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Article

Black Heart Detection in White Radish by Hyperspectral Transmittance Imaging Combined with Chemometric Analysis and a Successive Projections Algorithm

by
Dajie Song
1,2,†,
Lijun Song
3,†,
Ye Sun
1,
Pengcheng Hu
1,
Kang Tu
1,
Leiqing Pan
1,*,
Hongwei Yang
2 and
Min Huang
4
1
College of Food Science and Technology, Nanjing Agricultural University, NO.1 Weigang Road, Nanjing 210095, China
2
College of Science, Nanjing Agricultural University, No. 1 Weigang Road, Nanjing 210095, China
3
College of Life Science, Tarim University, Alar 843300, China
4
Key Laboratory of Advanced Process Control for Light Industry (Ministry of Education), Jiangnan University, Wuxi 214122, China
*
Author to whom correspondence should be addressed.
Appl. Sci. 2016, 6(9), 249; https://doi.org/10.3390/app6090249
Submission received: 22 June 2016 / Revised: 28 July 2016 / Accepted: 1 September 2016 / Published: 6 September 2016
(This article belongs to the Special Issue Applications of Hyperspectral Imaging for Food and Agriculture)
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Abstract

Radishes with black hearts will lose edible value and cause food safety problems, so it is important to detect and remove the defective ones before processing and consumption. A hyperspectral transmittance imaging system with 420 wavelengths was developed to capture images from white radishes. A successive-projections algorithm (SPA) was applied with 10 wavelengths selected to distinguish defective radishes with black hearts from normal samples. Pearson linear correlation coefficients were calculated to further refine the set of wavelengths with 4 wavelengths determined. Four chemometric classifiers were developed for classification of normal and defective radishes, using 420, 10 and 4 wavelengths as input variables. The overall classifying accuracy based on the four classifiers were 95.6%–100%. The highest classification with 100% was obtained with a back propagation artificial neural network (BPANN) for both calibration and prediction using 420 and 10 wavelengths. Overall accuracies of 98.4% and 97.8% were obtained for calibration and prediction, respectively, with Fisher's linear discriminant analysis (FLDA) based on 4 wavelengths, and was better than the other three classifiers. This indicated that the developed hyperspectral transmittance imaging was suitable for black heart detection in white radishes with the optimal wavelengths, which has potential for fast on-line discrimination before food processing or reaching storage shelves.
Keywords: hyperspectral transmittance imaging; black heart; detection; chemometric analysis; successive projections algorithm hyperspectral transmittance imaging; black heart; detection; chemometric analysis; successive projections algorithm
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MDPI and ACS Style

Song, D.; Song, L.; Sun, Y.; Hu, P.; Tu, K.; Pan, L.; Yang, H.; Huang, M. Black Heart Detection in White Radish by Hyperspectral Transmittance Imaging Combined with Chemometric Analysis and a Successive Projections Algorithm. Appl. Sci. 2016, 6, 249. https://doi.org/10.3390/app6090249

AMA Style

Song D, Song L, Sun Y, Hu P, Tu K, Pan L, Yang H, Huang M. Black Heart Detection in White Radish by Hyperspectral Transmittance Imaging Combined with Chemometric Analysis and a Successive Projections Algorithm. Applied Sciences. 2016; 6(9):249. https://doi.org/10.3390/app6090249

Chicago/Turabian Style

Song, Dajie, Lijun Song, Ye Sun, Pengcheng Hu, Kang Tu, Leiqing Pan, Hongwei Yang, and Min Huang. 2016. "Black Heart Detection in White Radish by Hyperspectral Transmittance Imaging Combined with Chemometric Analysis and a Successive Projections Algorithm" Applied Sciences 6, no. 9: 249. https://doi.org/10.3390/app6090249

APA Style

Song, D., Song, L., Sun, Y., Hu, P., Tu, K., Pan, L., Yang, H., & Huang, M. (2016). Black Heart Detection in White Radish by Hyperspectral Transmittance Imaging Combined with Chemometric Analysis and a Successive Projections Algorithm. Applied Sciences, 6(9), 249. https://doi.org/10.3390/app6090249

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