The manipulation and measurement of single droplets, ranging from micrometer to submicrometer size, are essential for precise in situ characterization of complex physicochemical processes in atmospheric aerosols. This represents a new direction in precision instrument development. Optical tweezer technology is a unique method that uses the gradient force of lasers to control suspended single droplets, enabling in situ precise measurements. The spontaneous and stimulated Raman signals from these droplets provide detailed information on the physicochemical composition, concentration, refractive index, and radius of particles, which serve as a novel approach for studying atmospheric aerosols. This paper discusses some types of optical tweezer techniques and their applications in measuring spontaneous and stimulated Raman signals in the field of atmospheric aerosols.

Raman spectroscopy can provide information on the molecular composition and structure of analyzed samples, used for analyzing and detecting the biochemical components of biological tissues and exploring the structure of biomolecules. It offers advantages such as non-destructiveness, high sensitivity, and rapid detection, and is widely applied in forensic identification and port sample testing. This review briefly outlines the basic principles of Raman spectroscopy and provides a detailed introduction to its advancements and latest research achievements in blood species identification from three aspects: detection methods, data preprocessing, and data classification. Additionally, it points out some current limitations of the technology and discusses the challenges encountered in data collection, processing, classification, and the establishment of spectral libraries. Finally, it explores the future research and development prospects of Raman spectroscopy, providing necessary references for subsequent research in this field.

In this paper, we propose a method based on porous alumina (AAO) substrate as a template, using Alternating current (AC) electric field assembly technology to assemble large gold ball nanoparticles coated with CTAB in AAO nanopores, and analyze the effects of AC electric field frequency and voltage on the assembly results, and obtain the optimal conditions for the preparation of self-assembled single particles in the pores. On this basis, the prepared substrate was soaked in a colloidal solution of small gold balls modified with sodium citrate, and the single particles modified by NDT molecules were quantitatively coupled with a single morphology and coated with different nanoparticles on the surface to obtain Heterodimers@AAO array substrate. Rhodamine 6G, Crystal violet and Aspartame were selected as probe molecules to explore the high Raman activity and enhanced uniformity of the substrates, and the obtained results provided experimental support for the engineering application of Raman spectroscopy.

A series of hydrotalcite materials are prepared using hydrothermal synthesis method, and their spectral near-infrared water absorption peaks are tested using a visible near-infrared spectrometer to simulate the near-infrared water peaks in jungle camouflage coating materials. The influence of experimental conditions on the size of water peaks is explored. A PSO-RBF predictive model is constructed using partial serpentine spectroscopic data and verifies the reliability of the model. The validation results indicate that the predictive accuracy of model exceeds 90%, demonstrating excellent practical capability. Employing the predictive model to forecast the development of serpentine facilitates the determination of optimal preparation conditions, thereby reducing experimental time and further enhancing the water absorption peak of traditionally synthesized serpentine products for camouflage applications.

Aiming at the problem of making and selling fake peony seed oil in the market, in situ, Raman spectroscopy and chemometrics were used to rapidly classify and identify the authenticity of peony seed oil. Peony seed oil mixtures with different adulteration concentrations were prepared, and their Raman spectra were collected. The spectral data were de-baseline processed by a wavelet algorithm. The characteristic information of the Raman spectrum was extracted based on principal component analysis. The adulteration quantitative models of peony seed oil and rapeseed oil were established by using three chemometrics methods, including multiple linear regression (MLR), principal component regression (PCR), and partial least squares regression (PLSR). The R2 of MLR, PCR, and PLSR were 0.9675, 0.9839, and 0.9846, respectively, and the RMSE were 0.057, 0.041, and 0.040, respectively. The results show that the PLSR algorithm has the best prediction effect. In this paper, a rapid detection method for the authenticity and adulteration of peony seed oil using in-situ Raman spectroscopy and chemometrics was proposed and implemented, which provides methodological reference and guidance for the rapid detection of peony seed oil adulteration.

With consumers' increasing attention to the ingredients of skincare products and their preference for natural and additive-free products, the issue of adding preservatives to skincare products has become one of the hot topics in analytical chemistry. This article proposes a method based on particle swarm optimization and backpropagation neural network algorithm combined with surface enhanced Raman spectroscopy (SERS) to achieve quantitative prediction of preservatives such as benzyl alcohol and phenoxyethanol in skincare products for the rapid and non-destructive quantitative analysis of preservatives. We collected and analyzed the Raman spectra of purified preservatives such as benzyl alcohol and phenoxyethanol in skincare products and predicted their vibrational spectra using density functional theory. The enhancement effects of different nanoparticles on preservatives such as benzyl alcohol and phenoxyethanol were compared in this innovative research. The Raman spectra of different concentrations of preservatives in skincare products were detected, and quantitative analysis models for preservatives such as benzyl alcohol and phenoxyethanol were constructed using the novel particle swarm optimization backpropagation neural network algorithm models. The predictive ability of the models was evaluated by the coefficient of determination and root mean square error. Among them, the PSO-BP model had the best prediction results for the test set, with R2 of 0.9518 and RMSE of 7.669×10-6. In summary, the particle swarm optimization backpropagation neural network algorithm combined with surface enhanced Raman spectroscopy proposed in this article has good performance in quantifying skincare preservatives. This study can provide strong technical reference and support for relevant law enforcement or quality supervision departments.

