Nutritional and sensory parameters of amazake from the recycling of stale bread

Stale bread is a waste product with a potential to be recycled. One way to manage this waste material is to process it by fermentation for the purpose of food production. This paper proposes the use of stale wheat and rye bread as ingredients in amazake, a liquid dessert traditionally obtained from rice by fermentation with the koji mould Aspergillus oryzae, followed by liquefaction by the action of fungal enzymes. The stale bread was introduced instead of rice at both the koji stage (wheat bread) and the liquefaction stage (wheat and rye bread). The resulting products had an extended volatile compound profile, from 5 to 15 compounds identified, and modified sensory parameters, compared to the traditional version. Amazake containing bread had an increased protein content, from 1.10 to 6.4 g/100 g, and were more abundant in dietary fibre (up to a maximum of 1.8 g/100 g), additionally enriched with a soluble fraction. The proposed procedure of obtaining of new formula amazake can be directly applied in households to reduce the amount of discarded bread. Due to its simplicity, it also has the potential for further modification in terms of production scale and product parameters.

Production of a Cheese-Like Aroma via Fermentation of Plant Proteins and Coconut Oil with the Basidiomycetes Cyclocybe aegerita and Trametes versicolor

Cheese is one of the most common dairy products and is characterized by its complex aroma. However, in times of climate change and resource scarcity, the possibility to mimic the characteristic cheese-like aroma from plant-based sources is in demand to offer alternatives to cheese. Accordingly, the production of a natural cheese-like aroma via fermentation of four plant-based proteins and coconut oil with basidiomycetes has been addressed. Mixtures of soy and sunflower protein with coconut oil (15 g/L) have shown the formation of a cheese-like aroma after 72 and 56 h after fermentation with Cyclocybe aegerita and Trametes versicolor, respectively. Isovaleric acid, butanoic acid, ethyl butanoate, 1-octen-3-ol, and various ketones were identified as the key odorants. Similarities to typical cheeses were observed by the principal component analysis. Overall, the finding offered an approach to a sustainable production of a natural cheese-like aroma from a plant source, thus contributing to the development of cheese alternatives.

Derivation of Kokumi γ-Glutamyl Peptides and Volatile Aroma Compounds from Fermented Cereal Processing By-Products for Reducing Bitterness of Plant-Based Ingredients

Current food production methods and consumption behaviours are unsustainable and contribute to environmental harm. One example is food waste-around 38% of food produced is wasted each year. Here, we show that two common food waste products, wheat bran and brewer's spent grain, can successfully be upcycled via miso fermentation. During the fermentation process, kokumi γ-glutamyl peptides, known to increase mouthfulness, are produced; these include γ-ECG (oxidized), γ-EVG, γ-EV, γ-EE, γ-EF, and γ-EL. The profiles of kokumi peptides and volatile aroma compounds are correlated with koji substrate, pH, and enzymatic activity, offering straightforward parameters that can be manipulated to increase the abundance of kokumi peptides during the fermentation process. Correlation analysis demonstrates that some volatile aroma compounds, such as fatty acid ethyl esters, are correlated with kokumi peptide abundance and may be responsible for fatty, greasy, and buttery aromas. Consumer sensory analysis conveys that the bitter taste of vegetables, such as that in endives, can be dampened when miso extract containing kokumi peptides is added. This suggests that kokumi peptides, along with aroma volatile compounds, can enhance the overall flavour of plant-based products. This study opens new opportunities for cereal processing by-product upcycling via fermentation, ultimately having the potential to promote a plant-based diet.

Discrimination rancidity degree of infant formula rice flour based on Headspace Solid-Phase Microextraction combined with Gas Chromatography-Mass Spectrometry as an alternative to sensory evaluation

To mitigating the serious threat of harmful volatile substances to the health of infants, an alternative method of odor evaluation were proposed based on Headspace solid-phase microextraction (HS-SPME) combined with Gas Chromatography-Mass Spectrometry (GC-MS) to discriminate the degree of rancidity of infant formula rice flour (IFRF). Inspectors can simply calculate the rancidity degree of infant formula rice flour according to the regression equation based on the concentration of rancidity markers. The results showed that the joint application of OPLS-DA, molecular sensory experiments, and unsaturated fatty acids (UFAs) degradation experiments could successfully recognize the rancidity markers without collinearity in multiple linear regression analysis. The rancidity markers curve fitting was helpful for the establishment of multivariate regression model of rancidity grading. The model had an accuracy of more than 92.90% by the verification of odor evaluation. The application of the model to investigate the market IFRF samples showed that about 3% of the samples collected in the experiment were unsuitable for infant feeding. Therefore, the established model was considered to be a robust and less workload method to replace the olfactory evaluation method for discriminating the rancidity degree of IFRF.

Rapid Assessment of Rice Quality Traits Using Low-Cost Digital Technologies

Aroma and other physicochemical parameters are important attributes influencing consumer perception and acceptance of rice. However, current methods using multiple instruments and laboratory analysis make these assessments costly and time-consuming. Therefore, this study aimed to assess rice quality traits of 17 commercial rice types using a low-cost electronic nose and portable near-infrared spectrometer coupled with machine learning (ML). Specifically, artificial neural networks (ANN) were used to classify the type of rice and predict rice quality traits (aromas, color, texture, and pH of cooked rice) as targets. The ML models developed showed that the chemometrics obtained from both sensor technologies successfully classified the rice (Model 1: 98.7%; Model 2: 98.6%) and predicted the peak area of aromas obtained by gas chromatography-mass spectroscopy found in raw (Model 3: R = 0.95; Model 6: R = 0.95) and cooked rice (Model 4: R = 0.98; Model 7: R = 0.96). Furthermore, a high R = 0.98 was obtained for Model 5 to estimate the color, texture, and pH of cooked rice. The proposed method is rapid, low-cost, reliable, and may help the rice industry increase high-quality rice production and accelerate the adoption of digital technologies and artificial intelligence to support the rice value chain.