Waveband selection of reagent-free determination for thalassemia screening indicators using Fourier transform infrared spectroscopy with attenuated total reflection

Changeable moving window-standard normal variable transformation for visible-NIR spectroscopic analyses

Based on the assumption of point-by-point local linearity, the changeable moving window-standard normal variable (CMW-SNV) was proposed as a reasonable improvement of the classical SNV. The three examples of quantitative and qualitative visible-near-infrared (Vis-NIR) analysis, quantifications of soil organic matter and corn meal moisture, and discriminant of rice seeds identification, were used to validate the effects of the CMW-SNV, SNV and equal segmentation SNV (ES-SNV) methods. The ES-SNV is another improvement of the SNV, but its algorithm would cause artificial discontinuities of the corrected spectrum. The SNV, ES-SNV and CMW-SNV corrected spectra were used to establish partial least squares (PLS) or partial least squares-discriminant analysis (PLS-DA) models respectively. For soil and corn meal datasets in modeling, the CMW-SNV-PLS models were both significantly better than the global SNV-PLS models; the root mean square errors of prediction in modeling (SEPM) values had the relative decrease of 26.4% and 6.6% respectively. For rice seeds dataset in modeling, the CMW-SNV-PLS-DA model was significantly better than the global SNV-PLS-DA model; the total recognition-accuracy rates in modeling (RARM) value increased by 2.1%. For all three datasets, the CMW-SNV models were better than (or close to) ones of the ES-SNV models. The equidistant combination (EC) and wavelength step-by-step phase-out (WSP) methods were used to perform wavelength selection on the CMW-SNV corrected spectra, determining the optimal EC-WSP-PLS or EC-WSP-PLS-DA models. In independent validation of three datasets, the high precision and high recognition accuracy rates validation results were all obtained. The CMW-SNV was a localized natural improvement of the classic global SNV method, and its correction maintained continuity of the spectra. The number of wavelengths m of the correction window represented the scale of localized SNV, and the algorithm platform of CMW-SNV also included the optimization of parameter m, making the localized CMW-SNV method more reasonable.

Infrared Spectroscopy: A New Frontier in Hematological Disease Diagnosis

Hematological diseases, due to their complex nature and diverse manifestations, pose significant diagnostic challenges in healthcare. The pressing need for early and accurate diagnosis has driven the exploration of novel diagnostic techniques. Infrared (IR) spectroscopy, renowned for its noninvasive, rapid, and cost-effective characteristics, has emerged as a promising adjunct in hematological diagnostics. This review delves into the transformative role of IR spectroscopy and highlights its applications in detecting and diagnosing various blood-related ailments. We discuss groundbreaking research findings and real-world applications while providing a balanced view of the potential and limitations of the technique. By integrating advanced technology with clinical needs, we offer insights into how IR spectroscopy may herald a new era of hematological disease diagnosis.

Optimal partner wavelength combination method applied to NIR spectroscopic analysis of human serum globulin

Human serum globulin (GLB), which contains various antibodies in healthy human serum, is of great significance for clinical trials and disease diagnosis. In this study, the GLB in human serum was rapidly analyzed by near infrared (NIR) spectroscopy without chemical reagents. Optimal partner wavelength combination (OPWC) method was employed for selecting discrete information wavelength. For the OPWC, the redundant wavelengths were removed by repeated projection iteration based on binary linear regression, and the result converged to stable number of wavelengths. By the way, the convergence of algorithm was proved theoretically. Moving window partial least squares (MW-PLS) and Monte Carlo uninformative variable elimination PLS (MC-UVE-PLS) methods, which are two well-performed wavelength selection methods, were also performed for comparison. The optimal models were obtained by the three methods, and the corresponding root-mean-square error of cross validation and correlation coefficient of prediction (SECV, R_P,CV) were 0.813 g L^-1 and 0.978 with OPWC combined with PLS (OPWC-PLS), and 0.804 g L^-1 and 0.979 with MW-PLS, and 1.153 g L^-1 and 0.948 with MC-UVE-PLS, respectively. The OPWC-PLS and MW-PLS methods achieved almost the same good results. However, the OPWC only contained 28 wavelengths, so it had obvious lower model complexity. Thus it can be seen that the OPWC-PLS has great prediction performance for GLB and its algorithm is convergent and rapid. The results provide important technical support for the rapid detection of serum.

Spectral noise-to-signal ratio priority method with application for visible and near-infrared analysis of whole blood viscosity

Whole blood viscosity (WBV) is a group of important clinical indicators of cardio-cerebral vascular diseases. Existing detection methods for WBV are complex, making them inconvenient for large population screening. Blood viscosity is closely related to the deformability and aggregation of erythrocytes, which are associated with haemoglobin. Haemoglobin has obvious near-infrared (NIR) spectral absorption. Scattering occurs when NIR light enters a viscous blood sample, and its scattering degree is correlated with blood viscosity. Based on repeated spectral measurements and spectral similarity, spectral noise-to-signal ratio (NSR) was proposed to quantify the spectral scattering effect in the blood sample. A novel selection method of piecewise-continuous wavelengths, named NSR priority-partial least squares (NSRP-PLS), was proposed and applied for visible-NIR quantitative analysis of WBV with high, medium and low shear rates [WBV(H), WBV(M), WBV(L)]. Modelling was separately performed by gender to allow for systematic gender differences in blood viscosity. For the NIR-predicted and clinically measured values of the three WBV indicators in independent validation, the root mean square errors for prediction (SEP) were 0.498, 0.222 and 0.193 (mPa·s), respectively. And the correlation coefficients (R_P) were 0.927, 0.934 and 0.927, respectively. Compared with the three current well-performing methods (MW-PLS, CARS-PLS and SPA-PLS), the proposed NSRP-PLS method achieved better predictive accuracy. Results indicated that visible-NIR spectroscopy combined with the NSRP-PLS method can be used for the quantitative analysis of WBV. The proposed analytical method is rapid, reagent-free and is scientific and meaningful for cardio-cerebral vascular diseases screening in large populations.

