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Recent Trends in Compressive Raman Spectroscopy Using DMD-Based Binary Detection
摘要： The collection of high-dimensional hyperspectral data is often the slowest step in the process of hyperspectral Raman imaging. With the conventional array-based Raman spectroscopy acquiring of chemical images could take hours to even days. To increase the Raman collection speeds, a number of compressive detection (CD) strategies, which simultaneously sense and compress the spectral signal, have recently been demonstrated. As opposed to conventional hyperspectral imaging, where full spectra are measured prior to post-processing and imaging CD increases the speed of data collection by making measurements in a low-dimensional space containing only the information of interest, thus enabling real-time imaging. The use of single channel detectors gives the key advantage to CD strategy using optical filter functions to obtain component intensities. In other words, the filter functions are simply the optimized patterns of wavelength combinations characteristic of component in the sample, and the intensity transmitted through each filter represents a direct measure of the associated score values. Essentially, compressive hyperspectral images consist of 'score' pixels (instead of 'spectral' pixels). This paper presents an overview of recent advances in compressive Raman detection designs and performance validations using a DMD based binary detection strategy.
关键词： Chemometrics,multivariate data analysis,digital light processor (DLP),digital micromirror device (DMD),optimal binary filters,spatial light modulators (SLM),Raman spectroscopy,chemical imaging,compressive detection
更新于2025-09-23 15:22:29
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Application of experimental design and multivariate analysis in the on-line reaction monitoring of a Suzuki cross-coupling reaction by Raman spectroscopy and multivariate analysis
摘要： A Suzuki-coupling reaction for the construction of CeC bond was monitored using Raman spectroscopy. The effects of temperature and concentration on the reaction yield were investigated following a central composite design. The percent yield for each data was measured off-line using GCeMS. On-line monitoring reactions using Raman spectroscopy coupled with partial least squares (PLS) were used for building a prediction model. Raman spectra collected from five experiments at different sampling times were used in building the model. The PLS model was then validated and results showed that the yield of reaction can be predicted well with high accuracy by the model, both within and outside the experimental limits. The results presented here have shown that such model is well suited for monitoring Suzuki-coupling reactions that are widely used in chemical and pharmaceutical industries.
关键词： Raman spectroscopy,Multivariate data analysis,Reaction monitoring,Suzuki cross-coupling
更新于2025-09-23 15:21:21
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Individual wheat kernels vigor assessment based on NIR spectroscopy coupled with machine learning methodologies
摘要： Knowledge of the seed vigor status of individual wheat kernels could provide scientific evidence for the screening of excellent germplasm and the breeding of seedlings. Although many factors collaborate to reduce or render seed vigor, many methods have been employed to detect individual kernel vigor. This study aims to demonstrate the feasibility for using near-infrared (NIR) spectroscopy to detect individual wheat seed vigor and determine suitable machine learning classification models. For this study, 1152 wheat kernel samples were selected, and five-sixths of the portion was treated by artificial aging (AA). All seeds spectra were acquired using a single-seed near-infrared system covering the spectral range of 1200–2400 nm. After NIR spectral collection, all kernels underwent a germination test to confirm their vigor. The spectral data from kernels within 3 germination days, 5 germination days and the non-germination kernels were further used for the development of three-category classification models. After pretreatment by using Savitzky-Golay (SG) second derivative-method and standard normal variate (SNV) correction, the high-dimension spectral data were smoothed, and then were reduced to select most effective wavelengths by two spectral dimensional reduction algorithms: principal component analysis (PCA) and successive projections algorithm (SPA). Four machine learning methodologies, support vector machine (SVM), extreme learning machine (ELM), random forest (RF) and adaptive boosting (AdaBoost) were combined with the two spectral dimensional reduction algorithms to build eight models to discriminate and predict each wheat kernel’s vigor. The results demonstrated that the eight three-category machine learning classification models developed with the two spectral dimensional reduction algorithms provided comparable results for individual wheat kernel vigor. The accuracies of the eight models were higher than 84.0%, and PCA-ELM and SPA-RF models afforded the two highest classification accuracies at 88.9% and 88.5%, respectively. The macro-average F1 of these two models were at the same level of 0.887, which means these two models had almost the same ability to assess kernel’s vigor. This study could serve as a major step towards the development of a fast and non-destructive high-throughput NIR-based sorting system of individual wheat kernel vigor determination for plant breeders, wheat quality inspectors, wheat processors, etc.
