Kennard Stone Research Articles

Design and combinatorial library generation of 1H 1,4 benzodiazepine 2,5 diones as photosystem-II inhibitors: A public QSAR approach

Exponential rise in the population around the word increased the demand of food grains/crops with limited expansion of the agricultural land. To meet the demand, generation of new herbicidal agents is of primary need for the manufacturing firm. In silico tool like QSAR is one of the regularly used in designing newer compounds along with wet experiment. Photosystem-II (PS-II) regarded as one of the major target for the herbicidal agents. With this aim in the present study a series of 1H, 1,4 benzodiazepine 2,5-dione analogues as herbicidal (PS-II inhibitors) agents were subjected to QSAR analysis using 2D PaDEL descriptors (open source). Two different splitting techniques namely, kennard stone based and k-means clustering splitting were used to divide the whole data set and GFA based on MAE criteria was used a statistical method to develop a model to investigate the physicochemical and structural requirement of potential PS-II inhibitors. All the models are statistically robust both internally and externally (Q2: 0.540–0.693, R2pred: 0.722–0.810). The activity is mostly affected by polarizabilities, electro negativities as well as substituents at the phenyl ring. Based on the results, a library of compounds was generated using SmiLib v2.0 tool (open source) and better predicted inside applicability domain compounds were identified by applying three different applicability domain (AD) approaches. Therefore the developed public QSAR models may be helpful for the scientific community for the further research.

Construction of the Calibration Set through Multivariate Analysis in Visible and Near-Infrared Prediction Model for Estimating Soil Organic Matter

The visible and near-infrared (VNIR) spectroscopy prediction model is an effective tool for the prediction of soil organic matter (SOM) content. The predictive accuracy of the VNIR model is highly dependent on the selection of the calibration set. However, conventional methods for selecting the calibration set for constructing the VNIR prediction model merely consider either the gradients of SOM or the soil VNIR spectra and neglect the influence of environmental variables. However, soil samples generally present a strong spatial variability, and, thus, the relationship between the SOM content and VNIR spectra may vary with respect to locations and surrounding environments. Hence, VNIR prediction models based on conventional calibration set selection methods would be biased, especially for estimating highly spatially variable soil content (e.g., SOM). To equip the calibration set selection method with the ability to consider SOM spatial variation and environmental influence, this paper proposes an improved method for selecting the calibration set. The proposed method combines the improved multi-variable association relationship clustering mining (MVARC) method and the Rank–Kennard–Stone (Rank-KS) method in order to synthetically consider the SOM gradient, spectral information, and environmental variables. In the proposed MVARC-R-KS method, MVARC integrates the Apriori algorithm, a density-based clustering algorithm, and the Delaunay triangulation. The MVARC method is first utilized to adaptively mine clustering distribution zones in which environmental variables exert a similar influence on soil samples. The feasibility of the MVARC method is proven by conducting an experiment on a simulated dataset. The calibration set is evenly selected from the clustering zones and the remaining zone by using the Rank-KS algorithm in order to avoid a single property in the selected calibration set. The proposed MVARC-R-KS approach is applied to select a calibration set in order to construct a VNIR prediction model of SOM content in the riparian areas of the Jianghan Plain in China. Results indicate that the calibration set selected using the MVARC-R-KS method is representative of the component concentration, spectral information, and environmental variables. The MVARC-R-KS method can also select the calibration set for constructing a VNIR model of SOM content with a relatively higher-fitting degree and accuracy by comparing it to classical calibration set selection methods.

Classification of fish meal produced in China and Peru by online near infrared spectroscopy with characteristic wavelength variables

Online near infrared reflectance spectroscopy combined with characteristic wavelength variables was used to establish a fast and nondestructive analytical method for the classification of fish meal produced in China and Peru. In this study, 117 fish meal samples (47 from China and 70 from Peru) were scanned in the spectral range of 1000–2500 nm by the online near infrared spectroscopy instrument applied on the conveyor belt. The K–S (Kennard–Stone) method was used for the division of samples into calibration and validation sets. Principal component analysis and partial least square discriminant analysis were applied to classify fish meal samples. The results showed that the discrimination accuracies with calibration and validation set samples were 100% and 89.74%, respectively, for the partial least square discriminant analysis model using the full spectrum after the optimimal spectral pre-treatment. Then competitive adaptive reweighted sampling (CARS) was used to select the characteristic wavelength variables for partial least square discriminant analysis model analysis, and the discrimination accuracy for the validation set increased to 94.87%. All the results indicated that online near infrared spectroscopy combined with characteristic wavelength variables could be used for discriminating fish meal samples produced in different places, which offers feed purchasers an effective, reliable, and real-time analysis method for the identification and authentication of the commercial fish meal product.

