Selection method of quasi-continuous wavelength combination with applications to the near-infrared spectroscopic analysis of soil organic matter

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.

Visible and Near-Infrared Spectroscopy Combined With Bayes Classifier Based on Wavelength Model Optimization Applied to Wine Multibrand Identification

The identification of high-quality wine brands can avoid adulteration and fraud and protect the rights and interests of producers and consumers. Since the main components of wine are roughly the same, the characteristic components that can distinguish wine brands are usually trace amounts and not unique. The conventional quantitative detection method for brand identification is complicated and difficult. The naive Bayes (NB) classifier is an algorithm based on probability distribution, which is simple and particularly suitable for multiclass discriminant analysis. However, the absorbance probability between spectral wavelengths is not necessarily strongly independent, which limits the application of Bayes method in spectral pattern recognition. This research proposed a Bayes classifier algorithm based on wavelength optimization. First, a large-scale wavelength screening for equidistant combination (EC) was performed, and then wavelength step-by-step phase-out (WSP) was carried out to reduce the correlation between wavelengths and improve the accuracy of Bayes discrimination. The proposed EC-WSP-Bayes method was applied to the 5-category discriminant analysis of wine brand identification based on visible and near-infrared (Vis-NIR) spectroscopy. Among them, four types of wine brands were collected from regular sales channels as identification brands. The fifth type of samples was composed of 21 other commercial brand wines and home-brewed wines from various sources, as the interference brand. The optimal EC-WSP-Bayes model was selected, the corresponding wavelength combination was 404, 600, 992, 2,070, 2,266, and 2,462 nm located in the visible light, shortwave NIR, and combination frequency regions. In modeling and independent validation, the total recognition accuracy rate (RAR_Total) reached 98.1 and 97.6%, respectively. The technology is quick and easy, which is of great significance to regulate the alcohol market. The proposed model of less-wavelength and high-efficiency (N = 6) can provide a valuable reference for small special instruments. The proposed integrated chemometric method can reduce the correlation between wavelengths, improve the recognition accuracy, and improve the applicability of the Bayesian method.

Rapid and simultaneous analysis of multiple wine quality indicators through near-infrared spectroscopy with twice optimization for wavelength model

Alcohol, total sugar, total acid, and total phenol contents are the main indicators of wine quality detection. This study aims to establish simultaneous analysis models for the four indicators through near-infrared (NIR) spectroscopy with wavelength optimization. A Norris derivative filter (NDF) platform with multiparameter optimization was established for spectral pretreatment. The optimal parameters (i.e., derivative order, number of smoothing points, and number of differential gaps) were (2, 9, 3) for alcohol, (1, 19, 5) for total sugar, (1, 17, 11) for total acid, and (1, 1, 1) for total phenol. The equidistant combination-partial least squares (EC-PLS) was used for large-scale wavelength screening. The wavelength step-by-step phase-out PLS (WSP-PLS) and exhaustive methods were used for secondary optimization. The final optimization models for the four indicators included 7, 10, 15, and 13 wavelengths located in the overtone or combination regions, respectively. In an independent validation, the root mean square errors, correlation coefficient for prediction (i.e., SEP and R_P), and ratio of performance-to-deviation (RPD) were 0.41 v/v, 0.947, and 3.2 for alcohol; 1.48 g/L, 0.992, and 6.8 for total sugar; 0.68 g/L, 0.981, and 5.1 for total acid; and 0.181 g/L, 0.948, and 2.9 for total phenol. The results indicate high correlation, low error, and good overall prediction performance. Consequently, the established reagent-free NIR analytical models are important in the rapid and real-time quality detection of the wine fermentation process and finished products. The proposed wavelength models provide a valuable reference for designing small dedicated instruments.

Water-based measured-value fuzzification improves the estimation accuracy of soil organic matter by visible and near-infrared spectroscopy

Visible and near-infrared (Vis-NIR) reflectance spectroscopy continues to emerge as a rapid and effective approach for estimating several soil physical and chemical properties including soil organic matter (SOM), but its accuracy is restricted by many factors including soil water. This study proposed the water-based measured-value fuzzification (WMF) method to decrease the influence of soil water, and combined with the partial least squares regression (PLSR) to develop SOM models. Vis-NIR spectral data was measured by an ASD FieldSpec 3 spectrometer. After WMF analysis, the PLSR method was used to develop SOM models. By comparison with the PLSR model, the WMF-PLSR model produced markedly better results (root mean square error of validation [RMSEV] = 2.776 g/kg, mean relative error of validation [MREV] = 8.111%, and ratio of performance to interquartile range [RPIQv] = 4.729). With these, the WMF method combined with PLSR shows the potential for estimating SOM content and expands the range of observation methods.

Rapid and simultaneous analysis of direct and indirect bilirubin indicators in serum through reagent-free visible-near-infrared spectroscopy combined with chemometrics

