Scatter Correction Research Articles

Detection of saffron adulteration with Crocus sativus style using NIR-hyperspectral imaging and chemometrics

Saffron is a valuable spice that is often adulterated. This study proposes using near-infrared hyperspectral imaging (NIR-HSI) and chemometrics as a fast and cost-effective method for detecting and quantifying adulteration in saffron stigmas. Adulterated saffron samples were prepared by adding Crocus sativus style to pure saffron stigmas in varying concentrations (20–90%). The spectral data were pre-treated using standard normal variate (SNV), and multiplicative scatter correction (MSC), while variable reduction was performed by Principal Component Analysis (PCA) and Partial Least Squares (PLS). Classification was done using Linear Discriminant Analysis (LDA), PLS-DA, Support Vector Machine (SVM), and Multi-layer Perceptron (MLP) models, while quantification was achieved by PLS, PCA, SVM, and MLP-based regression models. The HSI technique achieved correct classification rates of 95.6%–100% in discriminating authentic saffron from plant adulterants and adulterated saffron across all the models. Regression models to quantify the percentage style adulteration in saffron demonstrated excellent prediction abilities with almost all models achieving RPD (Residual Predictive Deviation) values of 3.0–5.4. The MLP model (1 hidden layer with 3 neurons) built from SNV pre-processed and PLS reduced data (15 LVs), showed exceptional predictive capabilities, with an R2p of 0.97, a Root Mean Squared Error of Prediction (RMSEP) of 4.3%, and an RPD of 5.4. The results demonstrate the potential of NIR-HSI and chemometrics for rapid and nondestructive detection and quantification of style in saffron stigmas.

Artificial Intelligence-Driven Single-Shot PET Image Artifact Detection and Disentanglement: Toward Routine Clinical Image Quality Assurance.

Medical imaging artifacts compromise image quality and quantitative analysis and might confound interpretation and misguide clinical decision-making. The present work envisions and demonstrates a new paradigm PET image Quality Assurance NETwork (PET-QA-NET) in which various image artifacts are detected and disentangled from images without prior knowledge of a standard of reference or ground truth for routine PET image quality assurance. The network was trained and evaluated using training/validation/testing data sets consisting of 669/100/100 artifact-free oncological 18 F-FDG PET/CT images and subsequently fine-tuned and evaluated on 384 (20% for fine-tuning) scans from 8 different PET centers. The developed DL model was quantitatively assessed using various image quality metrics calculated for 22 volumes of interest defined on each scan. In addition, 200 additional 18 F-FDG PET/CT scans (this time with artifacts), generated using both CT-based attenuation and scatter correction (routine PET) and PET-QA-NET, were blindly evaluated by 2 nuclear medicine physicians for the presence of artifacts, diagnostic confidence, image quality, and the number of lesions detected in different body regions. Across the volumes of interest of 100 patients, SUV MAE values of 0.13 ± 0.04, 0.24 ± 0.1, and 0.21 ± 0.06 were reached for SUV mean , SUV max , and SUV peak , respectively (no statistically significant difference). Qualitative assessment showed a general trend of improved image quality and diagnostic confidence and reduced image artifacts for PET-QA-NET compared with routine CT-based attenuation and scatter correction. We developed a highly effective and reliable quality assurance tool that can be embedded routinely to detect and correct for 18 F-FDG PET image artifacts in clinical setting with notably improved PET image quality and quantitative capabilities.

Reconstruction of multi-animal PET acquisitions with anisotropically variant PSF

Among other factors such as random, attenuation and scatter corrections, uniform spatial resolution is key to performing accurate quantitative studies in Positron emission tomography (PET). Particularly in preclinical PET studies involving simultaneous acquisition of multiple animals, the degradation of image resolution due to the depth of interaction (DOI) effect far from the center of the Field of View (FOV) becomes a significant concern. In this work, we incorporated a spatially-variant resolution model into a real time iterative reconstruction code to obtain accurate images of multi-animal acquisition. We estimated the spatially variant point spread function (SV-PSF) across the FOV using measurements and Monte Carlo (MC) simulations. The SV-PSF obtained was implemented in a GPU-based Ordered subset expectation maximization (OSEM) reconstruction code, which includes scatter, attenuation and random corrections. The method was evaluated with acquisitions from two preclinical PET/CT scanners of the SEDECAL Argus family: a Derenzo phantom placed 2 cm off center in the 4R-SuperArgus, and a multi-animal study with 4 mice in the 6R-SuperArgus. The SV-PSF reconstructions showed uniform spatial resolution without significant increase in reconstruction time, with superior image quality compared to the uniform PSF model.

