Wheat Flour Samples Research Articles

Elucidating the attributes of winter wheat grain quality under reduced nitrogen input through near‐infrared spectroscopy

AbstractBackground and ObjectivesResearch in crop science is usually conducted in studies with a small number of plants and crops. The aim of this project was to investigate whether near‐infrared spectroscopy (NIRS) can contribute to the analysis of such small data sets. For this purpose, NIR spectra of a small sample set of whole‐grain wheat flours of two winter wheat cultivars harvested from greenhouse and field experiments with different fertilization management were analyzed.FindingsPrincipal component analysis (PCA) facilitated the discrimination of samples based on fertilization, baking volume, and growing conditions for each wheat cultivar. In addition, PCA allowed the differentiation of wheat cultivars in a data set containing both cultivars. Spectra–structure relationships were also established. Accurate calibrations with R2 of .98, .98, and .54 were obtained for fertilization, crude protein concentration, and baking volume, respectively, even for a small data set of 10 samples of wheat cultivar Discus.ConclusionThe NIR spectra of whole‐grain wheat flour samples provided high‐resolution information on various parameters such as fertilization, baking volume, wheat cultivar, and growing site. This study demonstrates that NIRS is a valuable and efficient tool for the analysis of small data sets in crop science. This illustrates the effectiveness of the elegant NIRS method.Significance and NoveltyIn this study, small data sets of whole‐grain wheat flour from crop science experiments were analyzed by NIRS. Characteristic changes of the NIR spectra based on applied fertilizer amount, crude protein concentration, baking volume, growing site, and wheat cultivar were revealed. The detection of cause‐and‐effect relationships between spectra and chemical properties of samples contributes to a better understanding of NIR spectra of wheat flours in an interpretative way.

Barley and wheat beta-glucan content influenced by weather, fertilization, and genotype

IntroductionBeta-glucan is one of the most important dietary fibers in cereal grains, having a great impact on human health and food quality. Wheat and barley are strategic crops and their fibers contribute toward their nutraceutical impact. However, the health of the environment is also important to ensure sustainable crop production. Hence the European decision makers have agreed to move toward organic cropping and to reduce the use of mineral fertilizers. Environment and agricultural management have a vast impact on the content of β-glucan in cereal grains. To exploit the valuable properties of β-glucan, the knowledge of factors increasing the amount of this component is crucial. We hypothesized that annual weather conditions, nitrogen fertilization, and cropping system affect the β-glucan amount in wheat and barley.MethodsFor this purpose, spring barley and winter wheat flour samples from an 8-year-long period (2014-2021) were characterized. The experiment included conventional and organic cropping systems with different nitrogen rates between 0 and 150 kg ha−1. In addition, the variation of β-glucan content in different varieties was analyzed.Results and discussionThe results showed that the content of β-glucan in barley and wheat grains was affected mainly by weather conditions not by fertilization. The latter finding means that the regulation of crop nutrition is not going to impact dietary fiber content in our everyday food. Lower temperatures during tillering and higher 1,000 kernel weight and test weight showed a positive correlation with β-glucan content in barley grains, while precipitation during the grain filling period had a negative correlation. Our findings suggest that β-glucan can be obtained from low-input and organic systems as efficiently as from fertilized treatments. However, there is a need for adaptation strategies in industry, as β-glucan content can vary from year to year.

Front-face fluorescence spectroscopy combined with chemometrics for the discrimination of wheat flour and cassava flour

Fluorescence and chemometric methods were used in this study to discriminate wheat flour samples mixed with cassava flour at different percentages (from 0 to 60). The fluorescence emission spectra of the different flours were obtained at excitation wavelengths of 349 nm and 373 nm. Principal component analysis (PCA) and Cluster Analysis (CA) showed that pure wheat flour can be distinguished from mixed wheat flour. The overall predictive accuracy of the principal component analysis and discriminant analysis (PCA-DA) and partial least squares discriminant analysis (PLS-DA) of the two-class discriminant models (pure and mixed wheat) reached 100%, showing that the model was able to correctly differentiate a pure wheat sample from a mixed wheat sample. The predictions of the three-class models (pure wheat, 10%,20%−60%; pure wheat, 10%+20%, 30%−60% and pure wheat, 10%−30%, 40%−60%) showed an overall accuracy of over 90% for the PCA-DA and PLS-DA models, the latter being the best performing. Thus, the specificity, sensitivity and precision values (>0.8) for the 10%, 10% + 20% and 10%− 30% classes in each PLS-DA model showed the ability of these models to predict the classes of blended wheat flour samples according to their level of mixing by cassava.

