Physicochemical, Microbial, and Volatile Compound Characteristics of Gochujang, Fermented Red Pepper Paste, Produced by Traditional Cottage Industries

Optimization of Awaze paste formulations: The effects of using spices through a mixture design approach

Previous studies have revealed the microbial quality of Awaze paste. However, limited reports describe the effect of individual spices on Awaze paste quality. A mixture design approach was used to determine the appropriate proportions, with 15 experimental points for independent variables including RP (60-90 %), GA (10-30 %), RO (5-20 %), and GI (5-10 %). The techno-functional properties, particle size, antioxidant activity (DDPH radical assay), proximate composition, iron (Fe), zinc (Zn) content, viscosity, hardness, and microbiological quality of Awaze paste were assessed. The prepared Awaze paste showed a range of characteristics, with antioxidant activity (DDPH radical assay) ranging from 11.86 % to 62.5 %, crude protein content from 6.18 % to 16.22 %, crude fat from 5.7 % to 12.6 %, crude fiber from 16.86 % to 29.06 %, total ash content from 6.32 % to 9.94 %, total carbohydrate from 41.79 % to 60.61 %, energy from 264.3 to 329.2 k cal. , iron (Fe) content from 35.59 to 108.82 mg/100g, zinc (Zn) content from 1.72 to 26.93 mg/100g, viscosity from 65.5 to 125.5 cps, hardness from 8.48 to 55.09 g, yeast and mold count from 0.83 to 2.04 log cfu/g, and total bacterial count from 1.53 to 2.61 log cfu/g. Significant differences (p < 0.05) were observed in proximate composition, techno-functional properties, particle size, antioxidant activity, physicochemical properties, and microbiological characteristics among the formulations of Awaze paste. The selected formula showed a statistically significant difference (p < 0.05) compared to the control sample. The formulation containing 74.79 % RP, 10 % GA, 10.2 % RO, and 5.0 % GI was determined to be the optimal formula with a desirability of 0.73, based on the evaluated parameters. This preferred Awaze paste had a porosity of 28.12 %, particle size of 16.49 μm, antioxidant activity of 63.63 %, crude protein content of 17.28 %, iron (Fe) content of 98.06 mg/100g, and zinc (Zn) content of 15.04 mg/100g. Therefore, this optimal blend of ingredients could be used to produce a consumer accepted Awaze paste.

Potential neuroprotective benefits of plant-based fermented foods in Alzheimer's disease: an update on preclinical evidence

Alzheimer's disease (AD) currently lacks effective treatments, making its prevention a critical focus. While accumulating evidence supports that plant-based fermented foods may contribute to AD prevention, the neuroprotective effect of plant-based fermented foods on AD has not been comprehensively reviewed. In this study, we conducted a systematic review of preclinical studies on the efficacy of plant-based fermented foods in AD. The literature search was based on databases including PubMed, Embase, Web of Science, and Scopus. The PICO approach was employed for report inclusion, and each report was assessed for risk of bias using the SYRCLE's RoB tool. From the analysis of 25 retrieved reports, we extracted essential details, including bibliographic information, animal models and characteristics, sources of plant-based fermented foods, dosages, administration routes, durations, and outcome measures. Our findings indicate that plant-based fermented foods may positively impact acute and long-term cognitive function, as well as beta-amyloid-mediated neurodegeneration. This review sheds light on the potential neuroprotective benefits of plant-based fermented foods for various AD-related aspects, including oxidative stress, synaptotoxicity, neuroinflammation, tau hyperphosphorylation, dysfunctional amyloidogenic pathways, and cognitive deficits, as observed in rodent models of AD. However, the small number of studies obtained from our literature search and the finding that many of them were of moderate methodological quality suggest the need for further investigation to substantiate the beneficial potential of this class of functional food for the management of AD.

