Near infrared techniques applied to analysis of wheat-based products: Recent advances and future trends

Cookie composition analysis by Fourier transform near infrared spectroscopy coupled to chemometric analysis

The consumption ofcookies is ever growing and during the COVID-19 pandemic reached record consumption values and it is imperative to guarantee the quality and safety of the products.Fourier transform near infrared (FT-NIR) spectroscopy, combined with chemometric techniques, provides a promising solution in that regard, due to its speed and simple sample preparation. The objective of this study was to investigate the possibilities of using FT-NIR to predict lipids, carbohydrates, fibers, proteins, salt and energy contents, as well as to identify cookies type and main cereals present in a batch of 120 commercially acquired samples. The prediction models were performed using ordinary least squares (OLS), partial least squares (PLS), and PLS based classification models including discriminant analysis (PLS-DA), k-nearest neighbors (PLS-kNN) and naïve Bayes (PLS-NB). The best prediction models allowed for good accuracies, with correlation coefficients higher than 0.9 for all studied nutritional parameters. PLS-kNN methodology was able to identify all 5 main cereals (wheat, integral wheat, oat, corn and rice) as well as the 14 types of cookies based on the nutritional contents. The developed methods were able to accurately identify the cookies type and composition, confirming the proposed methodology as a fast, reliable, environmentally friendly and non-destructive alternative to standard analytical methods.

Discrimination of wheat flour grade based on PSO-SVM of hyperspectral technique

Rapid detection of wheat flour grade played an important role in the food industry. In this work, hyperspectral technology was used to detect five types of wheat flour. An analysis model was established based on the reflectance of samples at 968 ∼ 2576 nm. Moreover, multivariate scattering correction (MSC), standard normalized variate (SNV), and Savitzky-Golay (S-G) convolution smoothing were used for preprocessing, which was employed to reduce the influence of noise in the original spectrum. In order to simplify the model, competing adaptive reweighted sampling (CARS), successive projection algorithm (SPA), uninformative variable elimination (UVE) and the UVE-CARS algorithm were applied to extract feature wavelengths. Both partial least squares discriminant analysis (PLS-DA) model and support vector machine (SVM) model were established according to feature wavelengths. Furthermore, particle swarm optimization (PSO) algorithm was adopted to optimize the search of SVM model parameters, such as the penalty coefficient c and the regularization coefficient g. Experimental results suggested that the non-linear discriminant model for wheat flour grades was better than the linear discriminant model. It was considered that the MSC-UVE-CARS-PSO-SVM model achieved the best forecasting results for wheat flour grade discrimination, with 100% accuracy both in the calibration set and the validation set. It further shows that the classification of wheat flour grade can be effectively realized by using the hyperspectral and SVM discriminant analysis model, which proves the potential of hyperspectral reflectance technology in the qualitative analysis of wheat flour grade.

Prediction of Mineral Composition in Wheat Flours Fortified with Lentil Flour Using NIR Technology

Lentil flour is an important source of minerals, including iron, so its use in food fortification programs is becoming increasingly important. In this study, the potential of near infrared technology to discriminate the presence of lentil flour in fortified wheat flours and the quantification of their mineral composition is evaluated. Three varieties of lentils (Castellana, Pardina and Guareña) were used to produce flours, and a total of 153 samples of wheat flours fortified with them have been analyzed. The results show that it is possible to discriminate fortified flours with 100% efficiency according to their lentil flour content and to discriminate them according to the variety of lentil flour used. Regarding their mineral composition, the models developed have shown that it is possible to predict the Ca, Mg, Fe, K and P content in fortified flours using near infrared spectroscopy. Moreover, these models can be applied to unknown samples with results comparable to ICP-MS determination of these minerals.

Application of near-infrared spectroscopy for the nondestructive analysis of wheat flour: A review

The quality and safety of wheat flour are of public concern since they are related to the quality of flour products and human health. Therefore, efficient and convenient analytical techniques are needed for the quality and safety controls of wheat flour. Near-infrared (NIR) spectroscopy has become an ideal technique for assessing the quality and safety of wheat flour, as it is a rapid, efficient and nondestructive method. The application of NIR spectroscopy in the quality and safety analysis of wheat flour is addressed in this review. First, we briefly summarize the basic knowledge of NIR spectroscopy and chemometrics. Then, recent advances in the application of NIR spectroscopy for chemical composition, technological parameters, and safety analysis are presented. Finally, the potential of NIR spectroscopy is discussed. Combined with chemometric methods, NIR spectroscopy has been used to detect chemical composition, technological parameters, deoxynivalenol, adulterants and additives of wheat flour. Furthermore, NIR spectroscopy has shown great potential for the rapid and online analysis of the quality and safety of wheat flour. It is anticipated that the current review will serve as a reference for the future analysis of wheat flour by NIR spectroscopy to ensure the quality and safety of flour products.

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NIR spectroscopy is an ideal technique for analysis of wheat flour due to its rapid and nondestructive nature.

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Use of NIR spectroscopy for chemical composition, technological parameters, and safety analysis.

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Online and handheld NIR spectrometers for wheat flour detection are the future trends.