A rapid determination of wheat flours components based on near infrared spectroscopy and chemometrics

In this work, a rapid and simple analytical method for the quantitative determination of moisture, protein, wet gluten, starch and sedimentation index in the wheat flour was established by the combination of near infrared spectroscopy and chemometrics. The spectra of the 229 wheat flour samples were collected by a portable near infrared fast analyzer. The contents of these components were determined according to the relevant Chinese National Standards, and were taken as the corresponding reference database. Seven spectral pretreatment methods were employed to eliminate the optical interference from background and other noise information. The best result was obtained with FD+SG(15, 3)+MC method for moisture, protein, wet gluten and sedimentation index, FD+SG(15, 2)+MC method was more suitable for starch. The principal component numbers (PCs) were also optimized to obtain a superior model effect. Furthermore, partial least squares (PLS) and multiple linear regression (MLR) modeling methods were used to quantify the content of the components. When using FD+SG(15, 3)+MC pretreatment, all the PLS model parameters were significantly better than the MLR model. Both the predicted values and the reference values showed superior linear relationship within the calibration range. Moreover, the absolute error of the predicted values and their corresponding reference values in the PLS model were within their confidence intervals, respectively. The relative errors for moisture, protein, wet gluten and starch fluctuated little, only sedimentation index fluctuated greatly. The actual prediction correct rate of moisture, protein, wet gluten, starch and sedimentation index were 96.8%, 96.8%, 90.3%, 100.0% and 80.6%, respectively, which indicated the prediction was excellent.

Proximate, vitamins and sensory analyses of biscuit produced from blends of wheat and potato flour enriched with chia seed (Salvia Hispanica)

Proximate, vitamin and sensory analyses of biscuit produced from blends of wheat and potato flour enriched with Chia seed (Salvia Hispanica) were carried out in this study. Flour blends of wheat and potato were composite at replacement ratios of 50:50 %, 70:30 %, 60:40 % and 100 % wheat which served as the control and the three formulated samples were enriched with 5% Chia seed per sample. The biscuits produced were evaluated for their sensory, proximate and vitamins attributes. The result of sensory evaluation revealed that the biscuit was accepted with a mean score of 6.40% similar to the 100% wheat flour with mean score of 6.80 %. There were significant difference among the samples at (P≤0.05) based on appearance, aroma, taste, crispiness and texture. The results of proximate analysis shows that the protein content of the enriched blends increased with mean score of 7.95 % higher than that of the control (100 %) wheat flour of 7.04%. The 100 % wheat flour (sample D) had high moisture content with sample A, B, and C, having 6.38 %, 7.17 % and 6.77 % respectively. The carbohydrate and energy value of the samples were significantly different at (P<0.05). Sample A had the highest carbohydrate and energy value of 75.68 % and 391 % respectively. The result of the vitamins content shows that the chia seed enriched samples of A, B, and C recorded 1.83 to 1.47 % respectively for Vitamin A and 0.64 to 0.62 % for Vitamin E. Sample D (100 % wheat flour) recorded the highest Vitamin C content of 4.64% higher than the enriched samples. The samples were significantly different at (P<0.05). In conclusion, biscuit of acceptable quality were produced from the formulation of wheat and potato flour enriched with chia seed (Salvia Hispanica).

