No abstract

Agarwood and its related products are important, useful and valuable for many applications, such as medicine, incense, and perfume. In general, the grading method of agarwood is based on its physical properties, which is inefficient, time consuming and lacks repeatability. In this study, non-targeted headspace solid-phase microextraction (HS-SPME) combined with gas chromatography/mass spectrometry (GC/MS) and multivariate analysis was developed to classify and differentiate agarwoods based on their aromatic characteristics. Five samples from Indonesia and Vietnam were extracted with polydimethylsiloxane (PDMS) fiber and analyzed by HS-SPME-GC/MS. GC/MS data were processed using MZmine for statistical purposes. Principle component analysis (PCA) was applied to establish the relationship between samples and aromatic characteristics. In PCA results, samples were classified successfully according to the source, price, and types. A total of 17 markers were adopted and identified by GC/MS, and also confirmed. This result demonstrates that the proposed method is efficient, simple, and useful for grading of agarwoods.

The effect of corn oil addition on mycelium growth and polysaccharide productions in the medicinal mushroom Ganoderma lucidum was studied. The results showed that when a level of 2% corn oil was added at the beginning of culture, the biomass and polysaccharide productions reached a maximum of 12.9 and 1.038 g/L, respectively, during 13-day cultivation. The pH variation along with morphology observation in culture provided an indirect inference to the promotional effect of oil addition. Moreover, a curve fitting analysis was carried out to assay the elevated effect on biomass and exopolysaccharide productions in oil added culture. The experimental data of substrates consumption and products formation in culture with oil addition were predicted through the fitting equations obtained in single carbon source culture. The numerical results further clarified the stimulatory effects of oil addition in G. lucidum culture.

After completing the production of preserved eggs, traditionally, the degree of gelling is judged by allowing workers to tap the preserved eggs with their fingers and sense the resulting oscillations. The amount of oscillation is used for the quality classification. This traditional method produces varying results owing to the differences in the sensitivity of the individual workers, who are not objective. In this study, dielectric detection technology was used to classify the preserved eggs nondestructively. The impedance in the frequency range of 2–300 kHz was resolved into resistance and reactance, and was plotted on a Nyquist diagram. Next, the diagram curve was fitted in order to obtain the equivalent circuit, and the difference in the compositions of the equivalent circuits corresponding to gelled and non-gelled preserved eggs was analyzed. A preserved egg can be considered an RLC series circuit, and its decay rate is consistent with the decay rate given by mechanical vibration theory. The Nyquist diagrams for the resistance and reactance of preserved eggs clearly showed that the resistance and reactance of gelled and non-gelled eggs were quite different, and the classification of the eggs was performed using Bayesian network (BN). The results showed that a BN classifier with two variables, i.e., resistance and reactance, can be used to classify preserved eggs as gelled or non-gelled, with an accuracy of 81.0% and a kappa value of 0.62. Thus, a BN classifier based on resistance and reactance demonstrates the ability to classify the quality of preserved egg gel. This research provides a nondestructive method for the inspection of the quality of preserved egg gel, and provides a theoretical basis for the development of an automated preserved egg inspection system that can be used as the scientific basis for the determination of the quality of preserved eggs.

No abstract