Successive Projections Algorithm Method Research Articles

Identification of MDA in seized ecstasy-like samples using atmospheric solids analysis probe mass spectrometry and machine learning

In this study, a total of 64 samples of ecstasy tablets seized during operations conducted within the State of Rio Grande do Sul – Brazil in 2021–2022 were analyzed. The instrumental analysis was performed using the Radian ASAP MS instrument with a single quadrupole mass spectrometer and an Atmospheric Solids Analysis Probe (ASAP) interface. Data preprocessing was conducted using MATLAB to improve data quality for subsequent chemometric analyses. Classification models seeking to identify samples containing MDA (3,4-methylenedioxyamphetamine) were developed using Linear Discriminant Analysis (LDA) with variable selection through the genetic algorithm (GA), stepwise (SW), and successive projections algorithm (SPA). Additionally, Partial Least Squares Discriminant Analysis (PLS-DA) models were explored. The GA-LDA model achieved an accuracy of approximately 95% for both training and testing sets, demonstrating its effectiveness in distinguishing between sample classes. It selected only five variables during model building. The SW-LDA model displayed exceptional performance in the training set, achieving 100% for all metrics. However, its accuracy for the testing set was slightly lower at 84.7%, suggesting potential overfitting due to the inclusion of numerous variables. The SPA-LDA model, while not the best performer in the testing set, selected variables that were more easily attributed to specific ions/fragments, aiding in the interpretation of results. Interpretation of selected variables revealed key m/z values associated with different compounds found in the samples, such as MDMA, cocaine, and amphetamine-related fragments. Notably, the SPA method selected six variables that played a crucial role in distinguishing between sample groups. These findings contribute to the advancement of forensic drug analysis and quality control processes.

Hyperspectral estimation of maize (Zea mays L.) yield loss under lodging stress

The frequency and intensity of maize (Zea mays L.) yield disturbance caused by lodging stress are getting higher and higher, so it is of great significance to take effective methods to monitor the yield loss of lodging maize. This study aimed to explore the ability of hyperspectral technology to estimate maize yield loss under lodging. The lodging control experiment of maize was carried out, the changes of canopy hyperspectral and per unit yield loss of maize in multiple growth stages were analyzed. First, the successive projections algorithm (SPA) and recursive feature elimination (RFE) methods were used to select yield-sensitive wavelengths from original canopy spectrum (OCS) to achieve dimensionality reduction. Then, the fractional-order differential (FOD) transform was applied for the canopy spectrum, and the RFE method was used to select the optimal wavelength combination. Finally, the models of estimating per unit yield of maize with different lodging periods and days after lodging (DAL) were constructed by using the optimal wavelength combination, and the model was verified by leave-one-out cross-validation (LOOCV) method. It was found that the more serious the lodging severity, the greater the maize per unit yield loss. On 1, 7, 14, 21 DAL at vegetative tasseling (VT) stage and 1, 7 DAL at reproductive milk (R3) stage, the model accuracy of RFE method was 5.26%, 17.39%, 9.46%, 6.41%, 20.37% and 11.11% higher than that of SPA method. The estimation accuracy of lodging maize yield model was improved by using FOD method and RFE method (FOD-RFE). The R2 of the maize yield model of 1.4-order, 0.9-order, 1.8-order, 0.6-order on 1, 7, 14 and 21 DAL at VT stage reached 0.90, 0.89, 0.92, 0.93, which were 12.5%, 11.11%, 13.58% and 12.05% higher than OCS-RFE. The R2 of the maize yield model of 1.5-order and 1.5-order on 1 and 7 DAL at R3 stage were 0.84 and 0.87, which were 29.23% and 24.29% higher than OCS-RFE. Therefore, fractional-order differential transform and spectral feature selection could improve the estimation accuracy of maize yield under lodging stress. The hyperspectral technology could be used to quickly estimate maize yield loss under lodging stress.

Comprehensive growth index monitoring of desert steppe grassland vegetation based on UAV hyperspectral.

