Direct Orthogonal Signal Correction Research Articles

In Situ Nondestructive Detection of Nitrogen Content in Soybean Leaves Based on Hyperspectral Imaging Technology

In this paper, hyperspectral imaging technology, combined with chemometrics methods, was used to detect the nitrogen content of soybean leaves, and to achieve the rapid, non-destructive and in situ detection of the nitrogen content in soybean leaves. Soybean leaves under different fertilization treatments were used as the research object, and the hyperspectral imaging data and the corresponding nitrogen content data of soybean leaves at different growth stages were obtained. Seven spectral preprocessing methods, such as Savitzky–Golay smoothing (SG), first derivative (1-Der), and direct orthogonal signal correction (DOSC), were used to establish the quantitative prediction models for soybean leaf nitrogen content, and the quantitative prediction models of different spectral preprocessing methods for soybean leaf nitrogen content were analyzed and compared. On this basis, successive projections algorithm (SPA), genetic algorithm (GA) and random frog (RF) were employed to select the characteristic wavelengths and compress the spectral data. The results showed the following: (1) The full-spectrum prediction model of soybean leaf nitrogen content based on DOSC pretreatment was the best. (2) The PLS model of soybean leaf nitrogen content based on the five characteristic wavelengths had the best prediction performance. (3) The spatial distribution map of soybean leaf nitrogen content was generated in a pixel manner using the extracted five characteristic wavelengths and the DOSC-RF-PLS model. The nitrogen content level of soybean leaves can be quantified in a simple way; this provides a foundation for rapid in situ non-destructive detection and the spatial distribution difference detection of soybean leaf nitrogen. (4) The overall results illustrated that hyperspectral imaging technology was a powerful tool for the spatial prediction of the nitrogen content in soybean leaves, which provided a new method for the spatial distribution of the soybean nutrient status and the dynamic monitoring of the growth status.

Comparative Study of Augmented Classical Least Squares Models for UV Assay of Co-Formulated Antiemetics Together with Related Impurities.

The classical least squares (CLS) model and three augmented CLS models are adopted and validated for the analysis of pyridoxine HCl (PYR), cyclizine HCl (CYC), and meclizine HCl (MEC) in a quinary mixture with two related impurities: the CYC main impurity, Benzhydrol (BEH), which has carcinogenic and hepatotoxic effects, and the MEC official impurity, 4-Chlorobenzophenone (BEP). The proposed augmented CLS models are orthogonal signal correction CLS (OSC-CLS), direct orthogonal signal correction CLS (DOSC-CLS), and net analyte processing CLS (NAP-CLS). These models were applied to quantify the three active constituents in their raw materials and their corresponding dosage forms using their UV spectra. To evaluate the CLS-based models sensibly, we design a comparative study involving two sets: the training set to construct models and the validation set to assess the prediction abilities of these models. A five-level, five-factor calibration design was established to produce 25 mixtures for the calibration set. In addition, 16 experiments were performed for a test set distributed equally between the in-space and out-space samples. The primary criterion for comparing the models' performance was the validation set's root mean square error of prediction (RMSEP) value. Finally, augmented CLS models showed acceptable results for assaying the three analytes. The results were compared statistically with the reported HPLC methods; however, the DOSC-CLS model proved the best for assaying the dosage forms.

Quantitative analysis of blood glucose by FT‐Raman spectroscopy and multivariate statistical analysis

AbstractDiabetes is one of the common chronic diseases in worldwide and increasing among young children. Testing glycated hemoglobin levels as well as blood glucose levels is a method for detecting diabetes. In this article, with multivariate statistical analysis was used to monitor the blood glucose levels by the Fourier Transform Raman spectroscopy. Principal component regression with direct orthogonal signal correction is developed and applied to analyze the glucose concentration with the blood Raman spectroscopy. The root means square error of calibration is 3.0824 mg/dL (0.1712 mmol/L), the root means square error of prediction is 3.2688 mg/dL (0.1816 mmol/L), the correlation coefficients (R2) is .9946 and ratio of performance to deviation is 6.4789 and, respectively. The results show that this method can be used as a strong potential diagnostic tool for diabetes mellitus.