Single-shot quantitative x-ray imaging using a primary modulator and dual-layer detector.

Conventional x-ray imaging and fluoroscopy have limitations in quantitation due to several challenges, including scatter, beam hardening, and overlapping tissues. Dual-energy (DE) imaging, with its capability to quantify area density of specific materials, is well-suited to address such limitations, but only if the dual-energy projections are acquired with perfect spatial and temporal alignment and corrected for scatter. In this work, we propose single-shot quantitative imaging (SSQI) by combining the use of a primary modulator (PM) and dual-layer (DL) detector, which enables motion-free DE imaging with scatter correction in a single exposure. The key components of our SSQI setup include a PM and DL detector, where the former enables scatter correction for the latter while the latter enables beam hardening correction for the former. The SSQI algorithm allows simultaneous recovery of two material-specific images and two scatter images using four sub-measurements from the PM encoding. The concept was first demonstrated using simulation of chest x-ray imaging for a COVID patient. For validation, we set up SSQI geometry on our tabletop system and imaged acrylic and copper slabs with known thicknesses (acrylic: 0-22.5cm; copper: 0-0.9mm), estimated scatter with our SSQI algorithm, and compared the material decomposition (MD) for different combinations of the two materials with ground truth. Second, we imaged an anthropomorphic chest phantom containing contrast in the coronary arteries and compared the MD with and without SSQI. Lastly, to evaluate SSQI in dynamic applications, we constructed a flow phantom that enabled dynamic imaging of iodine contrast. Our simulation study demonstrated that SSQI led to accurate scatter correction and MD, particularly for smaller focal blur and finer PM pitch. In the validation study, we found that the root mean squared error (RMSE) of SSQI estimation was 0.13cm for acrylic and 0.04mm for copper. For the anthropomorphic phantom, direct MD resulted in incorrect interpretation of contrast and soft tissue, while SSQI successfully distinguished them quantitatively, reducing RMSE in material-specific images by 38%-92%. For the flow phantom, SSQI was able to perform accurate dynamic quantitative imaging, separating contrast from the background. We demonstrated the potential of SSQI for robust quantitative x-ray imaging. The integration of SSQI is straightforward with the addition of a PM and upgrade to a DL detector, which may enable its widespread adoption, including in techniques such as radiography and dynamic imaging (i.e., real-time image guidance and cone-beam CT).

Near-infrared spectroscopy bioprobe estimation of metabolites’ responses to Pb2+ in Cladophora rupestris

Near-infrared (NIR) spectroscopy was used as a bioprobe to estimation chlorophyll, total sugar, proline, and metallothionein (MT) response to Pb2+ (0, 1, 2.5, 5, 7.5, 10, and 20 mg/l) in Cladophora rupestris. Spectra between 3775.94 and 12315.19 cm−1 were analysed using a partial least-squares regression (PLS). Principal component analysis (PCA) was performed by evaluating the distance between the C. rupestris samples treated with different concentrations of Pb2+. Results showed that 4000–7500 cm−1 of raw NIR spectra from C. rupestris was linked to carbohydrates and stretching of N–H, and the O–H overtone was associated with proline and MT. NIR spectroscopy technique coupled with PLS quantitative analysis model has high potential. Multiplicative scatter correction and standard normal variate combining first-order derivative with S-G smoothing filter are superior to the other models. The root mean square error of cross of chlorophyll, total sugar, proline, and MT was 0–1, indicating the accuracy of the model is high. The Rc2 values of chlorophyll, total soluble sugar, proline, and MT between measured and predicted values were higher than 0.99. PCA analysis indicate clear boundaries in the spatial distributions of seven Pb2+ concentrations, indicating that NIR spectroscopy has high potential as a tool assisting bioprobes for estimating metabolites’ responses to Pb2+ in Cladophora rupestris.

