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Label-free Evaluation of Myocardial Infarct in Surgically Excised Ventricular Myocardium by Raman Spectroscopy
摘要： Understanding the viability of the ischemic myocardial tissue is a critical issue in determining the appropriate surgical procedure for patients with chronic heart failure after myocardial infarction (MI). Conventional MI evaluation methods are; however, preoperatively performed and/or give an indirect information of myocardial viability such as shape, color, and blood flow. In this study, we realize the evaluation of MI in patients undergoing cardiac surgery by Raman spectroscopy under label-free conditions, which is based on intrinsic molecular constituents related to myocardial viability. We identify key signatures of Raman spectra for the evaluation of myocardial viability by evaluating the infarct border zone myocardium that were excised from five patients under surgical ventricular restoration. We also obtain a prediction model to differentiate the infarcted myocardium from the non-infarcted myocardium by applying partial least squares regression-discriminant analysis (PLS-DA) to the Raman spectra. Our prediction model enables identification of the infarcted tissues and the non-infarcted tissues with sensitivities of 99.98% and 99.92%, respectively. Furthermore, the prediction model of the Raman images of the infarct border zone enabled us to visualize boundaries between these distinct regions. Our novel application of Raman spectroscopy to the human heart would be a useful means for the detection of myocardial viability during surgery.
关键词： label-free evaluation,myocardial viability,Raman spectroscopy,partial least squares regression-discriminant analysis,myocardial infarction
更新于2025-09-11 14:15:04
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Determination of water activity, total soluble solids and moisture, sucrose, glucose and fructose contents in osmotically dehydrated papaya using near-infrared spectroscopy
摘要： Near-infrared spectroscopy (NIRS) is a rapid analysis method that is widely used for quantitative determination of the major constituents in many food products. NIRS was applied in conjunction with a chemometric algorithm, namely the partial least squares regression (PLSR), to develop the optimum model for predicting the qualities of osmotically dehydrated papaya (ODP). Two hundred ODP samples were collected from commercial products and from different laboratory ODP processes with varying sucrose concentrations (35oBrix, 45oBirx, 55oBrix and 65oBrix) at 40°C for 6 hr and drying times at 60°C for 2 hr, 4 hr, 6 hr, 8 hr, 10 hr and 12 hr. All samples were divided into a calibration set (n = 140) and a validation set (n = 60) before quality determination and NIRS analysis. Samples were scanned over the NIR spectral range of 800–2400 nm in reflectance mode and their spectra were pretreated using the second derivative method. Suitable predictive models were developed by applying full wavelength PLSR and two wavelength interval selection methods, named the moving window partial least squares regression (MWPLSR) and the searching combination moving window partial least squares regression (SCMWPLSR). The results showed that SCMWPLSR provided better performance than PLSR and MWPLSR. The root mean square error of prediction values of water activity, moisture content, total soluble solids and the sucrose, glucose and fructose contents from SCMWPLSR were 0.014, 0.69% (dry basis), 0.58oBrix, 14.44 g/100 g of sample, 6.72 g/100 g of sample and 4.89 g/100 g of sample, respectively, with correlation coefficients in the range 0.981–0.994.
关键词： Moving window partial least squares regression,Searching combination moving window partial least squares regression,Near-infrared spectroscopy,Partial least squares regression,Papaya
更新于2025-09-10 09:29:36
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Portable Mid-Infrared Device and Chemometrics for the Prediction of Low (0.5%) Total <i>Trans</i> Fat Content in Fast Foods
摘要： The ruling that partially hydrogenated oils (PHO) are no longer “generally recognized as safe (GRAS),” has accelerated the replacement of PHO ingredients with fat alternatives having increasingly lower or no trans fat content. In the present study, we developed a Fourier-transform infrared (FTIR) spectroscopic procedure in conjunction with multivariate partial least squares regression (PLSR) and found it suitable for the accurate prediction of low (0.5%) total trans fat content, as percentage of total fat, measured as fatty acid methyl esters (FAME), in the lipids extracted from 24 representative fast foods. This multivariate data analysis approach is relevant because the precision of the current univariate FTIR official method (AOCS Cd 14-09) is reportedly poor below 2% of total fat, while PLSR has allowed us to accurately predict the concentration of low trans fat in fast foods. The performance of a portable FTIR device was also evaluated and compared to that of a benchtop FTIR spectrometer. For both infrared data sets, PLSR-predicted concentrations of total trans FAME, ranging from approximately 0.47% to 11.40% of total FAME, were in good agreement with those determined by a primary reference gas chromatography (GC) method (R2>0.99); high prediction accuracy was also evidenced by low root mean square error of cross-validation (RMSECV) values. The lowest RMSECV error of 0.12% was obtained with the portable device. The lowest total trans FAME concentration, determined by GC to be 0.42%, was accurately predicted by the portable FTIR/PLSR procedure as 0.47% of total FAME.