Hyperspectral imaging-based detection of soluble solids content of loquat from a small sample

The flavor and market value of loquats are substantially affected by their soluble solids content (SSC). This study explored the ability of hyperspectral imaging combined with multiple regression models to detect SSC of loquats using small sample size. In this study, one hundred and fifteen loquats were collected as samples, and regions of interest in the hyperspectral images of the samples were extracted. The spectral data processed through direct orthogonal signal correction (DOSC), the best among the five processing methods examined, was selected for the feature spectrum process. Successive projection algorithm (SPA), competitive adaptive reweighted sampling (CARS), improved uninformative variable elimination algorithm (imUVE), and imUVE-SPA algorithms were used to extract feature spectra and establish partial least squares regression (PLSR), support vector machine regression (SVR), back-propagation neural network (BPNN), and customized convolutional neural network (CNN) models, respectively. The results demonstrated that the CNN model based on the 18 feature spectra extracted by CARS was the best (correlation coefficient of validation = 0.904, root mean square error of validation = 0.336 %, and residual predictive deviation = 2.339). Consequently, hyperspectral images with small sample size combined with appropriate regression models can be used for nondestructive detection of loquat brix. Furthermore, the wavelength feature extraction algorithm effectively improves the performance of CNN models with small sample sizes. This study can serve as a valuable reference for the nondestructive detection of fruit brix in other limited sample situations.

Identifying Freshness of Shrimp Following Refrigeration Using Near-Infrared Hyperspectral Imaging

Shrimp tends to deteriorate during the refrigeration process. To monitor the freshness of shrimp during refrigeration, near-infrared (NIR) hyperspectral imaging was utilized to non-destructively identify the freshness of shrimp. In the process, three preprocessing methods (multivariate scatter correction [MSC], standard normal variate [SNV], and direct orthogonal signal correction [DOSC]) were employed to preprocess the full-wavelength spectral data, and three characteristic wavelength extraction algorithms (competitive adaptive reweighted sampling [CARS], and random forest [RF] simulated annealing [SA]) were used to extract the best-pre-processed data. Because extreme learning machine (ELM) and kernel extreme learning machine (KELM) are easily affected by parameters, ELM (based on teaching-learning-based optimization [TLBO]) and KELM (based on teaching-learning-based optimization [TLBO]) were proposed. In this study, four discriminant models (ELM, TLBO– ELM, KELM, and TLBO–KELM) were used for the full wavelength modeling analysis and the characteristic wavelength modeling analysis. In this work, the results of the final selected models are presented.

NIR quantitative model trans-scale calibration from small scale to pilot scale via directed DOSC-SBC algorithm

In order to solve the problem of inapplicability of NIR quantitative models due to the large difference between the modeling samples and the samples to be tested, Directed DOSC-SBC（DDOSC-SBC）algorithm is proposed in this paper based on Direct Orthogonal Signal Correction combined with Slope/Bias Correction (DOSC-SBC) algorithm. To obtain the suitable spectral matrix transfer parameters for the test set during DDOSC spectral preprocessing, several representative test samples in the test set were selected, then the spectral systematic errors between the modeling set and the test set were corrected with the SBC method in order to realize the trans-scale prediction of the NIR quantitative model. NIR data and the critical quality attributes（CQAs）were detected in the small scale and pilot scale pharmaceutical process of the fluidized bed granulation of dextrin and water extraction of honeysuckle. After the small scale model was calibrated via the directed DOSC-SBC algorithm which was guided by representative pilot scale samples, the small scale model was able to predict the pilot scale test samples more accurately. The NIR quantitative model trans-scale calibration from small scale to pilot scale was also successfully realized with a RPD value higher than 3.5 and RSEP value lower than 10%. DDOSC-SBC algorithm is a successful model trans-scale calibrated method that can be applied to NIR real-time monitoring of CQAs in the preparation process of Chinese herbal medicine.