Optimization of spicy red pepper paste (Awaze) formulation by D-optimal mixture design

The aim of this study was to produce spicy red pepper paste (Awaze) by addition of various sources of antioxidant, mineral, and fiber and optimize better processing methods of the paste. For this purpose, D-optimal mixture design was used, Awaze considering color value (a*), antioxidant content, mineral content, fiber content, chewiness, and viscosity of the paste as the main parameters. Various properties of the optimized formulation were evaluated. The optimal formulation contained 65.66% red pepper, 10% garlic, 19.08% red onion, and 5.25% ginger. In the optimized formulation, the redness value (a*) increased by more than 3.12 times compared to the control with the average antioxidant activity (44.6%). The optimal formulation had a higher fiber content, chewiness, and viscosity value compared to control which is probably related to the ingredient proportion difference. Due to the higher nutritional and processing quality obtained, this formulation can be suggested for commercial and household producers as a guide to manufacture Awaze. The results obtained indicate that it is possible to production of Awaze with high nutritional value and improved processing quality by utilizing a blend of red pepper, garlic, red onion, and ginger. Therefore, this formulation stands as a viable recommendation for both commercial enterprises and household producers due to its demonstrated ability to yield Awaze with enhanced nutritional content and superior processing quality.

Impact of Modified Atmosphere Packaging Conditions on Quality of Dates: Experimental Study and Predictive Analysis Using Artificial Neural Networks

Dates are highly perishable fruits, and maintaining their quality during storage is crucial. The current study aims to investigate the impact of storage conditions on the quality of dates (Khalas and Sukary cultivars) at the Tamer stage and predict their quality attributes during storage using artificial neural networks (ANN). The studied storage conditions were the modified atmosphere packing (MAP) gases (CO2, O2, and N), packaging materials, storage temperature, and storage time, and the evaluated quality attributes were moisture content, firmness, color parameters (L*, a*, b*, and ∆E), pH, water activity, total soluble solids, and microbial contamination. The findings demonstrated that the storage conditions significantly impacted (p < 0.05) the quality of the two stored date cultivars. The use of MAP with 20% CO2 + 80% N had a high potential to decrease the rate of color transformation and microbial growth of dates stored at 4 °C for both stored date cultivars. The developed ANN models efficiently predicted the quality changes of stored dates closely aligned with observed values under the different storage conditions, as evidenced by low Root Mean Square Error (RMSE) and Mean Absolute Percentage Error (MAPE) values. In addition, the reliability of the developed ANN models was further affirmed by the linear regression between predicted and measured values, which closely follow the 1:1 line, with R2 values ranging from 0.766 to 0.980, the ANN models demonstrate accurate estimating of fruit quality attributes. The study's findings contribute to food quality and supply chain management through the identification of optimal storage conditions and predicting the fruit quality during storage under different atmosphere conditions, thereby minimizing food waste and enhancing food safety.

Comparison of the laxative effects of Korean Gochujang containing different microbiota on loperamide-induced constipation in ICR mice

The prevalence of constipation, one of the common gastrointestinal (GI) diseases, has been gradually increasing. Gochujang, a traditional Korean fermented paste, has various microbiota and exerts diverse health beneficial effects. However, the ameliorative effect of Gochujang on constipation is unexplored. Seven-week-old ICR mice were divided into five groups: the normal group, the loperamide (LOP) group, the LOP + mosapride citrate (3 mg per kg BW, MOSA) treated group, the LOP + BMG Gochujang (2 g per kg BW) group, and the LOP + VMG Gochujang (2 g per kg BW) group. Gochujang alleviated constipation by increasing defecation frequency and water content in feces by reducing AQP3 mRNA expression. Additionally, Gochujang increased GI transit time and excitatory neurotransmitter levels and decreased inhibitory neurotransmitter levels. Moreover, Gochujang reduced mitogen-activated protein kinase (MAPK) activation and increased the c-Kit/SCF signaling pathway, suggesting that Gochujang regulates the enteric nervous system (ENS). Interestingly, BMG and VMG differently influenced the gut microbiota composition. Both Gochujang groups significantly decreased the Bacteroidetes and Firmicutes ratio compared to the LOP group. However, among Firmicutes genera, Acetatifactor was only reduced in BMG, and VMG only decreased Caproiciproducens and Acutalibacter. In summary, Gochujang effectively alleviated LOP-induced constipation outcomes regardless of their different microbial communities by ameliorating GI motility and changing the gut microbiota composition.