Prediction of Potassium (K) Content in Soil Analysis Utilizing Near-Infrared (NIR) Spectroscopy

The relationship between NIR spectroscopy spectral absorbance and potassium (K) is studied as a representative factor for analysing soil's nutritional content. To determine the potassium content of soil samples without resorting to intrusive and time-consuming chemical analysis techniques, NIR spectroscopy sampling techniques have been tested. Spectrum absorption data from 900 nm to 1600 nm were discovered to correspond with potassium values and were then analysed using a number of pre-processing procedures. Five techniques, Multiplicative Scatter Correction (MSC), Multiplicative Scatter Correction using Common Amplification (MSSCA), Multiplicative Scatter Correction using Common Offset (MSCCO), Detrending (DT), and Mean Normalization (MN), have been identified as the most effective. Using the Partial Least Squares Regression (PLSR) model, both calibration and prediction data are evaluated. In the end study, the MSCCA method was determined to be the most effective pre-processing method for both calibration and prediction outcomes, with R2 values of 0.9998 for calibration and RMSE values of 0.0600 for prediction. Utilising PLSR model and the MSCCA preprocessing method, the relationship between NIRS absorbance data and potassium may be determined. Consequently, we may infer that the NIRS approach can be utilised to detect amount of potassium in soil analysis employing a less time-consuming, non-invasive, and labour-intensive sampling technique.

Non-destructive detection of protein content in mulberry leaves by using hyperspectral imaging.

Protein content is one of the most important indicators for assessing the quality of mulberry leaves. This work is carried out for the rapid and non-destructive detection of protein content of mulberry leaves using hyperspectral imaging (HSI) (Specim FX10 and FX17, Spectral Imaging Ltd., Oulu, Finland). The spectral range of the HSI acquisition system and data processing methods (pretreatment, feature extraction, and modeling) is compared. Hyperspectral images of three spectral ranges in 400-1,000 nm (Spectral Range I), 900-1,700 nm (Spectral Range II), and 400-1,700 nm (Spectral Range III) were considered. With standard normal variate (SNV), Savitzky-Golay first-order derivation, and multiplicative scatter correction used to preprocess the spectral data, and successive projections algorithm (SPA), competitive adaptive reweighted sampling, and random frog used to extract the characteristic wavelengths, regression models are constructed by using partial least square and least squares-support vector machine (LS-SVM). The protein content distribution of mulberry leaves is visualized based on the best model. The results show that the best results are obtained with the application of the model constructed by combining SNV with SPA and LS-SVM, showing an R 2 of up to 0.93, an RMSE of just 0.71 g/100 g, and an RPD of up to 3.83 based on the HSI acquisition system of 900-1700 nm. The protein content distribution map of mulberry leaves shows that the protein of healthy mulberry leaves distributes evenly among the mesophyll, with less protein content in the vein of the leaves. The above results show that rapid, non-destructive, and high-precision detection of protein content of mulberry leaves can be achieved by applying the SWIR HSI acquisition system combined with the SNV-SPA-LS-SVM algorithm.

Comparison between Variable-Selection Algorithms in PLS Regression with Near-Infrared Spectroscopy to Predict Selected Metals in Soil.