Rapid and accurate determination of prohibited components in pesticides based on near infrared spectroscopy

Highly toxic pesticides are forbidden because of threatening to human lives, property, and the environment. However, some of them are still used illegally by added into normal pesticides to enhance the effectiveness and lower the cost. Therefore, it is very important to detect the illegally added ingredients in pesticides, such as Isofenphos-methyl in Tolfenpyrad. In this research, a rapid and accurate method for determining the concentration of Isofenphos-methyl in Tolfenpyrad is studied by near infrared spectroscopy with only nine characteristic wavelengths. Different pre-treating methods of direct orthogonal signal correction and first-order derivative on raw spectra and analysis methods of back propagation neural network and partial least squares are investigated to establish quantitative analysis models for comparison. Among them, the back propagation neural network model pre-treated with the direct orthogonal signal correction method has good prediction accuracy and prevents the data from being over-corrected. The correlation coefficients of the model calibration set and prediction set are as high as 0.999 and 0.989, respectively. Root Mean Square Error of Prediction and Ratio of Prediction to Deviation are 0.27% and 9.17, respectively. The prediction concentration limit can be as low as 0.5%. The results show that the direct orthogonal signal correction pre-treating method combined with back propagation neural network can be used for precisely quantitative determination of pesticide doping concentration. It provides an effective approach for rapid and accurate determination of prohibited components in pesticides.

Longitudinal Study Comparing Orthogonal Signal Correction Algorithms Coupled with Partial Least-Squares for Quantitative Near-Infrared Spectroscopy

Several orthogonal signal correction (OSC) methods coupled with partial least-squares (PLS) were applied to near-infrared spectra in a longitudinal study for the prediction of glucose, urea, and triacetin in liquid samples. The OSC methods included Wold’s OSC, Fearn’s OSC, orthogonal projection to latent structures (OPLS), and direct orthogonal signal correction (DOSC). These preprocessing methods are designed to simplify the spectra and remove information that is orthogonal to the analyte. Samples consisted of various concentrations of glucose, urea, and triacetin in the range from 1 to 19 mM. Calibration and prediction data were collected over 1.7 years to test how well each of the OSC methods helped to maintain analyte prediction performance over time. When comparing the OSC methods, it was found that all of the OSC methods improved the predictions in most data sets throughout the study but that DOSC clearly outperformed the others. On the basis of 18 prediction sets collected across 617 days after the collection of the calibration data, the average percent improvement in the standard error of prediction (SEP) when applying DOSC was 4.6%, 83.1%, and 43.0%, respectively, for glucose, urea, and triacetin. These comparisons were made relative to the SEP values obtained with unprocessed spectra.

Feasibility study on exhaled-breath analysis by untargeted Selected-Ion Flow-Tube Mass Spectrometry in children with cystic fibrosis, asthma, and healthy controls: Comparison of data pretreatment and classification techniques

Selected-Ion Flow-Tube Mass Spectrometry (SIFT-MS) has been applied in a clinical context as diagnostic tool for breath samples using target biomarkers. Exhaled breath sampling is non-invasive and therefore much more patient friendly compared to bronchoscopy, which is the golden standard for evaluating airway inflammation. In the actual pilot study, 55 exhaled breath samples of children with asthma, cystic-fibrosis and healthy individuals were included. Rather than focusing on the analysis of target biomarkers or on the identification of biomarkers, different data analysis strategies, including a variety of pretreatment, classification and discrimination techniques, are evaluated regarding their capacity to distinguish the three classes based on subtle differences in their full scan SIFT-MS spectra. Proper data-analysis strategies are required because these full scan spectra contain much external, i.e. unwanted, variation. Each SIFT-MS analysis generates three spectra resulting from ion-molecule reactions of analyte molecules with H3O+, NO+ and O2+. Models were built with Linear Discriminant Analysis, Quadratic Discriminant Analysis, Soft Independent Modelling by Class Analogy, Partial Least Squares - Discriminant Analysis, K-Nearest Neighbours, and Classification and Regression Trees. Perfect models, concerning overall sensitivity and specificity (100% for both) were found using Direct Orthogonal Signal Correction (DOSC) pretreatment. Given the uncertainty related to the classification models associated with DOSC pretreatments (i.e. good classification found also for random classes), other models are built applying other preprocessing approaches. A Partial Least Squares - Discriminant Analysis model with a combined pre-processing method considering single value imputation results in 100% sensitivity and specificity for calibration, but was less good predictive. Pareto scaling prior to Quadratic Discriminant Analysis resulted in 41/55 correctly classified samples for calibration and 34/55 for cross-validation. In future, the uncertainty with DOSC and the applicability of the promising preprocessing methods and models must be further studied applying a larger representative data set with a more extensive number of samples for each class. Nevertheless, this pilot study showed already some potential for the untargeted SIFT-MS application as a rapid pattern-recognition technique, useful in the diagnosis of clinical breath samples.