Effect of Sichuan Pepper (Zanthoxylum genus) Addition on Flavor Profile in Fermented Ciba Chili (Capsicum genus) Using GC-IMS Combined with E-Nose and E-Tongue

This study examined the flavor profiles of fermented Ciba chili, comparing samples with Sichuan pepper (HJ) to those without Sichuan pepper (CK), using three analytical techniques: E-tongue, E-nose, and gas chromatography-ion mobility spectrometry (GC-IMS). The results obtained from the E-tongue and E-nose exhibited a clear difference in taste and flavor between CK and HJ. In detail, CK mainly exhibited a sour flavor profile, whereas HJ displayed an intricate and rich flavor. The HS-GC-IMS results identified a total of 60 compounds in the samples, with terpenes, alcohols, and esters being the primary volatile flavor compounds. Additionally, Zanthoxylum was found to significantly enhance the concentration of these compounds in fermented Ciba chili. Through robust principal component analysis (rPCA), 17 distinct flavor compounds were selected. Correlation analysis revealed that most terpenes exhibited positive correlations with LY2/LG, LY2/gCT1, LY2/Gct, LY2/G, LY2/Gh, and terpenes were found in higher concentrations in HJ. This study contributes a theoretical basis and provides data support for optimizing the fermentation process and elucidating the underlying mechanism of characteristic aroma formation in Ciba chili after fermentation.

Starmerella lactis-condensi, a yeast that has adapted to the conditions in the oenological environment

The yeast Starmerella (Candida) lactis-condensi is considered a food contaminant microorganism. The aim of our research was to determine why St. lactis-condensi could become the dominant species of Essences, the top sweet wine speciality of Tokaj wine region in Hungary. We investigated the physiological properties of these yeasts based on parameters that may influence their ability to selectively proliferate and persist during maturation in wines with very high sugar content. These include glucose and fructose, alcohol, and sulphur tolerance. Our studies have shown that St. lactis-condensi is a fructophilic yeast that is able to adapt quickly to very high sugar concentrations (up to 500 g/L) in the Essences. The high glucose concentration inhibits its growth, as well as that of the St. bacillaris (Candida zemplinina) strains tested. The type and amount of sugars in the Essences, together with the sulphur and alcohol content, influence the composition of the dominant yeast biota. Analysis of (GTG)₅ microsatellite in the nuclear genome and mtDNA-RFLP studies demonstrate that a diverse population of St. lactis-condensi occurs in the Tokaj wine region, in the Essences. This yeast species is characterised by both physiological and genetic biodiversity. GC-MS analysis of Essences colonised exclusively with these yeasts showed no deterioration in quality.

Correlation between the microbiome and pack burst spoilage of Allium sativum supplemented fermented hot pepper paste

Gochujang (fermented hot pepper paste) products are well known for their distinct, spicy flavor. However, frequent pack burst spoilage of gochujang products occurs during transportation and storage because of microbial aerogenesis, resulting in considerable economic losses. The present study aimed to prevent pack burst spoilage of gochujang products by supplementing them with garlic ethanol extract. A simulated pack burst experiment revealed that 42.86 % of normal gochujang products were spoiled. Garlic ethanol extract significantly inhibited the growth of Zygosaccharomyces rouxii in gochujang products, with low minimum inhibitory concentration values (12.5-25 mg/mL). Gochujang products supplemented with various concentrations (1 % and 2.5 %) of garlic ethanol extract exhibited marked inhibition of microbial growth, particularly Z. rouxii, and pack burst spoilage. Microbiome analysis revealed that the pack burst samples harbored a high abundance of Z. rouxii. Supplementation of gochujang with 1 % garlic ethanol extract drastically reduced Z. rouxii abundance and prevented pack burst. Moreover, gochujang products supplemented with 1 % garlic ethanol extract exhibited a high hedonic score in the sensory analysis. Based on the results of this study, we concluded that supplementation of gochujang products with 1 % garlic ethanol extract before packaging could be effective in preventing pack burst spoilage of gochujang.

