Optimization of beef broth processing technology and isolation and identification of flavor peptides by consecutive chromatography and LC-QTOF-MS/MS

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.

Rapid screening based on machine learning and molecular docking of umami peptides from porcine bone

BACKGROUND

The traditional screening method for umami peptide, extracted from porcine bone, was labor-intensive and time-consuming. In this study, the rapid screening method and molecular mechanism of umami peptide was investigated.

RESULTS

This article showed that a more precisely rapid screening method with composite machine learning and molecular docking was used to screen the potential umami peptide from porcine bone. As reference, 24 reported umami peptides were predicated by composite machine learning, with the accuracy of 86.7%. In this study, potential umami peptide sequences from porcine bone were screened by UMPred-FRL, Umami-MRNN Demo, and molecular docking was used to provide further screening. Finally, nine peptides were screened and verified as umami peptides by this method: LREY, HEAL, LAKVH, FQKVVA, HVKELE, AEVKKAP, EAVEKPQS, KALSEEL and KKMFETES. The hydrogen bonding was deemed to be the main interaction force with receptor T1R3, and domain binding sites were Ser146, His121 and Glu277. The result demonstrated the feasibility of machine learning assisted T1R1/T1R3 receptor for rapid screening umami peptides. The screening method would not only adapt to screen umami peptides from porcine bone but possibly applied for other sources. It also provided a reference for rapid screening of umami peptides.

CONCLUSION

The manuscript lays a rapid screening method in screening umami peptide, and nine umami peptides from porcine bone were screened and identified. © 2022 Society of Chemical Industry.

Insights into flavor and key influencing factors of Maillard reaction products: A recent update

During food processing, especially heating, the flavor and color of food change to a great extent due to Maillard reaction (MR). MR is a natural process for improving the flavor in various model systems and food products. Maillard reaction Products (MRPs) serve as ideal materials for the production of diverse flavors, which ultimately improve the flavor or reduce the odor of raw materials. Due to the complexity of the reaction, MR is affected by various factors, such as protein source, hydrolysis conditions, polypeptide molecular weight, temperature, and pH. In the recent years, much emphasis is given on conditional MR that could be used in producing of flavor-enhancing peptides and other compounds to increase the consumer preference and acceptability of processed foods. Recent reviews have highlighted the effects of MR on the functional and biological properties, without elaborating the flavor compounds obtained by the MR. In this review, we have mainly introduced the Maillard reaction-derived flavors (MF), the main substances producing MF, and detection methods. Subsequently, the main factors influencing MF, from the selection of materials (sugar sources, protein sources, enzymatic hydrolysis methods, molecular weights of peptides) to the reaction conditions (temperature, pH), are also described. In addition, the existing adverse effects of MR on the biological properties of protein are also pointed out.

Metabolomics identify landscape of food sensory properties

Sensory evaluation is a key component of food production strategy. The classical food sensory evaluation method is time-consuming, laborious, costly, and highly subjective. Since flavor (taste and smell), texture, and mouthfeel are all related to the chemical properties of food, there has been a growing interest in how they affect the senses of food. In the past decades, emerging metabolomics has received much attention in the field of sensory evaluation, because it not only offers a broad picture of chemical composition for sensory properties but also revealed their changes and functions in food proceeding. This article reviewed food chemicals regarding the flavor, smell, and texture of foods, and discussed the advantages and limitations of applying metabolomics approaches to sensory evaluation, including GC-MS, LC-MS, and NMR. Taken together, this review gives a comprehensive, critical overview of the current state, future challenges, and trends in metabolomics on food sensory properties.

Antioxidant, Antimicrobial and Metmyoglobin Reducing Activity of Artichoke (Cynara scolymus) Powder Extract-Added Minced Meat during Frozen Storage

The present study aimed to investigate the bioactive compounds in artichoke (Cynara scolymus) powder, having antioxidant and antimicrobial activity, and to determine the effectiveness of artichoke (C. scolymus) powder extract within the minced meat. C. scolymus was extracted using two different methods. The method incorporating high phenolic and flavonoid content levels was used in other analyses and the phenolic and flavonoid contents in C. scolymus extract was determined using LC-QTOF-MS. Antioxidant, antimicrobial, and metmyoglobin (metMb) reducing activities and pH values of the extract-added minced meat samples were measured for 10 days during storage. DPPH, FRAP, and ABTS were used in the antioxidant analyses. The antimicrobial activity of C. scolymus extract was evaluated on five different food pathogens by using the disc diffusion method. The most resistant bacterium was found to be Listeria monocytogenes (18.05 mm ± 0.24). The amount of metMb was measured in the minced meat sample that was added to the extract during storage (p < 0.05). MetMb formation and pH value on the sixth day of storage were found to be at lower levels than in the control group. In conclusion, C. scolymus exhibited a good antimicrobial and antioxidant effect and can be used in storing and packaging the food products, especially the meat and meat products.