The goal of this study was to establish a comprehensive growth index (CGI) of grassland vegetation for monitor the overall condition of the grassland. Taking the desert grassland in Otuoke Banner, Ordos City, Inner Mongolia as the research object, this study integrates five indicators. First, the optimal band of the unmanned aerial vehicle hyperspectral data is optimized using the correlation analysis, successive projection algorithm (SPA), optimum index factor method, and band combination index method. A dual-band spectral index in good correlation with the CGI is then constructed in the optimal band. Afterwards, a CGI characterization model is established in accordance with the partial least squares regression (PLSR) algorithm and its accuracy is analyzed. Finally, the CGI of the study area is estimated. The experimental results are as follows. 1) The R2 of models built using the training samples of the spectral indices corresponding to the optimal spectra screened by the SPA method was 0.7835, RMSE was 0.0712, and RE was 6.89%, less than 10%. The R2 of the Validation samples was 0.7698, RMSE was 0.0471, and RE was 6.36%, less than 10%, highest precision. 2) Models were built using the spectral indices corresponding to the optimal spectra screened by the SPA method, and the CGI mean values were inverted. A comparison of the mean measured CGI values of the sample quadrat of the test area showed that the mean relative error was 3.82%. The results show that the vegetation growth of desert-steppe grasslands can be adequately monitored, providing technical support for the rapid and accurate diagnosis of grassland conditions. However, there are still shortcomings in this study. 1) The research area for this study was mainly in the desert steppe in Otuoke Banner, Ordos, hence the relevance and universality of the findings need to be verified, and subsequent experiments need to be carried out on desert steppes in other regions or even other types of grasslands to test the universality of the model. 2) In this study, the influence of soil background and litter on the spectral reflectance is not considered in depth. In addition, the influence of sensor observation angle and solar elevation angle on the inversion model demands further investigation efforts.

Nontradional Detection of Soil Moisture Content Based On Hyperspectral Imaging Technique

In the study, Vis-NIR hyperspectral imaging technique was investigated for non-destructive determination of soil moisture content. A total of 208 spectral information of soil samples by hyperspectral imaging system were collected. The differences between soil's moisture content and spectral changes were compared. Different methods of spectral data preprocessing methods, characteristic wavelengths extraction, and building models had influences on the performance of model established. Results from this investigation demonstrated that the reflectivity of soil spectrum were declining with the increase of soil moisture content. Once soil moisture content reached the limit of the field water holding capacity, the reflectivity of soil spectrum were increasing with the increase of soil moisture content. The methods of different pretreatment were analyzed, and the pretreatment method of normalization of unit vector was proposed. The number of characteristic wavelengths extracted by the method of uninformative variable eliminate (UVE), competitive adaptive reweighted sampling (CARS), beta coefficient (β), successive projections algorithm (SPA) were 49, 30, 5, 7, respectively. In order to reduce data redundancy, characteristic wavelengths extracted by the method of UVE and CARS were further extracted by SPA method. The number of characteristic wavelengths extracted by the method of UVE + SPA and CARS + SPA were 5, 8, respectively. The MLR model built based on the characteristic wavelengths extracted by the β coefficient was chosen as the best model, which of the optimal characteristic wavelengths were 411, 440, 622, 713, 790 nm, respectively. The correlation coefficient (Rp) and the root mean square error (RMSEP) for prediction set were 0.979 and 0.763, respectively. Therefore, it is feasible to predict soil moisture content using hyperspectral imaging technique. Bangladesh J. Bot. 51(4): 1039-1049, 2022 (December) Special

ResNet Model Automatically Extracts and Identifies FT-NIR Features for Geographical Traceability of Polygonatum kingianum.

Medicinal plants have incredibly high economic value, and a practical evaluation of their quality is the key to promoting industry development. The deep learning model based on residual convolutional neural network (ResNet) has the advantage of automatic extraction and the recognition of Fourier transform near-infrared spectroscopy (FT-NIR) features. Models are difficult to understand and interpret because of unknown working mechanisms and decision-making processes. Therefore, in this study, artificial feature extraction methods combine traditional partial least squares discriminant analysis (PLS-DA) and support vector machine (SVM) models to understand and compare deep learning models. The results show that the ResNet model has significant advantages over traditional models in feature extraction and recognition. Secondly, preprocessing has a great impact on the feature extraction and feature extraction, and is beneficial for improving model performance. Competitive adaptive reweighted sampling (CARS) and variable importance in projection (VIP) methods screen out more feature variables after preprocessing, but the number of potential variables (LVs) and successive projections algorithm (SPA) methods obtained is fewer. The SPA method only extracts two variables after preprocessing, causing vital information to be lost. The VIP feature of traditional modelling yields the best results among the four methods. After spectral preprocessing, the recognition rates of the PLS-DA and SVM models are up to 90.16% and 88.52%. For the ResNet model, preprocessing is beneficial for extracting and identifying spectral image features. The ResNet model based on synchronous two-dimensional correlation spectra has a recognition accuracy of 100%. This research is beneficial to the application development of the ResNet model in foods, spices, and medicinal plants.

