Wheat Flour Type Research Articles

Wheat Flour Discrimination Using Two-Dimensional Correlation Spectroscopy and Deep Learning.

The continuous evolution of deep learning has garnered significant attention in spectroscopy. This study focuses on identifying wheat flour, presenting a more efficient and accurate method by combining two-dimensional correlation spectroscopy (2D-COS) and deep learning techniques. A data set of 316 near-infrared (NIR) spectral samples of four types of wheat flour was collected. By applying three disparate 2D-COS techniques, i.e., synchronous, asynchronous, and integrated, we crafted 948 2D-COS images. These images, obtained by transforming the original one-dimensional spectra into 2D representations, offer richer information for deep learning analysis. The study introduced an 18-layer residual network incorporating a convolutional attention mechanism, specifically tailored for the 2D-COS analysis of wheat flour, aimed at enhancing the model's discriminative capabilities by refining the residual neural network's structure. Achieving an unprecedented recognition accuracy of 100% through methodical optimization and rigorous training on the synchronous 2D-COS data set of wheat flour, the proposed model is a testament to the efficacy of deep learning in spectroscopic analysis. To further exhibit the confluence of 2D-COS with deep learning, t-distributed stochastic neighbor embedding was employed to visualize the distinctive 2D-COS features within the deep learning architecture. Additionally, the model's performance was juxtaposed with prevailing NIR spectral recognition methods, including random forest, gradient boosting decision tree, and artificial neural network. This comparison cemented the proposed approach's superiority in wheat flour categorization. The findings of this study not only introduce a novel and efficient solution for wheat flour quality analysis but also underscore the significant potential of deep learning techniques in spectroscopy applications.

Classification of different wheat flour types using hyperspectral imaging and machine learning techniques

Different wheat flour types are used to produce various baked products. Due to the whiteness of the four types, hyperspectral imaging can be used due to receiving infrared wavelength. The technique was applied to distinguish confectionery flour and the flours of Samoun, Sangak, and Tafton breads using a line scanning system in the range of 400–950 nm. Effective wavelengths were selected and different image features were extracted from the corresponding image channels. The selected wavelengths were 601.33, 620.34, 696.41, 730.31, 821.26, and 841.11 nm. The extracted features were used in classification step using linear discriminant analysis, support vector machine, and artificial neural network methods in MATLAB software. The classification accuracy of artificial neural network was higher than the other methods. The efficient features gave higher classification accuracy (98.1 %) than all extracted features (96.9 %). The results showed the high ability of hyperspectral imaging combined with artificial neural network to distinguish different wheat flour types.

SLNIR-Net: A stacked ensemble approach using neural network meta-learners for comprehensive wheat flour detection

Wheat flour, a critical component in a myriad of nutritional applications, is both extensively cultivated and diversely utilized across the globe. However, accurately distinguishing between different types of wheat flour remains a significant challenge. This study presents an innovative identification method marrying near-infrared spectroscopy (NIRS) with ensemble learning. We collected spectral data from four unique types of wheat flour using NIRS, subjecting them to a series of preprocessing steps to optimize our analysis. The Competitive Adaptive Re-weighted Sampling (CARS) algorithm facilitated the extraction of pivotal spectral features, thereby enhancing the generalizability of our ensemble learning model. Diverging from conventional meta-learner designs, we forwent traditional logistic regression in favor of constructing a neural network-based Stacking integrated learning model, the SLNIR-Net. This model stands as a seminal tool for the precise identification of wheat flour varieties. Comparative results highlight SLNIR-Net’s superior performance over established methodologies, including Partial Least Squares Discriminant Analysis (PLS-DA) and four other notable ensemble learning methods, with our model achieving the highest recognition accuracy. Notably, the fine-tuned SLNIR-Net model attained a 100 % accuracy rate, eclipsing the benchmark Stacking model. This leap in performance is a testament to the enhancements made to circumvent overfitting and amplify the model’s generalization capacity. Our study marks a substantial leap forward in the swift and accurate classification of wheat flour types, charting a new course for broader product identification exploits using NIRS.