Soil is one of the Earth's most important natural resources. The presence of metals can decrease environmental quality if present in excessive amounts. Analyzing soil metal contents can be costly and time consuming, but near-infrared (NIR) spectroscopy coupled with chemometric tools can offer an alternative. The most important multivariate calibration method to predict concentrations or physical, chemical or physicochemical properties as a chemometric tool is partial least-squares (PLS) regression. However, a large number of irrelevant variables may cause problems of accuracy in the predictive chemometric models. Thus, stochastic variable-selection techniques, such as the Firefly algorithm by intervals in PLS (FFiPLS), can provide better solutions for specific problems. This study aimed to evaluate the performance of FFiPLS against deterministic PLS algorithms for the prediction of metals in river basin soils. The samples had their spectra collected from the region of 1000-2500 nm. Predictive models were then built from the spectral data, including PLS, interval-PLS (iPLS), successive projections algorithm for interval selection in PLS (iSPA-PLS), and FFiPLS. The chemometric models were built with raw data and preprocessed data by using different methods such as multiplicative scatter correction (MSC), standard normal variate (SNV), mean centering, adjustment of baseline and smoothing by the Savitzky-Golay method. The elliptical joint confidence region (EJCR) used in each chemometric model presented adequate fit. FFiPLS models of iron and titanium obtained a relative prediction deviation (RPD) of more than 2. The chemometric models for determination of aluminum obtained an RPD of more than 2 in the preprocessed data with SNV, MSC and baseline (offset + linear) and with raw data. The metals Be, Gd and Y failed to obtain adequate models in terms of residual prediction deviation (RPD). These results are associated with the low values of metals in the samples. Considering the complexity of the samples, the relative error of prediction (REP) obtained between 10 and 25% of the values adequate for this type of sample. Root mean square error of calibration and prediction (RMSEC and RMSEP, respectively) presented the same profile as the other quality parameters. The FFiPLS algorithm outperformed deterministic algorithms in the construction of models estimating the content of Al, Be, Gd and Y. This study produced chemometric models with variable selection able to determine metals in the Ipojuca River watershed soils using reflectance-mode NIR spectrometry.

Identifying the authenticity and geographical origin of rice by analyzing hyperspectral images using unsupervised clustering algorithms

The present study evaluated the capability of the hyperspectral imaging system (HSI) as a rapid and non-destructive technique to identify the authenticity and origin of three Iranian rice varieties (Shiroudi, Khazar, and Hashemi) from three main origins. Captured raw spectral data were pre-processed using de-trending (DT), multiplicative scatter correction (MSC), and standard normal variant (SNV), and then were fed to principal component analysis (PCA) for the visual discrimination of the samples and data reduction. Next, since in real applications, the system might face unknown rice samples with unknown patterns, three unsupervised algorithms, self-organizing map (SOM), automatic clustering by artificial bee colony (ABC), and k-means algorithms were applied for clustering the samples in their original group. Results illustrated that SOM and k-means clustering algorithms led to the reliable grouping of the rice varieties. The models indicated that the high-yielding rice (Shiroudi and Hashemi) from two different origins with similar weather conditions were distinguished close to each other and completely separated from the third variety (Khazar). However, the automatic clustering method separated the varieties with less accuracy than the other two methods. Finally, the HSI system coupled with unsupervised algorithms provided satisfactory results and could be used as a reliable, out-lab, and fast method for ‎authenticating rice‎ varieties from different geographical origins.

A study on energy window-based scatter correction methods in 99mTc and 123I imaging

Tc-99m and I-123 are radionuclides commonly used for diagnostic examination in nuclear medicine. Detection of the scattered photons by the Compton interactions in the photopeak energy window is a factor affecting the image quality. DEW and TEW scatter correction methods are used to improve the image quality degrade by Compton scattered photons. In the study, planar and SPECT images of sphere, rod and cardiac phantom containing different activities were acquired with a gamma camera system. DEW/TEW corrected and uncorrected images for Tc-99m/I-123 were evaluated qualitatively and quantitatively. Both for planar and SPECT images, the DEW and TEW scatter correction methods were found to improve the image quality at varying rates.

Scatter Corrected Cone-Beam CT for Radiomics in SBRT: A Feasibility Study

The brilliant therapeutic effect and longer patient survival time has made stereotactic body radiation therapy (SBRT) the major technology in radiation therapy. In the therapy procedure, cone-beam computed tomography (CBCT) imaging is routinely performed for patient set-up and positioning verification. CBCT images also have valuable information about day-to-day changes of the lesion during the treatment procedure, which may have huge potential influence in the therapy outcome. we perform scatter correction on CBCT and analyze the radiomics features from scatter corrected CBCT to explore its feasibility for radiomics analysis in SBRT. The dataset consisted of 12 patients with two-stage abdomen CBCT and planning CT (pCT) images. Firstly, the scatter artifacts are suppressed using a customized ultrafast Monte Carlo simulation-based scatter correction algorithm. The regions of interest (ROIs) are contoured randomly in CBCT, pCT and uncorrected CBCT images to extract the radiomics features. A total number of 271 radiomics features which include 59 unfiltered and 212 wavelet-filtered features are calculated. The Spearman's correlation coefficients (CCs) are calculated for statistical analysis. The percentages of strongly-correlated features (PSCFs) (CC>0.8) are improved by 11.8% and 9.9% in two stages respectively after the scatter correction. Specifically, for the features after applying low-pass filter in both x and y directions (LL-wavelet-filtered features), the PRFs increase from 17.0%, 18.9% to 37.7% and 34.0%, respectively. As the results shows, effective scatter correction can improve the correlation between CBCT and pCT features. The LL-wavelet-filtered features are improved most significantly. The features extracted from CBCT images have the possibility to be applied in radiomics.

