Spectral Processing Methods Research Articles

Correction to: Effects of different spectral processing methods on soil organic matter prediction based on VNIR‑SWIR spectroscopy in karst areas, Southwest China

Correction to: Effects of different spectral processing methods on soil organic matter prediction based on VNIR‑SWIR spectroscopy in karst areas, Southwest China

Non-Destructive Identification of Virgin Cashmere and Chemically Modified Wool Fibers Based on Fractional Order Derivative and Improved Wavelength Extraction Algorithm Using NIR Spectroscopy and Chemometrics

ABSTRACT Virgin cashmere is highly prized for its superior quality, and chemically modified wool is often used to fake it. However, traditional spectral analysis and image processing methods struggle to identify the two fibers. To address this, combining chemometrics with near-infrared (NIR) spectroscopy is proposed to identify virgin cashmere and chemically modified wool based on fractional order derivatives and improved wavelength extraction algorithm. The original spectra of the two fibers are very close, making identification difficult. Therefore, fractional order derivatives are used to amplify tiny spectral differences, which are often overlooked in conventional analysis. Meanwhile, common wavelength extraction algorithms few consider inherent relationship between spectral features and chemical properties, so an improved wavelength extraction algorithm using Shuffled Frog Leaping Algorithm (SFLA) and Beluga Whale Optimization (BWO) is employed to explore the connection of the two. The proposed method with Partial Least Squares Discriminant Analysis (PLS-DA) achieves an accuracy of 94.4%. Experimental results demonstrate that fractional order derivatives enhance tiny spectral features and differences, while extracted feature wavelengths correspond to main chemical characteristic bands of the fibers. This study shows the feasibility of identifying virgin cashmere and chemically modified wool and offers a novel idea for identifying fibers in the textile industry.

Cocoa bean metabolomics reveals polyphenols as potential markers relating to fine dark chocolate color shades.

This study aimed to evaluate the color and the discriminating compounds for two types of cocoa beans (black and brown beans) related to 70% dark chocolates of black and brown colors from a previous work of our group. Color analysis and untargeted high-resolution mass spectrometry-based metabolomic analysis were performed on eight beans of each type. Mass spectral data processing, univariate and multivariate statistical methods were conducted for classification of beans and selection of discriminant features. The results showed that the color difference already observed for black and brown chocolates preexists in the beans. Black and brown beans had 45 and 50 discriminant features, respectively, of which 16 and 41 were phenolic compounds. Most of them were also previously identified as discriminating compounds for black and brown chocolates. Black beans predominantly contained glycosylated flavanols, ranging from monomers to trimers, with dimers and trimers being A-type procyanidins, along with a phenolic acid (protocatechuic acid), and an O-glycosylated flavonol (quercetin-3-O-glucoside). In contrast, brown beans mostly contained non-glycosylated B-type procyanidins ranging from dimers to decamers, but also dimers and trimers of A-type procyanidins, and a glycosylated and sulfated flavanol ((epi) catechin hexoside-sulfate). These markers may be useful for quality control purposes and may contribute to the selection of beans that yield black or brown dark chocolates.

LSTM-based spectral confocal signal processing method

Traditional spectral confocal signal processing methods have problems such as difficulty in accurately extracting the peak wavelength, nonlinear error in the polynomial fitting calibration of the peak wavelength and position, and high dependence on hardware equipment. In this paper, the method of the LSTM neural network is introduced to achieve the direct characterization from the full spectrum signal to the position information by using its nonlinear mapping capability. The experimental results show that the measurement accuracy and measurement resolution of the new, to the best of our knowledge, method are improved, and it can still maintain a good measurement effect when using a low-performance spectrometer.