Unraveling chemical changes associated with the sensory quality of Chinese steamed bread as altered by wheat flour type

Chinese steamed bread (CSB) is an important staple of the Chinese people, and its flavor profile is mostly affected by wheat varieties among others. This study selected wheat flour made from three different wheat varieties and investigated their contribution to the CSB flavor profile in terms of metabolism. Thirteen aroma-active compounds identified by GC-O were determined as the main contributors to the different aroma profiles of three CSBs. 350 sensory trait-related metabolites were obtained from five key modules via weighted gene co-expression network analysis. It was found that the sensory characteristics of CSBs made of different wheat flour were significantly different. The higher abundance of lipids in Yongliang No.4 (YL04) wheat flour was converted to large number of fatty acids in fermented dough, which led to the bitterness of CSB. Besides, the abundance in organic acids and fatty acids contributed to the sour, milky, wetness and roughness attributes of YL04-CSB. More fatty amides and flavonoids in Jiangsu Red Durum wheat flour contributed to the fermented and winey attributes of CSB. Carbohydrates with higher abundance in Canadian wheat flour was involved in sugar-amine reaction and glucose conversion, which enhanced the sweetness of CSB. In addition, fatty acids, organic acids, amino acids, and glucose were crucial metabolites which can further formed into various characteristic compounds such as hexanal, hexanol, 2,3-butanediol, acetoin, and 2,3-butanedione and thus contributed to the winey, fresh sweet, and green aroma properties. This study is conductive to better understand the evolution of the compounds that affect the quality and aroma of CSBs.

Quality Assessment of Developed Cumin Cookies

Abstract: Cookies are good carrier of nutrients like carbohydrate and fat which can be enriched with protein. Refined Flour is the finest type of wheat flour containing high calorie content but minimal nutritional value that is used to make pizza, bread, samosa, kachoris, puri, cakes and biscuits.by partially replacing refined wheat flour with wheat flour up to an acceptable level. It is a fast and quick recipe for cookies made with Refined wheat flour, wheat flour and cumin seeds. The special characteristic of these cookies is that, as opposed to other cookie recipes, they do not contain plain flour, sugar or even jaggery. It is an excellent snack or coffee snack for munching and is enjoyed by all age groups, including children and adults because of its rich nutrients.

Nutritional composition, in vitro carbohydrates digestibility, textural and sensory characteristics of bread as affected by ancient wheat flour type and sourdough fermentation time

This study aimed to investigate the effect of ancient wheat flour type and sourdough fermentation time on the nutritional, textural and sensorial properties of fiber-rich sourdough bread. The proximate composition, minerals, carbohydrates, organic acids, volatiles, total phenolic content, simulated gastrointestinal digestion, textural and sensorial characteristics were investigated. Bread's minerals, total phenolics, cellulose contents and radical scavenging activity variations clearly indicates an increasing trend with sourdoughs fermentation time. Compared to maltose and glucose, fructose was predominant in all bread samples. Sourdough fermentation time and wheat type had non-significant influence on fructose content from digested fraction. Excepting emmer bread, fermentation time increased in vitro digestibility values for tested samples. The crumb textural parameters (hardness, gumminess, chewiness, cohesiveness and springiness index) were positively influenced by fermentation time. The specific clustering of the analysed characteristics distinguished emmer bread from other samples in terms of volatile compounds, textural and overall acceptability, being preferred by panellists.

Persistence comparison of two Shiga-toxin producing Escherichia coli (STEC) serovars during long-term storage and thermal inactivation in various wheat flours.

