Quantitation and Taste Contribution of Sensory Active Molecules in Oat (Avena sativa L.)

The insights into sour flavor and organic acids in alcoholic beverages

Alcoholic beverages have developed unique flavors over millennia, with sourness playing a vital role in their sensory perception and quality. Organic acids, as crucial flavor compounds, significantly impact flavor. This paper reviews the sensory attribute of sour flavor and key organic acids in alcoholic beverages. Regarding sour flavor, research methods include both static and dynamic sensory approaches and summarize the interaction of sour flavor with aroma, taste, and mouthfeel. In addition, this review focuses on identifying key organic acids, including sample extraction, chromatography, olfactometry/taste, and mass spectrometry. The key organic acids in alcoholic beverages, such as wine, Baijiu, beer, and Huangjiu, and their primary regulatory methods are discussed. Finally, future avenues for the exploration of sour flavor and organic acids by coupling machine learning, database, sensory interactions and electroencephalography are suggested. This systematic review aims to enhance understanding and serve as a reference for further in-depth studies on alcoholic beverages.

A cooperative combination of non-targeted metabolomics and electronic tongue evaluation reveals the dynamic changes in metabolites and sensory quality of radish during pickling

Pickled radish is a traditional fermented food with a unique flavor after long-term preservation. This study analyzed the organoleptic and chemical characteristics of pickled radish from different years to investigate quality changes during pickling. The results showed that the sourness, saltiness, and aftertaste-bitterness increased after pickling, and bitterness and astringency decreased. The levels of free amino acids, soluble sugars, total phenols, and total flavonoids initially decreased during pickling but increased with prolonged pickling. The diversity of organic acids also increased over time. Through non-targeted metabolomics analysis, 349 differential metabolites causing metabolic changes were identified to affect the quality formation of pickled radish mainly through amino acid metabolism, phenylpropane biosynthesis and lipid metabolism. Correlation analysis showed that L*, soluble sugars, lactic acid, and acetic acid were strongly associated with taste quality. These findings provide a theoretical basis for standardizing and scaling up traditional pickled radish production.

BitterMasS: Predicting Bitterness from Mass Spectra

Bitter compounds are common in nature and among drugs. Previously, machine learning tools were developed to predict bitterness from the chemical structure. However, known structures are estimated to represent only 5-10% of the metabolome, and the rest remain unassigned or "dark". We present BitterMasS, a Random Forest classifier that was trained on 5414 experimental mass spectra of bitter and nonbitter compounds, achieving precision = 0.83 and recall = 0.90 for an internal test set. Next, the model was tested against spectra newly extracted from the literature 106 bitter and nonbitter compounds and for additional spectra measured for 26 compounds. For these external test cases, BitterMasS exhibited 67% precision and 93% recall for the first and 58% accuracy and 99% recall for the second. The spectrum-bitterness prediction strategy was more effective than the spectrum-structure-bitterness prediction strategy and covered more compounds. These encouraging results suggest that BitterMasS can be used to predict bitter compounds in the metabolome without the need for structural assignment of individual molecules. This may enable identification of bitter compounds from metabolomics analyses, for comparing potential bitterness levels obtained by different treatments of samples and for monitoring bitterness changes overtime.

Objective quantification technique and widely targeted metabolomic reveal the effect of drying temperature on sensory attributes and related non-volatile metabolites of black tea

Drying temperature (DT) considerably affects the flavor of black tea (BT); however, its influence on non-volatile metabolites (NVMs) and their correlations remain unclear. In this study, an objective quantification technique and widely targeted metabolomics were applied to explore the effects of DT (130 °C, 110 °C, 90 °C, and 70 °C) on BT flavor and NVMs conversion. BT with a DT of 90 °C presented the highest umami, sweetness, overall taste, and brightness color values. Using the weighted gene co-expression network and multiple factor analysis, 455 sensory trait-related NVMs were explored across six key modules. Moreover, 169 differential NVMs were screened, and flavonoids, phenolic acids, amino acids, organic acids, and lipids were identified as key differential NVMs affecting the taste and color attributes of BT in response to DT. These findings enrich the BT processing theory and offer technical support for the precise and targeted processing of high-quality BT.