Rapid quantitative detection of mineral oil contamination in vegetable oil by near-infrared spectroscopy

This study provides a rapid method for quantification of mineral oil in rapeseed oil using near-infrared spectroscopy. The data were processed by direct orthogonal signal correction (DOSC), successive projections algorithm (SPA), partial least squares, and principal component regression (PCR). Good correlation coefficients (R) of 0.998 and root-mean-squared error (RMSE) of 0.005 were obtained, and the DOSC-SPA-PCR model was identified as the optimal method. A satisfactory accuracy with R and RMSE of prediction by DOSC-SPA-PCR of 0.990 and 0.006, was obtained. The results demonstrate that the proposed methodology is a promising method for the rapid quantitative detection of mineral oil in vegetable oil.

A new calibration model transferring strategy maintaining the predictive abilities of NIR multivariate calibration model applied in different batches process of extraction

Extraction process plays an important role in the pretreatment of Flos Lonicerae Japonicae for Qingkailing oral liquid and is also one of the most popular preparation processes of traditional Chinese medicine (TCM). In order to monitor the content of API in the extraction solution, a PLS model was developed based on NIR spectroscopy. However, due to the raw materials’ natural variability introduced into the extraction process of TCM or variations in spectral measurement conditions, it was unable to foresee the changes in the spectral response for new source batches, which could render a calibration model invalid. The development of a reliable multivariate calibration model for each new source batch is usually time-consuming and costly. In this work, a new calibration model transfer strategy which combined direct orthogonal signal correction with slope and bias correction (DOSC-SBC) was proposed and applied to maintain the predictive abilities of the original calibration model for new source batches. Performance of this model transfer strategy has been validated by two new individual batches which raw materials were from different sources, and the predictive relative errors of the samples from the above new batches decreased from 15.67% and 19.82% to 8.35% and 6.80% respectively after using DOSC-SBC strategy, demonstrating the good performance of the proposed model transfer strategy.

A biplot correlation range for group-wise metabolite selection in mass spectrometry

BackgroundAnalytic methods are available to acquire extensive metabolic information in a cost-effective manner for personalized medicine, yet disease risk and diagnosis mostly rely upon individual biomarkers based on statistical principles of false discovery rate and correlation. Due to functional redundancies and multiple layers of regulation in complex biologic systems, individual biomarkers, while useful, are inherently limited in disease characterization. Data reduction and discriminant analysis tools such as principal component analysis (PCA), partial least squares (PLS), or orthogonal PLS (O-PLS) provide approaches to separate the metabolic phenotypes, but do not offer a statistical basis for selection of group-wise metabolites as contributors to metabolic phenotypes.MethodsWe present a dimensionality-reduction based approach termed ‘biplot correlation range (BCR)’ that uses biplot correlation analysis with direct orthogonal signal correction and PLS to provide the group-wise selection of metabolic markers contributing to metabolic phenotypes.ResultsUsing a simulated multiple-layer system that often arises in complex biologic systems, we show the feasibility and superiority of the proposed approach in comparison of existing approaches based on false discovery rate and correlation. To demonstrate the proposed method in a real-life dataset, we used LC-MS based metabolomics to determine spectrum of metabolites present in liver mitochondria from wild-type (WT) mice and thioredoxin-2 transgenic (TG) mice. We select discriminatory variables in terms of increased score in the direction of class identity using BCR. The results show that BCR provides means to identify metabolites contributing to class separation in a manner that a statistical method by false discovery rate or statistical total correlation spectroscopy can hardly find in complex data analysis for predictive health and personalized medicine.