Quantitative detection of tapered fiber SERS probes prepared in batches by electrostatic adsorption self-assembly method

The tapered fiber Surface-enhanced Raman scattering (SERS) probe is a highly sensitive SERS substrate for on-site detection. However, fiber probes prepared by the current technology lack interchangeability and are difficult to meet the practical quantitative detection demands. In this paper, we develop a batch preparation technology of tapered fiber SERS probes, with good interchangeability between each of the 20 tapered fiber SERS probes prepared in batches. Furthermore, we propose and demonstrate a spectral data assimilation method, through which the differences in interchangeability between different batches can be eliminated. Using 10 batches of spectral data assimilated with this method, we obtain a high-precision calibrated quantitative relationship curve with a fitting degree of 0.99464, with recovery rates of the two thiram spiked samples 106.88% and 95.75%, respectively. The batch preparation technology and the spectral data processing method provide a new option for practical quantitative detection.

Influence of the peel on online detecting soluble solids content of pomelo using Vis-NIR spectroscopy coupled with chemometric analysis

The application of Visible-near infrared spectroscopy (Vis-NIRs) to internal quality detection of pomelo with large sizes and thick peels is still challenging. Considering that the peel has a large mass ratio in pomelo and a great added value in industrial processing, a postharvest processing mode of grading after peeling without affecting consumption of pomelos may be an approach to avoid adverse influence of peel on soluble solids content (SSC) evaluation. In this study, we investigated the performance variation of Vis-NIRs for SSC online determination of pomelos with and without peel for the first time. An online detection system for pomelo spectra acquisition was developed, and the spectral characteristic difference of the intact and peeled pomelos was analyzed. Subsequently, the performance of calibration models was gradually improved and comparatively analyzed by various spectral processing and wavelength selection methods. Furthermore, convolutional neural network (CNN) was utilized to explore its ability for feature extraction. The results showed that combined with standard normal variate (SNV) and second order detrending and changeable-size moving window algorithms, the partial least squares regression (PLSR) model using spectra of peeled pomelos achieved the best prediction results. Specifically, the model attained a determination coefficient of prediction (Rp2) of 0.88, a root mean square error of prediction (RMSEP) of 0.294%, and a residual predictive deviation (RPD) value of 2.57. This study demonstrates that the peel has a significantly negative effect on the prediction performance and the CNN could be an alternative to conventional PLSR method. Our work may open new avenues for the internal quality assessment of agro-products with complex tissue structure.

Comparison of convolutional-neural-networks-based method and LCModel on the quantification of in vivo magnetic resonance spectroscopy.

Quantification of metabolites concentrations in institutional unit (IU) is important for inter-subject and long-term comparisons in the applications of magnetic resonance spectroscopy (MRS). Recently, deep learning (DL) algorithms have found a variety of applications on the process of MRS data. A quantification strategy compatible to DL base MRS spectral processing method is, therefore, useful. This study aims to investigate whether metabolite concentrations quantified using a convolutional neural network (CNN) based method, coupled with a scaling procedure that normalizes spectral signals for CNN input and linear regression, can effectively reflect variations in metabolite concentrations in IU across different brain regions with varying signal-to-noise ratios (SNR) and linewidths (LW). An error index based on standard error (SE) is proposed to indicate the confidence levels associated with metabolite predictions. In vivo MRS spectra were acquired from three brain regions of 43 subjects using a 3T system. The metabolite concentrations in IU of five major metabolites, quantified using CNN and LCModel, exhibit similar ranges with Pearson's correlation coefficients ranging from 0.24 to 0.78. The SE of the metabolites shows a positive correlation with Cramer-Rao lower bound (CRLB) (r=0.46) and absolute CRLB (r=0.81), calculated by multiplying CRLBs with the quantified metabolite content. In conclusion, the CNN based method with the proposed scaling procedures can be employed to quantify in vivo MRS spectra and derive metabolites concentrations in IU. The SE can be used as error index, indicating predicted uncertainties for metabolites and sharing information similar to the absolute CRLB.

