Detection of moldy core in apples and its symptom types using transmittance spectroscopy

Abstract

Abstract: A detection method based on transmittance spectroscopy and support vector machine (SVM) was proposed to achieve rapid nondestructive detection of moldy core in apples. A visible to near-infrared (Vis/NIR) spectroradiometer was used for scanning transmittance spectra of 215 apple samples in the wavelength range of 200-1025 nm. Wavelet transform was used to reduce the dimensionality of the spectra and extract wavelet coefficients. Two classification algorithms including artificial neural network (ANN) and SVM were used to develop models whose parameters were optimized by genetic algorithms (GA) for determination of the presence and types of moldy core in apples. Comparisons results of the models showed that the GA-SVM model obtained the optimal result with an accuracy of 96.92% for detecting the presence of moldy core and 81.48% for distinguishing symptom types of the disease. These results indicate that it is feasible to detect moldy core in apples nondestructively and rapidly based on transmittance spectroscopy and that wavelet transform is an effective method for extraction of characteristics from spectra. Moreover, the GA-SVM algorithm in conjunction with Vis/NIR transmittance spectroscopy can accurately achieve fast and nondestructive detection of the presence and types of moldy core in apples.
Keywords: moldy core of apples, transmittance spectrum, wavelet transform, support vector machine, genetic algorithm, symptom types
DOI: 10.3965/j.ijabe.20160906.2235

Citation: Zhou Z Y, Lei Y, Su D, Zhang H H, He D J, Yang C H. Detection of moldy core in apples and its symptom types using transmittance spectroscopy. Int J Agric & Biol Eng, 2016; 9(6): 148－155.