Comparison of sensitivity to artificial spectral errors and multivariate LOD in NIR spectroscopy – Determining the performance of miniaturizations on melamine in milk powder

This study compared three commercially available spectrometers – whereas two of them were miniaturized – in terms of prediction ability of melamine in milk powder (infant formula). Therefore all spectra were split into calibration- and validation-set using Kennard Stone and Duplex algorithm in comparison. For each instrument the three best performing PLSR models were constructed using SNV and Savitzky Golay derivatives. The best RMSEP values were 0.28g/100g, 0.33g/100g and 0.27g/100g for the NIRFlex N-500, the microPHAZIR and the microNIR2200 respectively. Furthermore the multivariate LOD interval [LODmin, LODmax] was calculated for all the PLSR models unveiling significant differences among the spectrometers showing values of 0.20g/100g – 0.27g/100g, 0.28g/100g – 0.54g/100g and 0.44g/100g – 1.01g/100g for the NIRFlex N-500, the microPHAZIR and the microNIR2200 respectively. To assess the robustness of all models, artificial introduction of white noise, baseline shift, multiplicative effect, spectral shrink and stretch, stray light and spectral shift were applied. Monitoring the RMSEP as function of the perturbation gave indication of robustness of the models and helped to compare the performances of the spectrometers.Not taking the additional information from the LOD calculations into account one could falsely assume that all the spectrometers perform equally well which is not the case when the multivariate evaluation and robustness data were considered.

Quantum modelling of the Structure-Activity and toxicity relationship studies of some potent compounds on SR leukemia cell line

Abstract In this study, a data set of one hundred (100) compounds from NCI database were optimized at the density functional theory (DFT) level using Becke's three-parameter Lee–Yang–Parr hybrid functional (B3LYP) in combination with the 6–31G* basis set. The optimized structures were employed in the generation of quantum chemical and molecular descriptors. These were then divided into training and test sets by Kennard Stone algorithm. The QSAR models were generated using the Genetic Function Approximation (GFA). The developed models were then subjected to internal and external validation and Y-randomization tests in order to establish their predictability and reliability. Descriptors such as number of methanal group (nMethanal), Secondary butyl, Sum of atom-type E-State:-F (S_Sf), Excessive molar refraction (MLFER_E) and 3D topological distance based autocorrelation - lag 3/weighted by first ionization potential (TDB3i) were found to be principally responsible for the activity nature of the compounds on SR cell lines, while Geary autocorrelation - lag 7/weighted by first ionization potential (GATS7i), Spectral mean absolute deviation from Barysz matrix/weighted by van der Waals volumes (SpMAD_Dzv) and number of high lowest atom weighted BCUTS (BCUTW-1i) chemical descriptors mean effects in the LC 50 model indicates their high influence on the toxicity of these compounds on SR cell line.

Identifying CPPU-Treated Kiwifruits Using Near-Infrared Hyperspectral Imaging Technology

The application of forchlorfenuron (N-(2-chloro-4-pyridyl)-N′-phenylurea), short form CPPU, on kiwifruits has become an important factor that influences kiwifruit economic efficiency and the health development of kiwifruit industry. This study aims to investigate the feasibility of using hyperspectral imaging technology to identify kiwifruits treated with CPPU (named as treated kiwifruits) from kiwifruits without CPPU treatment (named as untreated kiwifruits), and to investigate which model, developed for a single variety or for two varieties together, has better identification performance. Two hundred and forty “Xixuan” kiwifruits and 240 “Huayou” kiwifruits (120 treated kiwifruits and 120 untreated kiwifruits for each variety) were used to obtain hyperspectral from 865.11 to 1711.71 nm. The samples were divided into calibration set and prediction set based on Kennard–Stone method as the ratio of 3:1. Standard normal variate transformation was used to preprocess obtained spectra. Successive projections algorithm (SPA) was applied to extract the characteristic wavelengths from full spectra (FS). Support vector machine (SVM) and extreme learning machine (ELM) modeling methods were used to establish identification models of treated kiwifruits based on FS, characteristic wavelengths extracted by SPA, and universal wavelengths (UWs) extracted from characteristic wavelengths selected by SPA for “Xixuan,” “Huayou,” and two varieties together, respectively. The results showed that the number of characteristic wavelengths selected by SPA were 18, 18, and 21 for “Xixuan,” “Huayou,” and two varieties together, respectively. Five UWs were found for the three different samples. The best model was SPA-ELM for both “Xixuan” (99.8 % accuracy rate for predication set) and “Huayou” (100.0 % accuracy rate for predication set). Models of SPA-SVM and SPA-ELM, whose accuracy rate reached 100 % for both calibration and predication sets, had the best performance when the two varieties were used together. The performances of models built for two varieties together were better than that for “Xixuan,” but they were worse than that for “Huayou.”.The study indicates that NIR hyperspectral imaging technique can be used as a noninvasive method for identifying CPPU-treated kiwifruits from untreated ones, and it is potential to develop a model based on multi-varieties together.