Bitter flavors and bitter compounds in foods: identification, perception, and reduction techniques

Bitterness is one of the five basic tastes generally considered undesirable. The widespread presence of bitter compounds can negatively affect the palatability of foods. The classification and sensory evaluation of bitter compounds have been the focus in recent research. However, the rigorous identification of bitter tastes and further studies to effectively mask or remove them have not been thoroughly evaluated. The present paper focuses on identification of bitter compounds in foods, structural-based activation of bitter receptors, and strategies to reduce bitter compounds in foods. It also discusses the roles of metabolomics and virtual screening analysis in bitter taste. The identification of bitter compounds has seen greater success through metabolomics with multivariate statistical analysis compared to conventional chromatography, HPLC, LC-MS, and NMR techniques. However, to avoid false positives, sensory recognition should be combined. Bitter perception involves the structural activation of bitter taste receptors (TAS2Rs). Only 25 human TAS2Rs have been identified as responsible for recognizing numerous bitter compounds, showcasing their high structural diversity to bitter agonists. Thus, reducing bitterness can be achieved through several methods. Traditionally, the removal or degradation of bitter substances has been used for debittering, while the masking of bitterness presents a new effective approach to improving food flavor. Future research in food bitterness should focus on identifying unknown bitter compounds in food, elucidating the mechanisms of activation of different receptors, and developing debittering techniques based on the entire food matrix.

Characterization of Seven New Steroidal Saponins from Korean Oat Cultivars by UPLC-QTOF-MS and UPLC-MS/MS

Oat saponins are composed of triterpenoid and steroidal saponins, and their potential biological activities, such as antibacterial, antifungicidal, osteogenic, and anticancer activities, have been reported. In this study, qualitative and quantitative analyses of oat saponins were conducted by using UPLC-QToF-MS and UPLC-Triple Q-MS/MS. A total of 22 saponins were analyzed in seven Korean oat cultivars. Among them, 7 saponins were identified as new compounds in this source, which were tentatively confirmed as nuatigenin-type saponins with 26-O-diglucoside and 3-O-malonylglucoside forms and (25S)-furost-5-en-3β,22,26-triol-type saponins. In addition, the total content of these saponins ranged from 70.61 to 141.38 mg/100 g dry weight, and it was affected by the type of oat cultivar and the presence or absence of hulling. These detailed profiles will be suggested as fundamental data for breeding superior oat cultivars, evaluating of related products, and various industries.

Genomic prediction of seed nutritional traits in biparental families of oat (Avena sativa)

Selection for more nutritious crop plants is an important goal of plant breeding to improve food quality and contribute to human health outcomes. While there are efforts to integrate genomic prediction to accelerate breeding progress, an ongoing challenge is identifying strategies to improve accuracy when predicting within biparental populations in breeding programs. We tested multiple genomic prediction methods for 12 seed fatty acid content traits in oat (Avena sativa L.), as unsaturated fatty acids are a key nutritional trait in oat. Using two well-characterized oat germplasm panels and other biparental families as training populations, we predicted family mean and individual values within families. Genomic prediction of family mean exceeded a mean accuracy of 0.40 and 0.80 using an unrelated and related germplasm panel, respectively, where the related germplasm panel outperformed prediction based on phenotypic means (0.54). Within family prediction accuracy was more variable: training on the related germplasm had higher accuracy than the unrelated panel (0.14-0.16 and 0.05-0.07, respectively), but variability between families was not easily predicted by parent relatedness, segregation of a locus detected by a genome-wide association study in the panel, or other characteristics. When using other families as training populations, prediction accuracies were comparable to the related germplasm panel (0.11-0.23), and families that had half-sib families in the training set had higher prediction accuracy than those that did not. Overall, this work provides an example of genomic prediction of family means and within biparental families for an important nutritional trait and suggests that using related germplasm panels as training populations can be effective.