Foodborne outbreaks associated with Shiga toxin-producing Escherichia coli (STEC) contaminated wheat flour have been an increasing food safety concern in recent decades. However, there is little literature aimed at investigating the impact of different flour types on the persistence of STEC during storage and thermal inactivation. Therefore, two serovars of STEC, O121 and O157, were selected to inoculate each of five different types of common wheat flours: whole wheat, bleached, unbleached, bread, and self-rising. Inoculated flours were examined for the stability of STEC during storage for up to 42 days at room temperature (RT) and aw ~0.56. Additionally, the thermal resistance of O121 and O157 under isothermal conditions at 60, 70, 80, and 90°C was analyzed for the inoculated flours. STEC storage persistence at RT was generally not affected by flour type, however, decreases of 1.2 and 2.4 log CFU/day within whole wheat flour for O121 and O157, respectively, were significantly lower than other flours. Though few differences were identified in relation to flour type, O121 exhibited significantly better survival rates than O157 during both equilibrium and storage periods. Compared to an approximate 6 log reduction in the population of O157, O121 population levels were reduced by a significantly lower amount (~3 log) during the entire storage period at RT. At each isothermal temperature, the impact of flour type on the thermal resistance capabilities of O121 or O157 was not a significant factor and resulted in similar survival curves regardless of serovar. Instead of exhibiting linear survival curves, both O121 and O157 displayed nonlinear curves with some shoulder/tail effect. Similar for both O121 and O157, the predicted decimal reduction time (D-value) decreased from approximately 25 min to around 8 min as the isothermal temperature increased from 60°C to 90°C. Results reported here can contribute to risk assessment models concerning contamination of STEC in wheat flour and add to our understanding of the impacts of flour type and STEC serovar on desiccation stability during storage and isothermal inactivation during thermal treatment.

Precision in wheat flour classification: Harnessing the power of deep learning and two-dimensional correlation spectrum (2DCOS)

Wheat flour is a ubiquitous food ingredient, yet discerning its various types can prove challenging. A practical approach for identifying wheat flour types involves analyzing one-dimensional near-infrared spectroscopy (NIRS) data. This paper introduces an innovative method for wheat flour recognition, combining deep learning (DL) with Two-dimensional correlation spectrum (2DCOS). In this investigation, 316 samples from four distinct types of wheat flour were collected using a near-infrared (NIR) spectrometer, and the raw spectra of each sample underwent preprocessing employing diverse methods. The discrete generalized 2DCOS algorithm was applied to generate 3792 2DCOS images from the preprocessed spectral data. We trained a deep learning model tailored for flour 2DCOS images – EfficientNet. Ultimately, this DL model achieved 100% accuracy in identifying wheat flour within the test set. The findings demonstrate the viability of directly transforming spectra into two-dimensional images for species recognition using 2DCOS and DL. Compared to the traditional stoichiometric method Partial Least Squares Discriminant Analysis (PLS_DA), machine learning methods Support Vector Machines (SVM) and K-Nearest Neighbors (KNN), and deep learning methods one-dimensional convolutional neural network (1DCNN) and residual neural network (ResNet), the model proposed in this paper is better suited for wheat flour identification, boasting the highest accuracy. This study offers a fresh perspective on wheat flour type identification and successfully integrates the latest advancements in deep learning with 2DCOS for spectral type identification. Furthermore, this approach can be extended to the spectral identification of other products, presenting a novel avenue for future research in the field.

Headspace volatiles profiles of different spring varieties and a wild relative of wheat flour.