Non-destructive detection of fusarium head blight in wheat kernels and flour using visible near-infrared and mid-infrared spectroscopy

Fusarium head blight (FHB) is one of the most severe fungal diseases that reduces yield of cereal crops and degrades kernel quality with mycotoxins, which are harmful to human and animal health. The majority of FHB identification at post-harvest stage is through lab-based analysis, whilst effective it is a time consuming, expensive, and laborious process. Hence, a non-destructive, rapid, accurate, and robust method is required for FHB detection at post-harvest. This study explores the potential of visible near-infrared (vis-NIR) in the wavelength range from 400 to 1700 nm and the mid-infrared (MIR) in the wavenumber range from 4000 to 650 cm−1 to predict FHB infection of wheat kernels and flour. A total of 143 ear samples (93 infected, and 50 healthy) were collected from an inoculated trial covering several winter wheat varieties. The collected spectral data was analysed with two different machine learning algorithms, namely, random forest (RF) and linear discriminant analysis (LDA). Both models produced a higher test accuracy of 96.6 % and 100 %, respectively, for the flour samples than that (e.g., 93.1 %) for the kernels, using the MIR spectroscopy. Recursive feature elimination (RFE) demonstrated notable improvements in accuracy of the vis-NIR for the kernels, with LDA model providing 100 % classification accuracy. While RFE failed to improve the accuracy of MIR-LDA models. The results highlight the effectiveness of vis-NIR and MIR spectroscopy with RFE and machine learning for classifying FHB in wheat kernel and flour samples.

Nutritional Properties of De-fatted Coconut (<em>Cocos nucifera</em> L.) Testa Flour Incorporated Sri Lankan Traditional Food: Roti

Coconut testa (brown outer skin of coconut kernel) is a by-product of coconut industry and it does not have proper usage at present. The aim of this research was to evaluate the potential of coconut testa flour (TF) to substitute for wheat flour in preparation of unleavened bread or roti. TF is the ground defatted dehydrated coconut parings obtained from paring oil extraction. The dehydrated brown testa was subjected to oil extraction using cold press expeller followed by grinding into a fine flour. The quality of raw material (testa and TF) was evaluated. The samples of roti were prepared by incorporating 0%, 10%, 20% and 30% of TF instead of wheat flour and the sensory attributes of the roti were evaluated. Functional and proximate composition of flour blends and selected roti samples were compared with a control (100% wheat flour). Fat and moisture contents of dehydrated parings were 58.06% and 4.04% respectively, and the concentrations were reduced to 16.97% and 2.56% respectively while producing TF. All sensory attributes (taste, texture, appearance, smell, overall acceptability) of the TF incorporated roti were significantly different (P<0.05) compared to the roti made with 100% wheat flour (WF). According to the results, TF can be successfully substituted for wheat flour up to a level of 20% to enhance nutritional qualities of roti. No significant difference of swelling capacity, bulk density, tapped density and water absorption capacity (P>0.05) between the selected flour blend sample (20% TF: 80% WF) and 100% WF sample was observed. However, crude fiber and protein contents of the 20% TF and 80% WF blend were significantly (P<0.05) higher than the WF only samples. The TF incorporation (20%) contributed to increase the crude fiber content of roti from 1.81% to 7.04% and protein content from 12.43% to 16.06%.

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.

Overview of test methods used to classify wheat flour and bread samples – REVIEW

In Hungary, common wheat (Triticum aestivum ssp. vulgare) is of good quality and world famous. In addition, it plays an important role in the human diet. The classification of flours ground from wheat is quite decisive and there are several methods for its examination. The most important flour testing requirements include moisture content, protein content gluten properties and the most important bakery value number. The measured characteristics give us the opportunity to conclude about the properties of the dough, and then bakery products. Several dynamic and static methods have been developed to study the physical properties of dough. The evaluation of products can be carried out in several respects with the help of a baking test. The multitude of methods currently used to qualify flour, dough and finished products also proves that the overview of the methods is quite topical.