Fourier Transform Near-Infrared Spectroscopy and Chemometrics To Predict Zygosacchromyces rouxii in Apple and Kiwi Fruit Juices

This study investigated the capability of near-infrared spectroscopy (NIRS) to predict the concentration of Zygosaccharomyces rouxii in apple and kiwi fruit juices. The yeast was inoculated in fresh kiwi fruit juice ( n = 68), reconstituted kiwi juice ( n = 85), and reconstituted apple juice ( n = 64), followed by NIR spectra collection and plate counting. A principal component analysis indicated direct orthogonal signal correction preprocessing was suitable to separate spectral samples. Parameter optimization algorithms increased the performance of support vector machine regression models developed in a single variety juice system and a multiple variety juice system. Single variety juice models achieved accurate prediction of Z. rouxii concentrations, with the limit of quantification at 3 to 15 CFU/mL ( R2 = 0.997 to 0.999), and the method was also feasible for Hanseniaspora uvarum and Candida tropicalis. The best multiple variety juice model obtained had a limit of quantification of 237 CFU/mL ( R2 = 0.961) for Z. rouxii. A Bland-Altman analysis indicated good agreement between the support vector machine regression model and the plate counting method. It suggests that NIRS can be a high-throughput method for prediction of Z. rouxii counts in kiwi fruit and apple juices.

Five modified classical least squares based models for stability indicating analysis of cyclobenzaprine HCl with its major degradation products: A comparative study

Five modified multivariate calibration models based on classical least squares (CLS) in addition to traditional CLS model are developed and validated for assaying cyclobenzaprine HCl (CZ) with its major degradants; dibenzocycloheptatrienone (DZ) and anthraquinone (AQ), whether in its pure form or in pharmaceutical dosage form. The five models are net analyte processing CLS (NAP-CLS), orthogonal signal correction CLS (OSC-CLS), direct orthogonal signal correction CLS (DOSC-CLS) and hybrid linear analysis following the strategy of Xu and Schechter (HLA-XS) or Goicoechea et al. (HLA-GO). The five modified CLS models in addition to traditional CLS were subjected to a comparative study through manipulation of ultra-violet absorption data in the region of 220–350 nm. Three factor four level experimental design was adopted which results in 16 mixtures calibration set covering various concentrations of CZ, DZ and AQ. An extra validation set, composed of nine mixtures, was prepared for validation of the prediction power of the presented models. Experimental results showed high capability of the proposed modified CLS models for assaying CZ successfully without any interference from the co-existing degradation products (DZ and AQ). A statistical comparison between the results of CZ analysis in its dosage form by the six CLS based models and the reported HPLC method was carried out presenting no significant difference in regards to precision and accuracy. Significance of CLS based models is a consequent of their high quantitative and qualitative power for assaying multi-components mixtures.

Artificial neural networks (ANNs) and partial least squares (PLS) regression in the quantitative analysis of cocrystal formulations by Raman and ATR-FTIR spectroscopy

