Performance Evaluation of Pre-Processing and Pre-Treatment Algorithm for Near-Infrared Spectroscopy Signals: Case Study pH of Intact Mango “Arumanis”

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"