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Two seaweed species were examined (Alaria esculenta and Saccharina latissima) as a fermentation feedstock for the development of a beverage. Three microbial groups were selected for experimentation; a single-species bacterium; Lactiplantibacillus plantarum, a single-species yeast; Saccharomyces cerevisiae and a multi-species culture; kombucha SCOBY (Symbiotic Culture of Bacteria and Yeast). Results showed SCOBY cultures were more successful in fermenting A. esculenta than S. latissima due to the lowered glucose content (4.069 ± 0.174 mg / mL and 12.224 ± 2.249 mg / mL, respectively). Likewise, L. plantarum played a major role in fermenting S. latissima, over A. esculenta, resulting in a lowered pH in the former but not the latter (6.44 ± 0.040 and 4.825 ± 0.045, respectively). S. cerevisiae does not appear to have been suitable as a fermentation medium for these two seaweed species under these conditions due to the lack of pH lowering, glucose reduction and acid production. All treatments showed an increased production of lactic acid, especially the SCOBY treated samples, which is an indicator of fermentation. There is potential for optimization of the seaweed fermentation process through strain selection, fermentation conditions optimization (temperature, time, pH) and seaweed biomass pretreatment. Glutamic acid, the amino acid responsible for umami flavour, was found in greater quantities in the S. cerevisiae treated seaweed, indicating that fermentation using S. cerevisiae cultures may improve the umami flavour profile of a seaweed fermented beverage product. In order to see if the seaweed species used had an impact on the SCOBY microbial makeup, the DNA profiles of the two SCOBYs used were analyzed. There were significant differences between the two SCOBYs. The A. esculenta SCOBY showed slightly rarer species of fungi and the species abundance of fungi was greater than that of S. latissima SCOBY. Furthermore, the number of fungal operational taxonomic units (

Coffee silverskin (CSS) is a coffee by-product obtained during the coffee roasting process and a potential source of proteins (14–19% protein content). In this study, conventional extraction (CE) and novel extraction methods were investigated for protein extraction from CSS. Optimum extraction pH and concentration for conventional acid and alkali extraction were studied. In addition, ultrasound-assisted extraction (UAE) and microwave-assisted extraction (MAE), which have been widely recognised as efficient and economic novel extraction techniques, were employed to enhance the protein extraction from CSS using alkaline and acid extraction. Among the extraction techniques studied, MAE was found to generate the highest yield of protein recovery, 43.53% of recovery with an average molecular weight of 6.64 kDa. Comparing with the conventional sequential extraction using NaOH followed by HCl at two different concentrations (0.2 M and 0.6 M), the recovery yields were significantly increased by 2.8-fold and 2.3-fold by applying UAE, and by 5.8-fold and 7-fold by applying MAE. The present work shows that UAE and MAE have potential to be rapid and effective tools for protein extraction from coffee silverskin. In this study, extracted proteins and raw material were characterised considering the molecular weight and the amino acid profile as well as its nutritional composition and its mineral contents.

The low image acquisition speed of terahertz (THz) time-domain imaging systems limits their application in biological products analysis. In the current study, a local pixel graph neural network was built for THz time-domain imaging super-resolution. The method could be applied to the analysis of any heterogeneous biological products as it only required a small number of sample images for training and particularly it focused on THz feature frequencies. The graph network applied the Fourier transform to graphs extracted from low-resolution (LR) images bringing an invariance of rotation and flip for local pixels, and the network then learnt the relationship between the state of graphs and the corresponding pixels to be reconstructed. With wood cores and seeds as examples, the images of these samples were captured by a THz time-domain imaging system for training and analysed by the method, achieving the root mean square error (RMSE) of pixels of 0.0957 and 0.1061 for the wood core and seed images, respectively. In addition, the reconstructed high-resolution (HR) images, LR images and true HR images at several feature frequencies were also compared in the current study. Results indicated that the method could not only reconstruct the spatial details and the useful signals from high noise signals at high feature frequencies but could also operate super-resolution in both spatial and spectral aspects.

Modern Techniques for Food Authentication, Second Edition presents a comprehensive review of the novel techniques available to authenticate food products, including various spectroscopic technologies, methods based on isotopic analysis and chromatography, and other techniques based on DNA, enzymatic analysis and electrophoresis. This new edition pinpoints research and development trends for those working in research, development and operations in the food industry, giving them readily accessible information on modern food authentication techniques to ensure a safe and authentic food supply. It will also serve as an essential reference source to undergraduate and postgraduate students, and for researchers in universities and research institutions. Presents emerging imaging techniques that have proven to be powerful, non-destructive tools for food authenticationIncludes applications of hyperspectral imaging to reflect the current trend of developments in food imaging technology for each topic areaProvides pixel level visualization techniques needed for fast and effective food sample testingContains two new chapters on Imaging Spectroscopic Techniques

The internal quality of intact persimmon cv. Rojo Brillante was assessed trough visible and near infrared hyperspectral imaging. Fruits at three stages of commercial maturity were exposed to different treatments with CO2 to obtain fruit with different ripeness and level of astringency (soluble tannin content). Spectral and spatial information were used for building classification models to predict ripeness and astringency trough multivariate analysis techniques like linear and quadratic discriminant analysis (LDA and QDA) and support vector machine (SVM). Additionally, flesh firmness was predicted by partial least square regression (PLSR). The full spectrum was used to determine the internal properties and later principal component analysis (PCA) was used to select optimal wavelengths (580, 680 and 1050 nm). The correct classification was above 92% for the three classifiers in the case of ripeness and 95% for QDA in the case of astringency. A value of R2 = 0.80 and a ratio of prediction deviation (RPD) of 1.86 were obtained with the selected wavelengths for the prediction of firmness which demonstrated the potential of hyperspectral imaging as a non-destructive tool in the assessment of the firmness, ripeness state and astringency level of Rojo Brillante persimmon.