Nondestructive detection of dried apricots quality based on machine vision and near-infrared diffuse reflectance spectroscopy technology

Quality is the most important factor for marketing of dried fruits. The machine vision and near infrared spectroscopy were explored to detect the external and internal quality of dried apricots nondestructively. Color images of dried apricots in four different locations were captured, the filling algorithm based on the regional skeleton was used for segmentation of dried apricots on those images and then area of dried apricot was calculated. Among 100 normal samples, 75 samples were randomly selected as calibrating set, 25 samples were used as forecast set. Evaluation model based on co-relationship between actual weight and pixels of dried apricots was developed via multiple linear regressions, the correlation coefficient of calibrating set and forecast set were 0.9374 and 0.9307 respectively, and the weights detection accuracy was 90%. Regional growth based on average gray value was put forward to extract surface defects of dried apricots, defects detection accuracy was 84.5%. SNV method was used to pretreat the near infrared spectrum of dried apricots. Then the partial least squares (PLS), back interval partial least squares (biPLS) and synergy interval partial least square (siPLS) were used to establish the prediction models of sugar content, respectively. Experimental results showed that the optimal biPLS model was obtained with 22 intervals divided and the optimal combinations of intervals 17、2、3、9、20、13、7、18、15、11、6 and its factor number being 10. The optimal biPLS model was achieved with correlation coefficient of 0.8983 and root mean square error of cross validation of 1.23 for calibration set and correlation coefficient of 0.8814 and the root mean square error of 1.46 for prediction set. The results indicate that machine vision and near infrared technology can be a good method to synthetically detect the internal and external quality of dried apricot.