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.

Visible and Near-Infrared Spectroscopic Discriminant Analysis Applied to Identification of Soy Sauce Adulteration

The identification of soy sauce adulteration can avoid fraud, and protect the rights and interests of producers and consumers. Based on two measurement models (1 mm, 10 mm), the visible and near-infrared (Vis-NIR) spectroscopy combined with standard normal variate-partial least squares-discriminant analysis (SNV-PLS-DA) was used to establish the discriminant analysis models for adulterated and brewed soy sauces. Chubang soy sauce was selected as an identification brand (negative, 70). The adulteration samples (positive, 72) were prepared by mixing Chubang soy sauce and blended soy sauce with different adulteration rates. Among them, the “blended soy sauce” sample was concocted of salt water (NaCl), monosodium glutamate (C5H10NNaO5) and caramel color (C6H8O3). The rigorous calibration-prediction-validation sample design was adopted. For the case of 1 mm, five waveband models (visible, short-NIR, long-NIR, whole NIR and whole scanning regions) were established respectively;in the case of 10 mm, three waveband models (visible, short-NIR and visible-short-NIR regions) for unsaturated absorption were also established respectively. In independent validation, the models of all wavebands in the cases of 1 mm and 10 mm have achieved good discrimination effects. For the case of 1 mm, the visible model achieved the optimal validation effect, the validation recognition-accuracy rate (RARV) was 99.6%;while in the case of 10 mm, both the visible and visible-short-NIR models achieved the optimal validation effect (RARV = 100%). The detection method does not require reagents and is fast and simple, which is easy to promote the application. The results can provide valuable reference for designing small dedicated spectrometers with different measurement modals and different spectral regions.

Effect of head model on Monte Carlo modeling of spatial sensitivity distribution for functional near-infrared spectroscopy

Modeling Light propagation within human head to deduce spatial sensitivity distribution(SSD)is important for Near-infrared spectroscopy(NIRS)/imaging(NIRI)and diffuse correlation tomography.Lots of head models have been used on this issue,including layered head model,artificial simplified head model,MRI slices described head model,and visible human head model.Hereinto,visible Chinese human(VCH)head model is considered to be a most faithful presentation of anatomical structure,and has been highlighted to be employed in modeling light propagation.However,it is not practical for all researchers to use VCH head models and actually increasing number of people are using magnet resonance imaging(MRI)head models.Here,all the above head models were simulated and compared,and we focused on the effect of using di®erent head models on predictions of SSD.Our results were in line with the previous reports on the effect of cerebral cortex folding geometry.Moreover,the in fluence on SSD increases with thefidelity of head models.And surprisingly,the SSD percentages in scalp and gray matter(region of interest)in MRI head model were found to be 80%and 125%higher than in VCH head model.MRI head models induced nonignorable discrepancy in SSD estimation when compared with VCH head model.This study,as we believe,is the first to focus on comparison among full serials of head model on estimating SSD,and provided quantitative evidence for MRI head model users to calibrate their SSD estimation.

Application of Visible/Near-Infrared Spectra in Modeling of Soil Total Phosphorus

Overabundance of phosphorus (P) in soils and water is of great concern and has received much attention in Florida, USA. Therefore, it is essential to analyze and predict the distribution of P in soils across large areas. This study was undertaken to model the variation of soil total phosphorus (TP) in Florida. A total of 448 soil samples were collected from different soil types. Soil samples were analyzed by chemical reference method and scanned in the visible/near-infrared (VNIR) region of 350-2 500 nm. Partial least squares regression (PLSR) calibration model was developed between chemical reference values and VNIR values. The coefficient of determination (R2) and the root mean squares error (RMSE) of calibration and validation sets, and the residual prediction deviation (RPD) were used to evaluate the models. The R2in calibration and validation for log-transformed TP (log TP) were 0.69 and 0.65, respectively, indicating that VNIR calibration obtained in this study accounted for at least 65% of the variance in log TP using only VNIR spectra, and the high RPD of 2.82 obtained suggested that the spectral model derived in this study was suitable and robust to predict TP in a wide range of soil types, being representative of Florida soil conditions.