Quantitative detection of zearalenone in wheat using intervals selection coupled to near-infrared spectroscopy

Wheat is a globally cultivated cereal crop. However, it is susceptible to Fusarium head blight, which leads to the production of zearalenone (ZEN), significantly impacting the value of wheat. This paper proposed a novel technique for quantitatively detecting ZEN in wheat using near-infrared spectroscopy. The absorbance of wheat flour samples with varying degrees of mildew were collected using a spectral detection system based on the spectrometer. And this paper introduced two methods for feature intervals selection, interval combination optimization (ICO) and interval variable iterative space shrinkage approach (iVISSA), combined with partial least squares (PLS) and support vector machine (SVM) established a regression model based on feature intervals selection for quantitatively detecting ZEN in wheat, and then compared the performance of them. The research results showed that the SVM model outperformed the PLS model in terms of detection performance. Additionally, the SVM model detecting the level of ZEN in wheat based on the feature intervals selected by ICO achieved the best generalization performance, with a root mean square error of prediction (RMSEP) of 31.4148 μg·kg−1, a coefficient of determination of prediction (RP2) of 0.9434, and a relative percent deviation (RPD) of 4.2768. These results demonstrated that the SVM regression model, established by using ICO for selecting the near-infrared spectral intervals, can accurately detect ZEN in wheat. Near-infrared spectroscopy combined with appropriate data analysis methods, which can be used as a simple and fast tool for quantitative detection of mycotoxins of wheat and other grains.

Development and Application of Isotope Labelled Internal Standards in a Sum Parameter Method for Ergot Alkaloid Screening of Food

Ergot alkaloids are a group of toxic compounds, formed by fungi on infested grasses. In 2022, the European Commission set into effect maximum levels for the sum of the twelve major ergot alkaloids in multiple foods. To facilitate the laborious and costly individual quantification of the twelve major ergot alkaloids by HPLC–MS/MS or -FLD, we recently reported a sum parameter method (SPM) for ergot alkaloid quantification. Here, derivatization to lysergic acid hydrazide—a derivative of the mutual ergoline backbone in all ergot alkaloids—allowed simplified determination of all ergot alkaloids in flour via HPLC-FLD. For the measurement of more complex matrices like processed foods, we now developed a MS/MS-based SPM. Two internal standards (IS), isotopically labelled at different positions of the molecule, were synthesized and employed in the MS/MS-measurements. Method performance using either the 13CD3-labelled or the 15N2-labelled IS was evaluated on naturally contaminated rye and wheat flour samples as well as on processed food matrices. Employing the 13CD3-labelled IS leads to lower variances and better consistency with the reference data (obtained by the FLD-based SPM) in flour samples compared to the 15N2-labelled IS. The novel method significantly improves the measurement of ergot alkaloids in complex food matrices, due to their increased selectivity and thus lower interferences. Furthermore, the application of isotope labelled IS obviates the need for time-consuming steps like the determination of recovery rate based, matrix specific correction factors as described in the MS/MS-based European standard method for ergot alkaloid quantification (EN 17425).

Quality of Fortified Zea Mays L (Maize) and Triticum Durum (Wheat) Flours

Maize flour is obtained through the milling process and contributes about 20% energy and 15% protein. wheat flour is rich in starch (70 to75%), water (12 to 14%), protein (8 to16%), a small portion of non-starch polysaccharides (2 to 3%), lipids (2%) and ash (1%). This study aimed to evaluate the centesimal quality of maize and wheat flour. 8 samples of flour were evaluated, being 2 ((First Choice and Mpupu) and 6 of wheat flour (Faspão, Babita, Xiluva, Eagle, Pasta and Ntombi)). Moisture by loss on dissection at 105°C, ash by incineration, fat by goldfish method, protein by biuret method, fulling number by reading on mininfra grain analyzer Scan-T, gluten, wet and dry by glutomatic system, gluten index by centrifugation and fiber by enzymatic-gravimetric method were determined as quality parameters. The results showed fat content 0.61 to 1.59%, moisture 13.3 and 13.4%, fiber 0.22 to 0.38% and protein around 5.99 to 7.09% for the corn flours. Moisture ranging from 13.6 to 13.8%, protein from 10.30 to 11.22%, ash from 0.50 to 0.70%, water absorption from 58.23 to 59.73%, falling number 276 to 354s, gluten around 24 to 29.1%, gluten index from 0 to 95%, wet gluten from 28.46 to 29.80 and dry gluten ranging from 9.36 to 10.50%. No significant differences were observed among the wheat flour samples for moisture, protein, ash, water absorption and wet gluten. Significant differences were found for falling number (FN), gluten and dry gluten. There was sufficient evidence of compliance with the flour quality criteria.