A prediction model of rubber content in the dried root of Taraxacum kok-saghyz Rodin based on near-infrared spectroscopy

BackgroundTaraxacum kok-saghyz Rodin (TKS) is a highly potential source of natural rubber (NR) due to its wide range of suitable planting areas, strong adaptability, and suitability for mechanized planting and harvesting. However, current methods for detecting NR content are relatively cumbersome, necessitating the development of a rapid detection model. This study used near-infrared spectroscopy technology to establish a rapid detection model for NR content in TKS root segments and powder samples. The K445 strain at different growth stages within a year and 129 TKS samples hybridized with dandelion were used to obtain their near-infrared spectral data. The rubber content in the root of the samples was detected using the alkaline boiling method. The Monte Carlo sampling method (MCS) was used to filter abnormal data from the root segments of TKS and powder samples, respectively. The SPXY algorithm was used to divide the training set and validation set in a 3:1 ratio. The original spectrum was preprocessed using moving window smoothing (MWS), standard normalized variate (SNV), multiplicative scatter correction (MSC), and first derivative (FD) algorithms. The competitive adaptive reweighted sampling (CARS) algorithm and the corresponding chemical characteristic bands of NR were used to screen the bands. Partial least squares (PLS), random forest (RF), Lightweight gradient augmentation machine (LightGBM), and convolutional neural network (CNN) algorithms were employed to establish a model using the optimal spectral processing method for three different bands: full band, CARS algorithm, and chemical characteristic bands corresponding to NR. The model with the best predictive performance for high rubber content intervals (rubber content > 15%) was identified.ResultThe results indicated that the optimal rubber content prediction models for TKS root segments and powder samples were MWS–FD CASR–RF and MWS–FD chemical characteristic band RF, respectively. Their respective RP2\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$${\ ext{R}}_{{\ ext{P}}}^{2}$$\\end{document}, RMSEP, and RPDP values were 0.951, 0.979, 1.814, 1.133, 4.498, and 6.845. In the high rubber content range, the model based on the LightGBM algorithm had the best prediction performance, with the RMSEP of the root segments and powder samples being 0.752 and 0.918, respectively.ConclusionsThis research indicates that dried TKS root powder samples are more appropriate for constructing a rubber content prediction model than segmented samples, and the predictive capability of root powder samples is superior to that of root segmented samples. Especially in the elevated rubber content range, the model formulated using the LightGBM algorithm has superior predictive performance, which could offer a theoretical basis for the rapid detection technology of TKS content in the future.

Simultaneous Rapid Detection of Multiple Physicochemical Properties of Jet Fuel Using Near-Infrared Spectroscopy.

Jet fuel is the primary fuel used in the aviation industry, and its quality has a direct impact on the safety and operational efficiency of aircraft. The accurate quantitative detection and analysis of various physicochemical property indicators are important for improving and ensuring the quality of jet fuel in the domestic market. This study used near-infrared (NIR) spectroscopy to establish a suitable model for the simultaneous and rapid detection of multiple physicochemical properties in jet fuel. Using more than 40 different sources of jet fuel, a rapid detection model was established by optimizing the spectral processing methods. The measurement models were separately built using the partial least-squares (PLS) and orthogonal PLS algorithms, and the model parameters were optimized. The results show that after the Savitzky-Golay second derivative preprocessing, the PLS model built using the feature spectra selected by the uninformative variable elimination wavelength algorithm achieved the best measurement performance. Compared with the PLS model without preprocessing, the range of the resulting accuracy improvement was at least 15.01%. Under the optimal model parameters, the calibration set regression coefficient (Rc2) of the 11 jet fuel property index models ranged from 0.9102 to 0.9763, with the root-mean-square error of calibration values up to 0.8468 °C (for flash points). The regression coefficient (Rp2) of the validation set ranged from 0.8239 to 0.9557, with the root-mean-square error of prediction values up to 1.1354 °C (for flash points). The ratios of prediction to deviation (RPD) values were all in the range of 1.9-3.0, indicating high accuracy and reliability of the model. The rapid NIR analysis method established in this study enables the simultaneous and rapid detection of multiple physicochemical properties of jet fuel, thereby providing effective technical support for ensuring the quality of jet fuel in the market.

