Salt is very important for human health and food seasoning. Recently, several peptides isolated from natural food products have been reported exhibiting a salty taste or a saltiness-enhancing function. In this investigation, taste-active peptides occurring in commercial Chinese fermented soybean curd were isolated and identified using ultrafiltration, gel permeation chromatography, ion-exchange chromatography, and nano-LC/Q–TOF MS/MS. The salty taste-enhancing function of the target fractions was confirmed by both a rat taste cell model and/or human sensory evaluation. Four decapeptides were found as taste-active compounds. Among them, peptide E (EDEGEQPRPF) was the most potent saltiness-enhancing peptide: 0.4 mg/mL in 50 mmol/L NaCl solution could increase its salty perception equivalent to the salt level of 63 mmol/L NaCl reference solution. The sequence of the peptide has been found in the α’-subunit of β-conglycinin [Glycine max]. The remaining peptides V (VGPDDDEKSW), DD (DEDEQPRPIP), and DG (DEGEQPRPFP) showed umami and kokumi tastes as well as a weak saltiness-enhancing sensation. These findings suggest that the decapeptide EDEGEQPRPF could be a possible alternative to partially reduce the amount of sodium intake without compromising for saltiness.

Jinhua dry-cured ham (JDH) is a traditional fermented meat product favored by Chinese consumers. In this paper, the impact of steaming on the key odorants of JDH was investigated using the sensomics approach. Compounds with odor activity values (OAV) ≥1 were re-engineered in a triglyceride matrix to imitate the odor profiles of both raw and steamed JDHs. The aromaactive compounds were then confirmed by recombination and omission tests using triangle tests. The odor profiles of raw and steamed JDHs were obtained by quantitative descriptive analysis to compare the differences between the original and recombined models. The results showed that pentanal, hexanal, dimethyl trisulfide, (E,E)-2,4-decadienal, (E)-2-heptenal, furaneol, 3methylbutanoic acid, 1-octen-3-one, and methional influenced the overall raw JDH odor significantly. Furaneol was first reported as a key compound that provides a caramel smell to the raw JDH. Apart from (E)-2-heptenal, dimethyl trisulfide, furaneol, 3methylbutanoic acid, and methional, the remaining three compounds including 2-furfurylthiol, benzeneacetaldehyde, and phenylethyl alcohol showed a significant influence on the odor profile of steamed JDH. The statistical analysis of the odor profiles showed an 80.0% similarity between the recombination raw JDH and the real raw JDH, and a 76.3% similarity between the model and the real steamed JDH.

Accurate remaining useful life (RUL) estimation is crucial for the maintenance of complex systems, e.g. aircraft engines. Thanks to the popularity of sensors, data-driven methods are widely used to evaluate RULs of systems especially deep learning approaches. Though remarkably capable at non-linear modeling, deep learning-based prognostics techniques lack powerful spatio-temporal learning ability. For instance, convolutional neural networks are restricted to only process grid structures rather than general domains, recurrent neural networks neglect spatial relations between sensors and suffer from long-term dependency learning. To solve these problems, we construct a graph structure on sensor network with Pearson Correlation Coefficients among sensors and propose a method for combining the power of graph convolutional network on spatial learning and sequence learning success of temporal convolutional networks. We conduct the proposed method on aircraft engine dataset provided by NASA. The experimental results demonstrate that the established graph structure is appropriate and the proposed approach can model spatio-temporal dependency accurately as well as improve the performance of RUL estimation.

Jinhua dry‐cured ham (JDH) is a traditional Chinese food with a unique flavor. Its sensory profiles have not been studied using a developed lexicon. In this study, a lexicon to profile sensory characterization of JDHs was developed to characterize samples starting from smelling, during oral processing, and after swallowing. Then the lexicon was composed to profile JDHs using descriptive analysis (DA) and check‐all‐that‐apply (CATA). Principal component analysis (PCA) was used to examine DA data, and correspondence analysis (CA) was used to examine CATA data. The DA data from trained panelists and CATA data from naive consumers were compared by multiple factor analysis (MFA). The result showed a total of 33 attributes composed of the lexicon to describe JDHs including seven terms for odor, six terms for texture, seven terms for flavor, four terms for taste, and nine terms for aftertaste. The application of the lexicon to describe JDHs using DA and CATA showed good discrimination among samples. The comparison between DA and CATA using MFA showed attributes with distinct intensity or significant variances among tested samples located closely on the map, but attributes with weak intensity could varied largely on the map. This lexicon can be useful for manufacturers, producers, and consumers and helpful for the standardization of JDHs. Practical Applications The development of a sensory lexicon for JDH can be helpful for the breeding of pigs, and the production of JDH with desired characteristics. It also assists manufacturers in differentiating products, optimizing processes, and developing new products with a consistent and expected JDH. The development of a lexicon will help both the trained panelists and consumers to fill knowledge gaps in JDH research from a sensory and consumer point of view. Such data are critical in sensory studies for conducting JDHs from different regions and countries in the future.