Simultaneously determining amino acid contents using near-infrared reflectance spectroscopy improved by pre-processing method in rice

Rice, the most consumed in Asian and constitute important source of amino acids. The present study evaluated the suitability of near-infrared reflectance spectroscopy to predict amino acid contents (AACs) using 1210 rice samples representing a wide range of grain qualities and genetic background. Calibration models established by combining different spectral derivatives, scatter and baseline correction methods, and regression algorithm were compared using full cross validation. A modified partial least square model (MPLS) with the spectral derivative treatment “2, 3, 3, 1” (2nd order derivative computed based on 3 data points, and 3 and 1 data points in the 1st and 2nd smoothing, respectively) and weighted multiplicative scattering correction (WMSC) method was identified as the best model for simultaneously measurement of 14 AACs in brown rice flour. The high model quality is reflected by RSQ of 0.914∼0.969, standard error of calibration (SEC) of 0.0187%∼0.0553%, 1-VR of 0.901∼0.955, SECV of 0.0214%∼0.0579%, and residual prediction deviation (RPD) of 2.58∼3.85. These results indicated that NIRS coupled with an appropriate pre-processing, multivariate regression could be a rapid and reliable method for quantification of AACs in rice breeding programs and quality control in the food industry. Factors that would influence model quality were investigated and discussed.

Predicting Quality Properties of Pears during Storage Using Hyper Spectral Imaging System

This paper presents a comprehensive analysis of the application of visible–near-infrared (Vis/NIR) spectroscopy for the estimation of various chemical attributes of pear fruit. Specifically, the paper investigates how pH, titratable acidity (TA), soluble solids content (SSC), and Vitamin C change as the pear undergoes different storage durations and temperatures. To obtain the most accurate prediction models, we applied a variety of pre-processing techniques to the acquired spectra. Notably, the combination of Savitzky-Golay (S.G.), Multiplicative Scatter Correction (MSC), and second derivatives (D2) emerged as the most effective method for predicting the fruit’s pH, with an impressive rp = 0.95 and SDR = 4.9. In contrast, combining S.G., MSC, and first derivatives (D1) yielded the most accurate predictions for TA, with a robust rp = 0.98 and SDR = 9.6. The research further delved into understanding how the storage period and temperature can significantly influence the pear fruit’s chemical properties. Our findings established that as the storage duration and temperature rise, the pH of the fruit also escalates, while TA sees a decline. The research further elucidates that prolonged storage periods and elevated temperatures lead to the pear fruit shedding its intrinsic qualities, resulting in a reduction in soluble solids and Vitamin C content. To summarize, this paper underscores the immense potential of Vis/NIR spectroscopy as a non-destructive and expedient tool for monitoring the chemical attributes of pear fruit during storage, especially when subjected to diverse temperature and time conditions. These insights not only add to the existing body of knowledge but also align with earlier research on how storage conditions can affect fruit quality.

