ABSTRACT
Abstract Hydroxymethylfurfural (HMF) is a quality indicator, especially in foods where changes in protein-carbohydrate interactions are observed during the applied process. In this study absorbance and L*, a*, b* values of red color emerged due to the relationship between hydroxymethylfurfural (HMF) and resorcinol during the modified Seliwanoff test were used as input data artificial neural network (ANN) to determine the HMF concentration for the first time. A linear relationship, between HMF concentration and absorbance of red color, can be represented by equation absorbance = 0.0020 + 0.0012* concentration of HMF (mg L-1) with R2 = 99.6%, Fisher ratio: 0.18, p value of lack of fit: 0.975, correlation coefficient: 0.9960. Intra-day and inter-day precision expressed as relative standard deviation (RSD) %, were 2.35 - 3.65% and 3.16 - 4.73%, respectively. Recovery rates and RSDs were in the range of 99.34 - 100.47% and 1.58 - 3.68%. It showed high correlation compared to HPLC method used as reference method (0.998). The R2 values of ANN for estimation of HMF concentration were found 0.90 for training, 0.96 for validation, and 0.99 for testing and AARD was found 8.85%. Evaluation of the absorbance and L*, a*, b* values of the red color with artificial intelligence is a reliable way to determine the HMF concentration.
ABSTRACT
Abstract This article aims to monitor the development of Orchis purpurea Huds., salep orchids, of different sizes over a period of two years, and to investigate the relationship between the parameters studied. In the first step, the measurements taken at the time of planting and harvesting of tubers divided into eight different groups according to their size were subjected to variance and Duncan's test. In the second step, the relationship between the parameters was investigated by ignoring seedling groups. The relationship between the two variables was determined by correlation analysis. The significance of the relationships between planting and harvest data sets, and variable contributions were determined by canonical correlation analysis. Finally, leaf area prediction modeling was performed by applying multiple regression analysis. In variance analysis all parameters were significant. The canonical correlation between the first pair of canonical variables was 0.988 (p<0.01). The data obtained from the tubers made the greatest contribution to the explanatory power of the canonical variables. The leaf area model was formulized as LA (mm2) = -1237.0204 + 57.7912 × LW + 16.6211 × LL where LA is leaf area, LW is leaf width, LL is leaf length and a, b, and c are coefficients.