Identification of Four Chicken Breeds by Hyperspectral Imaging Combined with Chemometrics

The current study aims to explore the potential of the combination of hyperspectral imaging and chemometrics in the rapid identification of four chicken breeds. The hyperspectral data of four chicken breeds were collected in the range of 400–900 nm. Five pretreatment methods were used to pretreat the original spectra. The important characteristic wavelength variables were extracted by random frog (RF), successive projection algorithm (SPA), and competitive adaptive reweighted sampling (CARS) algorithms. The classification models were established by using support vector machine (SVM), k-nearest neighbor (KNN), and partial least squares-discriminant analysis (PLS-DA). The results showed that the mean normalization pretreatment method was preferable, and overall classification accuracy of SVM-based models was higher than that of KNN-based and PLS-DA-based models. The correct classification rate (CCR) of the full-spectrum SVM model (Full-SVM) could reach 96.25%. The SPA method extracted 13 important wavelengths, and the SVM model based on SPA (SPA-SVM) achieved 90% CCR. This study can provide a theoretical reference for the discriminant analysis of chicken breeds.

Nondestructive Identification of Litchi Downy Blight at Different Stages Based on Spectroscopy Analysis

Litchi downy blight caused by Peronophythora litchii is the most serious disease in litchi production, storage and transportation. Existing disease identification technology has difficulty identifying litchi downy blight sufficiently early, resulting in economic losses. Thus, the use of diffuse reflectance spectroscopy to identify litchi downy blight at different stages of disease, particularly to achieve the early identification of downy blight, is very important. The diffuse reflectance spectral data of litchi fruits inoculated with P. litchii were collected in the wavelength range of 350–1350 nm. According to the duration of inoculation and expert evaluation, they were divided into four categories: healthy, latent, mild and severe. First, the SG smoothing method and derivation method were used to denoise the spectral curves. Then, the wavelength screening methods competitive adaptive reweighted sampling (CARS) and successive projections algorithm (SPA) were compared to verify that the SPA method was more effective. Eleven characteristic wavelengths were selected, accounting for only 1.1% of the original data. Finally, the characteristic wavelengths were tested by six different classification models, and their accuracy was calculated. Among them, the ANN model performed best, with an accuracy of 90.7%. The results showed that diffuse reflectance spectroscopic technology has potential for identifying litchi downy blight at different stages, providing technical support for the subsequent development of related automatic detection devices.

Moisture spectral characteristics and hyperspectral inversion of fly ash-filled reconstructed soil

To explore problems with the fast estimation method of moisture content (MC) in reconstructed soil under human disturbance, this paper used a fly ash-filled reconstructed soil region as the research object and obtained experimental data by Fieldspec4 high spectrometry and the laboratory drying method. The response characteristics of MC were analyzed from the original spectral data that underwent mathematical transformation and the spectral index data, and a corresponding inversion model was established. Combined with the successive projections algorithm (SPA), the model was optimized with a better fitting effect, and the optimal inversion model was obtained. The results showed that the composition of soil and fly ash were different, resulting in obvious differences in the shape of the spectral curve, but both had large moisture absorption peaks near 1420 nm and 1920 nm. After mathematical transformation, the correlation between the spectral reflectance and MC was enhanced, in which the absolute value of the maximum correlation between the soil moisture content (SMC) was 0.839, and the absolute value of the maximum correlation between the fly ash moisture content (FMC) was 0.801. Among them, the first-order differential of multivariate scattering correction (MSC′) and the first-order differential of logarithm ((lgR)′) had higher fitting accuracy for FMC and SMC, respectively. The scale and sensitivity of significance variables were greatly improved based on the spectral index of two-band operation. Better FMC and SMC models were constructed based on the difference soil index (DSI) under mathematical transformation, and R2 were 0.73 and 0.87, respectively. After SPA optimization, the predictive ability of the model was further improved, in which the predictive accuracy R2 of FMC and SMC reached up to 0.87 and 0.96, respectively, and the RPD was greater than 3. This shows that the DSI model based on MSC′ and (lgR)′ combined with the SPA method can be used as an effective means of predicting the MC in fly ash-filled reconstructed soil. These research results provide the theoretical basis and technical support for the application of soil near-earth sensing technology and rapid estimation of the MC of reconstructed soil under human disturbance.

