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

Abstract

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 R 2 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 R 2 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. Estimation of nutritional parameters M. H. Ahmad et al. † Flour type 405 contains less than 0.5% mineral contents. ‡ Flour type 550 contains 0.5-0.63% mineral contents. § Flour type 1050 contains 0.91-1.2% mineral contents. ¶ Whole wheat flour contains 1.2-1.8% mineral contents. Estimation of nutritional parameters M. H. Ahmad et al. † Root mean square error of cross-validation. ‡ Coefficient of determination for cross-validation. Estimation of nutritional parameters M. H. Ahmad et al. † Root mean square error of cross-validation. ‡ Coefficient of determination for cross-validation. Estimation of nutritional parameters M. H. Ahmad et al.

Prediction of wheat quality parameters combining Raman, fluorescence, and near‐infrared spectroscopy (NIRS)

“…The use of all three spectrometric techniques is also useful for predicting water absorption, as all three important water binders (starch, proteins, and pentosanes) can be detected in the spectra. Starch and proteins have been discussed above, pentosans can be detected as arabinoxylans in Raman spectra at 895 cm −1 , and as ferulic acid in fluorescence spectra at an excitation of 310–370 nm and emission of 410–470 nm (Ahmad, Nache, Hinrichs, et al, 2016; Piot et al, 2000).…”

“…Also, specific amino acids such as cysteine, proline, tryptophan, and phenylalanine can be found at 1995-1214 cm −1 (Talari et al, 2015). The aromatic amino acids tryptophan, phenylalanine, and tyrosine show fluorescence at 270-310 nm excitation and 310-370 nm emission (Ahmad, Nache, Hinrichs, et al, 2016). Besides proteins, starch is also in high quantities in wheat available and plays a decisive role in the baking quality.…”

“…Because of its high accuracy, fluorescence spectroscopy is also used in food analysis (Ludescher, 1990; Karoui & Blecker, 2011). The few studies on the use of fluorescence spectroscopy to predict wheat quality already showed great potential, but were limited in the number of analyzed samples (Ahmad, Nache, Hinrichs, et al, 2016; Ahmad, Nache, Waffenschmidt, et al, 2016).…”

Functional quality of optimized peach‐based beverage developed by application of ultrasonic processing

“…Multivariate data analysis was done by principal component analysis (PCA) because it can explain the original data which help to characterize the quality of juices (Jolliffe & Cadima, 2016). PCA is a method to identify patterns in data by comparing data sets which is further expressed based on their similarities and discrimination, respectively (Ahmad, Nache, Hinrichs, & Hitzmann, 2016…”

Supervision of Food Manufacturing Processes Using Optical Process Analyzers – An Overview

“…Analytical, rheological, and baking parameters of various wheat flours were determined by Ahmad et al 88, 89. Furthermore, they were able to estimate nutritional parameters 92.…”