Sri Agustina, Devianti, Ramayanty Bulan, Muhamad Muslih, Agustami Sitorus
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". © 2022 WITPress. All rights reserved.
Department of Agroindustri, Polytechnic of Indonesia Venezuela, Aceh Besar, 23372, Indonesia; Department of Agricultural Engineering, Faculty of Agriculture, Syiah Kuala University, Banda Aceh, 23111, Indonesia; Department of Information System, Nusa Putra University, Sukabumi, 43155, Indonesia; Department of Agricultural Engineering, King Mongkut’s Institute of Technology Ladkrabang, Ladkrabang, 10520, Thailand; Research Center for Appropriate Technology, National Research and Innovation Agency (BRIN), Subang, 41213, Indonesia
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