| Summary: | Abstract Recently, the application of Fourier transform infrared (FT‐IR) spectroscopy as a noninvasive technique combined with chemometric methods has been widely noted for quality evaluation of agricultural products. Mulberry (Morus alba var. nigra L.) is a native fruit of Iran and there is limited information about its quality characteristics. The present study aims at assessing a nondestructive optical method for determining the internal quality of mulberry juice. To do so, first, FT‐IR spectra were acquired in the spectral range 1000–8333 nm. Then, the principal component analysis (PCA) was used to extract the principal components (PCs) which were given as inputs to three predictive models (support vector regression (SVR), partial least square (PLS), and artificial neural network (ANN)) to predict the internal parameters of the mulberry juice. The performance of predictive models showed that SVR got better results for the prediction of ascorbic acid (R2 = .84, RMSE = 0.29), acidity (R2 = .71, RMSE = 0.0004), phenol (R2 = .35, RMSE = 0.19), total anthocyanin (R2 = .93, RMSE = 5.85), and browning (R2 = .89, RMSE = 0.062) compared to PLS and ANN. However, the ANN predicted the parameters TSS (R2 = .98, RMSE = 0.003) and pH (R2 = .99, RMSE = 0.0009) better than the other two models. The results indicated that a good prediction performance was obtained using the FT‐IR technique along with SVR and this method could be easily adapted to detect the quality parameters of mulberry juice.
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