Inspection of Spongy Tissue in ‘Namdokmai Sithong’ Mango Fruit by Near Infrared Spectroscopy
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Abstract
This study aimed to apply near infrared spectroscopy (NIRS) to investigate spongy tissue symptom in ‘Namdokmai Sithong’ mango. Spectral data were acquired from healthy (1,470 points) and spongy (97 points) tissues from 163 mango fruits prior to developing training models using artificial neural network (ANN). The ANN models were thereafter tested for their efficacies using K-fold cross validation to predict healthy and spongy tissues of mangoes. The results indicated that spectral data of a short-wave near infrared (12500 - 9000 cm-1 or 800 - 2500 nm) preprocessed by second derivative provided the highest coefficient of determination (R2) during model training and validating; 0.60 and 0.75, respectively. The ANN model also provided high accuracy for predicting healthy and spongy tissues; 99.32 and 73.68 percent, respectively. Therefore, NIRS combined with ANN might be a possible nondestructive technique for inspecting spongy tissue in 'Namdokmai Sithong' mango.
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