Rapid and non-destructive analysis for the identification of multi-grain rice seeds with near-infrared spectroscopy

The rapid and non-destructive discriminant analysis of rice seeds has great significance for large-scale agriculture. Using near-infrared (NIR) diffuse-reflectance spectroscopy with partial least squares-discriminant analysis (PLS-DA), a variety identification method of multi-grain rice seeds was developed. The equidistant combination method was adopted for large-range wavelength screening. A step-by-step phase-out method was proposed to eliminate interference wavelengths and improve the predicted effect. The optimal wavelength model was a combination of 54 wavelengths within 808-974 nm of the short-NIR region. One type of pure rice variety (Y Liangyou 900) was used for identification (negative). Positive samples included the other four pure varieties and contamination of Y Liangyou 900 by the above four varieties. The recognition-accuracy rates for positive, negative and total validation samples reached 93.1%, 95.1%, and 94.3%, respectively. In the long-NIR region, the local optimal wavelength model was a combination of 49 wavelengths within 1188-1650 nm, and the recognition-accuracy rates for positive, negative and total validation samples were 90.3%, 94.1%, and 92.5%, respectively. Results confirmed the feasibility of NIR spectroscopy for variety identification of multi-grain rice seeds. The proposed two discrete-wavelength models located in the short- and long-NIR regions can provide valuable reference to a dedicated spectrometer.

A rapid quantification method for the screening indicator for β-thalassemia with near-infrared spectroscopy

Near-infrared (NIR) spectroscopy combined with chemometrics was applied to rapidly analyse haemoglobin A2 (HbA₂) for β-thalassemia screening in human haemolysate samples. The relative content indicator HbA₂ was indirectly quantified by simultaneous analysis of two absolute content indicators (Hb and Hb∙HbA₂). According to the comprehensive prediction effect of the multiple partitioning of calibration and prediction sets, the parameters were optimized to achieve modelling stability, and the preferred models were validated using the samples not involved in modelling. Savitzky-Golay smoothing was firstly used for the spectral pretreatment. The absorbance optimization partial least squares (AO-PLS) was used to eliminate high-absorption wave-bands appropriately. The equidistant combination PLS (EC-PLS) was further used to optimize wavelength models. The selected optimal models were I=856nm, N=16, G=1 and F=6 for Hb and I=988nm, N=12, G=2 and F=5 for Hb∙HbA₂. Through independent validation, the root-mean-square errors and correlation coefficients for prediction (RMSEP, R_P) were 3.50gL^-1 and 0.977 for Hb and 0.38gL^-1 and 0.917 for Hb∙HbA₂, respectively. The predicted values of relative percentage HbA₂ were further calculated, and the calculated RMSEP and R_P were 0.31% and 0.965, respectively. The sensitivity and specificity for β-thalassemia both reached 100%. Therefore, the prediction of HbA₂ achieved high accuracy for distinguishing β-thalassemia. The local optimal models for single parameter and the optimal equivalent model sets were proposed, providing more models to match possible constraints in practical applications. The NIR analysis method for the screening indicator of β-thalassemia was successfully established. The proposed method was rapid, simple and promising for thalassemia screening in a large population.

Joint analyses model for total cholesterol and triglyceride in human serum with near-infrared spectroscopy

The development of a small, dedicated near-infrared (NIR) spectrometer has promising potential applications, such as for joint analyses of total cholesterol (TC) and triglyceride (TG) in human serum for preventing and treating hyperlipidemia of a large population. The appropriate wavelength selection is a key technology for developing such a spectrometer. For this reason, a novel wavelength selection method, named the equidistant combination partial least squares (EC-PLS), was applied to the wavelength selection for the NIR analyses of TC and TG in human serum. A rigorous process based on the various divisions of calibration and prediction sets was performed to achieve modeling optimization with stability. By applying EC-PLS, a model set was developed, which consists of various models that were equivalent to the optimal model. The joint analyses model of the two indicators was further selected with only 50 wavelengths. The random validation samples excluded from the modeling process were used to validate the selected model. The root-mean-square errors, correlation coefficients and ratio of performance to deviation for the prediction were 0.197 mmol L(-1), 0.985 and 5.6 for TC, and 0.101 mmol L(-1), 0.992 and 8.0 for TG, respectively. The sensitivity and specificity for hyperlipidemia were 96.2% and 98.0%. These findings indicate high prediction accuracy and low model complexity. The proposed wavelength selection provided valuable references for the designing of a small, dedicated spectrometer for hyperlipidemia. The methodological framework and optimization algorithm are universal, such that they can be applied to other fields.