Volatile profiling was conducted on four wheat varieties Triticum aestivum cv. Chinese Spring (CS), Highbury (High), Paragon (Para), Pavon76 (Pav76), and one wild relative Triticum timopheevii (P95). Headspace solid-phase microextraction (SPME) combined with gas chromatography-mass spectrometry (GC-MS) was used to explore differences in flavor formation mechanisms in different flours before and after starch gelatinization. Solvent retention capacity (SRC) analysis revealed subtle differences in water absorption, gluten strength, and starch characteristics across wheat flour types. Rapid Visco Analysis (RVA) of whole wheat flour demonstrated significant variations in pasting properties among wheat varieties, with P95 exhibiting higher viscosities compared to CS and High potentially influenced by starch gelatinization, protein-starch interactions, and lipid content. Aroma contributions of P95 clustered positively in PCA plots, contrasting with the four main varieties, indicative of species-level differentiation. Furthermore, the study highlighted the roles of viscosity, protein structure, lipid content, and fatty acid composition in modulating the release and perception of volatile aroma compounds during heating. This study sheds light on how the distinct characteristics of wheat flour influence aroma profiles, revealing species-level differences and the pivotal role of physiochemical properties in shaping flavor development mechanisms.

Comprehensive Characterization of Micronized Wholemeal Flours: Investigating Technological Properties across Various Grains.

With a suitable milling system, it is achievable to produce wholegrain flours that match the granulation and technological properties of refined flours while maintaining a complete nutritional profile. This process also minimizes the generation of additional industrial waste. This study aimed to characterize wholemeal flours with a fine granulation size of less than 160 µm: wheat (MWF), rye (MRF), spelt (MSF), barley (MBF), buckwheat (MBWF), and sorghum (MSGF). For comparison, the plain wheat flour type 530 (T530) was analyzed. The flours were assessed in terms of their chemical compositions and alpha amylase activities (the Falling Number assay), pasting properties (amylograph and a Rapid Visco Analyser (RVA)), water absorption using a farinograph, and technological quality based on their water (WRC) and sodium carbonate solvent retention capacity (SRC) profiles. Among the micronized wholemeal flours, wheat flour (MWF) exhibited the highest nutritional value, greatest water absorption, and highest final gelatinization temperature, but had the lowest energy value, carbohydrate content, water SRC, and sodium carbonate SRC. Wholemeal rye flour (MRF) displayed the lowest nutrient content and the highest amylolytic activity, water absorption, and sodium carbonate SRC. The plain wheat flour type 530 (T530) had the lowest water absorption. Special buckwheat flour (MBWF) showed the highest energy value due to its elevated carbohydrate content, along with the lowest sugar and TDF contents, amylolytic activity, and pasting temperature.

Detection of microorganisms and afla toxins (B1) in local wheat grains and local flour produced in Iraq

It can cause scattered dust and usually loaded with various germs that adversely affect the health of silos workersin silos. The main objective of the study is to measure microbial contamination. (bacterial, fungal, mycotoxins), which are widely prevalent in Iraqi silos and mills and negatively It affects the health of silos workers in particular and the general health of the community. The study included 40 samples of wheat grains and 40 samples of wheat flour, which were randomly collected for the winter seasons from 4 silos in Baghdad, silo Maysan and siloAL- Basra, and silo Erbil and silo Kirkuk during December and January 2022. The results indicated that the most isolated types of wheat and wheat flour are pathological spores in addition to the presence of a percentage of microbial toxins, and the chemical analysis with HPLC during the winter season showed the presence of toxic mycotoxin contamination for a number of silos, and the khan was about (76.6 ppb) by 12.5%, while the silos (cycle, coronary) were about 37.5%, with statistically significant differences (p. ≤ 0.05). The results of the statistical analysis indicated that there were statistically Statistically significant differences (p≤0.05) were recorded for wheat and flour in (temperature and humidity associated with storage,) and most of them samples contained more Coliforms and Mold than the permissible limit, while some of the samples contained an allowable percentage of mycotoxins.

Molecular Characterization of Lactic Acid Bacteria in Bakery and Pastry Starter Ferments.