Evaluation of extra-virgin olive oils shelf life using an electronic tongue—chemometric approach

Physicochemical quality parameters, olfactory and gustatory–retronasal positive sensations of extra-virgin olive oils vary during storage leading to a decrease in the overall quality. Olive oil quality decline may prevent the compliance of olive oil quality with labeling and significantly reduce shelf life, resulting in important economic losses and negatively condition the consumer confidence. The feasibility of applying an electronic tongue to assess olive oils’ usual commercial light storage conditions and storage time was evaluated and compared with the discrimination potential of physicochemical or positive olfactory/gustatory sensorial parameters. Linear discriminant models, based on subsets of 5–8 electronic tongue sensor signals, selected by the meta-heuristic simulated annealing variable selection algorithm, allowed the correct classification of olive oils according to the light exposition conditions and/or storage time (sensitivities and specificities for leave-one-out cross-validation: 82–96 %). The predictive performance of the E-tongue approach was further evaluated using an external independent dataset selected using the Kennard–Stone algorithm and, in general, better classification rates (sensitivities and specificities for external dataset: 67–100 %) were obtained compared to those achieved using physicochemical or sensorial data. So, the work carried out is a proof-of-principle that the proposed electrochemical device could be a practical and versatile tool for, in a single and fast electrochemical assay, successfully discriminate olive oils with different storage times and/or exposed to different light conditions.

Quantification of Soil Variables in a Heterogeneous Soil Region With VIS–NIR–SWIR Data Using Different Statistical Sampling and Modeling Strategies

Estimation accuracies obtained for soil properties from spectroradiometer data markedly depend on the individual sample set. The choice of the statistical method to sample a calibration set and the extension of the multivariate modeling approach with bagging and/or spectral variable selection may optimize predictions. We studied this with a set of 172 arable topsoils from a region near Trier (Germany) that covered—as often typical for medium to large-scale applications of soil spectroscopy—a wide range of different soil situations. Yet, differences concerning target variables—organic carbon (OC), nitrogen (N), microbial biomass ( ${\rm{C}_{{{\rm mic}}}}$ ) and thermostable carbon ( ${\rm{C}_{{{\rm inert}}}}$ )—were small. Based on a split of calibration and validation data with the Kennard–Stone algorithm, we found only moderate improvements towards partial least squares regression (PLSR) when combining PLSR with bagging and, for spectral variable selection, with “competitive adaptive reweighted sampling” (CARS). R 2 improved for OC (from 0.75 to 0.79), N (from 0.72 to 0.77) and ${\rm{C}_{{{\rm inert}}}}$ (from 0.66 to 0.68) in the validation. Additionally, we used individual calibration sets for each validation sample. In this “local” approach, we clustered calibration samples in the spectral feature space and selected individually the most similar sample from each cluster. Combining bagging-CARS-PLSR with this local approach improved R 2 markedly to 0.76 for ${\rm{C}_{{{\rm inert}}}}$ , and slightly to 0.82 for OC and to 0.76 (previously 0.73) for ${\rm{C}_{{{\rm mic}}}}$ . Effects of the local approach were twofold, as it removed improper samples from the calibration and balanced skewness in the data distribution.

Probing the origins of human acetylcholinesterase inhibition via QSAR modeling and molecular docking.