Determination of manganese content in cottonseed meal using near-infrared spectrometry and multivariate calibration

BackgroundManganese (Mn) is an essential microelement in cottonseeds, which is usually determined by the techniques relied on hazardous reagents and complex pretreatment procedures. Therefore a rapid, low-cost, and reagent-free analytical way is demanded to substitute the traditional analytical method.ResultsThe Mn content in cottonseed meal was investigated by near-infrared spectroscopy (NIRS) and chemometrics techniques. Standard normal variate (SNV) combined with first derivatives (FD) was the optimal spectra pre-treatment method. Monte Carlo uninformative variable elimination (MCUVE) and successive projections algorithm method (SPA) were employed to extract the informative variables from the full NIR spectra. The linear and nonlinear calibration models for cottonseed Mn content were developed. Finally, the optimal model for cottonseed Mn content was obtained by MCUVE-SPA-LSSVM, with root mean squares error of prediction (RMSEP) of 1.994 6, coefficient of determination (R2) of 0.949 3, and the residual predictive deviation (RPD) of 4.370 5, respectively.ConclusionsThe MCUVE-SPA-LSSVM model is accuracy enough to measure the Mn content in cottonseed meal, which can be used as an alternative way to substitute for traditional analytical method.

SPA-Based Methods for the Quantitative Estimation of the Soil Salt Content in Saline-Alkali Land from Field Spectroscopy Data: A Case Study from the Yellow River Irrigation Regions

The problem of soil salinization has always been a global problem involving resource, environmental, and ecological issues, and is closely related to the sustainable development of the social economy. Remote sensing provides an effective technical means for soil salinity identification and quantification research. This study focused on the estimation of the soil salt content in saline-alkali soils and applied the Successive Projections Algorithm (SPA) method to the estimation model; twelve spectral forms were applied in the estimation model of the spectra and soil salt content. Regression modeling was performed using the Partial Least Squares Regression (PLSR) method. Proximal-field spectral measurements data and soil samples were collected in the Yellow River Irrigation regions of Shizuishan City. A total of 60 samples were collected. The results showed that application of the SPA method improved the modeled determination coefficient (R2) and the ratio of performance to deviation (RPD), and reduced the modeled root mean square error (RMSE) and the percentage root mean square error (RMSE%); the maximum value of R2 increased by 0.22, the maximum value of RPD increased by 0.97, the maximum value of the RMSE decreased by 0.098 and the maximum value of the RMSE% decreased by 8.52%. The SPA–PLSR model, based on the first derivative of reflectivity (FD), the FD–SPA–PLSR model, showed the best results, with an R2 value of 0.89, an RPD value of 2.72, an RMSE value of 0.177, and RMSE% value of 11.81%. The results of this study demonstrated the applicability of the SPA method in the estimation of soil salinity, by using field spectroscopy data. The study provided a reference for a subsequent study of the hyperspectral estimation of soil salinity, and the proximal sensing data from a low distance, in this study, could provide detailed data for use in future remote sensing studies.

Identification of mildew degrees in honeysuckle using hyperspectral imaging combined with variable selection

Mildew is one of the main reasons for the quality degradation of honeysuckle, which can lead to economic loss and threaten human safety. In order to detect different mildew degrees of honeysuckle, a method based on hyperspectral imaging technology was investigated. Different spectral pre-processing methods including Savizky–Golay filter (SG), standard normalized variable (SNV), multiple scatter correct (MSC), SG–MSC (a combination of SG and MSC) and SG–SNV were conducted for raw spectral data. A comparison was made among different pre-processing methods based on partial least squares regression models, of which the best method was SG–SNV. Then the uninformative variable elimination (UVE), competitive adaptive reweighted sampling (CARS) and successive projections algorithm (SPA) were applied to extract effective variables (wavelengths) from the preprocessed data by SG–SNV. The extreme learning machine (ELM) models, which the ‘Sigmoidal’ was chosen as the incentive function and the number of neurons in the hidden layer was 40, were developed for identifying honeysuckle with different mildew degrees using full-spectrum data and the selected variables obtained by UVE, CARS, SPA, UVE–CARS, UVE–SPA, CARS–SPA and UVE–CARS–SPA. The classification results showed that the UVE–SPA–ELM model performed the highest accuracy of 100% for both training set and testing set and the proposed UVE–SPA method was optimal and powerful for the variable selection. The results of this study indicated that hyperspectral imaging technology could be a rapid and non-destructive analytical tool for identifying different mildew degrees of honeysuckle.