Investigation on the Integration of Low-Cost NIR Spectrometers in Mill Flour Industries for Protein, Moisture and Ash Content Estimation.

The flour milling industry-a vital component of global food production-is undergoing a transformative phase driven by the integration of smart devices and advanced technologies. This transition promises improved efficiency, quality and sustainability in flour production. The accurate estimation of protein, moisture and ash content in wheat grains and flour is of paramount importance due to their direct impact on product quality and compliance with industry standards. This paper explores the application of Near-Infrared (NIR) spectroscopy as a non-destructive, efficient and cost-effective method for measuring the aforementioned essential parameters in wheat and flour by investigating the effectiveness of a low-cost handle NIR spectrometer. Furthermore, a novel approach using Fuzzy Cognitive Maps (FCMs) is proposed to estimate the protein, moisture and ash content in grain seeds and flour, marking the first known application of FCMs in this context. Our study includes an experimental setup that assesses different types of wheat seeds and flour samples and evaluates three NIR pre-processing techniques to enhance the parameter estimation accuracy. The results indicate that low-cost NIR equipment can contribute to the estimation of the studied parameters.

Quality and Adulteration in Ethnic Spices and Food Ingredients in Local Market

The quality of Food depends on the nature of the ingredients used to prepare it, if the ingredient remains in best eminence without any adulteration then only we can get the pure quality food. This study aimed to detect the quality and adulterants in commonly used food ingredients and spices from local markets of Kolhapur city of India, which are usually used to prepare local ethnic food. Some tests from (the Food Safety and Standards Authority of India) are performed to check adulterants like insoluble impurities, chalk powder, washing soda, red lead salts, brick powder, and artificial colors in food ingredients. 5 sugar samples, 8 chili powder samples, 3 Jaggery samples, 6 turmeric powder samples, 6 common salt samples, 2 black pepper samples, 2 ghee or butter samples, 3 dal samples, 3 wheat flour samples, 2 honey samples, 2 cinnamon samples, and 3 coffee powder samples were analyzed. 2 chili, 1 black pepper, 1 cinnamon, 1 Jaggery 1 Honey, and 1 coffee powder samples are found with adulterants whereas most were without adulterants. Most of the local food ingredients are of good quality.

Разработка технологии функциональных хлебобулочных изделий

Peas (Pisum sativum L.) are rich in protein, B vitamins, and dietary fiber, represented by hemicellulose and pectins. In terms of amino acids, pea proteins are as close to the reference protein as possible. The limiting amino acids of pea protein are sulfur-containing, i.e., methionine and cysteine. Peas are also rich in lysine, which is the limiting amino acid for wheat flour. Therefore, products of pea processing can expand the range of commercial high-protein foods, including functional bakery products. The research objective was to develop a technology for functional bread from a mix of wholemeal pea flour and wheat gluten.
 The study featured baking wheat flour, wheat gluten, wholemeal pea flour of San Cipriano and Vega varieties, dough samples, and ready-made bread. The carbohydrate-amylase complex of flour was studied using an Amilotest AT-97 device. The starch content was determined by the polarimetric method. The spectrophotometric method served to test the flower samples for protein while the Kjeldahl method was applied to the bread samples. The rheological properties of the dough were studied on a Reotest 2 viscometer. The sensory evaluation relied on a panel of experts, and the chemical composition was revealed by calculation and analytically.
 The wholemeal pea flour had a lower starch gel viscosity compared to the wheat flour sample. A greater amount of flour added during kneading increased the viscosity of the resulting dough. The acidity was rather high: 7.2 and 9.4 degrees for San Cipriano and Vega samples, respectively, and so was the autolytic activity (≤ 80 s). These useful qualities made it possible to reduce the technological process by 115 and 145 min. The resulting bread demonstrated good physical, chemical, and sensory indicators. The high-protein raw materials increased the protein content in bread by 41.9–46.4%, compared to the control sample, which equaled 33.1–34.2% of the recommended daily intake per 100 g of bread.
 The optimal ratio of wholemeal high-protein pea flour and wheat gluten was 20/80 for the San Cipriano samples and 30/70 for the Vega variety.