Whole-genome analysis guided molecular mechanism of cyanogenic glucoside degradation by yeast isolated from Prunus mume fruit syrup

Consumption of fermented Prunus mume fruit (maesil) sugar syrup raise a health concern due to the presence of the cyanogenic glucoside amygdalin. The goal of the present study was to investigate the amygdalin degradation potential and genome profile of the native microbes found in maesil syrup. The microbial profile analysis revealed that yeast is the predominant microorganism native to maesil syrup and that the isolated yeast cells showed a remarkable potential for amygdalin reduction (99.7%). Moreover, the reduction in amygdalin was inversely proportional to the growth of the isolated yeast. The whole-genome analysis revealed that the isolated yeast is Zygosaccharomyces rouxii (genome size 10 Mb, 39.25% of GC content). Of the 5250 genes (64.88%) predicted in the Z. rouxii genome, 5245 (99.90%) were annotated using NCBI Non-Redundant, UniProt, and InterProScan databases. The genome of the isolated Z. ruoxii harbored 2.03% of repeats and 0.68% of non-coding RNAs. Protein prediction indicated that β-glycosidases and hydroxynitrile lyase may play a key role in amygdalin degradation. The predicted degradation initiated by β-glycosidases that hydrolyze α-glucosidic bonds of amygdalin results in α-hydroxy nitriles (cyanohydrins) that are subsequently converted into carbonyl compounds (benzaldehyde) and hydrogen cyanide catalyzed by hydroxynitrile lyases. Present findings provide valuable data for constructing engineered microorganisms that can degrade amygdalin. Further analysis of Z. rouxii may elucidate the exact mechanism of amygdalin reduction in the production of maesil syrup.

Prediction of Date Fruit Quality Attributes during Cold Storage Based on Their Electrical Properties Using Artificial Neural Networks Models

Evaluating and predicting date fruit quality during cold storage is critical for ensuring a steady supply of high-quality fruits to meet market demands. The traditional destructive methods take time in the laboratory, and the results are based on one specific parameter being tested. Modern modeling techniques, such as Machine Learning (ML) algorithms, offer unique benefits in nondestructive methods for food safety detection and predicting quality attributes. In addition, the electrical properties of agricultural products provide crucial information about the interior structures of biological tissues and their physicochemical status. Therefore, this study aimed to use an alternative approach to predict physicochemical properties, i.e., the pH, total soluble solids (TSS), water activity (a_w), and moisture content (MC) of date fruits (Tamar), during cold storage based on their electrical properties using Artificial Neural Networks (ANNs), which is the most popular ML technique. Ten date fruit cultivars were studied to collect data for the targeted parameters at different cold storage times (0, 2, 4, and 6 months) to train and test the ANNs models. The electrical properties of the date fruits were measured using a high-precision LCR (inductance, capacitance, and resistance) meter from 10 Hz to 100 kHz. The ANNs models were compared with a Multiple Linear Regression (MLR) at all testing frequencies of the electrical properties. The MLR models were less accurate than ANNs models in predicting fruit pH and had low performance and weak predictive ability for the TSS, a_w, and MC at all testing frequencies. The optimal ANNs prediction model consisted of the input layer with 14 neurons, one hidden layer with 15 neurons, and the output layer with 4 neurons, which was determined depending on the measurements of the electrical properties at a 10 kHz testing frequency. This optimal ANNs model was able to predict the pH with R² = 0.938 and RMSE = 0.121, TSS with R² = 0.954 and RMSE = 2.946, a_w with R² = 0.876 and RMSE = 0.020, and MC with R² = 0.855 and RMSE = 0.803 b by using the measured electrical properties. The developed ANNs model is a powerful tool for predicting fruit quality attributes after learning from the experimental measurement parameters. It can be suggested to efficiently predict the pH, total soluble solids, water activity, and moisture content of date fruits based on their electrical properties at 10 kHz.