关键词： Multiple classification,Machine learning,Near-infrared spectroscopy,Multivariate data analysis,Wheat seed vigor
更新于2025-09-19 17:13:59
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Tea types classification with data fusion of UV–Vis, synchronous fluorescence and NIR spectroscopies and chemometric analysis
摘要： The potential of selected spectroscopic methods - UV-Vis, synchronous fluorescence and NIR as well a data fusion of the measurements by these methods - for the classification of tea samples with respect to the production process was examined. Four classification methods - Linear Discriminant Analysis (LDA), Quadratic Discriminant Analysis (QDA), Regularized Discriminant Analysis (RDA) and Support Vector Machine (SVM) - were used to analyze spectroscopic data. PCA analysis was applied prior to classification methods to reduce multidimensionality of the data. Classification error rates were used to evaluate the performance of these methods in the classification of tea samples. The results indicate that black, green, white, yellow, dark, and oolong teas, which are produced by different methods, are characterized by different UV-Vis, fluorescence, and NIR spectra. The lowest error rates in the calibration and validation data sets for individual spectroscopies and data fusion models were obtained with the use of the QDA and SVM methods, and did not exceed 3.3% and 0.0%, respectively. The lowest classification error rates in the validation data sets for individual spectroscopies were obtained with the use of RDA (12,8%), SVM (6,7%), and QDA (2,7%), for the UV-Vis, SF, and NIR spectroscopies, respectively. NIR spectroscopy combined with QDA outperformed other individual spectroscopic methods. Very low classification errors in the validation data sets - below 3% - were obtained for all the data fusion data sets (SF+UV-Vis, SF+NIR, NIR+UV-Vis combined with the SVM method). The results show that UV-Vis, fluorescence and near infrared spectroscopies may complement each other, giving lower errors for the classification of tea types.
关键词： Fluorescence spectroscopy,Food adulteration,NIR,Teas classification,Multivariate data analysis,Data fusion,UV-Vis
更新于2025-09-10 09:29:36
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Calibration modelling for non-destructive estimation of external and internal quality parameters of ‘Marsh’ grapefruit using Vis/NIR spectroscopy
摘要： Consumer preference for fruit without disorder influences purchase of fruit at both local and international markets. Recent trends in horticulture show that consumer preference is influenced by assurance that external appearance is linked with rewarding internal sensory quality. Therefore, the need for non-destructive evaluation of external and internal quality parameters is important. This study was conducted to develop and test calibration models for integrated prediction of external and internal quality of 'Marsh' grapefruit. Visible to near infrared (Vis/NIR) spectroscopy (Vis/NIRS) was used to acquire spectral information from 522 intact fruit. Reference quality parameters such as colour indices (luminosity (L*), greenness (a*) and yellowness (b*)), rind dry matter (DM), rind total phenolics concentration, BrimA, carbohydrates, sweetness index (SI) and total sweetness index (TSI) were obtained using conventional methods. Principal component analysis was applied to analyse spectral data to identify outliers. Savitzky-Golay second derivative with second order polynomial was employed as pre-processing method to correct light scattering properties of the spectra. The spectra were subjected to a test set validation by categorising the spectra into calibration (60%) and validation (40%) sets. Partial least square regression was used as chemometric tool to develop models for predicting each parameter. The model validation results showed that external and internal quality parameters of grapefruit could be predicted with satisfactory accuracy with R2 value of 0.99 for rind quality parameters (L*, a*, b*, DM) and 0.77, 0.99, 0.99 for BrimA, SI and TSI, respectively. The residual predictive deviation (RPD) results for L*, a*, b*, DM, BrimA, SI and TSI were 64.1, 61.4, 123.4, 12.9, 1.4, 9.0 and 13.9, respectively. Vis/NIR calibration and validation results demonstrated that quality parameters of 'Marsh' grapefruit could be predicted using Vis/NIRS.
关键词： citrus fruit,multivariate data analysis,rind,chemometrics,near infrared spectroscopy,'Marsh' grapefruit
更新于2025-09-09 09:28:46