RESUMO
The research on nondestructive test for detecting the sugar content of Hami melon by the technology of hyperspectral imaging was put forward. The research used the hyperspectral imaging system to get the diffuse reflective spectrum information (400 - 1 000 nm) of anilox class Hami melon sugar content, chose effective whole wavelength (500 - 820 nm)to do the modeling regression analysis the sugar content of Hami melon. The research compared the correction method of MSC and SNV, and also compared the influence of accuracy of modeling in terms of the spectrum pretreatment methods of original spectrum, first order differential, second order differential; Using the methods of PLS, SMLR and PCR, the comparative analysis of sugar content detection model effect with skin Hami melon and peel Hami melon was conducted. The results showed that after the original spectrum being processed by MSC and first order differential spectrum, modeling effect could be very good using the method of PLS and SMLR. Synthesizing correction set correlation coefficient and forecast modeling effect, it's feasible to detect the sugar content of skin Hami melon by the PLS method, with a correction sample correlation coefficient (R(c)) of 0.861 and the lower root mean square errors of correction (RMSEC) of 0.627, and a prediction sample correlation coefficient (R(p)) of 0.706 and root mean square errors of prediction (RMSEP) of 0.873. The best effect to detecti the sugar content of peel Hami melon was obtained by the SMLR method with a correction sample correlation coefficient (R(c)) of 0.928 and the lower root mean square errors of correction (RMSEC) of 0.458, with a Prediction sample correlation coefficient (R(p)) of 0.818 and root mean square errors of prediction (RMSEP) of 0.727. The results of this study indicate that the technology of hyperspectral imaging can be used to predict the sugar content of Hami melon.
Assuntos
Carboidratos/análise , Cucurbitaceae/química , Análise Espectral/métodos , Interface Usuário-Computador , DifusãoRESUMO
BACKGROUND: Maintaining gamma-aminobutyric acidergic (GABAergic) inhibition in the amygdala within a physiological range is critical for the appropriate expression of emotions such as fear and anxiety. The synaptic GABA type A receptor (GABAAR) is generally known to mediate the primary component of amygdala inhibition and prevent inappropriate expression of fear. However, little is known about the contribution of the extrasynaptic GABAAR to amygdala inhibition and fear. METHODS: By using mice expressing green fluorescent protein in interneurons (INs) and lacking the δ subunit-containing GABAAR (GABAA(δ)R), which is exclusively situated in the extrasynaptic membrane, we systematically investigated the role of GABAA(δ)R in regulating inhibition in the lateral amygdala (LA) and fear learning using the combined approaches of immunohistochemistry, electrophysiology, and behavior. RESULTS: In sharp contrast to the established role of synaptic GABAAR in mediating LA inhibition, we found that either pharmacological or physiological recruitment of GABAA(δ)R resulted in the weakening of GABAergic transmission onto projection neurons in LA while leaving the glutamatergic transmission unaltered, suggesting disinhibition by GABAA(δ)R. The disinhibition arose from IN-specific expression of GABAA(δ)R with its activation decreasing the input resistance of local INs and suppressing their activation. Genetic deletion of GABAA(δ)R attenuated its role in suppressing LA INs and disinhibiting LA. Importantly, the GABAA(δ)R facilitated long-term potentiation in sensory afferents to LA and permitted the expression of learned fear. CONCLUSIONS: Our findings suggest that GABAA(δ)R serves as a brake rather than a mediator of GABAergic inhibition in LA. The disinhibition by GABAA(δ)R may help to prevent excessive suppression of amygdala activity and thus ensure the expression of emotion.