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    Item type:Publication,
    Development of wireless device prototype for measurement of rumen pH and temperature continuously in cattle
    (2023-01-01)
    Prasomsri, Piyanat
    ;
    Tudsorn, Apirak
    ;
    Leklerdsiriwong, Aekaluck
    ;
    Inchaisri, Chaidate
    ;
    Tooprakai, Siraphop
    This study aims to develop a prototype rumen pH and rumen temperature measuring device for continuous measurement. The benefit is for the monitoring and early diagnosis of metabolic diseases in cattle, especially rumen acidosis and subacute ruminal acidosis. The developed prototype device was approximately 50 millimeters in diameter, 145 millimeters in length and weighed 400 grams. The outer structure of the prototype was made from abs plastic made by a 3D printer, while the internal device contained pH and temperature sensors, a microcontroller and battery. The prototype was placed in the ventral sac of the rumen of fistulated cattle for three days to test the transfer of pH and temperature data. It was found that the pH drifted over time while the temperature remained stable. The pH and temperature signals showed a fast response after feeding. The rumen pH and rumen temperature measured with the prototype were 5.3 - 7.0 and 37.25 - 38.75, respectively. The pH drift over 48 and 72 hours was around 0.15 and 0.28, respectively.
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    Performance Evaluation of Pre-Processing and Pre-Treatment Algorithm for Near-Infrared Spectroscopy Signals: Case Study pH of Intact Mango “Arumanis”
    (2022-08-01)
    Agustina, Sri
    ;
    Devianti
    ;
    Bulan, Ramayanty
    ;
    Muslih, Muhamad
    ;
    Sitorus, Agustami
    pH is one of the important physical parameters to characterize mango damage because it can indicate changes in the structure and chemical content of the fruit. Thus, the present work evaluated the possibility of NIRs as a rapid and non-destructive tool for measuring the pH properties of intact mango from the cultivar "Arumanis" (Mangifera indica L.) using several algorithms for pre-processing, pre-treatment, and prediction. Three different algorithm predictions, namely principal component regression (PCR), partial least squares regression (PLSR), and support vector machine regression (SVMR), were used and compared to predict the pH of mangos. A total of 16 pre-processing and pretreatment algorithms are used to support algorithm prediction, and the results are also compared with the raw data spectra. The NIR spectral data used range from 1000 to 2500 nm. Algorithm performance will be evaluated using RMSE, error differences and concluded using RPD. The results show that the prediction of the PLSR algorithm can be performed with an RPD of 8.17, which is more significant than the PCR and SVMR algorithms, which are 1.04, and 1.90, respectively. To support this, pre-processing and pretreatment of the second derivative Savitzky–Golay is the best algorithm that can be used to predict the pH of the whole mango cultivar "Arumanis".