Quantitative analysis of blended corn-olive oil based on Raman spectroscopy and one-dimensional convolutional neural network.

Blended vegetable oil is a vital product in the vegetable oil market, and quantifying high-value vegetable oil is of great significance to protect the rights and interests of consumers. In this study, we established a one-dimensional convolutional neural network (1D CNN) quantitative identification model based on Raman spectra to identify the amount of olive oil in a corn-olive oil blend. The results show that the 1D CNN model based on 315 extended average Raman spectra can quantitatively identify the content of olive oil, with R2p and RMSEP values of 0.9908 and 0.7183 respectively. Compared with partial least squares regression (PLSR) and support vector regression (SVR), although the index is not optimal, it provides a new analytical method for the quantitative identification of vegetable oil.

Total synchronous fluorescence spectroscopy coupled with deep learning to rapidly identify the authenticity of sesame oil.

The quality of sesame oil (SO) has been paid more and more attention. In this study, total synchronous fluorescence (TSyF) spectroscopy and deep neural networks were utilized to identify counterfeit and adulterated sesame oils. Firstly, typical samples including pure SO, counterfeit sesame oil (CSO) and adulterated sesame oil (ASO) were characterized by TSyF spectra. Secondly, three data augmentation methods were selected to increase the number of spectral data and enhance the robustness of the identification model. Then, five deep network architectures, including Simple Recurrent Neural Network (Simple RNN), Long Short-Term Memory (LSTM) network, Gated Recurrent Unit (GRU) network, Bidirectional LSTM (BLSTM) network and LSTM fortified with Convolutional Neural Network (LSTMC), were designed to identify the CSO and trace the source with 100% accuracy. Finally, ASO samples were also 100% correctly identified by training these network architectures. These results supported the feasibility of the novel method.

Exploration of total synchronous fluorescence spectroscopy combined with pre-trained convolutional neural network in the identification and quantification of vegetable oil.

In order to distinguish different vegetable oils, adulterated vegetable oils, and to identify and quantify counterfeit vegetable oils, a method based on a small sample size of total synchronous fluorescence (TSyF) spectra combined with convolutional neural network (CNN) was proposed. Four typical vegetable oils were classified by three ways of fine-tuning the pre-trained CNN, the pre-trained CNN as a feature extractor, and traditional chemometrics. The pre-trained CNN was combined with support vector machines to distinguish adulterated sesame oil and counterfeit sesame oil separately with 100% correct classification rates. The pre-trained CNN combined with partial least square regression was used to predict the level of counterfeit sesame oil. The coefficient of determination for calibration (Rc2) values were all greater than 0.99, and the root mean square errors of validation were 0.81% and 1.72%, respectively. These results show that it is feasible to combine TSyF spectra with CNN for vegetable oil identification.

Identification and quantification of counterfeit sesame oil by 3D fluorescence spectroscopy and convolutional neural network.

The method of 3D fluorescence spectroscopy combined with convolutional neural network (CNN) was developed to identify the counterfeit sesame oil. AlexNet, a pre-trained CNN architecture, was transferred to extract spectral characteristics. Then these features extracted by AlexNet were used as the input of the support vector machine (SVM) to determine whether the sample was counterfeit and its ingredients simultaneously, and both the accuracy were 100%. According to different counterfeit ingredients, these features extracted by AlexNet were used as the input of partial least squares (PLS) to predict the volume percentage concentration of sesame oil essence. There was a good linear relationship between the predicted and actual values of the three sets of counterfeit samples (R2 > 0.99), and the root mean square error of prediction (RMSEP) values were 0.99%, 2.20% and 1.64%, respectively. The results confirmed the validity of this novel method in sesame oil identification.

Auditory Steady State Response; nature and utility as a translational science tool.

The auditory steady-state response (ASSR) has been used to detect auditory processing deficits in patients with psychiatric disorders. However, the methodology of ASSR recording from the brain surface has not been standardized in preclinical studies, limiting its use as a translational biomarker. The sites of maximal ASSR in humans are the vertex and/or middle frontal area, although it has been suggested that the auditory cortex is the source of the ASSR. We constructed and validated novel methods for ASSR recording using a switchable pedestal which allows ASSR recording alternatively from temporal or parietal cortex with a wide range of frequencies in freely moving rats. We further evaluated ASSR as a translational tool by assessing the effect of ketamine. The ASSR measured at parietal cortex did not show clear event-related spectral perturbation (ERSP) or inter-trial coherence (ITC) in any frequency bands or a change with ketamine. In contrast, the ASSR at temporal cortex showed clear ERSP and ITC where 40 Hz was maximal in both gamma-band frequencies. Ketamine exerted a biphasic effect in ERSP at gamma bands. These findings suggest that temporal cortex recording with a wide frequency range is a robust methodology to detect ASSR, potentially enabling application as a translational biomarker in psychiatric and developmental disorders.

Self-assembly formation of lipid bilayer coatings on bare aluminum oxide: overcoming the force of interfacial water.

Widely used in catalysis and biosensing applications, aluminum oxide has become popular for surface functionalization with biological macromolecules, including lipid bilayer coatings. However, it is difficult to form supported lipid bilayers on aluminum oxide, and current methods require covalent surface modification, which masks the interfacial properties of aluminum oxide, and/or complex fabrication techniques with specific conditions. Herein, we addressed this issue by identifying simple and robust strategies to form fluidic lipid bilayers on aluminum oxide. The fabrication of a single lipid bilayer coating was achieved by two methods, vesicle fusion under acidic conditions and solvent-assisted lipid bilayer (SALB) formation under near-physiological pH conditions. Importantly, quartz crystal microbalance with dissipation (QCM-D) monitoring measurements determined that the hydration layer of a supported lipid bilayer on aluminum oxide is appreciably thicker than that of a bilayer on silicon oxide. Fluorescence recovery after photobleaching (FRAP) analysis indicated that the diffusion coefficient of lateral lipid mobility was up to 3-fold greater on silicon oxide than on aluminum oxide. In spite of this hydrodynamic coupling, the diffusion coefficient on aluminum oxide, but not silicon oxide, was sensitive to the ionic strength condition. Extended-DLVO model calculations estimated the thermodynamics of lipid-substrate interactions on aluminum oxide and silicon oxide, and predict that the range of the repulsive hydration force is greater on aluminum oxide, which in turn leads to an increased equilibrium separation distance. Hence, while a strong hydration force likely contributes to the difficulty of bilayer fabrication on aluminum oxide, it also confers advantages by stabilizing lipid bilayers with thicker hydration layers due to confined interfacial water. Such knowledge provides the basis for improved surface functionalization strategies on aluminum oxide, underscoring the practical importance of surface hydration.