Identification of Bruise and Fungi Contamination in Strawberries Using Hyperspectral Imaging Technology and Multivariate Analysis
Mechanical bruise and fungi contamination are two typical defective features for strawberries, resulting in quick quality deterioration of the strawberries during transportation and storage. In this work, the approach of combined image processing with spectra analysis was successfully developed to identify defective strawberries (bruised and fungal infected) using hyperspectral reflectance imaging system. Hyperspectral image data was exploited by minimum noise fraction (MNF) transformation for strawberry defects distinguished by combining thresholding and morphology procedures, and defective regions were located and separated for spectra extracting. The linkages between quality parameters and spectra features were established based on the target defective regions of the fruit. After spectra normalization, three different spectral regions (400 to 600 nm, 650 to 720 nm, and 900 to 1010 nm) were identified for healthy, bruised, or infected strawberries, and eight optimal wavelengths were selected by the successive projection algorithms (SPA) from the whole range of wavelengths. Both linear and non-linear algorithms were developed to identify defective types in strawberries. The results indicated that based on full wavelengths, SVM model performed the highest overall identification accuracy, with the accuracy of 96.91% for calibration and 92.59% for prediction of the fruit. This work shows that hyperspectral reflectance imaging technology has the potential for identifying defective strawberries and provides theoretical basis for the development of online classification of different defected fruits.
KeywordsStrawberry Hyperspectral imaging Infection Bruise
This work was financially supported by the National Natural Science Foundation of China (31671925) and the Priority Academic Program Development of Jiangsu Higher Education Institutions (PAPD), and 2017 graduate students’ innovation project in Jiangsu province (2017-1520).
Compliance with Ethical Standards
Conflict of Interest
Qiang Liu declares that he has no conflict of interest. Ke Sun declares that he has no conflict of interest. Jing Peng declares that she has no conflict of interest. Mengke Xing declares that she has no conflict of interest. Leiqing Pan declares that he has no conflict of interest. Kang Tu declares that he has no conflict of interest.
This article does not contain any studies with human participants or animals performed by any of the authors.
- Giusti MM, Wrolstad RE (2001) Characterization and measurement of anthocyanins by UV‐visible spectroscopy. In: Current Protocols in Food Analytical Chemistry, John Wiley & Sons, Inc, pp 143–167Google Scholar
- He Y (2016) Spectroscopy and imaging technology in agriculture. The Science Publishing Company, BeijingGoogle Scholar
- Huang M, He Y, Cen H, Zhu D (2008) Rapeseed nitrogen status estimation of Vis-NIR spectra based on partial least square and BP neural network. IEEE International Conference on Control and Automation, Guangzhou, pp 1799–1803. https://doi.org/10.1109/ICCA.2007.4376671
- Keresztes JC, Diels E, Goodarzi M, Nguyen-Do-Trong N, Goos P, Nicolai B, Saeys W (2017) Glare based apple sorting and iterative algorithm for bruise region detection using shortwave infrared hyperspectral imaging. Postharvest Biol Technol 130:103–115. https://doi.org/10.1016/j.postharvbio.2017.04.005 CrossRefGoogle Scholar
- Li XL, He Y (2010) Evaluation of least squares support vector machine regression and other multivariate calibrations in determination of internal attributes of tea beverages. Food Bioprocess Technol 3(5):651–661. https://doi.org/10.1007/s11947-008-0101-y
- Lopes DS, Escribanobailón MT, Pérez Alonso JJ, Rivasgonzalo JC, Santosbuelga C (2007) Anthocyanin pigments in strawberry. LWT- Food Sci Technol 40(2):374–382. https://doi.org/10.1016/j.lwt.2005.09.018
- Mireei SA (2010) Nondestructive determination of effective parameters on maturity of Mozafati and Shahani date fruits by NIR spectroscopy technique. PhD diss. University of Tehran, Department of Mechanical Engineering of Agricultural Machinery, Persian, IranGoogle Scholar
- Shen CY, Jin SZ (2013) Principles of optics. Tsinghua University Press, BeijingGoogle Scholar
- Sun Y, Gu X, Sun K, Hu H, Xu M, Wang Z, Tu K, Pan L (2016) Hyperspectral reflectance imaging combined with chemometrics and successive projections algorithm for chilling injury classification in peaches. LWT Food Sci Technol 75:557–564. https://doi.org/10.1016/j.lwt.2016.10.006 CrossRefGoogle Scholar
- Wold JP, Jakobsen T, Krane L (1996) Atlantic salmon average fat content estimated by near-infrared transmittance spectroscopy. J Food Sci 61(1):74–77. https://doi.org/10.1111/j.1365-2621.1996.tb14728.x CrossRefGoogle Scholar
- Wu D, Sun DW (2013) Hyperspectral imaging technology: A nondestructive tool for food quality and safety evaluation and inspection. In: Yanniotis S, Taoukis P, Stoforos N, Karathanos V (eds) Advances in food process engineering research and applications. Food engineering series. Springer, Boston, pp. 581–606. https://doi.org/10.1007/978-1-4614-7906-2_2