The present work describes the development of an efficient, fast and accurate method for the quantification of polymer-based cocrystal formulations. Specifically, the content of carbamazepine–nicotinamide (CBZ/NIC) and ibuprofen-nicotinamide (IBU/NIC) cocrystals in Soluplus®-based formulations was independently determined with the aid of either Raman or Attenuated Total Reflectance Fourier-Transform Infrared Spectroscopy (ATR-FTIR) spectroscopy. Spectra peaks from mixtures of IBU/NIC and CBZ/NIC cocrystals with Soluplus at a ratio ranging from 90/10 to 1/99 w/w (cocrystal to SOL) were evaluated and modelled with the aid of feed-forward, back-propagation artificial neural networks (ANNs) and partial least squares (PLS) regression analysis. A 25 full-factorial experimental design was employed in order to evaluate the effect of ANN’s structure (number of hidden units) and training (number of iteration cycles) parameters along with the effect of Raman or FTIR spectra region and data preprocessing (direct orthogonal signal correction – DOSC, second derivative, or no preprocessing) on ANN’s fitting performance. Results showed that when DOSC preprocessing was employed excellent ANN fitting in both Raman (root mean squared error of prediction (RMSEp) values of 0.43 and 0.34 for IBU/NIC-SOL and CBZ/NIC-SOL, respectively) and FTIR (RMSEp values of 0.04 and 0.03 for IBU/NIC-SOL and CBZ/NIC-SOL, respectively) spectra was obtained. Comparison of ANNs fitting results with PLS regression (RMSEp for IBU/NIC-SOL was 0.94 and 7.36, and for CBZ/NIC-SOL 7.29 and 15.63, using Raman and FTIR analysis, respectively) revealed ANN’s fitting superiority, which can be attributed to their inherent non-linear predictive ability.

MCEE: a data preprocessing approach for metabolic confounding effect elimination.

It is well recognized that physiological and environmental factors such as race, age, gender, and diurnal cycles often have a definite influence on metabolic results that statistically manifests as confounding variables. Currently, removal or controlling of confounding effects relies heavily on experimental design. There are no available data processing techniques focusing on the compensation of their effects. We therefore proposed a new method, Metabolic confounding effect elimination (MCEE), to remove the influence of specified confounding factors and make the data more accurate. The method consists of three steps: metabolites grouping, confounder-related metabolites selection, and metabolites modification. Its effectiveness and advantages were evaluated comprehensively by several simulated models and real datasets, and were compared with two typical methods, the principal component analysis (PCA)- and the direct orthogonal signal correction (DOSC)-based methods. MCEE is simple, effective, and safe, and is independent of sample number, association degree, and missing value. Hence, it may serve as a good complement to existing metabolomics data preprocessing methods and aid in better understanding the metabolic and biological status of interest. Graphical Abstract Algorithm flow and demo performance of MCEE.

Prediction of the level of astringency in persimmon using visible and near-infrared spectroscopy

Early control of fruit quality requires reliable and rapid determination techniques. Therefore, the food industry has a growing interest in non-destructive methods such as spectroscopy. The aim of this study was to evaluate the feasibility of visible and near-infrared (NIR) spectroscopy, in combination with multivariate analysis techniques, to predict the level and changes of astringency in intact and in the flesh of half cut persimmon fruits. The fruits were harvested and exposed to different treatments with 95% CO2 at 20 °C for 0, 6, 12, 18 and 24 h to obtain samples with different levels of astringency. A set of 98 fruits was used to develop the predictive models based on their spectral data and another external set of 42 fruit samples was used to validate the models. The models were created using the partial least squares regression (PLSR), support vector machine (SVM) and least squares support vector machine (LS-SVM). In general, the models with the best performance were those which included standard normal variate (SNV) in the pre-processing. The best model was the PLSR developed with SNV along with the first derivative (1-Der) pre-processing, created using the data obtained at six measurement points of the intact fruits and all wavelengths (R2 = 0.904 and RPD = 3.26). Later, a successive projection algorithm (SPA) was applied to select the most effective wavelengths (EWs). Using the six points of measurement of the intact fruit and SNV together with the direct orthogonal signal correction (DOSC) pre-processing in the NIR spectra, 41 EWs were selected, achieving an R2 of 0.915 and an RPD of 3.46 for the PLSR model. These results suggest that this technology has potential for use as a feasible and cost-effective method for the non-destructive determination of astringency in persimmon fruits.

Simultaneous quantitative analysis of olmesartan, amlodipine and hydrochlorothiazide in their combined dosage form utilizing classical and alternating least squares based chemometric methods.