Bread is the oldest and most essential food consumed by humans, with its consumption exceeding nutritional needs and becoming part of cultural habits. Fermentation is an important step in the bread-making process, giving it its rheological, organoleptic, aromatic, and nutritional properties. Lactic acid bacteria and yeasts are both responsible for the fermentation step and part of the natural flour microbiota. In this study, we aimed to characterize LAB in three types of flour, namely, wheat, oat, and rice flour, using conventional phenotypic and biochemical assays and to carry out molecular-biology-based characterization via studying the rrn Operon using RFLP of the ITS region and via PCR using species-specific primers. Additionally, the effect of LAB diversity among the three types of flour and their influence on dough characteristics were assessed. Also, we evaluated the antagonistic effects of LAB on two bacterial (E. coli and S. aureus) and two fungal (Botrytis and Fusarium) pathogens. This study showed that LAB are not the predominant species in rice flour, while they were predominant in wheat and oat flour. Additionally, Lactobacillus sanfranciscencis was found to be the predominant species in wheat flour, while its presence in oat flour was minor. Finally, through their production of soluble substances, LAB exerted antagonistic effects on the four types of pathogenic microorganisms.

The classification of Iranian wheat flour varieties using FT-MIR spectroscopy and chemometrics methods

Wheat varieties are grown in many countries throughout the world to produce grain and flour for foods, an outstanding and remarkable foodstuff that fulfills human nutritional needs. Methodologically, there is little known about how to distinguish accurately the type of wheat flour and, in the meantime, to restrain food fraud. Therefore, non-destructive methods are necessary for identification, in turn, the spectroscopy method has the potential to be one of the most efficient non-destructive methods. Given the importance of high-purity wheat flour for consumers and food industries, this research introduces the five most cultivated flour varieties in Iran that were classified by FT-MIR spectroscopy. Different preprocessing techniques and unsupervised and supervised models investigated the spectral data of flour varieties. The best outcome came with the standard normal variate (D2 + SNV) preprocessing method and with all supervised models. The accuracy of Linear Discriminant Analysis (LDA), Support Vector Machine (SVM), and Artificial Neural Network (ANN) was 100% for the test dataset. In conclusion, it is recommended flour industries use the FT-MIR spectroscopy and chemometrics techniques, the combined form of these techniques helps these industries be aware of the purity of produced flour as possible.

Effect of Spirulina in Bread Formulated with Wheat Flours of Different Alveograph Strength.

Consumers within the EU are increasingly asking for natural and healthier food products, which are additive-free and environmentally friendly. The aim of this study was to assess the effects of Spirulina (Arthrospira sp.) in bread formulated with four wheat flours with different alveograph strengths. The flours used were Manitoba Flour (00/251), Ground-force wholemeal (Whole/126), Standard Bakery Flour (0/W105), and Organic Bakery Flour (2/W66). Powdered Spirulina biomass was used as a new ingredient with a high nutritional profile and bioactive compounds; incorporation was made at two levels: 1.5% and 2.5% of the flour amount. The same bread recipe was used for all formulations, but for the 1.5 and 2.5% variations, 6 g and 10 g of Spirulina were added, respectively. Antioxidant capacity increased with increasing microalgal biomass. The visual and taste attributes of the breads with microalgae underwent noticeable changes compared to their counterparts without microalgae. Biomass addition significantly (p < 0.05) affected bread weight and volume, and different trends were found based on the type of wheat flour. Spirulina-containing breads showed a greener coloration while the microalgae concentration was augmented. The moisture and texture were slightly affected by the addition of the biomass at both levels. The 2.5% concentration samples were well accepted in most cases by consumers, emphasizing the salty flavor as a pleasant feature. No significant sensory differences were observed between samples, and the acceptability index was always higher than 72%. The results show that Spirulina could be an environmentally friendly ingredient for the reformulation of nutritionally enhanced bread with a good texture that is well-accepted by consumers.

