FOOD SCIENCE ›› 2018, Vol. 39 ›› Issue (24): 289-296.doi: 10.7506/spkx1002-6630-201824043

• Safety Detection • Previous Articles     Next Articles

Development of a Predictive Model for Rapid Detection of Sulfur Content in Honeysuckle Based on Hyperspectral Imaging Technology

FENG Jie1, LIU Yunhong1,2,*, SHI Xiaowei1, WANG Qingqing1, XU Qian1   

  1. (1. College of Food and Bioengineering, Henan University of Science and Technology, Luoyang 471023, China; 2. Henan Engineering Technology Research Center of Food Materials, Luoyang 471023, China)
  • Online:2018-12-25 Published:2018-12-17

Abstract: For rapid and non-destructive detection of sulfur content in honeysuckle, the flowers of Lonicera japonica Thunb., hyperspectral imaging technology combined with chemometrics was applied to develop a predictive model for detecting sulfur-fumigated honeysuckle with different sulfur concentrations. Hyperspectral images of non-fumigated and sulfur-fumigated honeysuckle samples with four concentration gradients of 0%, 0.5%, 1% and 1.5% on a fresh mass basis were collected and preprocessed by Savitzky-Golay smoothing filter (S_G filter), multiple scatter correct (MSC) or standard normal variate transformation (SNV). S_G filter was selected as the optimal pretreatment method. Subsequently, the processed spectral data were used to establish models using either fisher discriminant analysis (FDA) or kernel Fisher discriminant analysis (KFDA), and the results showed that KFDA had a better discrimination accuracy of 98.2%. Considering that the full-range spectral data contain a great deal of redundancy, the characteristic wavelengths were extracted by three different methods, regression coefficients (RC), Wilks criterion and RC-Wilks. As a result, the discriminant models, RC-KFDA, Wilks-KFDA and RC-Wilks-KFDA were developed. A comparison was made between these models, and the RC-Wilks-KFDA model was found to be the best one with the highest discrimination accuracy of 100%, good classification efficiency and short running time of 0.69 s. Therefore, the S_G-RC-Wilks-KFDA model could allow fast, effective and non-destructive detection of sulfur content in honeysuckle.

Key words: honeysuckle, hyperspectral imaging, sulfur content, rapid detection

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