Continuous Wavelet Analysis of Leaf Reflectance Improves Classification Accuracy of Mangrove Species

Due to continuous degradation of mangrove forests, the accurate monitoring of spatial distribution and species composition of mangroves is essential for restoration, conservation and management of coastal ecosystems. With leaf hyperspectral reflectance, this study aimed to explore the potential of continuous wavelet analysis (CWA) combined with different sample subset partition (stratified random sampling (STRAT), Kennard-Stone sampling algorithm (KS), and sample subset partition based on joint X-Y distances (SPXY)) and feature extraction methods (principal component analysis (PCA), successive projections algorithm (SPA), and vegetation index (VI)) in mangrove species classification. A total of 301 mangrove leaf samples with four species (Avicennia marina, Bruguiera gymnorrhiza, Kandelia obovate and Aegiceras corniculatum) were collected across six different regions. The smoothed reflectance (Smth) and first derivative reflectance (Der) spectra were subjected to CWA using different wavelet scales, and a total of 270 random forest classification models were established and compared. Among the 120 models with CWA of Smth, 88.3% of models increased the overall accuracy (OA) values with an improvement of 0.2–28.6% compared to the model with the Smth spectra; among the 120 models with CWA of Der, 25.8% of models increased the OA values with an improvement of 0.1–11.4% compared to the model with the Der spectra. The model with CWA of Der at the scale of 23 coupling with STRAT and SPA achieved the best classification result (OA = 98.0%), while the best model with Smth and Der alone had OA values of 86.3% and 93.0%, respectively. Moreover, the models using STRAT outperformed those using KS and SPXY, and the models using PCA and SPA had better performances than those using VIs. We have concluded that CWA with suitable scales holds great potential in improving the classification accuracy of mangrove species, and that STRAT combined with the PCA or SPA method is also recommended to improve classification performance. These results may lay the foundation for further studies with UAV-acquired or satellite hyperspectral data, and the encouraging performance of CWA for mangrove species classification can also be extended to other plant species.

Nondestructive quality assessment of chili peppers using near-infrared hyperspectral imaging combined with multivariate analysis

There is an increasing demand of chili peppers due to their special taste and numerous applications through a number of markets, and high quality is crucial for both producers and customers. This research was aimed to investigate the potential of near-infrared hyperspectral imaging (HSI) for nondestructive quality assessment of chili peppers. Near-infrared HSI in the spectral range of 975–1646 nm was employed to acquire hyperspectral reflectance images of chili peppers. High-performance liquid chromatography and freeze-drying methods were conducted to obtain the reference values of capsaicinoid concentrations and water contents, respectively. Three different variable selection methods with successive projections algorithm (SPA), competitive adaptive reweighted sampling (CARS) and genetic algorithm-partial least squares (GA-PLS) were performed to remove the redundant information and select the optimal wavelengths. Quantitative models including partial least squares (PLS), extreme learning machine (ELM) and least-squares support vector machine (LS-SVM) were then developed to predict the capsaicinoid concentrations and the water content. The results show that the ELM models combined with the SPA method yielded the best prediction performances for the capsaicin and dihydrocapsaicin concentrations, and the water content, with the highest correlation coefficients of prediction (RP) of 0.83, 0.80 and 0.93, respectively. Distribution maps of capsaicin and dihydrocapsaicin concentrations for intact and cut chili peppers were obtained. Finally, classification models for discriminating pungent and non-pungent chili peppers with a classification accuracy of 98.0% were developed. The results demonstrate that near-infrared HSI technique is promising for pepper quality assessment.

Pixel Selection by Successive Projections Algorithm Method in Multivariate Image Analysis for a QSAR Study of Antimicrobial Activity for Cephalosporins and Design New Cephalosporins.