Simultaneous spectrophotometric analysis of a multi-component dosage form of olmesartan, amlodipine and hydrochlorothiazide used for the treatment of hypertension has been carried out using various chemometric methods. Multivariate calibration methods include classical least squares (CLS) executed by net analyte processing (NAP-CLS), orthogonal signal correction (OSC-CLS) and direct orthogonal signal correction (DOSC-CLS) in addition to multivariate curve resolution-alternating least squares (MCR-ALS). Results demonstrated the efficiency of the proposed methods as quantitative tools of analysis as well as their qualitative capability. The three analytes were determined precisely using the aforementioned methods in an external data set and in a dosage form after optimization of experimental conditions. Finally, the efficiency of the models was validated via comparison with the partial least squares (PLS) method in terms of accuracy and precision.

Soil Phosphorus and Potassium Estimation by Reflectance Spectroscopy

Visible and near-infrared (VNIR) diffuse reflectance spectroscopy has potential in site-specific measurement of soil properties. However, previous studies have reported VNIR estimates of plant-available soil phosphorus (P) and potassium (K) to be of variable accuracy. In this study, we used a database of over 1500 soil samples to investigate what factors influenced P and K estimation accuracy. Specifically, the effects of classifying soil samples by Major Land Resource Area (MLRA), cation exchange capacity (CEC), or organic matter (OM) were investigated. Additionally, calibrations using only those samples within the approximate range of interest for fertilizer application to field crops (P from 0 to 27 mg kg -1 and K from 0 to 192 mg kg -1 ) were compared to calibrations using the full range of soil samples. Pretreatments of log 10 (1/reflectance) plus mean normalization plus median filter smoothing with or without direct orthogonal signal correction (DOSC) were investigated. Models were developed using partial least squares regression (PLSR) with leave-one-out cross-validation. Reasonable estimates of P and K were obtained for soil samples from two Missouri MLRAs (109 and 115B) out of the eight analyzed. Model estimates were poor when soil samples were grouped by CEC or OM; however, there was some indication that VNIR estimation of P and K might be possible for soils low in OM. Accuracy was maintained when analyzing a reduced wavelength range of 1100 to 2450 nm, suggesting that this narrower sensing range might be used for lower-cost on-the-go sensors. Inclusion of the DOSC pretreatment reduced P and K estimation errors by 25% to 39%. The results of this research provided some insight into the factors affecting the accuracy of P and K estimation by VNIR models, but additional research is needed to determine if these findings can lead to P and K estimations sufficiently accurate to guide variable-rate fertilization.

Determination of Cefoperazone Sodium in Presence of Related Impurities by Improved Classical Least Squares Chemometric Methods: A Comparative Study

A comparative study is established among 4 chemometric models depending on classical least squares (CLS) approach, namely, spectral residual augmented CLS (SRACLS), net analyte processing CLS (NAP-CLS), orthogonal signal correction CLS (OSC-CLS), and direct orthogonal signal correction CLS (DOSC-CLS). The comparison is expressed through analysis of a case study dataset of UV spectral data of Cefoperazone Sodium (CEF) and its two related impurities: in pure powder form and in pharmaceutical dosage form. Four-level three-factor experimental design was established for optimum analysis. The adopted experimental design gave rise to a training set consisting of 16 mixtures (containing different ratios of interfering species). To test the prediction power of the suggested models, an independent test set consisting of 9 mixtures was used. The presented results show the ability of the proposed models to quantify CEF in presence of two related impurities with high accuracy and selectivity (103.76±1.03,102.07±0.91,101.61±0.72, and101.60±0.72for SRACLS, NAP-CLS, OSC-CLS, and DOSC-CLS, resp.). Dosage form analysis results were compared statistically to a published HPLC methodology showing insignificant difference in terms of precision and accuracy, indicating the suggested models reliability and their suitability for quality control analysis of drug product. Compared to other models, OSC-CLS and DOSC-CLS models gave more accurate results with lower prediction error for test set samples.