Amount of damaged starch in laboratory and production grinding flour

Due to the different approach to the Russian and European flour milling process in Russia, the amount of damaged starch (SC) at flour mills is rarely controlled. This issue needs a comprehensive study, since the indicator of damaged starch is of great importance: its assessment allows determining the intended purpose of flour (bread baking, confectionery and pasta production). Manage the flour-grinding process in terms of monitoring the operation of the mill rolls during grinding, namely, checking their parallelism and wear, optimizing the gap and pressure. Flour with a high content of damaged starch should not be used for the same purpose as flour with a low content of damaged starch. Also, the assessment of the damaged starch index allows improving the dough yield by selecting the optimal water absorption coefficient (water absorption), optimizing the volume, color and shelf life of the finished flour products. At the initial stage, the purpose of the study was to identify the relationship between the amount of damaged starch and flour quality indicators, as well as the rheological properties of dough from laboratory flour and industrial grinding, and to further clarify the level of damaged starch on a large number of samples of flour of different quality according to its intended purpose. The available literature data on this issue are contradictory. The amount of damaged starch in flour was determined on a damaged starch analyzer. The following were also determined: the quantity and quality of gluten, the falling number, dough stability, liquefaction, water absorption of 20 samples of wheat flour of laboratory and industrial grinding. The results of the study showed that the amount of damaged starch in industrial milled flour was higher, and amounted to 20-27 UCD (average 24 UCD) versus 15-21 (average 18 UCD) in laboratory milled flour. A correlation was found only in industrial milling flour between damaged starch and the falling number (0.632), liquefaction (0.781) and water absorption (0.690). In industrial flour, with an increase in the amount of damaged starch, there was an increase in water absorption and liquefaction of the dough, which is consistent with the literature data; there was no clear decrease in dough stability. In the flour of laboratory grinding, the dependences between the indicators are insignificant.

Defective zirconium/titanium bimetallic metal-organic framework as a highly selective and sensitive electrochemical aptasensor for deoxynivalenol determination in foodstuffs.

Anelectrochemical aptsensor for deoxynivalenol determination was successfully designed and constructed based on a defective bimetallic organic framework (denoted as ZrTi-MOF). The high porosity, large specific surface area, several structural defects, mixed metal clusters, and rich functionality of ZrTi-MOF markedly enhanced its electrochemical activity and facilitated the aptamer immobilization. As a result, the ZrTi-MOF-based aptasensor shows high sensitivity to detect deoxynivalenol via specific recognition between aptamer and deoxynivalenol, as well as the formation of aptamer-deoxynivalenol complex. On this basis, the developed ZrTi-MOF-based impedimetric aptasensor showed a low detection limit of 0.24fgmL-1 for deoxynivalenol determination in the deoxynivalenol concentration range 1fgmL-1-1ngmL-1 under optimized conditions, which also exhibited satisfactory selectivity, stability, reproducibility, and regenerability. Furthermore, determinationof deoxynivalenol was achieved in bread and wheat flour samples via the developed ZrTi-MOF-based deoxynivalenol aptasensor. The result from this study showed that the ZrTi-MOF-based electrochemical aptasensor could become a promising strategy for detecting deoxynivalenol in foodstuffs in the future.

