A Randomized Controlled Trial to Evaluate the Effects of Different Forms of Orange Juice on Dental Plaque Ph

Aim: To study the acidogenic response in human dental plaque in vivo with fresh orange juice compared to orange fruit drink with no added sugar and regular orange fruit drink that were currently available in the market. Materials and Methods: Ninety subjects aged 18-30 years were allocated randomly into 3 groups. 10 ml of each experimental drink was given to the subjects in each group and instructed to keep in mouth for 2 minutes before swallowing. Intrinsic pH of the test drinks was measured by the Digital pH meter. Plaque pH was measured at the baseline, immediately after consumption and at 1,15 and 30minutes post consumption. Result: Inter group comparison by ANOVA showed significant difference among mean pH values and time intervals. There was significant drop in plaque pH in all the three groups after 1minute. Tropicana group showed drop in pH even after 30 minutes while in other two groups, pH returned to initial levels at the end of 30minutes. Conclusion: The results of our study showed that, plaque pH dropped rapidly after 1 minute of consumption of all the three fruit juices. Plaque pH returned to initial levels in two groups while in tropicana group, it declined.

Klasifikasi Tekstur Kematangan Buah Jeruk Manis Berdasarkan Tingkat Kecerahan Warna dengan Metode Deep Learning Convolutional Neural Network

Sweet orange is very much consumed by humans because oranges are rich in vitamin C, sweet oranges can be consumed directly to drink. The classification carried out to determine proper (good) and unfit (rotten) oranges still uses manual methods, This classification has several weaknesses, namely the existence of human visual limitations, is influenced by the psychological condition of the observations and takes a long time. One of the classification methods for sweet orange fruit with a computerized system the Convolutional Neural Network (CNN) is algorithm deep learning to the development of the Multilayer Perceptron (MLP) with 100 datasets of sweet orange images, the classification accuracy rate was 97.5184%. the classification was carried out, the result was 67.8221%. Testing of 10 citrus fruit images divided into 5 good citrus images and 5 rotten citrus images at 96% for training 92% for testing which were considered to have been able to classify the appropriateness of sweet orange fruit very well. The graph of the results of the accuracy testing is 0.92 or 92%. This result is quite good, for the RGB histogram display the orange image is good

Development of an Effective and Economic Biosensor for Diabetic Blood Monitoring Based on MWCNTs, Artificial Redox Mediator Ferrocene, Nafion Polymer and a Local Extracted and Purified Glucose Oxidase Enzyme from Penicillium Notatum F-158 Fungus

Enhancement of the properties of glucose oxidase (GOx) enzyme is still receiving attention due to its extensive applications. Eight different fungal strains were isolated from soil and orange fruit samples for inexpensive GOx production. Penicillium notatum F-158 (P. notatum) strain produced a remarkable amount of GOx. Its culture condition was optimized for optimum GOx production. GOx was purified and its activity, stability and kinetic parameters were studied. An effective biosensor {GCE/[MWCNTs–Fc–GOx(FAD)–NF]} based on layer by layer immbolization of MWCNTs, aritificial ferrocene (Fc) redox mediator, extracted P. notatum GOx enzyme and nafion polymer (NF) on glassy carbon electrode (GCE) surface was developed for glucose determination. Fc acts as an electron relay between enzyme and MWCNTs/GCE. The synergy of MWCNTs and Fc enhances the electrocatalytic action of Fc to the enzymatic oxidation of glucose. Low potential (+0.3V) of Fc applied in amperometric measurements avoids interference of the main electroactive substances present in the real plasma samples. This biosensor showed broad linear ranges {2.80×10-4 to 14.99×10-3 M} and low detection limit (8.68×10-6 M) for glucose determination. The achieved glucose concentrations in six plasma samples are consistent with normal values in human blood indicating that such biosensor could be used clinically.

Distance transform-watershed segmentation and multi-layer perceptron algorithm to separate touching orange fruit in digital images

An algorithm to separate touching oranges using a distance transform-watershed segmentation is presented. In this study, there are four classes of oranges, such as class A, B, C, and D, respectively. The size of each class is based on the Indonesian National Standard (SNI), the sample used is 168 oranges of which 140 are for training and 28 oranges are for testing. The image of citrus fruits was captured using Kinect v2 camera with a camera resolution of 1920 × 1080 pixels, the distance from the camera to the background is 23 cm. The images were captured in PNG format. The watersheds were computed based on the distance transformed by orange regions. The corresponding basins were finally used to split the falsely connected corn kernel by intersecting the basins with the corn kernel regions. Experimental results show that the multi-layer perceptrons have classification accuracy rates of 92.85%. The algorithm appears to be robust enough to separate most of the multiple touching scenarios.

Genomic and transcriptomic analyses of Citrus sinensis varieties provide insights into Valencia orange fruit mastication trait formation

Valencia orange (Citrus sinensis Osbeck) (VO) is a type of late-ripening sweet orange whose ripening occurs 4 to 5 months later than that of the mid-ripening common sweet orange (CO). Notably, the mastication trait of VO fruit is inferior to that of CO fruit. To date, how inferior pulp mastication trait forms in VO has not been determined. In this study, 13 VO varieties and 12 CO varieties were subjected to whole-genome resequencing. A total of 2.98 million SNPs were identified from 25 varieties, and a SNP molecular marker was developed to distinguish VO and CO. Moreover, 144 and 141 genes identified by selective sweep analysis were selected during VO and CO evolution, respectively. Based on gene functional enrichment analysis, most of the selected VO genes were related to the stress response and lignin biosynthesis. Simultaneously, we comparatively analyzed the transcriptome profiles of peel and pulp tissues among three VO varieties and three CO varieties, and the results demonstrated differences in lignin biosynthesis between VO and CO fruits. Furthermore, coexpression network analysis was performed to identify hub genes of lignin-related and variety-specific networks, which included CsERF74, CsNAC25, CsHSFB3, CsSPL4/13, etc. Overall, this study provides important insights into the mastication trait formation of Valencia orange fruit.

The Activities Of Combination Citrus hystrix Peel Extract and Carica papaya Leaves Extract Against Candida albicans and Escherichia coli

Citrus hystrix Peel Extract and Carica papaya Leaves Extract contain several active components that can be used as antimicrobial compounds. The aim was to test a mixture of extracts from Citrus hystrix peel and Carica papaya leaves as growth inhibiting compounds for Candida albicans and Escherichia coli. This study used 4 levels of treatment, namely a combination of papaya leaves extract and orange fruit peel at concentrations of 0%, 25%, 50%, 75% and 100%. The research data were in the form of the diameter of the growth inhibition of Escherichia coli and Candida fungi, which were analyzed using the Kruskall-Wallis test at 5% confidence level and 5% Duncan's test. showed a significant difference between treatments, but the concentration of 100% showed better results to inhibit Escherichia coli (1.58 ± 0.28 d) and the growth of Candida albicans (1.53 ± 0.57b) compared to controls and other concentrations.