Recent scientific evidence indicates that protein hydrolysates contain bioactive peptides that have potential benefits for human health. However, the bitter‐tasting hydrophobic peptides in protein hydrolysates negatively affect the sensory quality of resulting products and limit their utilization in food and pharmaceutical industries. The approaches to reduce, mask, and remove bitter taste from protein hydrolysates have been extensively reported. This review paper focuses on the advances in the knowledge regarding the structure–bitterness relationship of peptides, the release mechanism of bitter peptides, and the debittering methods for protein hydrolysates. Bitter tastes generating with enzymatic hydrolysis of protein is influenced by the type, concentration, and bitter taste threshold of bitterness peptides. A “bell‐shaped curve” is used to describe the relationship between the bitterness intensity of the hydrolysates and the degree of hydrolysis. The bitter receptor perceives bitter potencies of bitter peptides by the hydrophobicity recognition zone. The intensity of bitterness is influenced by hydrophobic and electronic properties of amino acids and the critical spatial structure of peptides. Compared to physicochemical debittering (i.e., selective separation, masking of bitter taste, encapsulation, Maillard reaction, and encapsulation) and other biological debittering (i.e., enzymatic hydrolysis, enzymatic deamidation, plastein reaction), enzymatic hydrolysis is a promising debittering approach as it combines protein hydrolyzation and debittering into a one‐step process, but more work should be done to advance the knowledge on debittering mechanism of enzymatic hydrolysis and screening of suitable proteases. Further study can focus on combining physicochemical and biological approaches to achieve high debittering efficiency and produce high‐quality products.

Bitter peptides in the enzymatic hydrolysates were prepared and purified from wheat gluten using aqueous ethanol solutions and macroporous resin, which has opened a new road for the extraction and separation of bitter peptides. This report contains the release regularity of bitter peptides and the factors affecting the change of bitter intensity during enzymatic hydrolysis, providing a scientific basis for the research on debitterizing method. In this study, the effects of different degrees of hydrolysis (DH) and enzyme active sites on the bitter peptide content and bitter taste thresholds were discussed. The relationship between amino acid composition, molecular weight distribution, surface hydrophobicity and bitter taste thresholds was extensively researched. The results showed the exposure of hydrophobic amino acids and the bitterness intensity of the hydrolysates increased as the DH increased, and the bitterness of wheat gluten hydrolysates (WGHs) hydrolyzed by Alcalase was stronger than that of Trypsin. According to correlation analysis, the proportion of total hydrophobic amino acid is the first factor that affects the sensory properties of bitter peptide, and the release content of bitter peptides and the content of total bitter amino acids are the second, following by the content of peptide in the molecular weight range of 500–1,000 Da and the surface hydrophobicity. The amino acid sequence of bitter peptides from WGHs were identified and predicted using high performance liquid chromatography-mass spectrometry (HPLC-MS/MS) and bioinformatics. It was found that the molecular weight of most of the peptides was below 1,500 Da, and the Q value was higher than 5.86 kJ/mol.

Peanut meal (PM) has recently emerged as a potential protein source for wood adhesives, owing to superior features such as high availability, renewability and eco-friendliness. However, the poor properties of unmodified PM-based wood adhesives, compared with their petroleum-derived counterparts, limit their use in high-performance applications. In order to promote the application of PM-based wood adhesives in plywood industry, urea (U) and epichlorohydrin (ECH) were used to enhance the properties of the adhesives and the modification mechanism was investigated. PM-based wood adhesives made with U and ECH were shown to possess sufficient water resistance and exhibited higher apparent viscosity and solid content than without. Fourier-transform infrared spectroscopy results suggested that U denatured PM protein and expose more reactive groups, allowing ECH to react better with U-treated PM protein to form a dense, cross-linked network which was the main reason for the improvement of the properties. The crystallinity increased from 2.7% to 11% compared with the control, indicating that the molecular structure of the resultant adhesive modified by U and ECH became more regular and compact owing to the cross-linked network structure. Thermogravimetry tests showed that decomposition temperature of the protein skeleton structure increased from 307°C to 314°C after U and ECH modification. Scanning electron microscopy images revealed that using U and ECH for adhesives resulted in a smooth protein surface which prevented moisture penetration and improved water resistance. PM-based adhesives thus represent potential candidates to replace petroleum-derived adhesives in the plywood industry, which will effectively promote the rapid development of eco-friendly adhesives and increase the added value of PM.

Anticancer peptides (ACPs) have promising prospects for cancer treatment. Traditional ACP identification experiments have the limitations of low efficiency and high cost. In recent years, data-driven deep learning techniques have shown significant potential for ACP prediction. However, data-driven prediction models rely heavily on extensive training data. Furthermore, the current publicly accessible ACP dataset is limited in size, leading to inadequate model generalization. While data augmentation effectively expands dataset size, existing techniques for augmenting ACP data often generate noisy samples, adversely affecting prediction performance. Therefore, this paper proposes a novel augmented sample selection framework for the prediction of anticancer peptides (ACPs-ASSF). First, the prediction model is trained using raw data. Then, the augmented samples generated using the data augmentation technique are fed into the trained model to compute pseudo-labels and estimate the uncertainty of the model prediction. Finally, samples with low uncertainty, high confidence, and pseudo-labels consistent with the original labels are selected and incorporated into the training set to retrain the model. The evaluation results for the ACP240 and ACP740 datasets show that ACPs-ASSF achieved accuracy improvements of up to 5.41% and 5.68%, respectively, compared to the traditional data augmentation method.

Peanut meal (PM) as the raw material was crosslinked modification by different mass fractions of 4,4′-diphenylmethane diisocyanate (MDI), to prepare adhesives for label. The properties of the adhesives, including water resistance, the viscosity and solid content were investigated. The performance of the label adhesive modified using MDI were significantly higher than those of the unmodified adhesive, making it suitable for applications in the labels. The water resistance duration was up form 3 h to 77 h when the modifier was added at 15 wt.% (based on PM). Further, the viscosity of the modified adhesive increased to an appropriate 82.2 Pa·s, and its solid content increased from 16.01% to 18.16%. In addition, Fourier transform infrared spectroscopy (FTIR) and X-ray diffraction analysis (XRD) were used to characterize the chemical interactions in the cured adhesives, and the thermogravimetric analysis (TGA) and scanning electron microscopy (SEM) were used to analyze the reason of adhesive performance improvement. It was found that incorporating MDI into the adhesives resulted in the formation of a dense crosslinking network structure because of chemical reactions between MDI and active groups on the protein molecules and the introduction of hydrophobic groups in the adhesives; these factors effectively prevented water penetration, thus improving the water resistance of the cured adhesives.