Development of a smart spectral analysis method for the determination of mulberry (Morus alba var. nigra L.) juice quality parameters using FT‐IR spectroscopy

• Published in: Food science & nutrition Vol. 11; no. 4; pp. 1808 - 1817
• Main Authors: Soltanikazemi, Maryam, Abdanan Mehdizadeh, Saman, Heydari, Mokhtar, Faregh, Seyed Mojtaba
• Format: Journal Article
• Language: English
• Published: United States John Wiley & Sons, Inc 01.04.2023; John Wiley and Sons Inc; Wiley
• Subjects: Acidity; Agricultural products; ANN; Anthocyanins; Antioxidants; Artificial neural networks; Ascorbic acid; Food quality; Fourier transforms; Fruits; Infrared spectroscopy; Juices; Mathematical models; Morus alba; Neural networks; Nondestructive testing; Original; Original Research; Parameters; Performance prediction; Phenols; PLS; Prediction models; Principal components analysis; Quality assessment; Regression analysis; Spectral analysis; spectroscopy; Spectrum analysis; Support vector machines; ν‐SVR; United States > US; Japan; ANN; spectroscopy; PLS; ν‐SVR
• ISSN: 2048-7177; 2048-7177
• DOI: 10.1002/fsn3.3211

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. The aim of this work was to evaluate the potential of FT‐IR spectroscopy, as a quantitative analytical technique for the evaluation of TSS, ascorbic acid, acidity, phenol, anthocyanin, browning, and pH. In this study, PCA was used to extract features, and the ν‐SVR, PLS, and ANN were used as predictive models. Finally, the ability of each model to predict the internal parameters of mulberry juice is examined.