关键词： partial least squares regression,portable device,infrared spectroscopy,low trans fat content,fast foods
更新于2025-09-09 09:28:46
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Direct Determination of Ni2+-Capacity of IMAC Materials Using Near-Infrared Spectroscopy
摘要： The present paper reports a new method for the quanti?cation of the Ni2+-capacity of an immobilized metal af?nity chromatography (IMAC) material using near-infrared spectroscopy (NIRS). Conventional analyses using UV absorption spectroscopy or atomic absorption spectrometry (AAS) need to dissolve the silica-based metal chelate sorbent as sample pretreatment. In the ?rst step, those methods were validated on the basis of an ideal homogenous NiSO4-solution and unveiled that UV with an intermediate precision of 2.6% relative standard deviation (RSD) had an advantage over AAS with an intermediate precision of 6.5% RSD. Therefore, UV analysis was chosen as reference method for the newly established NIRS model which has the advantage of being able to measure the material directly in diffuse re?ection mode. Partial least squares regression (PLSR) analysis was used as multivariate data analysis tool for quanti?cation. The best PLSR result obtained was: coef?cient of determination (R2) = 0.88, factor = 2, root mean square error of prediction (RMSEP) = 22 μmol/g (test-set validation) or 7.5% RSDPLSR. Validation of the Ni2+-capacity using UV absorption spectroscopy resulted in an intermediate precision of ±18 μmol/g or 5.0% RSD. Therefore, NIRS provides a fast alternative analysis method without the need of sample preparation.
关键词： Ni2+-capacity,partial least squares regression,IMAC,near-infrared spectroscopy,method validation
更新于2025-09-09 09:28:46
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Visible-Near-Infrared Spectroscopy Prediction of Soil Characteristics as Affected by Soil-Water Content
摘要： Soil physical characteristics are important drivers for soil functions and productivity. Field applications of near-infrared spectroscopy (NIRS) are already deployed for in situ mapping of soil characteristics and therefore, fast and precise in situ measurements of the basic soil physical characteristics are needed at any given water content. Visible-near-infrared spectroscopy (vis–NIRS) is a fast, low-cost technology for determination of basic soil properties. However, the predictive ability of vis–NIRS may be affected by soil-water content. This study was conducted to quantify the effects of six different soil-water contents (full saturation, pF 1, pF 1.5, pF 2.5, pF 3, and air-dry) on the vis–NIRS predictions of six soil physical properties: clay, silt, sand, water content at pF 3, organic carbon (OC), and the clay/OC ratio. The effect of soil-water content on the vis–NIR spectra was also assessed. Seventy soil samples were collected from five sites in Denmark and Germany with clay and OC contents ranging from 0.116 to 0.459 and 0.009 to 0.024 kg kg-1, respectively. The soil rings were saturated and successively drained/dried to obtain different soil–water potentials at which they were measured with vis–NIRS. Partial least squares regression (PLSR) with leave-one-out cross-validation was used for estimating the soil properties using vis–NIR spectra. Results showed that the effects of water on vis–NIR spectra were dependent on the soil–water retention characteristics. Contents of clay, silt, and sand, and the water content at pF 3 were well predicted at the different soil moisture levels. Predictions of OC and the clay/OC ratio were good at air-dry soil condition, but markedly weaker in wet soils, especially at saturation, at pF 1 and pF 1.5. The results suggest that in situ measurements of spectroscopy are precise when soil-water content is below field capacity.