Thirty-one Cephalosporin compounds were modeled using the multivariate image analysis and applied to the quantitative structure activity relationship (MIA-QSAR) approach. The acid dissociation constants (pKa) of cephalosporins play a fundamental role in the mechanism of activity of cephalosporins. The antimicrobial activity of cephalosporins was related to their first pKa by different models. Bidimensional molecular structures (images) were used to calculate some pixel descriptors. The selection of pixels by successive projections algorithm (SPA) was done to achieve simple MIA-QSAR models; based on a small subset of pixels. In the present study, the performance of pixel selection technique using SPA for partial least squares (PLS) model was evaluated. The obtained model showed nice prediction ability with root mean square error of prediction (RMSEP) values of 0.402, 0.315, and 0.160 for principal component regression (PCR), PLS and SPA-PLS models respectively. Among the three methods, SPA-PLS was potentially useful in predicting the pKa of cephalosporins. The study showed the maximum structural efficacy is on pKa in a, b and c regions.

The Contrast of Selecting Wavelength Capability of SPA and GA for Soil EC Detecting Model

The electrical conductivity(EC) of the soil was an important index to evaluate soil salinity. Spectral detecting method was an effective method. The objectives of this study were to compare choosing ability of the successive projections algorithm (SPA) and Genetic Algorithm (GA) methods for sensitive wavelengths and establish hyper spectral inversion model. Firstly, 140 soil samples were taken back to the laboratory and measured the EC (soil: water=1:5, EC1:5) and spectral reflectance(900-1700nm). Secondly, the SPA and GA were used to select sensitive wavelengths for reducing the complexity of the model, then PLSR method was used to establish detecting model. Finally, the determination coefficient (R2), root-mean-square error (RMSE), ratio prediction to deviation (RPD) and the number of wavelengths selected were used to evaluate different kinds of methods. The experimental results showed the prediction models after using SPA were better than GA method because SPA had lesser band number and time, higher R2 and RPD, smaller RMSE(band number=5, R2=0.9707, RPD=8.4445 and RMSE=0.31178). Thus, it was validated the SPA method can be used to select sensitive wavelengths to improve the model accuracy of soil EC.

Hyperspectral prediction of leaf area index of winter wheat in irrigated and rainfed fields.

The growth status of winter wheat in irrigated field and rainfed field are obviously different and the field types may have an effect on the predictive accuracy of hyperspectral model. The objectives of the present study were to understand the difference of spectral sensitive wavelengths for leaf area index (LAI) in two field types and realize its hyperspectral prediction. In study, a total of 31 and 28 sample sites in irrigated fields and rainfed fields respectively were selected from Wenxi County, and the LAI and canopy spectra were also collected at the main grow stage of winter wheat. The method of successive projections algorithm (SPA) was applied by selecting the important wavelengths, and the multiple linear regression (MLR) and partial least squares regression (PLSR) were used to construct the predictive model based on the important wavelengths and full wavelengths, respectively. Moreover, the parameters of variable importance project (VIP) and B-coefficient derived from PLSR analysis were implemented to validate the evaluated wavelengths using the SPA method. The sensitive wavelengths of LAI for irrigated field and rainfed field were 404, 407, 413, 417, 450, 677, 715, 735, 816, 1127 and 404, 406, 432, 501, 540, 679, 727, 779, 1120, 1290 nm, respectively, and these wavelengths proved to be highly correlated with LAI. Compared with the model performance based on the SPA-MLR and PLSR methods, the method of SPA-MLR was proved to be better (rainfed field: R2 = 0.736, RMSE = 1.169, RPD = 1.6245; irrigated field: R2 = 0.716, RMSE = 1.059, RPD = 1.538). Moreover, the predictive model of LAI in rainfed fields had a better accuracy than the model in irrigated fields. The results from this study indicated that it was necessary to classify the field type while monitoring the winter wheat using the remote sensing technology. This study also demonstrated that the multivariate method of SPA-MLR could accurately evaluate the sensitive wavelengths and construct the predictive model of LAI.

