Visible and Near-Infrared Spectroscopy with Multi-Parameters Optimization of Savitzky-Golay Smoothing Applied to Rapid Analysis of Soil Cr Content of Pearl River Delta

Using visible and near-infrared (Vis-NIR) spectroscopy combined with partial least squares (PLS) regression, the rapid reagent-free analysis model for chromium (Cr) content in tideland reclamation soil in the Pearl River Delta, China was established. Based on Savitzky-Golay (SG) smoothing and PLS regression, a multi-parameters optimization platform (SG-PLS) covering 264 modes was constructed to select the appropriately spectral preprocessing mode. The optimal SG-PLS model was determined according to the prediction effect. The selected optimal parameters d, p, m and LV were 2, 6, 23 and 8, respectively. Using the validation samples that were not involved in modeling, the root mean square error (SEPV), relative root mean square error (R-SEPV) and correlation coefficients (RP, V) of prediction were 11.66 mg·kg-1, 10.7% and 0.722, respectively. The results indicated that the feasibility of using Vis-NIR spectroscopy combined with SG-PLS method to analyze soil Cr content. The constructed multi-parameters optimization platform with SG-PLS is expected to be applied to a wider field of analysis. The rapid detection method has important application values to large-scale agricultural production.

Near-Infrared Spectroscopy Combined with Partial Least Squares Discriminant Analysis Applied to Identification of Liquor Brands

The identification of liquor brands is very important for food safety. Most of the fake liquors are usually made into the products with the same flavor and alcohol content as regular brand, so the identification for the liquor brands with the same flavor and the same alcohol content is essential. However, it is also difficult because the components of such liquor samples are very similar. Near-infrared (NIR) spectroscopy combined with partial least squares discriminant analysis (PLS-DA) was applied to identification of liquor brands with the same flavor and alcohol content. A total of 160 samples of Luzhou Laojiao liquor and 200 samples of non-Luzhou Laojiao liquor with the same flavor and alcohol content were used for identification. Samples of each type were randomly divided into the modeling and validation sets. The modeling samples were further divided into calibration and prediction sets using the Kennard-Stone algorithm to achieve uniformity and representativeness. In the modeling and validation processes based on PLS-DA method, the recognition rates of samples achieved 99.1% and 98.7%, respectively. The results show high prediction performance for the identification of liquor brands, and were obviously better than those obtained from the principal component linear discriminant analysis method. NIR spectroscopy combined with the PLS-DA method provides a quick and effective means of the discriminant analysis of liquor brands, and is also a promising tool for large-scale inspection of liquor food safety.

Equidistant combination wavelength screening and step-by-step phase-out method for the near-infrared spectroscopic analysis of serum urea nitrogen

We applied near-infrared(NIR)spectroscopy with chemometrics for the rapid and reagent-fee analysis of serum urea nitrogen(SUN).The modeling is based on the average effect of multiple sample partitions to achieve parameter selection with stability.A multiparameter optimization platform with Norris derivative filter-partial least squares(Norris-PLS)was developed to select the most suitable mode(d=2,s=33,g=15).Using equidistant combination PLS(EC-PLS)with four parameters(initial wavelength I,number of wavelengths N,number of wavelength gaps G and latent variables LV),we performed wavelength screening after eliminating high-absorption wavebands.The optimal EC-PLS parameters were I=1228 nm,N=26,G=16 and LV=12.The root-mean square error(SEP),correlation coefficient(R_(p))for prediction and ratio of performance-to-deviation(RPD)for validation were 1.03 mmol L^(-1),0.992 and 7.6,respectively.We proposed the wavelength step-by-step phase-out PLS(WSP-PLS)to remove redun-dant wavelengths in the top 100 EC-PLS models with improved prediction performance.The combination of 19 wavelengths was identifed as the optimal model for SUN.The SEP,Rp and RPD in validation were 1.01 mmol L^(-1),0.992 and 7.7,respectively.The prediction effect and wavelength complexity were better than those of EC-PIS.Our results showed that NIR spectroscopy combined with the EC-PLS and WSP-PLS methods enabled the high-precision analysis ofSUN.WSP-PLS is a secondary optimization method that can further optimize any wavelength moc odel obtained through other continuous or discrete strategies to establish a simple and better model.

Discriminant Analysis of Liquor Brands Based on Moving-Window Waveband Screening Using Near-Infrared Spectroscopy

Partial least squares discriminant analysis (PLS-DA) with integrated moving-window (MW) waveband screening was applied to the discriminant analysis of liquor brands with near-infrared (NIR) spectroscopy. Luzhou Laojiao, a popular liquor with strong fragrant flavor, was used as the identified liquor brand (160 samples, negative, 52 vol alcoholicity). Liquors of 10 other brands with strong fragrant flavor were used as the interferential brands (200 samples, positive, 52 vol alcoholicity). The Kennard-Stone algorithm was used for the division of modeling samples to achieve uniformity and representativeness. Based on the MW-PLS-DA, a simplified optimal model set with 157 wavebands was further proposed. This set contained five types of wavebands corresponding to the NIR absorption bands of water, ethanol, and other micronutrients (i.e., acids, aldehydes, phenols, and aromatic compounds) in liquor for practical choice. Using five selected simple models with 4775 - 4239, 7804 - 6569, 6264 - 5844, 9435 - 7896, and 12066 - 10373 cm-1, the validation recognition rates were obtained as 99.3% or higher. Results show good prediction performance and low model complexity, and also provided a valuable reference for designing small dedicated instruments. The proposed method is a promising tool for large-scale inspection of liquor food safety.

