Vibrational Spectroscopy最新文献

In this investigation, a pioneering approach involving the fusion of matrix fragments-related spectral data was proposed to improve the underperformance observed in raw meat and bone meal (MBM) when employed for species discrimination analysis. Initially, the MBM matrix was characterized as a binary mixture comprising bone fragment (BF) and meat fragment (MF). Subsequently, the disparities in near infrared (NIR), mid infrared (MIR), and Raman spectra between BF and MF samples were individually identified and elucidated. Following, the spectral fusion data related to matrix fragments were synthesized and subjected to analysis using principal component analysis (PCA) and partial least squares discriminant analysis (PLS-DA) for species-specific evaluation. The suggested data fusion strategy was authenticated by its capacity to facilitate improved differentiation within the principal component space, along with reduced classification errors in PLS-DA. Further, complementarity of matrix fragments-related spectral variables for MBM species discrimination analysis was explicitly scrutinized and contributions to MBM derived from four species were meticulously traced. Additionally, the proposed analytical strategy for MBM could serve as a reference for the spectral characterization of other agricultural materials with complex matrices.

Carbohydrates are essential molecules in the metabolism of plant systems whose quantification is crucial. The study aims to estimate foliar glucose content using the Smartphone-based Color Grab app by color change upon reaction with a 3,5-dinitrosalicylic acid reagent and mid-infrared spectra. The hue showed a negative correlation of − 0.959 with glucose content with sensitivity, detection limit and precision of 13.46 μg/mL,μg/mL,0.035 μg/mL, and 0.229% respectively. The glucose concentration to color coordinates displayed a linear response between 50 to 600 µg/mL. The linear regression equation with hue of standards was used to predict spectrophotometrically measured glucose concentration of leaf extracts with R² = 0.934 and sensitivity of 13.46 μg/mL. Multivariate analysis of infrared spectrum (650–4000 cm^‐1−1) of powdered arecanut leaves indicated elastic net and partial least square regression as the best models with R² of 0.99. The study has practical implications in smartphone or infrared spectra-based glucose measurements for low glucose (< 1 mg/mL) samples.

In this work, a rapid and simple analytical method for the quantitative determination of moisture, protein, wet gluten, starch and sedimentation index in the wheat flour was established by the combination of near infrared spectroscopy and chemometrics. The spectra of the 229 wheat flour samples were collected by a portable near infrared fast analyzer. The contents of these components were determined according to the relevant Chinese National Standards, and were taken as the corresponding reference database. Seven spectral pretreatment methods were employed to eliminate the optical interference from background and other noise information. The best result was obtained with FD+SG(15, 3)+MC method for moisture, protein, wet gluten and sedimentation index, FD+SG(15, 2)+MC method was more suitable for starch. The principal component numbers (PCs) were also optimized to obtain a superior model effect. Furthermore, partial least squares (PLS) and multiple linear regression (MLR) modeling methods were used to quantify the content of the components. When using FD+SG(15, 3)+MC pretreatment, all the PLS model parameters were significantly better than the MLR model. Both the predicted values and the reference values showed superior linear relationship within the calibration range. Moreover, the absolute error of the predicted values and their corresponding reference values in the PLS model were within their confidence intervals, respectively. The relative errors for moisture, protein, wet gluten and starch fluctuated little, only sedimentation index fluctuated greatly. The actual prediction correct rate of moisture, protein, wet gluten, starch and sedimentation index were 96.8%, 96.8%, 90.3%, 100.0% and 80.6%, respectively, which indicated the prediction was excellent.

To improve the accuracy of multi-component gas analysis in infrared spectroscopy and simplify the workflow, an infrared spectroscopy gas detection method based on an improved convolutional neural network is proposed. This method can not only identify a variety of gas categories but also finely identify the concentration of gas. To verify the model identification effect proposed in this paper, eight kinds of gases such as CH4 and C2H6 were used as the sample gases for gas identification and concentration classification, and the corresponding hardware was used to complete the development of the system. The experimental results show that the accuracy of the model method for gas species identification can reach 90%, and the accuracy for concentration identification is the same. In addition, compared with the traditional CNN method, the recognition effect is significantly improved. With the improvement of the data set, the number of gas categories detected by this method and the measurement accuracy will be improved.

Confocal Raman microspectral imaging (CRMI) is a versatile non-invasive technique that allows in vitro cell imaging without any chemical fixation, fluorescent markers or other contrast-enhancing chemicals. In this study, dorsal root ganglion (DRG) neuron and its affiliated Schwann cells (SCs) were co-cultured to unveil their underlying sub-cellular constitutional and structural nature. Both DRG neurons and SCs were derived from neonatal Sprague-Dawley rats and seeded on CaF₂ subtracts for spectral analysis. After acquiring Raman hyperspectral datasets, multivariate data analyses, including K-mean cluster analysis (KCA) and principal component analysis (PCA), were successively adopted to study the subcellular structural and compositional information of the measured cells. Univariate spectral analysis was adopted to emphasize the spatial distribution of subcellular constitutions based on the acquired spectral characteristics. Results have shown Raman spectral characteristics of DRG neurons (cell membrane, cytoplasm, organelles, nucleus) and its affiliated SCs (myelin, cell membrane, cytoplasm, nucleus), as well as information on the subcellular distribution pattern of major biochemical components (proteins, cytochrome c, nucleic acids, lipids, carbohydrates). This in vitro spectral-imaging work provides a proof of principle of an analytical method for future studies on the developmental mechanisms of DRG neurons and their molecular bases for the treatment of diseases of the peripheral nervous system.

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.