Determination of the protective effects of olive leaf extracts on microbiological and physicochemical properties of pepper paste using the image processing methods

Abstract

Deterioration due to chemical and microbiological changes is the most important problem encountered in the production and consuming period of the traditional and modern red pepper paste. On the other side, it is stated in the literature that the olive leaf is an effective natural preservative for similar deteriorations. However, a research related to the preservative effect of olive leaf on pepper paste has not yet been presented in the literature. In the study, olive leaf extract (OLE) was added at different concentrations to the industrial sweet pepper paste and preservative effect of OLE was measured to complete the mentioned deficiency in the literature. However, the experimental results were not suitable for modeling since it contained too much local optimum. Therefore, the experimental results corresponding to not tested independent variable combinations were predicted by bicubic interpolation method. Then, the predicted values were transformed into images to visual determination of significant changes associated with OLE concentrations and storage days. According to the findings, the OLE supplementation between 2.5 and 3.5 g/kg provides optimum microbiological preservation along 15 days for the pepper paste stored as cover‐open. However, OLE supplementation did not cause any physicochemical or color changes for pepper paste stored as cover‐open. Also, there was no significant microbiological, physicochemical or color changes determined for the pepper paste stored as cover‐closed. As a result, OLE supplementation between 2.5 and 3.5 g/kg suggested to preserve the canned pepper paste microbiologically after the consumption has started. Thus, the effect of the OLE on pepper paste was determined and presented to the literature. Practical application OLE supplementation between 2.5 and 3.5 g/kg inhibits the microbiological growth in pepper paste stored as cover‐open. Thus, the canned pepper paste supplemented with OLE can be protected microbiologically after start of consumption. Olive leaf that is originally wasted also can be evaluated.

Modeling and analyzing rheological behavior of sour lemon juice concentrate using image processing methods

“…On the contrary, the color changes in Figures 4a and 5a show that the corresponding viscosity range change has also been statistically significant. Detailed explanation of the statistical difference of colors is also available in the literature(Gamli et al, 2018; Kalkan et al, 2019). That is, Figures 4b and 5b offer prediction in smaller value ranges, while Figures 4a and 5a provide prediction at statistically different value ranges.…”

“…The bit value determines how many shades of gray have in the image. Grayscale images can also be converted to color images by matching gray tones with rainbow colors and tones of these colors (Gamli et al, 2018). The data corresponding to one color in the color images has a statistically significant difference from the data corresponding to the other colors.…”

“…The bit value determines how many shades of gray have in the image. Grayscale images can also be converted to color images by matching gray tones with rainbow colors and tones of these colors (Gamli et al, 2018).…”

“…However, model equations cannot provide this advantage. Furthermore, with the help of image processing methods such as histogram stretching, histogram equalization, and so forth, the optimal range can be determined instead of a single optimum value or the regions where the dependent variable undergoes statistically significant changes can be identified (Akben, 2018; Çelik, Akben, & Mazlum, 2019; Gamli, Eraslan, & Akben, 2018; Kalkan, Akben, & Çanga, 2019).…”

Modeling the microbiological effect of essential peppermint oil‐supplemented chitosan film on Bonito fish (Sarda sarda) fillets and analysis via image processing methods

“…If the experimental results can be modeled with a high fit coefficient, the values predicted by model equations are transformed into images. Otherwise, the experimental results are directly transformed into images, and then the bicubic interpolation‐based image resize methods are used (Gamli, Eraslan, & Akben, ). The different colors in the images mean that the corresponding values are statistically different.…”