Stratified diagnosis of cotton canopy spectral characteristics based on CWT-SPA and its relationship with moisture, nitrogen, and SPAD values

ABSTRACT Cotton is an economically valuable crop worldwide, and accurate and rapid leaf moisture content, nitrogen content, and soil plant analysis development (SPAD) value estimations are crucial for cotton growth. In this study, high-resolution spectral data were collected for the Tahe 2 cotton variety in Alar City, Xinjiang. The raw spectra were preprocessed using four methods: first derivative, standard normal variate transformation, second derivative, and multiplicative scatter correction. Wavelet coefficients at multiple scales were generated from the raw spectra using a continuous wavelet transform (CWT) and CWT with first derivative (CWT-FD). A quantitative detection model combining CWT and CWT-FD with successive projection algorithm – partial least squares (SPA-PLS) was developed to estimate the leaf moisture content, nitrogen content, and SPAD values. The performances of the different scales and methods for estimating these parameters were compared. Six stratification methods were employed to determine the optimal modelling results and decomposition scales for the parameters under various stratification schemes. The results demonstrated that the CWT and CWT-FD methods significantly improved the accuracy of estimating cotton canopy spectral characteristics related to the parameters, surpassing that of conventional spectral processing methods. The mexh, db5, and db5 wavelet basis functions, along with scale 3, were identified as the optimal modelling choices for moisture content, nitrogen content, and SPAD values throughout the cotton plant growth period. Among the six vertical stratification structures of cotton, the CWT and CWT-FD methods, along with the three wavelet basis functions, achieved the best results for the three parameters in the upper layer. The optimal relative percent deviation values for the three parameters of typical leaves ranged from 1.603 to 2.016, indicating satisfactory modelling performance. These findings provide a theoretical foundation for accurately estimating leaf moisture content, nitrogen content, and SPAD values using spectral technology, enabling the simultaneous extraction and monitoring of multiple physiological indicators.

Quantitative analysis of heavy metals in soil by X-ray fluorescence: Fusion, intelligentization and Nonstandard-sample calculation

Qualitative and quantitative analysis of heavy metal elements in soil by X-ray fluorescence (XRF) has received widespread attention and research from scholars as an important method for assessing environmental pollution. As a detection sample for multi-component systems, the study of matrix correction has always been the key to XRF data analysis for geological samples. In this study, we reviewed the calculation and analysis methods of XRF data used for geological samples since the Sherman equation was proposed, and divided the development of XRF data processing for soil samples into three stages based on the changes in the matrix correction methods used. By reviewing the processing ideas from past research, this paper summarizes the process of quantitative analysis of geological samples into seven stages and reviews the commonly used methods for each stage. Due to limitations in instrument and standard sample costs, as well as methodological constraints, geological samples currently face three challenges: a shortage of standard samples, insufficient generalization ability of established models, and large measurement errors in low-content element determination. With the further cross-penetration of multiple fields and disciplines and the summary of past research trends, we propose three research trends that may break through these limitations: fusion, intelligentization, and nonstandard-sample calculation. We also discuss the technical solutions related to these three research trends. We extensively discussed the feasibility and advantages of using spectral co-use, knowledge engineering, and adversarial data augmentation techniques to address problems. Our review provides insights into the XRF spectral data processing methods and frameworks for evaluating geological samples, and provides technical solutions to address the current challenges faced by XRF analysis of geological samples.

Effects of different spectral processing methods on soil organic matter prediction based on VNIR-SWIR spectroscopy in karst areas, Southwest China

Effects of different spectral processing methods on soil organic matter prediction based on VNIR-SWIR spectroscopy in karst areas, Southwest China

Spatial and Spectral EEG Signal Analysis with Case Study of Slogans on Consumer’s Behaviour

Neuromarketing utilizes neuroscientific techniques to investigate consumer behavior, providing valuable insights beyond traditional research methods such as questionnaires and interviews which may not provide a complete understanding of consumer decision-making processes.Electroencephalography (EEG) has emerged as a promising tool for analyzing consumer responses to marketing stimuli. Nevertheless, the neural processing of slogans and their impact on short-term memory recall using EEG signals remains understudied. This research aims to bridge this gap by examining the neural activity associated with the recall of slogans using EEG analysis. By employing a spatial selection and spectral processingmethod, which involves Butterworth BPF filtering and L2-norm normalization to identify optimal channel combinations, active brain areas involved in slogan processing can be identified. Results reveal prominent activation in the frontal and occipital regions, particularly the F4 channel, indicating active recall and visual processing in individuals who correctly respond to slogans. These findings underscore the significance of slogans as visual marketing stimuli and offer insights for effective branding strategies. Leveraging EEG signals and understanding short-term memory processes enables marketers to optimize the impact of slogans on consumer engagement and brand recognition.