Myocardial perfusion imaging with retrospective gating and integrated correction of attenuation, scatter, respiration, motion, and arrhythmia

BackgroundAbsolute quantitative myocardial perfusion SPECT requires addressing of aleatory and epistemic uncertainties in conjunction with providing image quality sufficient for lesion detection and characterization. Iterative reconstruction methods enable the mitigation of the root causes of image degradation. This study aimed to determine the feasibility of a new SPECT/CT method with integrated corrections attempting to enable absolute quantitative cardiac imaging (xSPECT Cardiac; xSC).MethodsWe compared images of prototype xSC and conventional SPECT (Flash3DTM) acquired at rest from 56 patients aged 71 ± 12 y with suspected coronary heart disease. The xSC prototype comprised list-mode acquisitions with continuous rotation and subsequent iterative reconstructions with retrospective electrocardiography (ECG) gating. Besides accurate image formation modeling, patient-specific CT-based attenuation and energy window-based scatter correction, additionally we applied mitigation for patient and organ motion between views (inter-view), and within views (intra-view) for both the gated and ungated reconstruction. We then assessed image quality, semiquantitative regional values, and left ventricular function in the images.ResultsThe quality of all xSC images was acceptable for clinical purposes. A polar map showed more uniform distribution for xSC compared with Flash3D, while lower apical count and higher defect contrast of myocardial infarction (p = 0.0004) were observed on xSC images. Wall motion, 16-gate volume curve, and ejection fraction were at least acceptable, with indication of improvements. The clinical prospectively gated method rejected beats ≥20% in 6 patients, whereas retrospective gating used an average of 98% beats, excluding 2% of beats. We used the list-mode data to create a product equivalent prospectively gated dataset. The dataset showed that the xSC method generated 18% higher count data and images with less noise, with comparable functional variables of volume and LVEF (p = ns).ConclusionsQuantitative myocardial perfusion imaging with the list-mode-based prototype xSPECT Cardiac is feasible, resulting in images of at least acceptable image quality.

Predicting starch content of cassava with near infrared spectroscopy in Ugandan cassava germplasm

In Uganda, efforts are underway to improve starch content through conventional breeding as a strategy for increasing adoption of new cassava varieties for both food and industry. However, only few samples can be quantified, limiting the gains in breeding. A database of 115 clones was used to evaluate the potential of Near infrared spectroscopy to predict starch content in cassava. Starch content ranged from 21.48 to 73.97% dry basis. The performance of standard normal variate and de-trend with second derivative calculated on two data points and smoothing plus combination of standard multiplicative scatter correction with second derivative calculated on two data points and smoothing were the best fit mathematical treatments for the calibrations developed. Near infrared spectroscopy predictions for starch content (R2 = 0.85, and r2 = 0.55) developed were reliable, thus usable for screening of cassava starch content at early stages of breeding.

Performance study of a 360° CZT camera for monitoring 177Lu-PSMA treatment

BackgroundThe aim of this study was to investigate the quantification performance of a 360° CZT camera for 177Lu-based treatment monitoring.MethodsThree phantoms with known 177Lu activity concentrations were acquired: (1) a uniform cylindrical phantom for calibration, (2) a NEMA IEC body phantom for analysis of different-sized spheres to optimise quantification parameters and (3) a phantom containing two large vials simulating organs at risk for tests. Four sets of reconstruction parameters were tested: (1) Scatter, (2) Scatter and Point Spread Function Recovery (PSFR), (3) PSFR only and (4) Penalised likelihood option and Scatter, varying the number of updates (iterations × subsets) with CT-based attenuation correction only. For each, activity concentration (ARC) and contrast recovery coefficients (CRC) were estimated as well as root mean square. Visualisation and quantification parameters were applied to reconstructed patient image data.ResultsOptimised quantification parameters were determined to be: CT-based attenuation correction, scatter correction, 12 iterations, 8 subsets and no filter. ARC, CRC and RMS results were dependant on the methodology used for calculations. Two different reconstruction parameters were recommended for visualisation and for quantification. 3D whole-body SPECT images were acquired and reconstructed for 177Lu-PSMA patients in 2–3 times faster than the time taken for a conventional gamma camera.ConclusionQuantification of whole-body 3D images of patients treated with 177Lu-PSMA is feasible and an optimised set of parameters has been determined. This camera greatly reduces procedure time for whole-body SPECT.

Rapid detection of micronutrient components in infant formula milk powder using near-infrared spectroscopy.

