Vibrational Spectroscopy最新文献

Oysters are extensively cultivated worldwide. However, significant variations in chemical composition, quality, and price exist between oysters from different geographical origins. This study employed portable near-infrared spectroscopy in conjunction with chemometric analysis to determine the geographical origin and glycogen content of oysters. Pretreatment methods (multiplicative scattering correction, first derivative, and second derivative) were used to preprocess the raw spectra. Partial least squares discriminant analysis (PLS-DA), orthogonal partial least squares discriminant analysis (OPLS-DA), and support vector machine (SVM) were then adopted to establish the qualitative models. Partial least squares regression (PLSR) and support vector machine regression (SVMR) were compared for predicting the glycogen content. The results revealed that the PLS-DA, OPLS-DA, and SVM models classified the geographical origin of oysters with 100% accuracy. For quantitative analysis, the regression equations displayed high predictive ability. The SVMR model was superior to the PLSR model for glycogen content prediction, with a coefficient of determination of prediction (R²_P) of 0.9253 and a residual prediction deviation (RPD) of 3.62. Therefore, the proposed approach is suitable for the accurate and environmentally friendly determination of the geographical origin and glycogen content of oysters, thus representing an attractive alternative method for the traceability supervision and quantitative analysis of seafood products.

Rapid identification of the active state of foodborne bacteria is crucial for ensuring the safety and quality control of food or pharmaceutical products. In this study, a combination of hyperspectral microscope imaging (HMI) and machine learning algorithm is employed for the identification of active state of Escherichia coli (E. coli). Hyperspectral microscope images of live, 100 ℃ heat inactivation and 121 ℃ high-pressure inactivation of E. coli are collected in wavelength range of 370–1060 nm. Savitzky-Golay (SG) smoothing combing with normalization is used for spectra preprocessing. And principal component analysis (PCA) is employed for spectral dimension reduction. Four different regions of interest (ROIs), including the entire bacterial cell ROI (cell), the outer cell wall ROI (cell_r), the membrane structure ROI (cell_w) formed by the cell wall and cell membrane, and the central of the cell ROI (cell_cy), are extracted and used as model input variables to investigate the influence on the modeling results. Five model algorithms, support vector machines (SVM), random forests (RF), k-nearest neighbors (KNN) algorithms, discriminant analysis (DA) classifiers, and long short-term memory (LSTM) neural networks are used and compared. Modeling results with spectral data of cell_r perform better than those with other ROIs. Accuracy of the models with data of the cell_r ROI are as follows: 79.78% for SVM, 95.11% for RF, 91.33% for KNN, 98.22% for DA, and 93.78% for LSTM. DA achieves the highest classification accuracy. The results show that high-temperature inactivation induces changes in bacterial tissue and morphology, resulting in certain spectral differences among bacteria in three different states. The combination of hyperspectral microscope imaging and machine learning algorithm can provide an effective method for identification of active and inactive states of E. coli. Furthermore, the model, constructed with the data of cell_r ROI, exhibits the best performance in identification.

Once turmeric has been ground into powder, it is difficult to tell visually if it has been tampered with. In this study, ATR-FTIR spectroscopy was used in tandem with one-class support vector machine (OCSVM) to detect adulteration in turmeric powder. The OCSVM models were trained using 42 pure turmeric powder samples, optimized using 30 pure turmeric powder samples, and subsequently evaluated by classifying 30 pure and 120 adulterated (cornstarch, Metanil Yellow, Orange II, and Sudan I) samples. Preprocessing methods, such as Savitzky-Golay (SG)-derivatives, standard normal variate (SNV), and multiplicative scatter correction (MSC), were used individually and in combination to obtain the best-performing model. Models were assessed by comparing the sensitivity, specificity, and efficiency values and compared with one-class soft independent modeling of class analogy (OCSIMCA). The best performing OCSVM model (sensitivity = 1.00, specificity = 0.89) was obtained by first conducting an MSC on the raw data followed by SG-2nd derivative transformation. It also has an efficiency value of 0.94, which was 0.14 higher than when data preprocessing was not done. Compared to the results of OCSIMCA, the OCSVM model gave a higher efficiency value and can detect lower levels of cornstarch adulteration. Also, the results showed that inclusion of data preprocessing can lead to a better classification model. With the obtained evaluation parameter values, ATR-spectroscopy coupled with OCSVM demonstrated its potential for screening turmeric powder products.

Spectra obtained from fresh grapevine organs provide information on chemical composition but could also contain valuable information on the morphological and physical attributes. The prediction of grapevine organs physical attributes using infrared spectroscopy is explored for the first time in this study. Near infrared spectroscopy (NIR) using a solid probe (NIR-SP) and a rotating integrating sphere (NIR-RS) and mid infrared (MIR) were used to obtain spectra from fresh and intact grapevine shoots, leaves, and berries. Linear partial least squares (PLS) and non-linear least absolute shrinkage and selection operator (LASSO), and extreme gradient boost (XGBoost) were implemented to predict relevant physical attributes in grapevine organs. NIR-RS using XGBoost showed coefficients of determination in validation (R²val) of 91.01% and root mean square error of prediction (RMSEP) of 0.71 mm (6.80%) for berry diameter. Shoot diameter was predicted at R²val of 62.08% and RMSEP at 0.82 mm (12.75%) using NIR-RS with LASSO regression. Monitoring these attributes throughout the growing season can lead to important viticultural information on grapevine yield, growth, and health.

The Raman spectrum of furfural is computed and analyzed using the Vibrational Coupled Cluster Method (VCCM). Furfural has immense applications in organic synthesis, electrocatalysis and energy conversion process. The experimental Raman spectrum of furfural is congested and broad, even in the medium energy regions like C-C stretching and CO stretching regions. We extensively analyze the Fermi and higher quanta resonance effects on the Raman spectrum by examining the VCCM wavefunctions. In addition, a systematic study on the effect of incident frequency on the anharmonic Raman activity is carried out by comparing the results with incident wavelengths 325.0 nm and 632.8 nm against static polarizability.

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.