Challenges of Raman spectra to estimate carbonyl index of microplastics: A case study with environmental samples from sea surface

This study investigates the feasibility of using the carbonyl index (CI) derived from Raman spectra as an indicator of plastic degradation and its relationship with the CI calculated from Fourier transform infrared (FTIR) spectra, using microplastic samples of polyethylene (PE) from surface seawater. Multiple methods were used to calculate the CI values of FTIR spectra, while proposed methods were used to calculate the corresponding CI values of Raman spectra. Some significant relations between FTIR CI and Raman CI were observed. However, small R2 values suggest weak functional relationships, which can be attributed to the low signal-to-noise ratio (SNR) of Raman spectra. These results highlight the challenges of establishing a functional relationship between FTIR CI and Raman CI, including challenges such as the uniformity of Raman spectra, determining optimal Raman measurement parameters, selecting appropriate peaks for Raman CI calculation, deciding on spectral processing methods, and addressing the interdependence of these issues.

Estimation of Soil Surface Roughness Parameters Under Simulated Rainfall Using Spectral Reflectance in Optical Domain

AbstractThe purpose of the study was to evaluate the possibility of parameterizing the state of soil surface roughness (SSR) based on proximal measurements of spectral reflectance in the VIS‐NIR range, which is important for the needs of monitoring the state of soil surfaces. SSR should be constantly monitored as it provides an insight into a range of hydrological and erosive soil processes and improves the interpretation of remote sensing data. SSR and the spectral reflectance of three texturally different soils were measured under simulated rainfall in laboratory condition. The relationship between the SSR parameters and soil spectra was determined using regression random forest models. Various spectral data processing methods were tested and the best wavelengths for SSR description after rainfall were found. Two roughness indices were used to describe the SSR: Height Standard Deviation (HSD) and T3D (Tortuosity index). Although both shared a significant correlation with SSR, the T3D index demonstrated a more pronounced rainfall effect and a closer correlation with spectral data than HSD. The best determination of T3D was obtained with the raw spectra (RAW) (R2 = 0.71), as well as with spectra transformed with the baseline alignment first derivative (BA1d) method (R2 = 0.71) or the Savitzky‐Golay (SG) method (R2 = 0.69). Different wavelengths were the best SSR predictors depending on the spectral transformation method (VIPs ‐ Variable Importance in Projection). For both roughness indices, the NIR wavelengths (725–820 nm) yielded the highest VIP Score in models based on RAW spectra, while those in the VIS region (450–772 nm) were most important in models based on transformed spectra.

Application of Spectral Analysis and Image Processing Methods to Discriminate Hydrothermal Alteration Minerals Around the Tutakdağı (Şebinkarahisar-Giresun) Lead–Zinc Deposits, Northeastern Turkey

Application of Spectral Analysis and Image Processing Methods to Discriminate Hydrothermal Alteration Minerals Around the Tutakdağı (Şebinkarahisar-Giresun) Lead–Zinc Deposits, Northeastern Turkey

Effect of different spectral processing methods on the quantitative model of Astragalus polysaccharides detected by NIR spectroscopy

Quality control of Chinese herbal medicines requires reliable and rapid detection techniques, Many scholars have used NIR spectroscopy for research in Chinese herbal medicine industry. The aim of this study was to evaluate the feasibility of combining NIR spectroscopy and machine learning to predict the adulteration content of Astragalus polysaccharides. The astragalus polysaccharide and rice flour were mixed to form astragalus polysaccharide adulterant with different concentration. the Quantitative prediction models are built using the support vector machine (SVM). Different pre-processing methods of SG, SNV, WT, SG+SNV, SG+WT were used to process the spectra, and then the continuous projection algorithm (SPA) and uninformative variable elimination (UVE) were used to select the characteristic wavelengths. The SVM prediction model based on SG+SNV+SPA is optimal with an RPD of 2.653, indicating that the model has good predictive ability. The results showed that NIR technique could be used for quantitative analysis of Astragalus polysaccharides.

