Appearance quality, nutritional value, and aroma components of wild diguo (Ficus tikoua Bur.) fruit collected from southwest China.

Diguo (Ficus tikoua Bur.), an ancient wild fruit, is widely spread in southwest China. However, there is little information on the phenotypic traits, quality characteristics, and aroma compounds available to diguo fruit. The present study is an investigation into the effects of geographical origin on the phenotypic traits and quality characteristics of wild diguo fruit collected from southwest China. The volatile compounds in the mixed fruit samples were also investigated using gas chromatography-mass spectrometry. Our results indicated that significant variation existed among the sampling materials in all the phenotypic parameters. Fruit fresh weight ranged between 2.06 and 4.59 g. Moreover, significant variation existed among the selected materials in all macronutrients (dry matter, total soluble solids, crude protein, crude fat, and ash) and some nutritional parameters (glutamate, arginine, total soluble solids, maltose, and mannose, etc.). Regardless of their geographical origin, diguo fruit is relatively low in fat and fructose and high in fiber and glutamate. A total of 95 volatile constituents were identified in the frozen diguo fruit. In conclusion, diguo fruit with rich nutritional attributes has a promising future for commercial-scale production. The variability of the observed morphological and nutritional features of diguo fruit provides important characteristics for improving the breeding of diguo as a modern fruit crop.

Forecasting the atmospheric refractive index structure constant profile with an altitude-time correlations-inspired deep learning model.

An accurate forecast of the atmospheric refractive index structure constant (C n2) is vital to analyzing the influence of atmospheric turbulence on laser transmission in advance. In this paper, we propose a novel method to forecast the atmospheric refractive index structure constant C n2 profile, which is inspired by the turbulence characteristics (i.e., the altitude-time correlations). A deep convolutional neural network (DCNN) is adopted in the hope that with the stacked convolutional layers to abstract the altitude-time correlations of C n2, it can accurately forecast the C n2 profile in the near future based on the accumulated historical measurement data. While the sliding window algorithm is introduced to segment the measured time series data of the C n2 profiles to generate the input-output pair data for training and testing. Experimental results demonstrate its high forecast accuracy, as the obtained root mean square error and the correlation coefficient are 0.515 and 0.956 in the one-step-ahead C n2 profile forecast case, 0.753 and 0.9046 in the 36-step-ahead forecast case, respectively. Moreover, the forecast accuracy versus altitude and its relationship with the distribution of C n2 against altitude are analyzed. Most importantly, with a series of experiments of various input feature sizes, the appropriate sliding window width for C n2 forecast is explored, and the short-term correlation of C n2 is also verified.

[Potential Applicability of Fecal NIRs: A Review].

Near-infrared reflectance spectroscopy (NIRS) is an inexpensive, rapid, environment-friendly and non-invasive analytical technique that has been extensively applied in the analysis of the dietary attributes and the animal products. Acquisition of dietary attributes is essential for nutritional diagnoses to provide animals with reasonable diet. Traditionally, the calibration equations for the prediction of dietary attributes (e. g. crude protein) are developed from feed NIR spectra and the results of conventional chemical analysis (i. e. reference data). It is difficult to obtain the NIR spectra of forages consumed by grazing animals, so the method of this calibration is inappropriate for free-grazing herbivores. Feces, as the animal's metabolites, contain the information about both the animal's diet and the animal itself. Recently, Fecal-NIRS (F. NIRS) has been directly used to monitor diet information (botanical composition, chemical composition and digestibility), based on correlation between reference data and fecal NIR profile. Subsequently, some additional application (such as sex and species discrimination, reproductive and parasite status) of F. NIRS also is outlined. In the last, application of NIRS in animal manure is summarized. NIRS was shown to be an alternative to conventional wet chemical methods for analyzing some nutrient concentrations in animal manure rapidly. Overall, this paper proves that F. NIRS is a rapid and valid tool for the determination of the dietary attributes and of the physiological status of animal, although more efforts need to be done to improve the accuracy of the F. NIRS technique. Several researchers in English have reviewed the applications of F. NIRS. In China, however, there is a paucity of research and application regarding F. NIRS. We expect that this paper in Chinese will be helpful to the development of F. NIRS in China. At the same time, we propose NIRS as a simple and rapid analytical method for predicting the main chemical composition (dry matter, organ matter, total solid, volatile solid, total nitrogen, total Kjeldahl nitrogen and ammonium nitrogen) in animal manure.