RESUMEN
Electronic nose (E-nose) and hyperspectral image (HSI) were combined to evaluate mutton total volatile basic nitrogen (TVB-N), which is a comprehensive index of freshness. The response values of 10 E-nose sensors were collected, and seven responsive sensors were screened via histogram statistics. Reflectance spectra and image features were extracted from HSI images, and the effective variables were selected through random frog and Pearson correlation analyses. With multi-source features, an input-modified convolution neural network (IMCNN) was constructed to predict TVB-N. The seven E-nose sensors, spectra of effective wavelengths (EWs), and five important image features were combined with IMCNN to achieve the best result, with the root mean square error, correlation coefficient, and ratio of performance deviation of the prediction set of 3.039 mg/100 g, 0.920, and 3.59, respectively. Hence, the proposed method furnishes an approach to accurately analyze mutton freshness and provide a technical basis for investigation of other meat qualities.
Asunto(s)
Nariz Electrónica , Carne Roja , Imágenes Hiperespectrales , Carne/análisis , Redes Neurales de la Computación , Nitrógeno/análisis , Carne Roja/análisisRESUMEN
A novel polynomial correction method, order-adaptive polynomial correction (OAPC), was proposed to correct reflectance spectra with operator differences, and convolutional neural network (CNN) was used to develop analysis model to predict behenic acid in edible oils. With application of OAPC, CNN performed well with coefficient of determination of correction (R2cor) of 0.8843 and root mean square error of correction (RMSEcor) of 0.1182, outperforming partial least squares regression, support vector regression and random forest with OAPC, as well as the cases without OAPC. Based on 16 effective wavelengths selected by combination of bootstrapping soft shrinkage, random frog and Pearson's correlation, CNN and OAPC exhibited excellent performance with R2cor of 0.9560 and RMSEcor of 0.0730. Meanwhile, only 5% correction samples were selected by Kennard-Stone for OAPC. Overall, the proposed method could alleviate the impact of operator differences on spectral analysis, thereby providing potential to correct differences from measurement instruments or environments.
Asunto(s)
Aceites de Plantas , Máquina de Vectores de Soporte , Ácidos Grasos , Redes Neurales de la Computación , Análisis Espectral , VerdurasRESUMEN
In this study, an ordered mesoporous silica modified with lanthanum oxide was synthesized using diatomite as silica source and applied for adsorption of phosphate from aqueous solution. By taking cost-effectiveness for practical application into consideration, the adsorbent with a theoretical La/SiO2 molar ratio of 0.2 (La0.2M41) possessed a promising performance. In the batch adsorption tests, the adsorbents with La2O3 loading possessed markedly enhanced adsorption capacities. Phosphate uptake by La0.2M41 was pH-dependent with the highest sorption capacities observed over a pH range of 3.0-6.0. Coexistent anions displayed an adverse effect on phosphate adsorption following the order of CO3 2-⯠> F-⯠> NO3 - > Cl- > SO4 2-. In the kinetic study, phosphate adsorption onto La0.2M41 followed the pseudo-second-order equation better than the pseudo-first-order, suggesting chemisorption. The Langmuir isothermal model well described the adsorption isotherm data, showing a maximum adsorption capacity for phosphate of up to 263.16 mg/g at 298 K. In a real treated wastewater effluent with phosphate concentration of 2.5 mg P/L, La0.2M41 efficiently reduced the phosphate concentration to 28 µg P/L.