Different ages are closely related especially among the adjacent ages because aging is a slow and extremely non-stationary process with much randomness. To explore the relationship between the real age and its adjacent ages, an age group-n encoding (AGEn) method is proposed in this paper. In our model, adjacent ages are grouped into the same group and each age corresponds to n groups. The ages grouped into the same group would be regarded as an independent class in the training stage. On this basis, the original age estimation problem can be transformed into a series of binary classification sub-problems. And a deep Convolutional Neural Networks (CNN) with multiple classifiers is designed to cope with such sub-problems. Later, a Local Age Decoding (LAD) strategy is further presented to accelerate the prediction process, which locally decodes the estimated age value from ordinal classifiers. Besides, to alleviate the imbalance data learning problem of each classifier, a penalty factor is inserted into the unified objective function to favor the minority class. To compare with state-of-the-art methods, we evaluate the proposed method on FG-NET, MORPH II, CACD and Chalearn LAP 2015 Databases and it achieves the best performance.

Electronic nose (E-nose) and electronic tongue (E-tongue) can mimic the sensory perception of human smell and taste, and they are widely applied in tea quality evaluation by utilizing the fingerprints of response signals representing the overall information of tea samples. The intrinsic part of human perception is the fusion of sensors, as more information is provided comparing to the information from a single sensory organ. In this study, a framework for a multi-level fusion strategy of electronic nose and electronic tongue was proposed to enhance the tea quality prediction accuracies, by simultaneously modeling feature fusion and decision fusion. The procedure included feature-level fusion (fuse the time-domain based feature and frequency-domain based feature) and decision-level fusion (D-S evidence to combine the classification results from multiple classifiers). The experiments were conducted on tea samples collected from various tea providers with four grades. The large quantity made the quality assessment task very difficult, and the experimental results showed much better classification ability for the multi-level fusion system. The proposed algorithm could better represent the overall characteristics of tea samples for both odor and taste.

In this study, we investigated the effects of volatile organic compounds (VOCs) produced by Bacillus subtilis CF-3 on the growth and development of Colletotrichum gloeosporioides and evaluated the elicitation of active defense responses in harvested litchi fruits. In vitro experiments were conducted to explore the bacteriostatic effect of VOCs in inhibiting pathogenic fungi by means of plate enthalpy test, scanning electron microscopy, transmission electron microscopy, and gas chromatography–mass spectrometry (GC–MS). The results showed that 2,4-di-tert-butylphenol and CF-3 24-h fermentation broth (24hFB) can significantly inhibit the germination of fungal spores, disrupt hyphal and cell morphology, and decrease cell membrane fluidity and integrity, resulting in the changes of indexes. In addition, the bacteriostasis of VOCs in the defensive ability of litchi fruits to C. gloeosporioides was studied, and it was shown that 2,4-di-tert-butylphenol and CF-3 24hFB can inhibit the activity of the pathogenic enzymes (pectinase and cellulase) secreted by C. gloeosporioides to reduce the decomposition of plant tissues, activate antioxidant enzymes (peroxidase, polyphenol oxidase, catalase, and superoxide dismutase) in the fruit to eliminate excessive reactive oxygen species in fruits in order to reduce plant cell damage and activate disease resistance enzymes (phenylalanineammonialyase, chitinases, β-1,3-glucanase) to enhance the resistance of litchi fruits to C. gloeosporioides and inhibit its growth. This study investigated the bacteriostasis of VOCs in inhibiting C. gloeosporioides and inducing the resistance of litchi fruits, providing a theoretical basis for future applications.

Developing innovative products that satisfy various groups of consumers helps a company maintain a leading market share. The hedonic scale and just-about-right (JAR) scale are 2 popular methods for hedonic assessment and product diagnostics. In this paper, we chose to study flavored liquid milk because it is one of the most necessary nutrient sources in China. The hedonic scale and JAR scale methods were combined to provide directional information for flavored liquid milk optimization. Two methods of analysis (penalty analysis and partial least squares regression on dummy variables) were used and the results were compared. This paper had 2 aims: (1) to investigate consumer preferences of basic flavor attributes of milk from various cities in China; and (2) to determine the improvement direction for specific products and the ideal overall liking for consumers in various cities. The results showed that consumers in China have local-specific requirements for characteristics of flavored liquid milk. Furthermore, we provide a consumer-oriented product design method to improve sensory quality according to the preference of particular consumers.