Discriminating Drying Method of Tarhana Using Computer Vision

Optimization of computational intelligence approach for the prediction of glutinous rice dehydration

BACKGROUND

Five computational intelligence approaches, namely Gaussian process regression (GPR), artificial neural network (ANN), decision tree (DT), ensemble of trees (EoT) and support vector machine (SVM), were used to describe the evolution of moisture during the dehydration process of glutinous rice. The hyperparameters of the models were optimized with three strategies: Bayesian optimization, grid search and random search. To understand the parameters that facilitate intelligence model adaptation to the dehydration process, global sensitivity analysis (GSA) was used to compute the impact of the input variables on the model output.

RESULT

The result shows that the optimum computational intelligence techniques include the 3-9-1 topology trained with Bayesian regulation function for ANN, Gaussian kernel function for SVM, Matérn covariance function combined with zero mean function for GPR, boosting method for EoT and 4 minimum leaf size for DT. GPR has the highest performance with R2 of 100% and 99.71% during calibration and testing of the model, respectively. GSA reveals that all the models significantly rely on the variation in time as the main factor that affects the model outputs.

CONCLUSION

Therefore, the computational intelligence models, especially GPR, can be applied for an effective description of moisture evolution during small-scale and industrial dehydration of glutinous rice. © 2024 Society of Chemical Industry.

A Multi-Flow Production Line for Sorting of Eggs Using Image Processing

In egg production facilities, the classification of eggs is carried out either manually or by using sophisticated systems such as load cells. However, there is a need for the classification of eggs to be carried out with faster and cheaper methods. In the agri-food industry, the use of image processing technology is continuously increasing due to the data processing speed and cost-effective solutions. In this study, an image processing approach was used to classify chicken eggs on an industrial roller conveyor line in real-time. A color camera was used to acquire images in an illumination cabinet on a motorized roller conveyor while eggs are moving on the movement halls. The system successfully operated for the grading of eggs in the industrial multi-flow production line in real-time. There were significant correlations among measured weights of the eggs after image processing. The coefficient of linear correlation (R²) between measured and actual weights was 0.95.

Computer Vision Classification of Barley Flour Based on Spatial Pyramid Partition Ensemble

Imaging sensors are largely employed in the food processing industry for quality control. Flour from malting barley varieties is a valuable ingredient in the food industry, but its use is restricted due to quality aspects such as color variations and the presence of husk fragments. On the other hand, naked varieties present superior quality with better visual appearance and nutritional composition for human consumption. Computer Vision Systems (CVS) can provide an automatic and precise classification of samples, but identification of grain and flour characteristics require more specialized methods. In this paper, we propose CVS combined with the Spatial Pyramid Partition ensemble (SPPe) technique to distinguish between naked and malting types of twenty-two flour varieties using image features and machine learning. SPPe leverages the analysis of patterns from different spatial regions, providing more reliable classification. Support Vector Machine (SVM), k-Nearest Neighbors (k-NN), J48 decision tree, and Random Forest (RF) were compared for samples' classification. Machine learning algorithms embedded in the CVS were induced based on 55 image features. The results ranged from 75.00% (k-NN) to 100.00% (J48) accuracy, showing that sample assessment by CVS with SPPe was highly accurate, representing a potential technique for automatic barley flour classification.

Composition of volatile aromatic compounds and minerals of tarhana enriched with cherry laurel (Laurocerasus officinalis)

Different concentrations of cherry laurel pulp (0, 5, 10, 15 and 20%) were used to produce tarhana samples. Volatile aromatic compounds and minor mineral content were investigated. Volatile aromatic compounds were analyzed by using GC-MS with SPME fiber and minor mineral values were evaluated with inductively coupled plasma optical emission spectrometer. The statistical analysis showed that addition of pulp affected volatile aromatic compounds and minor mineral content significantly. Thirty five volatile aromatic compounds were found in tarhana samples. The octanoic acid from acids, benzaldehyde (CAS) phenylmethanal from aldehydes, 6-methyl-5-hepten-2-one from ketones, octadecane (CAS) n-octadecane form terpenes, ethyl caprylate from esters and benzenemethanol (CAS) benzyl alcohol from alcohols had the highest percentage of volatile aromatic compounds. Tarhana samples were rich source of Mn, Cu and Fe content.