Non-destructive detection for mold colonies in rice based on hyperspectra and GWO-SVR.

BACKGROUND Mold contamination of grains not only contributes to inedible food, resulting in economic losses, but also leads to mold in humans and livestock, and can even be carcinogenic to them. Rice, as one of the main grain varieties, if stored improperly, is easily susceptible to mildew. In order to detect the total number of mold colonies in rice more accurately, a method based on hyperspectral imaging technology was investigated. RESULTS In this paper, non-destructive detection for the total number of mold colonies in rice was performed from the angle of spectral analysis. A determination coefficient of 0.9621 for the calibration set and 0.9511 for the prediction set between the spectral data and number of mold colonies were eventually achieved by establishing the best support vector regression (SVR) model, optimized by the Gray Wolf Optimization (GWO) algorithm. CONCLUSION Hyperspectral imaging technology combined with the optimal model (GWO-SVR) is feasible for non-destructive detection of the total number of mold colonies in rice, providing a promising tool for the mold detection of other agricultural products. © 2017 Society of Chemical Industry.

[1]  Corinna Cortes,et al.  Support-Vector Networks , 1995, Machine Learning.

[2]  Dong Jinle.  Nondestructive Detection of Soluble Solid Content of Postharvest Kiwifruits Based on Hyperspectral Imaging Technology , 2015 .

[3]  A. Hossain,et al.  Comparison of the finite mixture of ARMA-GARCH, back propagation neural networks and support-vector machines in forecasting financial returns , 2011 .

[4]  S. Mirjalili,et al.  A new hybrid PSOGSA algorithm for function optimization , 2010, 2010 International Conference on Computer and Information Application.

[5]  Efkan Durmus,et al.  Detection of aflatoxin and surface mould contaminated figs by using Fourier transform near-infrared reflectance spectroscopy. , 2017, Journal of the science of food and agriculture.

[6]  Hongzhi Liu,et al.  A Review: Peanut Fatty Acids Determination Using Hyper Spectroscopy Imaging and Its Significance on Food Quality and Safety , 2014 .

[7]  Byoung-Kwan Cho,et al.  Qualitative properties of roasting defect beans and development of its classification methods by hyperspectral imaging technology. , 2017, Food chemistry.

[8]  V. Segtnan,et al.  Near-Infrared Hyperspectral Imaging of Fusarium-Damaged Oats (Avena sativa L.) , 2015 .

[9]  Xiaoli Li,et al.  Detection of Fungus Infection on Petals of Rapeseed (Brassica napus L.) Using NIR Hyperspectral Imaging , 2016, Scientific Reports.

[10]  Zou Xiaobo,et al.  Rapid prediction of phenolic compounds and antioxidant activity of Sudanese honey using Raman and Fourier transform infrared (FT-IR) spectroscopy. , 2017, Food chemistry.

[11]  Jiewen Zhao,et al.  Quantifying Total Viable Count in Pork Meat Using Combined Hyperspectral Imaging and Artificial Olfaction Techniques , 2016, Food Analytical Methods.

[12]  G Bonifazi,et al.  Early detection of toxigenic fungi on maize by hyperspectral imaging analysis. , 2010, International journal of food microbiology.

[13]  Jiewen Zhao,et al.  Determination of Amino Acid Nitrogen in Soy Sauce Using Near Infrared Spectroscopy Combined with Characteristic Variables Selection and Extreme Learning Machine , 2013, Food and Bioprocess Technology.

[14]  S. N. Deepa,et al.  Long-Term Wind Speed Forecasting using Spiking Neural Network Optimized by Improved Modified Grey Wolf Optimization Algorithm. , 2016 .

[15]  Ermias A. Haile,et al.  Rapid Prediction of Fatty Acid Content in Marine Oil Omega-3 Dietary Supplements Using a Portable Fourier Transform Infrared (FTIR) Device and Partial Least-Squares Regression (PLSR) Analysis. , 2017, Journal of agricultural and food chemistry.

[16]  D. Pollock.  Trend estimation and de-trending via rational square-wave filters , 2000 .