The Effect of Zinc Biofortified Wheat Produced via Foliar Application on Zinc Status: A Randomized, Controlled Trial in Indian Children

BackgroundAgronomic zinc biofortification of wheat by foliar application increases wheat zinc content and total zinc absorption in humans. ObjectivesTo assess the effect of agronomically biofortified whole wheat flour (BFW) on plasma zinc (PZC) compared with a postharvest fortified wheat (PHFW) and unfortified control wheat (CW) when integrated in a midday school meal scheme. MethodsWe conducted a 20-wk double-blind intervention trial in children (4–12 y, n = 273) individually randomly assigned to 3 groups to receive a daily school lunch consisting of 3 chapattis prepared with the 3 different wheat flour types. Measurements of anthropometry, blood biochemistry, and leukocyte DNA strand breaks were conducted. We applied sparse serial sampling to monitor PZC over time, and analysis was performed using linear mixed-effects models. ResultsMean zinc content in BFW, PHFW, and CW were 48.0, 45.1, and 21.2 ppm, respectively (P < 0.001). Mean (standard deviation) daily zinc intakes in the study intervention in BFW, PHFW, and CW groups were 4.4 (1.6), 5.9 (1.9) and 2.6 (0.6) mg Zn/d, respectively, with intake in groups PHFW and BFW differing from CW (P < 0.001) but no difference between BFW and PHFW. There were no time effect, group difference, or group × time interaction in PZC. Prevalence of zinc deficiency decreased in the BFW (from 14.1%–11.2%), PHFW (from 8.9%–2.3%), and CW (9.8%–8.8%) groups, but there was no time × treatment interaction in the prevalence of zinc deficiency (P = 0.191). Compliance with consuming the study school meals was associated with PZC (P = 0.006). DNA strand breaks were not significantly associated with PZC (n = 51; r = 0.004, P = 0.945). ConclusionsConsumption of either PHFW or BFW provided an additional ∼1.8 to 3.3 mg Zn/d, but it did not affect PZC or zinc deficiency, growth, or DNA strand breaks.This trial was registered on clinicaltrials.gov as NCT02241330 and ctri.nic.in as CTRI/2015/06/005913.

Influence of the Wheat and Corn Flour Types on the Biological Parameters on the Red Flour Beetle, Tribolium castaneum (Herbst)

Influence of the Wheat and Corn Flour Types on the Biological Parameters on the Red Flour Beetle, Tribolium castaneum (Herbst)

Bread spoilage fungi as creators of α amylase using two types of wheat flour

Fungal spoilage of bread can be a great problem; however, it can be explored as a producer of enzymes. The fungi were isolated from breads, and their activity for α-amylase production was planned. The results identified nine fungi on spoiled breads. Aspergillus fumigatus occurred with 85% frequency, followed by other isolates. Starch yeast (SY), white flour (WF), and black flour (BF) were applied as substrates for α-amylase activity using fungal isolates. The SY was the best, followed by WF and BF for α-amylase activity. Using SY, A. niger showed the greatest potency for α-amylase (7.67 U/mL) unlike Monilia sitophila, which reflected low α-amylase activity (2.69 U/mL). Using WF, A. fumigatus reflected high amylase activity (5.76 U/mL) while A. niger, A. terreus, and Penicillium expansum showed less activity (5.12 U/mL, 4.41 U/mL, and 3.56 U/mL, respectively). The temperature 30 °C and pH 6 were the optimum for α-amylase activity by A. niger, A. fumigatus, and P. chrysogenum, using the three media, but α-amylase activity of A. fumigatus at 40 °C was higher than at 20 °C. At the ninth day of incubation, the maximum α-amylase activity was reported using SY, while at the twelfth day, maximum activity was reported using WF and BF.