Establishment of a comprehensive indicator to nondestructively analyze watermelon quality at different ripening stages

Two nondestructive methods based on visible and near-infrared(VIS-NIR)spectroscopy and X-ray image have been used for the evaluation of watermelon quality.The prediction perform-ance based on partial least squares(PLS)by diffuse transmittance measurement(500-1010 nm)was evaluated for_chemical quality attributes SSC(Rc=0.903;RMSEC=0.572%Brix;Rp=0.862;RMSEP=0.717%Brix;RPD=1.83),lycopene(Rc=0.845;RMSEC=0.266 mg/100 gFW;Rp=0.751;RMSEP=0.439 mg/100 gFW;RPD=1.13)and moisture(Rc=0.917;RMSEC=0.280%;Rp=0.937;RMSEP=0.276%;RPD=2.79).The X-ray calibration linearequations developed by extracting the appropriate gray threshold were sufficiently precise forvolume(R?=0.986)and weight(R?=0.993).In order to optimize prediction model of water-melon quality in growth period,multivariate multi-block technique factor analysis enabled in-tegration of these traits:chemical information is related to physical infomation.Applyingprinciple component analysis to extract common factors and varimax with Kaiser normalizationto improve explanatory,the comprehensive indicator based on variances was established satis.factorily with Rc=0.94,RMSEC=0.244,Rp=0.93,RMSEP=0.344 and RPD=2.00.Acomparison of these models indicates that the comprehensive indicator determined only by portable VIS-NIR spectrometer appears as a suitable method for appraising watermelon qualitynondestructively on the plant at diferent ripen stages.This method contributes to infer the picking date of watermelon with higher accuracy and bigger economic benefits than that byexperience.

Paleoclimatic application of spectral parameters to the eolian red clay of the Jianzha Basin,northeastern margin of the Tibetan Plateau

Reflectance spectroscopy is rapid,inexpensive,and non-destructive and can provide important information about the mineralogy of rocks and sediments.We measured the reflectance spectroscopy of Miocene red clay deposits on the northeastern margin of the Tibetan Plateau,with the aim of developing a rapid methodology for detecting paleoclimatic changes.We obtained visible/near-infrared(VNIR)and short-wave infrared(SWIR)spectroscopy data from the red clay in the Jianzha Basin,and analyzed their relationship with independent paleoclimatic records,including mineral contents and environmental magnetic parameters.The results show that the VNIR parameters,including D500,D900,R500,and R900(where D and R represent the depth and reflectance of the absorption peaks around 500 and 900 nm,respectively)are temperature-sensitive and correlated with the magnetic susceptibility,frequency-dependent magnetic susceptibility,and the marine δ^(18)O record.The results of frequency-domain analysis of the VNIR parameters show that they reflect climate change on orbital timescales.SWIR parameters,such as AS1400,D1400/D1900 and D1900(where AS represents the asymmetry of the absorption peaks around1400 nm),are correlated with the illite and montmorillonite content,and they are sensitive to the weathering intensity.The spectral parameters of the eolian red clay in the Jianzha Basin reflect regional climatic changes caused by the uplift of the Tibetan Plateau at~8.5 Ma and global climatic cooling at~7.2 Ma,and thus they are applicable as both regional and global paleoenvironmental indicators.

Application of swarm intelligence algorithms to the characteristic wavelength selection of soil moisture content

Swarm intelligence algorithms own superior performance in solving high-dimensional and multi-objective optimization problems.The application of the swarm intelligence algorithms to visible and near-infrared(VIS-NIR)spectral analysis of soil moisture can contribute to the optimization of the soil moisture prediction model and the development of the real-time soil moisture sensor.In this study,a high-resolution spectrometer was used to obtain spectral data of different levels of soil moisture which were manually configured.Isolation Forest algorithm(iForest)was used to eliminate outliers from the data.Based on the root mean square error of prediction RMSEP of Back Propagation Neural Network(BPNN)model results,a series of new swarm intelligence algorithms,including Manta Ray Foraging Optimization(MRFO),Slime Mould Algorithm(SMA),etc.,were used to select the characteristic wavelengths of soil moisture.The analysis results showed that MRFO owned the best performance if only from the predictive capability perspective and SMA had a better performance when considering the proportion of the selecting wavelengths and the results of the model prediction.By comparing and analyzing the modeling results of traditional intelligence algorithms Genetic Algorithm(GA)and Particle Swarm Optimization(PSO),it was found that the new swarm intelligence had a better performance in selecting the characteristic wavelengths of soil moisture.Integrating the results of all intelligence algorithms used,soil moisture sensitive wavelengths were selected as 490 nm,513 nm,543 nm,900 nm and 926 nm,which provide the basis for the design of real-time soil moisture sensor based on VIS-NIR.