A novel approach to combine spatial and spectral information from hyperspectral images

This article proposes a generic framework to process jointly the spatial and spectral information of hyperspectral images. First, sub-images are extracted. Then each of these sub-images follows two parallel workflows, one dedicated to the extraction of spatial features and the other dedicated to the extraction of spectral features. Finally, the extracted features are merged, producing as many scores as sub-images. Two applications are proposed, illustrating different spatial and spectral processing methods. The first one is related to the characterization of a teak wood disk, in an unsupervised way. It implements tensors of structure for the spatial branch, simple averaging for the spectral branch and multi-block principal component analysis for the fusion process. The second application is related to the early detection of apple scab on leaves. It implements co-occurrence matrices for the spatial branch, singular value decomposition for the spectral branch and multiblock partial least squares discriminant analysis for the fusion process. Both applications demonstrate the interest of the proposed method for the extraction of relevant spatial and spectral information and show how promising this new approach is for hyperspectral imaging processing.

A non-invasive optical method for anaemia detection

The latest non-invasive biomedical electronic sensors are capable of providing continuous reliable information, for instance oxygenation level of blood, level of glucose etc. These biomedical sensors are in general based on the electro-chemical or spectral properties of the substances. The absorption of blood in infrared and visible range is prominently determined by haemoglobin. The coefficient of absorption for blood is different at different wavelengths. This optical absorption characteristic of the blood yields the vital information on the composition of the blood. Haemoglobin is primarily used to measure the status of anaemia, in developing countries like India and other African countries numerous people with greater health needs don’t have access to proper diagnostic facilities. Therefore, an introduction of portable, low cost and non-invasive Haemoglobin detector will give a vast opportunity for screening the population suffering with anaemia. The present article purposes a non-invasive method of haemoglobin detection comprising of an organic photovoltaic cell and three LED’s sources i.e., blue, green and red which are used with their respective radiation spectral range of 450–495 nm, 495–570 nm and 620–750 nm to illuminate an area of the skin on finger, this transmitted light after interacting with tissues is detected by an arrangement of Coumarin 30 : C60/NN′-QA/ZnPc active layer based organic solar cell. Thus, the approach provide in this article presents a simple, biocompatible and flexible means for assessing blood haemoglobin level by utilizing an multi spectral optical processing method. The method developed herein could further be integrated to wearable electronic devices.

Nondestructive evaluation of polyphenol oxidase activity in tomato based on segmentation of Vis-NIR spectral graph characteristics

In this study, a new prediction method for polyphenol oxidase (PPO) activity in tomato based on segmentation of Vis-NIR spectral graph characteristics has been proposed. Spectral data are miscellaneous and complex, which increases the prediction modeling complexity. However, spectral data depicted curve is usually approximately continuous and smooth, indicating spectral data is constrained by the smooth spectral curve. There is a strong correlation between adjacent spectral data, and spectral data information redundancy is large. To eliminate information redundancy, compress data, simplify modeling process and improve modeling effects, Vis-NIR spectral discrete points was imagined as a smooth curve, the smooth curve and coordinate axis enclosed area was regarded as a spectral graph. The whole spectral graph was divided into n small graphs, and the area/area-to-perimeter ratio characteristics of every small spectral graph were extracted. The extracted characteristics were used to establish prediction model of PPO activity. Experimental results showed that the proposed algorithm optimizes the modeling effect, reduces the modeling complexity and improves the modeling efficiency. When the spectral graph was averagely segmented into 21 small graphs, area-to-perimeter ratio characteristic was extracted, and multiple linear regression (MLR) was used, the modeling method performed the best. The root mean square error of calibration set (RMSEC) and prediction set (RMSEP) were 1.74 and 1.99 respectively, and the correlation coefficient of calibration set Rc and prediction set Rp were 0.98 and 0.97 respectively. This study provides a reference for nondestructive evaluation of tomato PPO activity using Vis-NIR spectral technology and the proposed spectral data processing method sheds light on spectral data processing.