed by any of the authors.
Ahmadi E (2012) Bruise susceptibilities of kiwifruit as affected by impact and fruit properties Research in Agricultural Engineering - UZEI (Czech Republic), p 107–113Google Scholar
Blasco J, Cubero S, Gómezsanchís J, Mira P, Moltó E (2009) Development of a machine for the automatic sorting of pomegranate (Punica granatum) arils based on computer vision. J Food Eng 90:27–34CrossRefGoogle Scholar
Bobelyn E, Serban AS, Nicu M, Lammertyn J, Nicolai BM, Saeys W (2010) Postharvest quality of apple predicted by NIR-spectroscopy: study of the effect of biological variability on spectra and model performance. Postharvest Biol Technol 55:133–143CrossRefGoogle Scholar
Cubero S, Aleixos N, Moltó E, Gómez-Sanchis J, Blasco J (2011) Erratum to: Advances in machine vision applications for automatic inspection and quality evaluation of fruits and vegetables. Food Bioprocess Technol 4:829–830CrossRefGoogle Scholar
Fei L, Ishikawa Y, Kitazawa H, Satake T (2010a) Impact damage to apple fruits in commercial corrugated fiberboard box packaging evaluated by the pressure-sensitive film technique. J Food Agric Environ 8:218–222Google Scholar
Fei L, Yutaka I, Hiroaki K, Takaaki S (2010b) Measurement of impact pressure and bruising of apple fruit using pressure-sensitive film technique. J Food Eng 96:614–620CrossRefGoogle Scholar
Lewis R, Yoxall A, Marshall MB, Canty LA (2008) Characterising pressure and bruising in apple fruit. Wear 264:37–46CrossRefGoogle Scholar
Lötze E, Huybrechts C, Sadie A, Theron KI, Valcke RM (2006) Fluorescence imaging as a non-destructive method for pre-harvest detection of bitter pit in apple fruit (Malus domestica Borkh.). Postharvest Biol Technol 40:287–294CrossRefGoogle Scholar
Mehmood T, Liland KH, Snipen L, Sæbø S (2012) A review of variable selection methods in partial least squares regression. Chemom Intell Lab Syst 118:62–69CrossRefGoogle Scholar
Rivera NV et al (2014) Early detection of mechanical damage in mango using NIR hyperspectral images and machine learning. Biosyst Eng 122:91–98CrossRefGoogle Scholar
Saracoglu T, Ucer N, Ozarslan C (2011) Engineering properties and susceptibility to bruising damage of table olive (Olea europaea) fruit. Int J Agric Biol 13:801–805Google Scholar
Vursavuş K, Ozguven F (2004) Determining the Effects of vibration parameters and packaging method on mechanical damage in golden delicious apples. Turk J Agric For 28:311–320Google Scholar
Wang H (2013) Marker identification technique for deformation measurement. Adv Mech Eng, (2013-10-29) 2013:1255–1260Google Scholar
Wang H, Kang Y (2002) Improved digital speckle correlation method and its application in fracture analysis of metallic foil. Opt Eng 41:436–445Google Scholar
Wang NN, Sun DW, Yang YC, Pu H, Zhu Z (2015) Recent advances in the application of hyperspectral imaging for evaluating fruit quality. Food Anal Methods 9:1–14Google Scholar
Xing J, Landahl S, Lammertyn J, Vrindts E, Baerdemaeker JD (2003) Effects of bruise type on discrimination of bruised and non-bruised ‘Golden Delicious’ apples by VIS/NIR spectroscopy. Postharvest Biol Technol 30:249–258CrossRefGoogle Scholar
Yu KQ, Zhao YR, Liu ZY, Li XL, Liu F, He Y (2014) Application of visible and near-infrared hyperspectral imaging for detection of defective features in loquat. Food Bioprocess Technol 7:3077–3087CrossRefGoogle Scholar
Yuwana Y, Duprat F (1997) Prediction of apple bruising based on the instantaneous impact shear stress and energy absorbed. Int Agrophys 11:771–772Google Scholar
Zeebroeck MV, Linden VV, Darius P, Ketelaere BD, Ramon H, Tijskens E (2007a) The effect of fruit factors on the bruise susceptibility of apples. Postharvest Biol Technol 46:10–19CrossRefGoogle Scholar
Zeebroeck MV, Linden VV, Darius P, Ketelaere BD, Ramon H, Tijskens E (2007b) The effect of fruit properties on the bruise susceptibility of tomatoes. Postharvest Biol Technol 45:168–175CrossRefGoogle Scholar
Zhang X, Chu X, Ji H, Wang Y (2016) Effect of freezing rate on the onion cell deformation evaluated by digital image correlation. Food Anal Methods 9:1–8CrossRefGoogle Scholar
Zhang B, Fan S, Li J, Huang W, Zhao C, Qian M, Zheng L (2015) Detection of early rottenness on apples by using hyperspectral imaging combined with spectral analysis and image processing. Food Anal Methods 8:2075–2086CrossRefGoogle Scholar
Zhang H, Huang G, Song H, Kang Y (2012) Experimental investigation of deformation and failure mechanisms in rock under indentation by digital image correlation. Eng Fract Mech 96:667–675CrossRefGoogle Scholar