Precautionary analysis of sprouting potato eyes using hyperspectral imaging technology

Yingwang Gao, Qiwei Li, Xiuqin Rao, Yibin Ying

Abstract


Sprouted potatoes are not allowed for healthy diet. A good knowledge of the sprouting stage of potatoes can help manage the storage conditions and guide market distribution, thus enabling the quality assurance of potatoes on table. This article presented an intelligent method for precautionary analysis of potato eyes based on hyperspectral imaging technique. Potential potato eyes were classified into two categories according to the time gap to the sprouting date, i.e. by-sprouting and pre-sprouting potato eyes, representing eyes about to sprout and eyes that will take a while to sprout. Features used for classification were extracted by two methods, including successive projections algorithm (SPA) and a newly-developed sine fit algorithm (SFA). Then classifiers of fisher discriminant analysis (FDA) and least square support vector machine (LSSVM) were utilized for classification of potential sprouting potato eyes. Results showed that FDA was more effective than LSSVM in classifying pre-sprouting and by-sprouting potato eyes, and SFA performed well in FDA classifier with the recognition accuracy of 95.3% for prediction set. It is concluded that hyperspectral imaging has the potential for predicting the sprouting stages of potato eyes.
Keywords: potato tuber, potato eyes, sprouting stage, hyperspectral imaging, sine fit algorithm(SFA), quality and safety, prediction
DOI: 10.25165/j.ijabe.20181102.2748

Citation: Gao Y W, Li Q W, Rao X Q, Ying Y B. Precautionary analysis of sprouting potato eyes using hyperspectral imaging technology. Int J Agric & Biol Eng, 2018; 11(2): 153–157.

Keywords


potato tuber, potato eyes, sprouting stage, hyperspectral imaging, sine fit algorithm(SFA), quality and safety, prediction

Full Text:

PDF

References


Rady A M, Guyer D E. Rapid and/or nondestructive quality evaluation methods for potatoes: A review. Computers & Electronics in Agriculture, 2015; 117: 31–48.

Farré E M, Bachmann A, Willmitzer L, Trethewey R N. Acceleration of potato tuber sprouting by the expression of a bacterial pyrophosphatase. Nature Biotechnology, 2001; 19: 268–272.

Coleman W K. Dormancy release in potato tubers: A review. American Potato Journal, 1987; 64: 57–68.

Sonnewald U. Control of potato tuber sprouting. Trends in Plant Science, 2001; 6(8): 333–335.

Ha M, Kwak J H, Kim Y, Zee O P. Direct analysis for the distribution of toxic glycoalkaloids in potato tuber tissue using matrix-assisted laser desorption/ionization mass spectrometric imaging. Food Chemistry, 2012; 133: 1155–1162.

Friedman M. Potato glycoalkaloids and metabolites: Roles in the plant and in the diet. Journal of Agricultural & Food Chemistry, 2006; 54: 8655–8681.

Huang T, Li X Y, Jin R, Ku J, Xu S M, Xu M L, et al. Multi-target recognition of internal and external defects of potato by semi-transmission hyperspectral imaging and manifold learning algorithm. Spectroscopy and Spectral Analysis, 2015; 35(4): 992–996.

Jeong J C, Ok H C, Hur O S, Kim C G. Prediction of sprouting capacity using near-infrared spectroscopy in potato tubers. American Journal of Potato Research, 2008; 85: 309–314.

Yu Z H, Hao H L, Zhang B C. Research on sprouted potato non-destructive detection based on euclidean distance algorithm. Journal of Agricultural Mechanization Research, 2015; 11: 174–177. (in Chinese)

Jin R, Li X Y, Yan Y Y, Xu M L, Ku J, Xu S M, et al. Detection method of multi-target recognition of potato based on fusion of hyperspectral imaging and spectral information. Transactions of the CSAE, 2015; 31(16): 258–263. (in Chinese)

Rady A, Guyer D, Lu R. Evaluation of sugar content of potatoes using hyperspectral imaging. Food & Bioprocess Technology, 2015; 8: 995–1010.

Dacal-Nieto A, Formella A, Carrión P, Vazquez-Fernandez E, Fernández-Delgado M. Common scab detection on potatoes using an infrared hyperspectral imaging system. International Conference on Image Analysis and Processing ICIAP 2011: LNCS, vol. 6979, Springer Berlin Heidelberg, 2011; pp.303–312.

Gao H L, Li X Y, Xu S M, Tao H L, Li X J, Sun J F. Comparative study of transmission and reflection hyperspectral imaging technology for potato damage detection. Macromolecular Symposia, 2013; 330: 150–165.

Al-Mallahi A, Kataoka T, Okamoto H. Discrimination between potato tubers and clods by detecting the significant wavebands. Biosystems Engineering, 2008; 100: 329–337.

Qiao J, Wang N, Ngadi M O, Singh S, Baljinder. Water content and weight estimation for potatoes using hyperspectral imaging. ASABE Annual Meeting 2005, Tampa, FL, July 17-20, 2005.

Reust W, Winiger F A, Hebeisen T, Dutoit J P. Assessment of the physiological vigour of new potato cultivars in Switzerland. Potato Research, 2001; 44: 11–17.

Guo H, Cai J M, Wang D G. Analysis of tempo-spatial patterns of beijing external vegetable supply and its effects under massive logistical system. Economic Geography, 2012; 32: 96–101.

Araújo M C U, Saldanha T C B, Galvão R K H, Yoneyama T, Chame H C, Visani V. The successive projections algorithm for variable selection in spectroscopic multicomponent analysis. Chemometrics & Intelligent Laboratory Systems, 2001; 57: 65–73.

Suykens J A K, Vandewalle J. Least squares support vector machine classifiers. Neural Processing Letters, 1999; 9: 293–300.

Ropodi A I, Panagou E Z, Nychas G J E. Data mining derived from food analyses using non-invasive/non-destructive analytical techniques; determination of food authenticity, quality and safety in tandem with computer science disciplines. Trends in Food Science and Technology, 2016; 50: 11–25.




Copyright (c)



2023-2026 Copyright IJABE Editing and Publishing Office