Type B trichothecenes in cakes and their interaction with matrix components

The study evaluated the detectable type B trichothecenes (TCTB) in cakes with different formulations in order to analyse their interaction with matrix components and the impact of daily exposure to these mycotoxins by cake consumption. A central composite design was carried out with the variables type of wheat flour (wholemeal and white flour) and ratios of milk, water and sugar. The TCTB reductions were correlated with the matrix components by principal component analysis (PCA) and the daily exposure was estimated in the condition that promoted the higher TCTB mitigation. The natural contamination of wheat flour, the ingredients dilution effect and the partial replacement of water by milk affected the TCTB levels in wholemeal cake (<1500 μg/kg) and white cake (<320 μg/kg). The PCA showed that the resistant starch was correlated with the TCTB detectable. The ingredients combination that promoted TCTB reductions of 68.9% in the white cake and 78.8% in the wholemeal cake, was the formulation that used 70 g of sugar and partial substitution of water by milk (20:20, mL/mL) in relation to 100 g of wheat flour. The daily exposure estimated by consumption of wholemeal cake was 3.5 times higher in comparison to white cake. The use of ingredients that lead to high resistant starch formation is a promising mitigation strategy.

Evaluation of Physico-Chemical and Microbiological Parameters of the Types of Wheat Flour Produced in Milling Company

Wheat flour is one of the most used ingredients in bakery products, based on its nutritional and technological properties. Also in recent years, with the increase in the variety of dough products, there has also been an increase in the types of wheat flour in a way that suits the needs of the market. This study aims to analyze the physico-chemical and microbiological characteristics of some types of wheat flour produced by a milling company, which are used for different purposes, such as: white flour, brown flour, whole meal flour, etc. In total, 6 wheat flour samples of different types were analyzed for: moisture, ash, gluten, acidity, water activity and total microbiological load. The results showed that the moisture content is below 14.5 % for all samples. The range of ash content varies from 0.46-1.29% and is different based on the type of flour, because it represents the total amount of minerals present in wheat flour. Acidity is the most used parameter to determine storage conditions and all samples had a low value of 0.09-0.21 %.The wet gluten content of the flour samples varies between the range of 27.3-34.4 %, the highest value of gluten was detected in brown flour. From the microbiological analyzes carried out, we see that the wheat flour samples analyzed result in a low microbial load. Based on the results of the analyzed samples, all types of wheat flour produced by this company have good quality in terms of the analyzed parameters. Usually, the quality of the final product depends on the quality of the raw ingredients. So, the study of the quality of wheat flour in terms of physico-chemical and microbiological parameters also means a higher quality and safety of bakery products.

Influence of Non-Thermal Plasma Treatment on Structural Network Attributes of Wheat Flour and Respective Dough.

Due to its "generally recognized as safe status" (GRAS) and moderate treatment temperatures, non-thermal plasma (NTP) has lately been considered a suitable replacement for chemicals in the modification of food properties and for preserving food quality. One of the promising areas for the application of NTP is the treatment of wheat flour, leading to improved flour properties and product quality and consequently to higher customer satisfaction. In the present research, the German wheat flour type 550, equivalent to all-purpose flour, was treated using NTP in a rotational reactor to determine the influence of short treatment times (≤5 min) on the properties of flour (moisture and fat content, protein, starch, color, microbial activity, and enzymes), dough (visco-elastic properties, starch, wet and dry gluten, and water absorption), and baking products (color, freshness, baked volume, crumb structure, softness, and elasticity). Based on the properties of NTP, it was expected that even very short treatment times would have a significant effect on the flour particles, which could positively affect the quality of the final baking product. Overall, the experimental analysis showed a positive effect of NTP treatment of wheat flour, e.g., decreased water activity value (<0.7), which is known to positively affect flour stability and product shelf life; dough stability increased (>8% after 5 min. treatment); dough extensibility increased (ca. 30% after 3 min treatment); etc. Regarding the baking product, further positive effects were detected, e.g., enhanced product volume (>9%), improved crumb whiteness/decreased crumb yellowness, softening of breadcrumb without a change in elasticity, and limited microorganism and enzymatic activity. Furthermore, no negative effects on the product quality were observed, even though further food quality tests are required. The presented experimental research confirms the overall positive influence of NTP treatment, even for very low treatment times, on wheat flour and its products. The presented findings are significant for the potential implementation of this technique on an industrial level.