Moving-window bis-correlation coefficients method for visible and near-infrared spectral discriminant analysis with applications

The moving window bis corelation coefficients(MW BiCC)was proposed and employed for the discriminant analysis of transgenic sugarcane leaves and B-thalassemia with visible and near-infrared(Vis NIR)spectroscopy.The well-performed moving window principal component analysis linear discriminant analysis(MWPCA-LDA)was also conducted for comparison.A total of 306 transgenic(positive)and 150 nont ransgenic(negative)leave samples of sugarcane were collected and divided to calibration,prediction,and validation.The diffuse reflection spectra were corected using Savitzky-Golay(SG)smoothing with first-order derivative(d=1),third-degree polynomial(p=3)and 25 smpothing points(m=25).The selected waveband was 736-1054nm with MW-BiCC,and the positive and negative validation recognition rates(V_REC^(+),VREC^(-))were 100%,98.0%,which achieved the same effect as MWPCA-LDA.Another example,the 93 B-thalassemia(positive)and 148 nonthalassemia(negative)of human hemolytic samples were colloctod.The transmission spectra were corrected using SG smoothing withd=1,p=3 and m=53.Using M W-BiCC,many best wavebands were selected(e.g.,1116-1146,17941848 and 22842342nm).The V_REC^(+)and V_REC^(-)were both 100%,which achieved the same effect as MW-PCA-LDA.Importantly,the BICC only required ca lculating correlation cofficients between the spectrum of prediction sample and the average spectra of two types of calibration samples.Thus,BiCC was very simple in algorithm,and expected to obtain more applications.The results first confirmed the feasibility of distinguishing B-thalassemia and normal control samples by NIR spectroscopy,and provided a promising simple tool for large population thalassemia screening.

Rapid Analysis of Soil Copper Content for the Pearl River Delta Based on Near-Infrared Spectroscopy Combined with SG-PLS

Using near-infrared (NIR) spectroscopy combined with an optimal method for Savitzky-Golay (SG) smoothing and partial least squares (PLS) regression, a rapid analysis method was established for copper content in the beach reclamation soil samples from Pearl River Delta in China. A framework with calibration, prediction and validation was established by considering randomness and stability. The parameters were optimized according to the comprehensive index (SEP+) to produce modeling stability. The validation results show that, based on the SG-PLS model in long-NIR region (1100 - 2498 nm) with first-order derivative, fifth degree polynomial, seven smoothing points and six PLS factors, the corresponding root mean square error (SEP), correlation coefficient of prediction (RP) and average relative error (ARE) were 0.31 mg·kg-1, 0.924 and 4.5%, respectively. The result indicates high prediction accuracy. The relevant parameter selection can also provide a reference for designing small and dedicated spectrometer.

AO-MW-PLS method applied to rapid quantification of teicoplanin with near-infrared spectroscopy

Teicoplanin(TCP)is an important lipoglycopeptide antibiotic produced by fermenting Acti-noplanes teichomyceticus.The change in TCP concentration is important to measure in the fermentation process.In this study,a reagent-free and rapid quantification method for TCP in the TCP-Tris-HCl mixture samples was developed using near infrared(NIR)spectroscopy by focusing our attention on the fermentation process for TCP.The absorbance optimization(AO)partial least squares(PLS)was proposed and integrated with the moving window(MW)PLS,which is called AO-MW-PLS method,to select appropriate wavebands.Amodel set that includes various wavebands that were equivalent to the optimal AO-MW-PLS waveband was,proposed based on statistical considerations.The public region of all equivalent wavebands was just one of the equivalent wavebands.The obtained public regions were 1540-1868 nm for TCP and 1114-1310 nm for Tris.The root-mean-square error and correlation coeficient for leave-one-out cross validation were 0.046 mg mL^(-1)and 0.9998 mg mL^(-1)for TCP,and 0.235 mg mL^(-1)and 0.9986 mg mL^(-1)for Tris,respectively.All the models achieved highly accurate prediction effects,and the selected wavebands provided valuable references for designing specialized spectrometers.This study provided a valuable reference for further application of the proposed methods to TCP fermentation broth and to other spectroscopic analysis fields.