Quantitative Analysis of Oat (Avena sativa L.) and Pea (Pisum sativum L.) Saponins in Plant-Based Food Products by Hydrophilic Interaction Liquid Chromatography Coupled with Mass Spectrometry

This work presents the sample extraction methods for solid and liquid sample matrices for simultaneous quantification of oat (Avena sativa L.) and pea (Pisum sativum L.) saponins: avenacoside A, avenacoside B, 26-desglucoavenacoside A, and saponin B and 2,3-dihydro-2,5-dihydroxy-6-methyl-4H-pyran-4-one (DDMP) saponin, respectively. The targeted saponins were identified and quantified using a hydrophilic interaction liquid chromatography with mass spectrometric detection (HILIC-MS) method. The simple and high-throughput extraction procedure was developed for solid oat- and pea-based food samples. In addition, a very simple extraction procedure for liquid samples, without the need to use lyophilisation, was also implemented. Oat seed flour (U-¹³C-labelled) and soyasaponin Ba were used as internal standards for avenacoside A and saponin B, respectively. Other saponins were relatively quantified based on avenacoside A and saponin B standard responses. The developed method was tested and successfully validated using oat and pea flours, protein concentrates and isolates, as well as their mixtures, and plant-based drinks. With this method, the saponins from oat- and pea-based products were separated and quantified simultaneously within 6 min. The use of respective internal standards derived from U-¹³C-labelled oat and soyasaponin Ba ensured high accuracy and precision of the proposed method.

Flavor challenges in extruded plant-based meat alternatives: A review

Demand for plant-based meat alternatives has increased in recent years due to concerns about health, ethics, the environment, and animal welfare. Nevertheless, the market share of plant-based meat alternatives must increase significantly if they are to support sustainable food production and consumption. Flavor is an important limiting factor of the acceptability and marketability of plant-based meat alternatives. Undesirable chemosensory perceptions, such as a beany flavor, bitter taste, and astringency, are often associated with plant proteins and products that use them. This study reviewed 276 articles to answer the following five research questions: (1) What are the volatile and nonvolatile compounds responsible for off-flavors? (2) What are the mechanisms by which these flavor compounds are generated? (3) What is the influence of thermal extrusion cooking (the primary structuring technique to transform plant proteins into fibrous products that resemble meat in texture) on the flavor characteristics of plant proteins? (4) What techniques are used in measuring the flavor properties of plant-based proteins and products? (5) What strategies can be used to reduce off-flavors and improve the sensory appeal of plant-based meat alternatives? This article comprehensively discusses, for the first time, the flavor issues of plant-based meat alternatives and the technologies available to improve flavor and, ultimately, acceptability.

Selection for seed size has uneven effects on specialized metabolite abundance in oat (Avena sativa L.)

Plant breeding strategies to optimize metabolite profiles are necessary to develop health-promoting food crops. In oats (Avena sativa L.), seed metabolites are of interest for their antioxidant properties, yet have not been a direct target of selection in breeding. In a diverse oat germplasm panel spanning a century of breeding, we investigated the degree of variation of these specialized metabolites and how it has been molded by selection for other traits, like yield components. We also ask if these patterns of variation persist in modern breeding pools. Integrating genomic, transcriptomic, metabolomic, and phenotypic analyses for three types of seed specialized metabolites—avenanthramides, avenacins, and avenacosides—we found reduced heritable genetic variation in modern germplasm compared with diverse germplasm, in part due to increased seed size associated with more intensive breeding. Specifically, we found that abundance of avenanthramides increases with seed size, but additional variation is attributable to expression of biosynthetic enzymes. In contrast, avenacoside abundance decreases with seed size and plant breeding intensity. In addition, these different specialized metabolites do not share large-effect loci. Overall, we show that increased seed size associated with intensive plant breeding has uneven effects on the oat seed metabolome, but variation also exists independently of seed size to use in plant breeding. This work broadly contributes to our understanding of how plant breeding has influenced plant traits and tradeoffs between traits (like growth and defense) and the genetic bases of these shifts.

Widely targeted metabolomics analysis of white peony teas with different storage time and association with sensory attributes

The sensory features of white peony teas (WPTs) significantly change with storage age; however, their comprehensive associations with composition are still unclear. This study aimed to clarify the sensory quality-related chemical changes in WPTs during storage. Liquid chromatography-tandem mass spectrometry based on widely targeted metabolomics analysis was performed on WPTs of 1-13 years storage ages. Weighted gene co-expression network analysis (WGCNA) was used to correlate metabolites with sensory traits including color difference values and taste attributes. 323 sensory trait-related metabolites were obtained from six key modules via WGCNA, verified by multiple factor analysis. The decline and transformation of abundant flavonoids, tannins and amino acids were related to the reduced astringency, umami and increased browning of tea infusions. In contrast, the total contents of phenolic acids and organic acids increased with storage. This study provides a high-throughput method for the association of chemical compounds with various sensory traits of foods.