The research on the quantification of peony seed oil adulteration is still focused on the adulteration of a single cheap vegetable oil, so it is necessary to consider more complex adulteration. This paper uses portable near-infrared Raman spectroscopy technology combined with particle swarm optimization generalized regression neural network algorithm (PSO-GRNN) to help interpret the complex adulteration behavior of peony seed oil. Firstly, the mixed oil of cheap sunflower seed oil and corn oil was used as the adulteration object. The adulteration concentration of peony seed oil was prepared from low to high, and the concentration gradient was relatively uniform. Secondly, a portable near-infrared Raman spectrometer was used to collect the Raman spectrum signals of all oil samples. The Raman spectra were manually reduced and weighted based on spectral analysis. Finally, a quantitative analysis model based on PSO-GRNN algorithm was established. The results show that the complex adulteration behavior of peony seed oil can be interpreted based on portable near-infrared Raman spectroscopy technology combined with the PSO-GRNN algorithm. This scheme can predict the concentration of peony seed oil and effectively evaluate the concentration of various cheap vegetable oils. The test set of the model R2>0.94, RMSE<0.036. This paper deeply studied the possible complex adulteration of peony seed oil and proposed the corresponding solutions. This method is significant for the market supervision and quality detection of peony seed oil.

Faced with the continuous improvement of counterfeiting methods, cheap olive pomace oil will probably become a potential raw material for adulterating extra virgin olive oil. Therefore, this study focuses on the deep learning algorithm-assisted non-contact, non-destructive spectral detection technology to quantify the adulteration behavior of extra virgin olive oil. Mix expired olive pomace oil and extra virgin olive oil in different volume proportions to prepare different adulterated, mixed oil concentrations. The 785 nm portable Raman spectrometer was used to collect the Raman spectra of these mixed oils, and the quantitative analysis model of adulteration was established by combining the one-dimensional convolutional neural network algorithm. The density functional theory B3LYP/6-31+G (d, p) basis set was used to calculate the Raman spectrum of linoleic acid molecules to further analyze the Raman spectrum of extra virgin olive oil. The experimental results show that the technical solution based on combining deep structured feedforward neural networks and 785 nm portable Raman spectroscopy technology is a powerful tool for quantitative analysis of plant oil adulteration. The decision coefficients of 4000 spectral data quantitative models from 80 mixed oil products are all better than 0.97, and the decision coefficient of quantitative analysis in the evaluation model test set reaches 0.9704, with a root mean square error less than 0.0499. This technology has great application potential in quickly evaluating the adulteration of extra virgin olive oil, providing a beneficial reference scheme for regulating the domestic olive oil market and safeguarding consumers' legitimate rights and interests.

Avocado oil is a new vegetable oil extracted from avocado pulp. Because of its high price and limited public awareness, it is likely to produce and sell fake products. To meet the requirements of rapid, non-destructive, and high-throughput detection, this paper proposes a detection method for avocado oil by using a one-dimensional convolutional neural network model and in-situ micro Raman spectroscopy. The mixture of rapeseed oil and sunflower oil was used as the main component of avocado oil adulteration, and the spectra of pure vegetable oil and mixed oil were detected by in-situ micro Raman spectroscopy technology. The chemical information of the Raman spectrum characteristic peaks of avocado oil was analyzed and interpreted. The spectral information with synergy and correlation with Avocado concentration changes was selected through the covariance difference and correlation coefficient, and it was used as the input of the network model. The one-dimensional convolutional neural network model has a good prediction effect in the test set, with overall R2>0.915 and rmsr<0.0755. The detection method is based on a one-dimensional convolutional neural network model combined with in-situ micro Raman spectroscopy technology to predict the adulteration concentration of avocado oil, which is feasible and meets the detection requirements of market applications. The results have significant value for standardizing the domestic avocado market and accelerating the functional management of market supervision.

