Effect of wheat roasting conditions and wheat type on short-wave infrared (SWIR) spectral data of whole and milled wheat by ANOVA-simultaneous component analysis.

ANOVA-simultaneous component analysis (ASCA) was used to investigate the effect of roasting and wheat type on shortwave-infrared (SWIR) spectra of whole wheat and flour through assessment of statistical significance and characterisation of the contributing spectral features. The full factorial experimental design included two wheat types, three roasting temperatures and three roasting frequencies. SWIR spectral images were collected from the two roasted wheat types and their two milled samples. Three ASCA models, one for each wheat conformation (kernel, whole wheat flour, white flour) were investigated. It was evidenced that all factors and interaction in the whole wheat flour model had a significant (p ≤ 0.05) effect on spectral data. Only the factor roasting frequency was not significant in white flour model and only the interaction between roasting frequency and wheat type was not significant for the kernel model. The main variations in the loading line plots were identified and characterised by chemical structural differences that occur within the sample. The effect of roasting frequency had a more adverse effect on protein stability, moisture evaporation, water soluble carbohydrates and aromatic amino acids, compared to roasting temperature. A Rapid Visco-Analyser (RVA) was used to further investigate difference in wheat type as almost all spectral data sets differed significantly. The most prominent difference between the two wheat types was observed as differences in amylase activity and presence of lipids. ASCA applied to SWIR whole wheat and flour spectral data effectively characterised the significant effect of roasting on wheat starch and protein structures.

Application of a generative adversarial network for multi-featured fermentation data synthesis and artificial neural network (ANN) modeling of bitter gourd-grape beverage production.

Artificial neural networks (ANNs) have in recent times found increasing application in predictive modelling of various food processing operations including fermentation, as they have the ability to learn nonlinear complex relationships in high dimensional datasets, which might otherwise be outside the scope of conventional regression models. Nonetheless, a major limiting factor of ANNs is that they require quite a large amount of training data for better performance. Obtaining such an amount of data from biological processes is usually difficult for many reasons. To resolve this problem, methods are proposed to inflate existing data by artificially synthesizing additional valid data samples. In this paper, we present a generative adversarial network (GAN) able to synthesize an infinite amount of realistic multi-dimensional regression data from limited experimental data (n = 20). Rigorous testing showed that the synthesized data (n = 200) significantly conserved the variances and distribution patterns of the real data. Further, the synthetic data was used to generalize a deep neural network. The model trained on the artificial data showed a lower loss (2.029 ± 0.124) and converged to a solution faster than its counterpart trained on real data (2.1614 ± 0.117).

Structure, in vitro starch digestibility and physicochemical properties of starch isolated from germinated Bambara groundnut.

This study investigated the effect of germination time (0, 24, 48 and 72 h) on the physicochemical characteristics, in vitro starch digestibility and microstructural changes in Bambara groundnut starch. The starch yield, lightness (L*) value, amylose content and resistant starch contents of isolated starches decreased significantly (p ≤ 0.05) with increasing germination time. Scanning electron microscopy revealed that starch from raw and germinated Bambara grains were smooth with no evidence of starch degradation and were mainly oval shaped, with some granule's irregular and kidney shaped. Water absorption capacity (1.33-1.90 g/g), swelling power (2.12-16.53 g/g), solubility index (1.14-13.04 g/g), and dispersibility (75.92-86.47%) greatly increased as germination timed increased. Germination did not alter the X-ray diffraction pattern (Type-A) but increased the relative crystallinity of the starches. The peak gelatinization temperatures (73.23-73.91 °C) of starch from germinated Bambara were significantly higher than native starch (72.81 °C). Native starch and starch from germinated Bambara grains had substantially high proportion of resistant starch (approx. 73%) and high pasting temperatures (approx. 88 °C). Conclusively, germination significantly changed starch structure at molecular level and impacted functionality.

Holistic View of Starch Chemistry, Structure and Functionality in Dry Heat-Treated Whole Wheat Kernels and Flour.

Heat treatment is used as a pre-processing step to beneficially change the starch properties of wheat flour to enhance its utilisation in the food industry. Heat-treated wheat flour may provide improved eating qualities in final wheat-based products since flour properties predominantly determine the texture and mouthfeel. Dry heat treatment of wheat kernels or milled wheat products involves heat transfer through means of air, a fluidising medium, or radiation-often resulting in moisture loss. Heat treatment leads to changes in the chemical, structural and functional properties of starch in wheat flour by inducing starch damage, altering its molecular order (which influences its crystallinity), pasting properties as well as its retrogradation and staling behaviour. Heat treatment also induces changes in gluten proteins, which may alter the rheological properties of wheat flour. Understanding the relationship between heat transfer, the thermal properties of wheat and the functionality of the resultant flour is of critical importance to obtain the desired extent of alteration of wheat starch properties and enhanced utilisation of the flour. This review paper introduces dry heat treatment methods followed by a critical review of the latest published research on heat-induced changes observed in wheat flour starch chemistry, structure and functionality.

Kinetics of Phenolic Compounds Modification during Maize Flour Fermentation.

This study aimed to investigate the kinetics of phenolic compound modification during the fermentation of maize flour at different times. Maize was spontaneously fermented into sourdough at varying times (24, 48, 72, 96, and 120 h) and, at each point, the pH, titratable acidity (TTA), total soluble solids (TSS), phenolic compounds (flavonoids such as apigenin, kaempferol, luteolin, quercetin, and taxifolin) and phenolic acids (caffeic, gallic, ferulic, p-coumaric, sinapic, and vanillic acids) were investigated. Three kinetic models (zero-, first-, and second-order equations) were used to determine the kinetics of phenolic modification during the fermentation. Results obtained showed that fermentation significantly reduced pH, with a corresponding increase in TTA and TSS. All the investigated flavonoids were significantly reduced after fermentation, while phenolic acids gradually increased during fermentation. Among the kinetic models adopted, first-order (R2 = 0.45-0.96) and zero-order (R2 = 0.20-0.82) equations best described the time-dependent modifications of free and bound flavonoids, respectively. On the other hand, first-order (R2 = 0.46-0.69) and second-order (R2 = 0.005-0.28) equations were best suited to explain the degradation of bound and free phenolic acids, respectively. This study shows that the modification of phenolic compounds during fermentation is compound-specific and that their rates of change may be largely dependent on their forms of existence in the fermented products.

Metabolomic approaches for the determination of metabolites from pathogenic microorganisms: A review.

Metabolomics is a high precision analytical approach to obtaining detailed information of varieties of metabolites produced in biological systems, including foods. This study reviews the use of metabolomic approaches such as liquid chromatography mass spectrometry (LCMS), gas chromatography mass spectrometry (GC-MS), matrix assisted laser desorption /ionization tandem time of flight mass spectrometry (MALDI-TOF-MS) and nuclear magnetic resonance (NMR) for investigating the presence of foodborne pathogens and their metabolites. Pathogenic fungi and their notable metabolites (mycotoxins) have been studied more extensively using metabolomics as compared to bacteria, necessitating further studies in this regard. Nevertheless, such identified fungal and bacteria metabolites could be used as biomarkers for a more rapid detection of these pathogens in food. Other important compounds detected through metabolomics could also be correlated to functionality of these pathogenic strains, determined by the composition of the foods in which they exist, thereby providing insights into their metabolism. Considering the prevalence of these food pathogens, metabolomics still has potentials in the determination of food-borne pathogenic microorganisms especially for the determination of pathogenic bacteria toxins and is expected to generate research interests for further studies and applications.