Application of NIR transmission spectroscopy with effective wavelength selection in non-destructive determination of essential amino acid content of foxtail millet

The near infrared (NIR) spectroscopy models developed for rapid and accurate determination of nine essential amino acids and protein in foxtail millet were investigated. The effects of the status of the samples (intact or ground), amino acid/protein correlation and variable selection methods on the predictive ability of the models were analysed. According to results, although the average spectral patterns of the intact and ground samples were similar, the absolute values of peaks and valleys for the intact samples were slightly higher. The modelling results of intact samples were similar to those obtained with ground samples. In the results of the partial least squares models with full spectra, 81-94% of the amino acid variance could be explained, except for lysine, tryptophan and methionine with the coefficients of determination of cross-validation less than 0.70. The correlation between various amino acids and protein might affect the predictive ability of NIR spectroscopy for essential amino acids. After variable selection, the models for isoleucine, leucine, phenylalanine, threonine, tryptophan and valine were remarkably improved by the successive projections algorithm method. The results indicated that short-wave NIR spectroscopy coupled with variable selection is a promising approach for determination of essential amino acids in foxtail millet.

An Ensemble Successive Project Algorithm for Liquor Detection Using Near Infrared Sensor

Spectral analysis technique based on near infrared (NIR) sensor is a powerful tool for complex information processing and high precision recognition, and it has been widely applied to quality analysis and online inspection of agricultural products. This paper proposes a new method to address the instability of small sample sizes in the successive projections algorithm (SPA) as well as the lack of association between selected variables and the analyte. The proposed method is an evaluated bootstrap ensemble SPA method (EBSPA) based on a variable evaluation index (EI) for variable selection, and is applied to the quantitative prediction of alcohol concentrations in liquor using NIR sensor. In the experiment, the proposed EBSPA with three kinds of modeling methods are established to test their performance. In addition, the proposed EBSPA combined with partial least square is compared with other state-of-the-art variable selection methods. The results show that the proposed method can solve the defects of SPA and it has the best generalization performance and stability. Furthermore, the physical meaning of the selected variables from the near infrared sensor data is clear, which can effectively reduce the variables and improve their prediction accuracy.

Detection in situ of carotenoid in microalgae by transmission spectroscopy

Carotenoids, which can be part of the food additives and drug additive, are one of important internal quality indexes for living microalgae. In the present study, visible/near infrared (Vis/NIR) transmission spectra in situ of Spirulina sp. suspension were required using an Ocean Optics USB 4000 Spectrometer in the wavelength range of 346–1038nm, and the loading weights (LW) analysis, uninformative variables elimination (UVE) and successive projections algorithm (SPA) were used to select important variables related to the carotenoids content (CC) for the Spirulina sp. suspension. Different concentrations of 100 samples of Spirulina sp. suspension were selected. The results showed the correlation coefficient (r), root mean square error (RMSE) and residual predictive deviation (RPD) in the prediction sets were 0.96, 0.23mg/L, 3.40, 0.89, 0.39mg/L, 1.59 and 0.96, 0.24mg/L, 3.44 for x-LW-PLS, UVE-PLS and SPA-PLS model respectively. It indicated that SPA-PLS gave the best result, while x-LW-PLS was better than UVE-PLS. So, Vis/NIR transmission spectra combined with SPA method was feasible to assess CC of Spirulina sp. suspension. And SPA variable selection method can simplify the prediction model and improve the model prediction precision. Furthermore, the method can be used as a good example for the detection in situ of other pigment content in other microalgae.

Improved partial least squares regression for rapid determination of reducing sugar of potato flours by near infrared spectroscopy and variable selection method

The feasibility of near infrared (NIR) spectroscopy coupled with variable selection methods for rapid determination of reducing sugar content in potato flours was investigated. Monte Carlo uninformative variable elimination (MCUVE), successive projections algorithm (SPA) and genetic algorithm (GA) were performed comparatively to choose characteristic variables associated with reducing sugar distributions. Eighty three and twenty seven samples were used to calibrate models and assess the performance of the models, respectively. Through comparing the performance of partial least squares regression models with new samples, the optimal models of reducing sugar was obtained with 12 selected variables by combination of MCUVE and SPA method. The correlation coefficient of prediction (r p ) and root mean square errors of prediction (RMSEP) were 0.981 and 0.243, respectively. The results suggest that NIR technique combining with MCUVE and SPA has significant potential to quantitatively analyze reducing sugar in potato flours; moreover, it could indicate the related spectral contributions.