关键词： Visible-Near-Infrared Spectroscopy,Soil Physical Properties,Soil Characteristics,Soil-Water Content,Partial Least Squares Regression
更新于2025-09-09 09:28:46
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Determination of Nitrogen Concentration in Fresh Pear Leaves by Visible/Near-Infrared Reflectance Spectroscopy
摘要： A rapid and reliable method is required to determine the N status of pear (Pyrus communis L.) leaves during the growing season for timely fertilization to improve the yields and fruit quality. In the present study, we evaluated visible and near-infrared reflectance (Vis/NIR) spectra of fresh pear leaves using partial least squares (PLS) regression to determine the N concentration of fresh pear leaves. In addition, we studied the performance of modified spectra generated using different preprocessing techniques. A total of 450 leaf samples were collected from 6-yr-old pear trees of two cultivars, and randomly separated into two subsets (calibration subset [294 samples] and validation subset [180 samples]) after excluding outliers by using principle component analysis. Results showed that the model built using full spectra performed better than that developed using characteristic wavelength segments. In addition, we found that original spectral proved to provide better accuracy than derivative spectra. Among the studied preprocessing techniques, moving average smoothing (MAS) technique improved accuracy the most. Overall results suggested that PLS regression with preprocessing of full spectra using MAS is optimal method for modeling N concentration of fresh pear leaves which yielded 0.961 and 0.953 coefficient of determination (R2) for calibration and cross-validation, respectively. The validation of this method resulted high R2 value (0.847) and low mean relative error (4.48%). In conclusion, this model could provide a rapid and more reliable method to determine the total N concentration in fresh pear leaves and could be useful for fertilization management in pear orchards.
关键词： partial least squares regression,preprocessing techniques,pear leaves,Nitrogen concentration,visible/near-infrared reflectance spectroscopy
更新于2025-09-09 09:28:46
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Predicting ambient aerosol thermal–optical reflectance measurements from infrared spectra: elemental carbon
摘要： Elemental carbon (EC) is an important constituent of atmospheric particulate matter because it absorbs solar radiation influencing climate and visibility and it adversely affects human health. The EC measured by thermal methods such as thermal–optical reflectance (TOR) is operationally defined as the carbon that volatilizes from quartz filter samples at elevated temperatures in the presence of oxygen. Here, methods are presented to accurately predict TOR EC using Fourier transform infrared (FT-IR) absorbance spectra from atmospheric particulate matter collected on polytetrafluoroethylene (PTFE or Teflon) filters. This method is similar to the procedure developed for OC in prior work (Dillner and Takahama, 2015). Transmittance FT-IR analysis is rapid, inexpensive and nondestructive to the PTFE filter samples which are routinely collected for mass and elemental analysis in monitoring networks. FT-IR absorbance spectra are obtained from 794 filter samples from seven Interagency Monitoring of PROtected Visual Environment (IMPROVE) sites collected during 2011. Partial least squares regression is used to calibrate sample FT-IR absorbance spectra to collocated TOR EC measurements. The FT-IR spectra are divided into calibration and test sets. Two calibrations are developed: one developed from uniform distribution of samples across the EC mass range (Uniform EC) and one developed from a uniform distribution of Low EC mass samples (EC < 2.4 μg, Low Uniform EC). A hybrid approach which applies the Low EC calibration to Low EC samples and the Uniform EC calibration to all other samples is used to produce predictions for Low EC samples that have mean error on par with parallel TOR EC samples in the same mass range and an estimate of the minimum detection limit (MDL) that is on par with TOR EC MDL. For all samples, this hybrid approach leads to precise and accurate TOR EC predictions by FT-IR as indicated by high coefficient of determination (R2; 0.96), no bias (0.00 μg m?3, a concentration value based on the nominal IMPROVE sample volume of 32.8 m3), low error (0.03 μg m?3) and reasonable normalized error (21 %). These performance metrics can be achieved with various degrees of spectral pretreatment (e.g., including or excluding substrate contributions to the absorbances) and are comparable in precision and accuracy to collocated TOR measurements. Only the normalized error is higher for the FT-IR EC measurements than for collocated TOR. FT-IR spectra are also divided into calibration and test sets by the ratios OC/EC and ammonium/EC to determine the impact of OC and ammonium on EC prediction. We conclude that FT-IR analysis with partial least squares regression is a robust method for accurately predicting TOR EC in IMPROVE network samples, providing complementary information to TOR OC predictions (Dillner and Takahama, 2015) and the organic functional group composition and organic matter estimated previously from the same set of sample spectra (Ruthenburg et al., 2014).
