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Salmonella typhi is a gram-negative bacteria that causes food poisoning. Salmonella typhi bacteria can cause typhus, which can produce lipopolysaccharide complex endotoxin, which plays an important role in the pathogenesis oftyphoid fever. Cases of food poisoning are still common and are one of the causes of death and death in Indonesia. In 2019, there were 77 cases of food poisoning in Indonesia. A fast, accurate, and specific detection method is needed to detect poisoning. In previous studies, the optimum conditions and formulas for detecting these bacteria have been obtained using Real Time PCR. The results of previous studies are used as the basis for the development of a prototypedetection kit. In this study, validation was carried out, which aimed to confirm the results of previous studies so that a reproducible and accurate product for the detection of Salmonella typhi bacteria could be obtained. The results of this study showed that fim-C primers for Salmonella typhi were amplified at 95 base pairs (bp) with an annealing temperature of 60ºC and a standard DNA concentration of 50 ng/µL. The results of the Real Time PCR confirmation test of Salmonellatyphi bacteria at a pre-denaturation of 3 minutes with a concentration of 10 pmol obtained the Ct value according to thestandard with previous studies. The Ct value obtained was 13.96 for S. typhi bacteria. Based on the results obtained, it can be conclude that the condition validation stage for pure cultures was successfully carried out by producing consistentand reproducible data.

Cronobacter sakazakii is one of the bacteria that causes food poisoning that contaminates infant formula. This pathogen causes necrotizing enterocolitis, sepsis, and meningitis in infants or neonates with reported case fatality rates ranging from 40% to 80%. Therefore, it is necessary to develop fast and accurate detection of C. sakazakii in infant formula milk. This research aims to develop a method for detecting C. sakazakii bacteria using real-time PCR with high sensitivity, specificity, and accuracy. A rapid detection method using real-time PCR with the target gene grxB successfully detects the presence of C. sakazakii DNA in artificially contaminated formula milk. The results of the real-time PCR test showed that C. sakazakii DNA with a concentration of 53 ng/µL could be amplified by the grxB gene primer pair with a Ct value of 12 and a Tm value of 85.8ºC. The specificity test showed that the grxB primer could differentiate between target and some non-target bacteria. The sensitivity test showed the ability of the grxB primer to detect the smallest concentration of 3,392 pg/µL with a Ct of 24,06. Based on the results obtained, it can be concluded that the grxB primer has the potential to be used as rapid detection method for C. sakazakii bacteria in infant formula using real-time PCR.

Objective: The purpose of this research was to evaluate and select the best architecture among native convolutional neural network (CNN), MobileNetV2, ResNet50V2, and EfficientNetB0 for predicting the endpoint of the high shear wet granulation process, with accuracy as the main evaluation metric. Methods: The dataset was captured from an industrial camera using static image analysis and was manually labeled as “NOT READY” and “READY” according to the traditional endpoint method based on the mixer’s ampere point in the granulator. The dataset contained a total of 180 images, which were split between training and validation sets. Native CNN and TensorFlow Keras application programming interface (API) were utilized with MobileNetV2, EfficientNetB0, and ResNet50V2 as base feature encoders. Hyperparameters, such as final Fully Connected (FC) layer width, dropout rate, and learning rate, were optimized for binary classification using Keras hyper tuning. Results: The best was the native CNN, it was also the fastest among the three other models, taking only 20-30 ms per step for inference during runtime, though it requires 9000 ms time for training, the longest time among the models. It achieved an accuracy of 98%, and a validation accuracy of 97%. Conclusion: The system was able to determine when a wet granulation process has reached its endpoint based on live images from a camera after being trained on previously labeled data. The native CNN was the best model, offering the fastest runtime performance and the highest accuracy. [ABSTRACT FROM AUTHOR]