Indirect (IBil), direct (DBil) and total (TBil) bilirubin are important clinical indicators of hepatobiliary diseases, which require rapid detection in diagnosis and treatment. IBil and DBil have a structural relationship with several macromolecules in hepatobiliary metabolism. Here, the rapid analysis models for bilirubin indicators using serum visible-near-infrared (Vis-NIR) spectroscopy were established. Norris derivative filter with optimisation was used for spectral pretreatment; the optimal parameters (derivative order, number of smoothing points, number of differential gaps) were (2, 15, 9) for IBil; (2, 13, 9) for DBil, respectively. Equidistant combination-partial least squares (EC-PLS) was used for large-scale wavelength screening. Wavelength step-by-step phase-out PLS (WSP-PLS) was used for secondary wavelength optimisation. The wavelength models of the optimal EC-WSP-PLS for IBil and DBil included 11 and 18 wavelengths, respectively. In independent validation, the root-mean-square errors and correlation coefficient for prediction (SEP, R_P), and ratio of performance-to-deviation (RPD) were 0.90 μmol L^-1, 0.975, and 4.4 for IBil; 0.71 μmol L^-1, 0.955, and 3.3 for DBil, respectively. TBil was subjected to spectral analysis, and the summation of the prediction values of IBil and DBil was compared. The latter was obviously better, and SEP, R_P, RPD were 0.82 μmol L^-1, 0.990, 7.1, respectively. The results for IBil, DBil and TBil indicated high correlation, low error and good overall prediction ability and confirmed the feasibility of the simultaneous analysis of bilirubin indicators through reagent-free serum Vis-NIR spectroscopy. The proposed method is crucial for the rapid screening of large populations and the treatment of hepatobiliary diseases.

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.

Performance of calibration model with different ratio of sample size to the number of wavelength: Application to hemoglobin determination by NIR spectroscopy

Near infrared spectroscopy is widely used in composition analysis in fields of food, medicines, environment, and so on. The proportion of sample size and the wavelength used is very important for the performance of the calibration model. In this research, we explored the influence of ratio of sample size to the number of wavelength (SWR) on the performance of calibration model, with hemoglobin determination as an example. The results showed that RMSEC increases with the increase of SWR, when SWR is less than 0.5, namely the samples in the calibration set were less than half of the number of wavelengths used in establishing the calibration model, while RMSEP decreases with the increase of SWR. The calibration model was lack of reliability at this range for SWR. RMSEC and RMSEP tend to be stable when SWR value is greater than 0.9. However, in most cases, the samples size was limited, and wavelength selection was commonly used in practical spectroscopy analysis. In order to confirm that the effect of SWR were caused by both sample size and wavelength number, we also studied the performance of calibration model with different WSR. Wavelengths were selected by equidistant combination multiple linear regression (ECMLR) method. The conclusion from results were consistent with the previous part, namely when establishing calibration model, the number of wavelengths used should be less than the twice amount of samples in the calibration set to ensure the validity of the model. We recommend that wavelength selection part was indispensable for small sample size cases. This research can be important evidence and guide for other researches with spectroscopy methods.

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.

Boosting the Performance of Genetic Algorithms for Variable Selection in Partial Least Squares Spectral Calibrations

A genetic algorithm (GA) for variable selection in partial least squares (PLS) regression that incorporates adaptive boosting to identify informative wavelengths in near-infrared (NIR) spectra has been developed. Three studies demonstrating the advantages of incorporating an adaptive boosting routine into a GA that employs the root mean square error of calibration as its fitness function are highlighted: (1) prediction of hydroxyl number of terpolymers from NIR diffuse reflectance spectra; (2) calibration of acetone from NIR transmission spectra of mixtures of water, acetone, t-butyl alcohol and isopropyl alcohol; and (3) determination of the active pharmaceutical ingredients in drug tablets from NIR diffuse reflectance spectra. The performance of the GA with adaptive boosting to select wavelengths was compared with one without adaptive boosting. For all three NIR data sets, variable selected PLS models developed by a GA with adaptive boosting performed better. Analysis of the wavelengths selected by the GA with adaptive boosting also demonstrate that chemical information indicative of the analyte was captured by the selected wavelengths.

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.

Detection of Nanoscale Soil Organic Matter by Middle Infrared Spectrum for Forensic Science

Soil is useful as a kind of trace evidence for forensic science. Thus it is very crucial to identify sources of soil. The nanoscale soil organic matter (NSOMs) can be used to differentiate soil sources because their constituents and contents are relatively stable with time but variant by location. In this study, NSOMs from eighteen regions of Shandong Province in China were examined by middle infrared spectrum (4000–400 cm−1). The results showed that the constituents and contents of NSOMs in eighteen samples were dramatically different; a NSOM fingerprint for each sample was drawn based on these characteristics. This suggests that a national or global NSOM fingerprint database could be rapidly established by the one-step middle infrared spectrum analysis for different soil samples, which will be helpful to determine crime scenes by comparing the middle infrared spectrum of forensic soil with the NSOMs fingerprint database.

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

A reagent-free determination method for the thalassemia screening indicators hemoglobin (Hb), mean corpuscular Hb (MCH), and mean corpuscular volume (MCV) was developed based on Fourier transform infrared spectrometers equipped with an attenuated total reflection accessory. A random and stability-dependent rigorous process of calibration, prediction, and validation was conducted. Appropriate wavebands were selected using the improved moving window partial least squares method with stability and equivalence. The obtained optimal wavebands were 1722 to 1504 cm⁻¹ for Hb, 1653 to 901 cm⁻¹ for MCH, and 1562 to 964 cm⁻¹ for MCV. A model set equivalent to the optimal model was proposed for each indicator; the public waveband of Hb equivalent wavebands was 1717 to 1510 cm⁻¹, and the public equivalent waveband for MCH and MCV was 1562 to 901 cm⁻¹. All selected wavebands were within the MIR fingerprint region and achieved high validation effects. The sensitivity and specificity were 100.0% and 96.9% for the optimal wavebands and 100.0% and 95.3% for the equivalent wavebands, respectively. Thus, the spectral prediction was highly accurate for determining negative and positive for thalassemia screening. This technique is rapid and simple in comparison with conventional methods and is a promising tool for thalassemia screening in large populations.