Estimation of the nutritional parameters of various types of wheat flours using fluorescence spectroscopy and chemometrics

The purpose of this study is to evaluate the potential of fluorescence spectroscopy to predict the nutritional parameters of twenty‐six commercially available wheat flours from different vendors. Principal component analysis (PCA) was used to clearly identify the correlations among different types of flours. A partial least square regression (PLSR) model gives a good prediction for moisture, fat and carbohydrates using cross‐validation, with a R² of 0.86, 0.88 and 0.89 respectively. However, the protein, sucrose and salt contents showed little correlation in PLSR. Locally weighted regression (LWR) provides a significant improvement in the prediction of all of the nutritional parameters. The error decreases with an increasing R² to 0.96, 0.93, 0.99, 0.98, 0.99, 0.88, 0.95 and 0.99 for the energetic value, protein, fat, moisture, carbohydrate, sucrose, salt and saturated fatty acid contents respectively, for different wheat flours. Hence, fluorescence, which is a non‐invasive and rapid method, can be used to evaluate the nutritional parameters of different types of wheat flours.