Infrared Spectroscopy for the Analysis of Bioactive Analytes in Wheat: A Proof-of-Concept Study

This study compared the performance of near-infrared spectroscopy (NIRS) and mid-infrared spectroscopy (MIRS) for the prediction of moisture, protein, total phenolic content (TPC), ferric reducing antioxidant potential (FRAP) and total monomeric anthocyanin (TMA) content in 65 samples of Australian wheat flour. Models were constructed on 50 of the wheat samples, with the 15 remaining samples used as a dependent test set. NIRS showed excellent results for the prediction of protein content (R2test = 0.991; RMSEP = 0.22% w/v) and acceptable to good results for TPC (R2test = 0.83; RMSEP = 3.9 mg GAE/100 g), FRAP (R2test = 0.92; RMSEP = 5.4 mg TE/100 g) and moisture content (R2test = 0.76, RMSEP = 0.62% w/v). Similarly, MIRS showed the best results for protein prediction (R2test = 0.93, RMSEP = 0.62% w/v) and acceptable results for moisture content (R2test = 0.83, RMSEP = 0.65% w/v), FRAP (R2test = 0.83, RMSEP = 7.0 mg TE/100 g) and TPC (R2test = 0.73, RMSEP = 5.6 mg GAE/100 g). However, the TMA content could not be predicted. Finally, moving window analysis was conducted to determine the optimum wavelength ranges for predicting selected analytes. On average, this improved RMSECV values by an average of 18–20% compared to the corresponding full wavelength models, when using the same component selection method. The results confirm that infrared spectroscopy may be useful for the real-time quantitation and/or screening of key quality parameters in wheat, such as protein, TPC and antioxidant capacity.

Innovative nanostructured aptasensor for the electrochemical detection of gluten in food samples

Gluten is a protein found in a wide variety of cereals, such as wheat, rye and barley. While important for the food fabrication process, the presence of gluten in these cereals can be dangerous for patients suffering from celiac disease or gluten allergy. Moreover, gluten can be found in trace amounts even in food products that should not theoretically contain it. For this reason, it is important to develop new, fast, and reliable methods for the detection of gluten in food samples.In this work, a nanostructured platform based on graphite and gold was developed, optimized and characterized. The platform was used for the construction of an aptasensor for the selective detection of gliadin, the component of gluten responsible for triggering celiac disease. The aptasensor was able to detect this analyte in a domain of 10–50000 ng/L with a limit of detection of 3.4 ng/L. The aptasensor proved to be sensitive and selective for the target analyte, even in wheat flour samples containing gluten, allowing the detection of gluten up to 10 μg/kg of flour, but also in products from the category of gluten-free products such as gluten-free flour substitute comprised of a mixture of rice flour, carob flour, soy flour and starch and gluten-free breakfast cereals (muesli and breakfast cereals filled with hazelnut cream). Thus, the aptasensor proved useful for applications in food quality control, including for products from the gluten-free category, for which a maximum gluten content of 20 ppm (20 mg gluten/kg of product) is accepted.

Molds and Mycotoxins in Ration Wheat Flour in Sulaimani Provence

Wheat flour is one of the main food sources of mycotoxin exposure, hence its safety warrants investigation. Aflatoxin B1 (AFB1) and Deoxynivalenol (DON), specifically, and their levels of contamination in wheat flour samples from ration wheat flour samples in Sulaimani City, Kurdistan, Iraq, were examined in the current study. According to the findings of the fungal study, Penicillium (35%) infected the bulk of the samples, with Aspergillus (27%) and Rhizopus (17%) coming in second and third, respectively, and Alternaria (9%), Fusarium (6%), Helminthosporium (4%), Mucor (1%), and Cladosporium (1%).In November and December, the number of molds reached intolerable heights. The findings of the enzyme-linked immunosorbent assay (ELISA) test revealed that AFB1 was present in varying amounts in 100% of the wheat flour samples. AFB1 surpassed the 2 μgkg-1 maximum permissible quantity in 37.5% of samples, particularly in November and December. In terms of DON, 100% of samples were contaminated with DON without going over the 750 μgkg-1 EU limit.