Correlation Analysis between Mean Corpuscular Hemoglobin and Mean Corpuscular Volume for Thalassemia Screening in Large Population

Correlation analysis between the hematological parameters mean corpuscular hemoglobin (MCH) and mean corpuscular volume (MCV) for thalassemia screening in large population was discussed. A total of 4920 peripheral blood samples of reproductive age persons were collected from Guangdong province of China. The hematological parameters MCH and MCV values of samples were first measured, and then the DNA analyses for thalassemia were conducted. All samples were composed by 4463 non-thalassemia and 457 thalassemia, and among 457 thalassemia samples, 311 were α-thalassemia, 133 were β-thalassemia, and 13 were α & β-thalassemia. In accordance with non-thalassemia, thalassemia, α-thalassemia, β-thalassemia, α & β-thalassemia and the entire group itself, a total of six sample groups were divided. The corresponding correlation coefficients between the measured MCH and MCV values for the six sample groups were 0.880, 0.968, 0.966, 0.962, 0.980 and 0.965 respectively. For the thalassemia carriers, highly significant correlation between MCH and MCV were observed. The fitting equations between MCH and MCV values were also obtained. The results indicated that the feasibility for thalassemia screening using MCV or MCH independently as parameter, and provided suitable strategy to select parameters and models for thalassemia screening in large population.

Application of Portable Near-Infrared Spectrometer for Analysis of Organic Carbon in Marine Sediments

The performance of a portable near-infrared (NIR) spectrometer to determine organic carbon (OC) in marine sediments was evaluated. The NIR reflection spectra of 180 samples in the range 950 - 1650 nm were acquired using an ultra-compact spectrometer. NIR spectroscopy combined with the partial least squares (PLS) regression and Savitzky-Golay (SG) smoothing was successfully applied to rapid and reagent-free determination of OC. Using the PLS-SG model with 1nd order derivative, 2th polynomial and eleven smoothing points, the root-mean-square errors (RMSEPM) and correlation coefficients (RP,M) of prediction for modeling were 0.073% and 0.894, respectively, the root-mean-square errors (RMSEPV) and correlation coefficients (RP,V) of prediction for validation were 0.075% and 0.883, respectively. Results showed that the small portable NIR instrument achieved well prediction effect for the analysis of OC in marine sediments, which had advantages of rapid, easy to carry and operate suitable for large-scale applications to analyze marine sediments.

Near-Infrared Spectroscopic Analysis of Hemoglobin with Stability Based on Human Hemolysates Samples

Near-infrared (NIR) spectroscopy combined with the partial least-squares (PLS) regression was successfully applied for the rapid quantitative analysis of hemoglobin (HGB) based on human hemolysates samples. Based on the varied divisions for the calibration and prediction sets, an effective modeling approach using stable model parameters was proposed. Among 255 samples, 80 were randomly selected as the validation set. The remaining 175 samples were divided into the calibration set (110 samples) and the prediction set (65 samples) for a total of 30 times with certain similarities based on partial least squares cross-validation predictive basis (PLSPB). The optimal PLS factor was 8, the modeling effects M-SEPAve, M-RP,Ave, M-SEPStd and M-RP,Std were 3.84g/L, 0.967, 0.16g/L and 0.006, respectively, the validation effects V-SEP, V-RP and V-RSEP were 3.59g/L, 0.980 and 2.7%, respectively. It indicated that the method has high prediction precision and well stability. The results show that NIR spectroscopy of hemolysates is accurate to HGB’s determination, and it is hopeful to be applied to clinic.

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 combinationpartial least squares(EC-PLS)was used for large-scale wavelength screening.The wavelength step-by-step phaseout 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 RP),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,and5.1 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.

Discrete combination method based on equidistant wavelength screening and its application to near-infrared analysis of hemoglobin

A wavelength selection method for discrete wavelength combinations was developed based on equi- distant combination-partial least squares （EC-PLS） and applied to a near-infrared （NIR） spectroscopic analysis of hemoglobin （Hb） in human peripheral blood samples. An allowable model set was established through EC-PLS on the basis of the sequence of the predicted error values. Then, the wavelengths that appeared in the allowable models were sorted, combined, and utilized for modeling, and the optimal number of wavelengths in the combina- tions was determined. The ideal discrete combination models were obtained by traversing the number of allowable models. The obtained optimal EC-PLS and discrete wavelength models contained 71 and 42 wave- lengths, respectively. A simple and high-performance discrete model with 35 wavelengths was also established. The validation samples excluded from modeling were used to validate the three models. The root-mean-square errors for the N1R-predicted and clinically measured Hb values were 3.29, 2.86, and 2.90 g.L ~, respectively; the correlation coefficients, relative RMSER and ratios of performance to deviation were 0.980, 0.983, and 0.981; 2.7%, 2.3%, and 2.4%; and 4.6, 5.3, and 5.2, respectively. The three models achieved high prediction accuracy. Among them, the optimal discrete combination model performed the best and was the most effective in enhancing prediction performance and removing redundant wave- lengths. The proposed optimization method for discrete wavelength combinations is applicable to NIR spectro- scopic analyses of complex samples and can improve prediction performance. The proposed wavelength models can be utilized to design dedicated spectrometers for Hb and can provide a valuable reference for non-invasive Hb detection.