Raman spectroscopy combined with multiple one-dimensional deep learning models for simultaneous quantification of multiple components in blended olive oil.

Blended vegetable oils are highly prized by consumers for their comprehensive nutritional profile. Therefore, there is an urgent need for a rapid and accurate method to identify the true content of blended oils. This study combined Raman spectroscopy with three deep learning models (CNN-LSTM, improved AlexNet, and ResNet) to simultaneously quantify extra virgin olive oil (EVOO), soybean oil, and sunflower oil in olive blended oil.

Manipulation of the photoluminescence of lead halide perovskite quantum dots with mechanically reconfigurable 3D photonic crystals.

Reconfigurable 3D photonic crystals (3DPCs) are promising for dynamic emission devices, owing to their unique properties. Here, we integrated the perovskite quantum dot film together with 3D reconfigurable photonic crystals (PCs) to form quantum dot/photonic crystal heterostructures and investigated their interactions at their interfaces. The photonic bandgaps of the presented 3DPCs can be dynamically tuned by heating and applying external mechanical forces, and they can be stably fixed in the intermediate states.

Identification and quantification of adulterated honey by Raman spectroscopy combined with convolutional neural network and chemometrics.

In this study, Raman spectroscopy combined with convolutional neural network (CNN) and chemometrics was used to achieve the identification and quantification of honey samples adulterated with high fructose corn syrup, rice syrup, maltose syrup and blended syrup, respectively. The shallow CNNs utilized to analyze honey mixed with single-variety syrup classified samples into four categories by the adulteration concentration with more than 97% accuracy, and the general CNN model for simultaneously detecting honey adulterated with any type of syrup obtained an accuracy of 94.79%.

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 Rp and RMSEP values of 0.