关键词： Elemental carbon,IMPROVE network,Fourier transform infrared,partial least squares regression,thermal–optical reflectance
更新于2025-09-04 15:30:14
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Rapid prediction of acid detergent fiber content in corn stover based on NIR-spectroscopy technology
摘要： Prediction of acid detergent fiber (ADF) content in corn stover depends on precise data and appropriate analytical methods. In this paper, the optimal PLSR-BPNN model was created for rapidly getting ADF content based on the optimal selection of crucial parameters and the combination of partial least squares regression (PLSR) and back propagation neural network (BPNN). Herein, Mahalanobis distance (MD) was proposed as a tool to recognize and remove outliers. Additionally, on the basis of the characteristic bands extracted by correlation coefficient method (CC), principal component analysis (PCA) was performed to select principal components (PCs) to further compress data of bands for obtaining few characteristic wavelengths. It turned out that the performance of PLSR calibration model based on the selected 10 wavelengths was best. The correlation coefficient (R2), root mean square error of prediction (RMSEP), residual predictive deviation (RPD) and relative standard deviation (RSD) of test set successively were 0.9936, 0.3765, 12.5869, and 0.0087. Besides, BPNN was proposed to cut down the nonlinear regression residual of PLSR model. Genetic algorithm (GA) was applied to avoid the problem of local minimum in network. When RMSEP decreased to the minimum value of 0.2181, PLSR-BPNN model was proven to further improve performance and reached for the best level. Finally, the result of external validation shown that the R2, RMSEP, RPD, RSD were 0.9856, 0.4590, 8.3264, 0.0110, respectively, the created model presented the best predictive performance. Hence, the proposed methods combining with NIR-spectroscopy technology can be used to determine ADF content in corn stover.
关键词： Principal component analysis,Corn stover,Acid detergent fiber,Back propagation neural network,Genetic algorithm,Partial least squares regression
更新于2025-09-04 15:30:14
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A near-infrared spectroscopy routine for unambiguous identification of cryptic ant species
摘要： Species identification—of importance for most biological disciplines—is not always straightforward as cryptic species hamper traditional identification. Fibre-optic near-infrared spectroscopy (NIRS) is a rapid and inexpensive method of use in various applications, including the identification of species. Despite its efficiency, NIRS has never been tested on a group of more than two cryptic species, and a working routine is still missing. Hence, we tested if the four morphologically highly similar, but genetically distinct ant species Tetramorium alpestre, T. caespitum, T. impurum, and T. sp. B, all four co-occurring above 1,300 m above sea level in the Alps, can be identified unambiguously using NIRS. Furthermore, we evaluated which of our implementations of the three analysis approaches, partial least squares regression (PLS), artificial neural networks (ANN), and random forests (RF), is most efficient in species identification with our data set. We opted for a 100% classification certainty, i.e., a residual risk of misidentification of zero within the available data, at the cost of excluding specimens from identification. Additionally, we examined which strategy among our implementations, one-vs-all, i.e., one species compared with the pooled set of the remaining species, or binary-decision strategies, worked best with our data to reduce a multi-class system to a two-class system, as is necessary for PLS. Our NIRS identification routine, based on a 100% identification certainty, was successful with up to 66.7% of unambiguously identified specimens of a species. In detail, PLS scored best over all species (36.7% of specimens), while RF was much less effective (10.0%) and ANN failed completely (0.0%) with our data and our implementations of the analyses. Moreover, we showed that the one-vs-all strategy is the only acceptable option to reduce multi-class systems because of a minimum expenditure of time. We emphasise our classification routine using fibre-optic NIRS in combination with PLS and the one-vs-all strategy as a highly efficient pre-screening identification method for cryptic ant species and possibly beyond.
关键词： Random forests,Ants,Species identification tool,One-vs-all strategy,Formicidae,Neural networks,Cryptic-species complex,Partial least squares regression,Tetramorium
更新于2025-09-04 15:30:14