In order to achieve rapid detection of galactooligosaccharides (GOS), fructooligosaccharides (FOS), calcium (Ca), and vitamin C (Vc), four micronutrient components in infant formula milk powder, this study employed four methods, namely Standard Normal Variate (SNV), Multiplicative Scatter Correction (MSC), Normalization (Nor), and Savitzky-Golay Smoothing (SG), to preprocess the acquired original spectra of the milk powder. Then, the Competitive Adaptive Reweighted Sampling (CARS) algorithm and Random Frog (RF) algorithm were used to extract representative characteristic wavelengths. Furthermore, Partial Least Squares Regression (PLSR) and Support Vector Regression (SVR) models were established to predict the contents of GOS, FOS, Ca, and Vc in infant formula milk powder. The results indicated that after SNV preprocessing, the original spectra of GOS and FOS could effectively extract feature wavelengths using the CARS algorithm, leading to favorable predictive results through the CARS-SVR model. Similarly, after MSC preprocessing, the original spectra of Ca and Vc could efficiently extract feature wavelengths using the CARS algorithm, resulting in optimal predictive outcomes via the CARS-SVR model. This study provides insights for the realization of online nutritional component detection and optimization control in the production process of infant formula.

Multiple scatter correction for single plane Compton camera imaging in nuclear decommissioning

AbstractThis paper focuses on the application of Compton cameras in nuclear decommissioning, aiming to develop efficient and affordable radiological characterization methods. Compton cameras offer advantages over traditional Anger cameras, including increased sensitivity and a larger field of view. We showcase the use of monolithic scintillation detectors in a Compton camera setup to compute two‐dimensional reconstructions of the source location of incident gamma rays. The presence of multiple scattering effects in the large, spatially non‐resolved detectors can lead to spatial artifacts in the reconstructed source activity. To address this, the paper proposes an extended forward model that accounts for double scattering interactions within a single detector element. Experimental and simulation results demonstrate the effectiveness of the proposed approach.

Study on the dimension correction of juicy peach based on diffuse transmittance online detection

At present, visible/near infrared (VIS/NIR) spectroscopy has been widely used in fruit quality detection, but the different dimensions of fruit will affect the accuracy of quality detection, especially the online detection. At this time, it is necessary to study a dimension correction method to improve the accuracy of spectral detection. Therefore, in this paper, VIS/NIR spectroscopy technology was used to collect the spectra and soluble solid content (SSC) of three fruit types of peach(small, medium and large fruit) under different integration time. The analysis of their spectral attenuation revealed that the spectral energy of medium and large fruit, all attenuated under short integration time, and their complete spectrum could be collected under long integration time, indicating that adjusting the integration time was feasible. After that, partial least square method (PLS) was used to develop the combination model with the spectral data of different dimensions of peach under different integration time, and the optimal model was obtained. When the integration time of small fruit, medium fruit and large fruit was 80 ms, 100 ms and 120 ms respectively, the prediction correlation coefficient (Rp) of the model was 0.846. Then multiplicative scatter correction (MSC) was used to pretreat the optimal model, and the Rp of the model was 0.857. The results showed that using different integration time could reduce the influence of dimension difference on the online quality detection of peach, realize dimension correction, and more accurately detect the quality of peach. In addition, it also showed that the dimension correction method can improve the accuracy of spectral detection, and has certain application value for spectral detection.

Improvement on the discrimination of Amomum tsao-ko origins using NIR combined with sequential pre-processing through orthogonalization (SPORT) and PLS-DA

Amomum tsao-ko Crevost et Lemaire (A. tsao-ko) is mainly used as spices in Chinese hotpot and meat cooking for improving taste and removing peculiar smell. To guarantee the quality of A. tsao-ko, the demand of quick and efficient analysis method is in urge. In this study, A. tsao-ko from 5 regions (including 248 individuals) were analyzed by NIR. Furthermore, sequential preprocessing through orthogonalization (SPORT) was performed to fuse the complementary information of several scatter correction techniques for obtaining the high-quality model. The result showed that in the best results obtained on the scatter correction modeling, the accuracy values of training set and test set were respectively identical to 0.8 and 0.84. However, SPORT method can correctly discriminate all the samples. Hence, SPORT model outperformed the single scatter correction modeling. Furthermore, compared with the single scatter correction modeling, SPORT could not only take the user out of the loop of the selection of the best pre-processing but also could give insight into the incremental contributions of each processed NIR data. Therefore, SPORT could be used as a reliable method for quality control of A. tsao-ko.