Vibrational Spectroscopy最新文献

As a routine, lipstick is daily used by females and can easily be transferred to clothes, cups, tissue papers, and other objects. The analysis of lipsticks is a relatively new and exciting field in forensics, helping to identify suspects in criminal cases where lipstick evidence has been left at crime scenes. By matching a specific brand of lipstick to a sample, investigators can positively connect certain individuals to locations or people, helping to aid in their investigation and subsequent proceedings. In this present study, 20 different pink shade lipsticks of the same manufacturer were analyzed using Vacuum FT-IR, and Raman spectroscopy to show a differentiation percentage of 95.8% between the samples. Data analysis using data mining techniques was performed on FT-IR spectra. Principle Component Analysis (PCA) was used as a data mining model for classification purposes, and it was able to distinguish between lipsticks samples based on their FT-IR spectra.

The recycling of plastics from small household appliances is of great significance in improving the environment and addressing resource shortages, and has gradually become a focus of attention in various countries. Firstly, spectra were collected from samples with different colors, oxidation levels, and flame retardants. It was found that samples with different colors and oxidation levels exhibited different reflectivity, while samples with flame retardants showed smaller absorption peaks. Subsequently, the spectrum was preprocessed and analyzed, and the results showed that the samples collected under different conditions had little effect on plastic classification. Finally, plastic spectral classification was carried out using algorithms such as support vector machine (SVM), backpropagation neural network (BP), k-nearest neighbor (k-NN), partial least squares discriminant analysis (PLS-DA), and linear discriminant analysis (LDA). Overall, the classification accuracy of each algorithm exceeds 92 %, with SVM and PLS-DA having the best classification performance, while K-NN has relatively poor classification performance. In summary, the plastic classification algorithm for small household appliance recycling based on infrared spectroscopy can meet the actual plastic classification needs of plastic recycling plant production lines.

Most of the research on intact fruit spectroscopy is derivative in nature as it primarily showcase application of existing spectroscopy devices which are often proprietary in nature. The regression models developed by researchers to predict physicochemical attributes using spectra remain theoretical due to lack of mechanism to integrate the developed models back into proprietary devices. This poses challenge for commercial adaptation of this technology in commercial food quality supply chain. The present study addresses this research gap by presenting first of its kind innovative approach to classify tomatoes based on lycopene content using chemometrics-machine learning framework driven portable short-wave near infra-red (SWNIR) spectrophotometer developed by integration of open-source hardware (AS7265x multispectral chipset having wavelength range 410–940 nanometre (nm), Arduino Uno microcontroller) and software (R platform), housed in ergonomically designed and 3-dimension printed cabinet ensuring noise-free spectra acquisition. The lycopene content was observed to have strong negative correlation with wavelengths (nm) 485, 560 and 585 at ρ = – 0.65, – 0.70, – 0.70, whereas strong positive correlation with 760 nm at ρ = +0.64. Similar associations were qualitatively observed using principal component analysis. Atypical of literature, feature selection was performed based on analysis of variance and 14 wavelengths which exhibited statistically significant difference with respect to 15-days storage study (p ≤ 0.05) were selected for model development. Chemometrics-machine learning framework was used for development of optimised probabilistic and non-probabilistic models including logistic regression, Linear Discriminant Analysis (LDA), Random Forest (RF), Artificial Neural Networks (ANN) and Support Vector Machine (SVM) models using 10-fold cross validation subjected to 80–20% train-test split of the dataset. In agreement with literature, 500–750 nm wavelength range dominated the classification of lycopene content. Notably, specific wavelengths for logistic regression (560 nm), LDA (730 nm, 645 nm, 560 nm, 535 nm), RF (760 nm, 585 nm, 560 nm, 645 nm), and ANN (585 nm, 560 nm) significantly influenced outcome instances across classifiers. Accuracy obtained from confusion matrix on test dataset was used as performance metric to compare different models. Logistic regression and RF showcased accuracy of 80%, LDA and SVM at 90% while ANN outperformed all models with accuracy of 95%. This study successfully augmented technological advancement in field of spectroscopy for non-invasive quality assessment of fruit. It is recommended to conduct similar studies on other climacteric fruits for wider adoption of this technology.

The polymorphism of drugs exists widely in solid chemical drugs. It will affect the physical and chemical properties of drugs, as well as bioavailability. So it is very necessary to establish an quantitative method to improve the quality control level of polymorphic drugs. Attenuated total reflectance-Fourier transform infrared (ATR-FTIR) and Raman spectra have been included in many countries’ pharmacopoeia as the drug polymorph analytical technique, and they have many unique advantages. However, for multiple mixed systems, due to the complexity of optical signals, it is difficult to obtain an ideal content prediction model by classical linear regression, so the application of chemometric methods shows advantages. Pyrazinamide is a typical polymorphism drug, three polymorphic forms (α, δ, γ) were obtained. The model prediction ability of two kinds of spectroscopy combined with three kinds of stoichiometric methods was investigated by orthogonal experiment. On this basis, the influence of different combinations of five data preprocessing methods on improving modeling quality was investigated. In this research, Raman spectra combined with partial least squares (PLS), multiplicative scatter correction (MSC), denoise, median and first derivative at the whole spectral range resulted in a better calibration model. It had a RMSEP of 5.3%, 21.6%, and 20.8% for polymorphs α, δ, and γ, respectively. Several methods were used for preprocessing the spectral data could remove unimportant baseline (offset) interference from samples or to correct scattering effects and emphasize spectral the interesting signals. PLS can derive a few components from the independent variable system. Therefore, it may be an effective method to establish a quantitative model for a multi-polymorphism component mixed system.

Explanation and prediction for the product of trehalose dihydrate dehydration process was realized in this work. β form is the thermodynamic dehydration product of trehalose dihydrate. And α form is the kinetic dehydration product of trehalose dihydrate, which was analyzed by low-frequency Raman spectra, mid-frequency Raman difference spectra and IR difference spectra, and the analysis results confirmed that the crystal structure of trehalose dihydrate and α form are similar. The selective dehydration process from trehalose dihydrate to α form is due to their similar short-range orders. Amorphous trehalose is the uncontrollable dehydration product of trehalose dihydrate due to the collapse of water channels. The dehydration product of trehalose dihydrate by freeze-drying was a mixture of α form and amorphous phase, and the content of amorphous trehalose in the freeze-drying product increases with the decrease of the particle size of dihydrate. Study on the dehydration principle of organic hydrates will guide the preparation, storage and desolvation of drugs and foods.

Stratum corneum, and epidermis, regions of human skin were analyzed in vivo using confocal Raman spectroscopy to evaluate age-related biochemical and spectral changes. The data consisted of two defined age groups comprising 71 volunteers (27 ± 3 and 55 ± 4 years old). Multivariate statistical analyses were used to interpret and classify the average spectral data for each skin layer. The analyses demonstrated the measurement of two different groups of skin with different ages and revealed the most representative peaks for both the stratum corneum and epidermis. The Amide III and Amide I, both in α-helix conformation, exhibited increased signals in the spectra of the epidermis and stratum corneum of the younger group, and it was observed that an increased crosslinking of keratin filaments with age is a potential contributor to the stiffness increment, which consequently leads to a decrease in the Raman signal in the older group. The opposite occurred for the lipids signal, as changes in the lateral packing of lipids indicate skin ageing and an increase in the Raman signal. The disparity in the means of total natural moisturizing factor, was statistically significant between the two age groups. The statistical results demonstrated the emergence of distinct groups pertaining to the epidermis and stratum corneum, as well as pertaining to group I or II.