Accurately identifying vegetable oil species is significant in oil quality control, fraud detection, nutrition and health, and grain and oil trade. Accurate and rapid identification of vegetable oils is essential to ensure oil quality and maintain market supervision. This paper proposes a Principal component analysis-Ant colony optimization-Support vector machine (PCA-ACO-SVM) algorithm combined with Fourier transform infrared spectroscopy (FTIR) technology for rapidly identifying vegetable oil species. Six different kinds of vegetable oils were collected, and the absorption and transmission infrared spectra of the samples were measured by FTIR. PCA reduced the dimension of infrared spectral data, and the infrared spectral characteristics of oil products were extracted. The ACO algorithm optimizes the core parameters of the SVM classification algorithm. The optimized core parameters of the SVM classification model are C=1.1024043 and Gamma=0.1476193. This study uses the PCA-ACO-SVM algorithm to establish the identification model of vegetable oil species. The classification model was trained, and the parameters were optimized using the known types of oil products. The model was further applied to identify unknown oil products. By comparing with other algorithms, the accuracy and efficiency of the PCA-ACO-SVM algorithm in identifying vegetable oil types were verified. The results show that the PCA-ACO-SVM algorithm combined with FTIR technology can quickly and accurately identify the types of vegetable oil. This method has high classification accuracy and high computational efficiency in data processing, which is very suitable for the practical application of large-scale vegetable oil classification. In summary, the PCA-ACO-SVM algorithm proposed in this paper, combined with FTIR technology, provides a feasible solution for rapidly identifying vegetable oil species, which has high efficiency and accuracy. The scheme has broad application prospects in the food industry and quality supervision and is of great significance to the quality supervision of vegetable oil..

Palm leaf manuscriptsarea kind of the oldestliterature with significant historical and literary values. The leaves are easilydamagedby external factors which can result in diseases. Water stainsareone of the most common diseaseson palm leaf manuscripts. They are permeable contamination depositsthatcan affect the appearance of palm leafmanuscriptsand even damage the subjects' stability. In this paper, visible-reflected images andultravioletfluorescence images oftwo sets of palm leaf manuscript collectionsare captured. Physical parametersincluding pH values and chromatic aberration in water-stain and non-water-stain areasare measured by portable pH meter and colorimeter. The results show that “tideline” is more obvious and water-stain areasareeasier to distinguish under ultraviolet light. Meanwhile, pHvalues are all smaller in water-stain areasand the color differencebetween water-stain and non-water-stain areas is obvious. The results of micro-X-ray fluorescence and Fourier transform infrared spectroscopy coupled with ATR show that elements including K, Ca, P, S, and Mn are enriched in water-stain areas, with the decrease of the relative contents of cellulose and hemicellulose. Acomprehensive determination methodof water stain on palm leaf manuscripts based on nondestructive techniquesis first establishedin this study, whichprovides a deep understanding ofwater stain pollution. The method established in this study is meaningfulfor the scientific and rational preservation of palm leaf manuscriptsand can be a direction for furthercleaning and removal of water stain pollution.

The dragon shaped horsehead bronze Jian unearthed from Zhangzhuang Qiao Tomb Group King Zhao M1, which was a national first-class bronze cultural relic cast in the early Warring States period and buried in the Eastern Han Dynasty. Based on the methods such as polarizing microscope, Raman spectroscopy, X-ray diffraction, scanning electron microscopy energy spectrum, etc., the green, blue, and gray white corrosion on its surface were respectively determined to be malachite, azurite, and quartz. After cleaned, derusted, repaired, and sealed treatment, the state of bronze jian had reached stability. As a practical tool was soughted after by the nobles of Zhao in the Handan region, the horsehead bronze jian had important archaeological and historical research value. The use of horsehead as ear decoration pattern was a concentrated reflection and microscopic portrayal of the important role played by the horse in the development, inheritance, and evolution of Zhao culture.

In order to solve the problem of handwriting identification in the field of scientific document examination in court, we usedreflectance transformation imaging to observe the three-dimensional characteristics of sister lines and quantitatively analyze them for the first time. In this experiment, the writing robot was employed to regulate the writing strength, writing angle, azimuth angle, and pen and paper clamp angle. A total of 21 common domestic pens were utilized to create “Lin” samples under the pen and paper clamp angles of 30°, 45°, and 60°. The experimental samples were produced at azimuth angles of 0°, 30°, 45°, and 60°, and at 0°, 30°, and 45° with respect to the line of sight. In total, 336 sets of samples were produced and imaged under the normal line visualization mode. The sister line characteristics and quantitative analysis were then conducted. A total of 336 sets of experimental samples were imaged and analyzed in the normal visualization mode, and the sister line characteristics of each set of samples were analyzed to derive the optimal conditions for the appearance of sister lines. Subsequently, under the optimal conditions, the factors affecting the sister lines were investigated by 3D reconstruction and quantitative analysis based on RTI technology. Finally, the probability of sister line appearance of the samples under different pen and paper clip angles was analyzed by ANOVA. Preliminary experimental results show that the sister line feature is most obvious when the azimuth angle is 45° and the pen and paper clamp angle is 45°, and the probability of the sister line appearing is as high as 99% when the experiment is repeated; the pen and paper clamp angle has a decisive influence on the formation of the sister line. The combination of RTI technology and sister lines provides new possibilities for the supplementation and enrichment of the handwriting inspection method system, and has a broad development prospect in the field of document inspection.