ABSTRACT
Carbon nanomaterials, due to their catalytic activity and high surface area, have potential as cell immobilization supports to increase the production of xylanase. Recombinant Kluyveromyces lactis used for xylanase production was integrated into a polymeric gel network with carbon nanomaterials. Carbon nanomaterials were pretreated before cell immobilization with hydrochloric acid (HCl) treatment and glutaraldehyde (GA) crosslinking, which contributes to cell immobilization performance. Carbon nanotubes (CNTs) and graphene oxide (GO) were further screened using a Plackett-Burman experimental design. Cell loading and agar concentration were the most important factors in xylanase production with low cell leakage. Under optimized conditions, xylanase production was increased by more than 400% compared to free cells. Immobilized cell material containing such high cell densities may exhibit new and unexplored beneficial properties because the cells comprise a large fraction of the component. The use of carbon nanomaterials as a cell immobilization support along with the entrapment method successfully enhances the production of xylanase, providing a new route to improved bioprocessing, particularly for the production of enzymes. KEY POINTS: ⢠Carbon nanomaterials (CNTs, GO) have potential as cell immobilization supports. ⢠Entrapment in a polymeric gel network provides space for xylanase production. ⢠Plackett-Burman design screen for the most important factor for cell immobilization.
Subject(s)
Kluyveromyces , Nanostructures , Nanotubes, Carbon , Enzymes, Immobilized , Kluyveromyces/genetics , Research DesignABSTRACT
Exported fresh intact pineapples must fulfill the minimum internal quality requirement of 12 degree brix. Even though near-infrared (NIR) spectroscopic approaches are promising to non-destructively and rapidly assess the internal quality of intact pineapples, these approaches involve expensive and complex NIR spectroscopic instrumentation. Thus, this research evaluates the performance of a proposed pre-dispersive NIR light sensing approach in non-destructively classifying the Brix of pineapples using K-fold cross-validation, holdout validation, and sensitive analysis. First, the proposed pre-dispersive NIR sensing device that consisted of a light sensing element and five NIR light emitting diodes with peak wavelengths of 780, 850, 870, 910, and 940 nm, respectively, was developed. After that, the diffuse reflectance NIR light of intact pineapples was non-destructively acquired using the developed NIR sensing device before their Brix values were conventionally measured using a digital refractometer. Next, an artificial neural network (ANN) was trained and optimized to classify the Brix values of pineapples using the acquired NIR light. The results of the sensitivity analysis showed that either one wavelength that was near to the water absorbance or chlorophyll band was redundant in the classification. The performance of the trained ANN was tested using new pineapples with the optimal classification accuracy of 80.56%. This indicates that the proposed pre-dispersive NIR light sensing approach coupled with the ANN is promising to be an alternative to non-destructively classifying the internal quality of fruits.
ABSTRACT
Visible and near infrared spectroscopy is a non-destructive, green, and rapid technology that can be utilized to estimate the components of interest without conditioning it, as compared with classical analytical methods. The objective of this paper is to compare the performance of artificial neural network (ANN) (a nonlinear model) and principal component regression (PCR) (a linear model) based on visible and shortwave near infrared (VIS-SWNIR) (400-1000 nm) spectra in the non-destructive soluble solids content measurement of an apple. First, we used multiplicative scattering correction to pre-process the spectral data. Second, PCR was applied to estimate the optimal number of input variables. Third, the input variables with an optimal amount were used as the inputs of both multiple linear regression and ANN models. The initial weights and the number of hidden neurons were adjusted to optimize the performance of ANN. Findings suggest that the predictive performance of ANN with two hidden neurons outperforms that of PCR.