Alzheimer’s disease (AD) is a chronic neurodegenerative disease which leads to the gradual loss of neuronal cells. Several hypotheses for AD exists (e.g., cholinergic, amyloid, tau hypotheses, etc.). As per the cholinergic hypothesis, the deficiency of choline is responsible for AD; therefore, the inhibition of AChE is a lucrative therapeutic strategy for the treatment of AD. Acetylcholinesterase (AChE) is an enzyme that catalyzes the breakdown of the neurotransmitter acetylcholine that is essential for cognition and memory. A large non-redundant data set of 2,570 compounds with reported IC50 values against AChE was obtained from ChEMBL and employed in quantitative structure-activity relationship (QSAR) study so as to gain insights on their origin of bioactivity. AChE inhibitors were described by a set of 12 fingerprint descriptors and predictive models were constructed from 100 different data splits using random forest. Generated models afforded R2, n}{}{Q}_{mathrm{CV }}^{2} and n}{}{Q}_{mathrm{Ext}}^{2} values in ranges of 0.66–0.93, 0.55–0.79 and 0.56–0.81 for the training set, 10-fold cross-validated set and external set, respectively. The best model built using the substructure count was selected according to the OECD guidelines and it afforded R2, n}{}{Q}_{mathrm{CV }}^{2} and n}{}{Q}_{mathrm{Ext}}^{2} values of 0.92 ± 0.01, 0.78 ± 0.06 and 0.78 ± 0.05, respectively. Furthermore, Y-scrambling was applied to evaluate the possibility of chance correlation of the predictive model. Subsequently, a thorough analysis of the substructure fingerprint count was conducted to provide informative insights on the inhibitory activity of AChE inhibitors. Moreover, Kennard–Stone sampling of the actives were applied to select 30 diverse compounds for further molecular docking studies in order to gain structural insights on the origin of AChE inhibition. Site-moiety mapping of compounds from the diversity set revealed three binding anchors encompassing both hydrogen bonding and van der Waals interaction. Molecular docking revealed that compounds 13, 5 and 28 exhibited the lowest binding energies of −12.2, −12.0 and −12.0 kcal/mol, respectively, against human AChE, which is modulated by hydrogen bonding, π–π stacking and hydrophobic interaction inside the binding pocket. These information may be used as guidelines for the design of novel and robust AChE inhibitors.

Strategy for constructing calibration sets based on a derivative spectra information space consensus

Constructing an excellent calibration set is crucial to ensuring accurate multivariate calibration of spectra data. The purpose of this paper is to present an improved Kennard–Stone (KS) calibration set construction strategy based on different derivative spectra information spaces, termed Consensus Kennard–Stone (CKS). The core idea is to make full use of different derivative spectra information spaces when constructing the calibration set using a consensus selection method as well as to improve the prediction performance of the multivariate regression model. The experimental results from two public spectra datasets indicate that the proposed CKS strategy can use a more appropriate subset of samples for constructing the calibration set in the multivariate regression model and has superior predictive performance compared with the existing classic sample-selection KS strategies.

A combination of chemometrics methods and GC–MS for the classification of edible vegetable oils

The authenticity of edible vegetable oils is a very important issue due to consumer health and commercial reasons. Gas chromatography–mass spectrometry (GC–MS) was applied to analyze the fatty acid composition of sixty six samples from six different kinds of edible vegetable oils. The fatty acid profiles of these edible vegetable oils were used to classify the type of edible oils. For improving the classification accuracy of vegetable oils with respect to type, the support vector machine (SVM) technique, optimized using the genetic algorithm (GA), was employed to construct the classification model. The effectiveness of the GA–SVM combination in classification was compared with that of other well-known strategies for classification, such as minimum distance classification (MDC) and linear discriminant analysis (LDA). In addition, the Kennard–Stone algorithm was used to select the representative training samples and compared with the random sampling method. The misclassification rates were 8.48% and 3.03% for training and test set, respectively, by the GA–SVM model using the linear kernel. Only one or two samples will be misclassified in the process of GA–SVM classification. The classification task based on fatty acid data can be successfully achieved by the GA–SVM technique combined with the Kennard–Stone algorithm. The results reveal that this strategy is of great promise in flexible and accurate classification of edible vegetable oils.

Peach variety identification using near-infrared diffuse reflectance spectroscopy

More than 1000 peach varieties with significant differences in qualities are cultivated in China. Distinguishing peach varieties is not only needed by peach sellers, but also demanded by consumers. To offer information on identifying peach varieties, near-infrared (NIR) diffuse reflectance spectra between 833 and 2500nm were collected for four peach varieties, 100 samples for each variety. Kennard–Stone algorithm method was used to divide all samples into calibration set (320 peaches) and prediction set (80 peaches). Eight principal components (PCs), 1067 and 10 characteristic wavelengths were extracted by principal component analysis (PCA), uninformative variable elimination based on partial least squares (UVE-PLS) and successive projections algorithm (SPA) from full spectra (FS) with 2074 initial wavelengths, respectively. Least squares support vector machine (LSSVM) and extreme learning machine (ELM) were used to establish peach varieties identification models using the FS, selected PCs and characteristic wavelengths as input variables. Experimental results showed that all models based on PCA reached 100% accuracy for identifying the four peach varieties. The accuracy of LSSVM models based on UVE-PLS also reached 100%. This study indicated that peach varieties could be distinguished successfully by using NIR spectroscopy.

Soft sensing of product quality in the debutanizer column with principal component analysis and feed-forward artificial neural network

In this work, data-driven soft sensors are developed for the debutanizer column for online monitoring of butane content in the debutanizer column bottom product. The data set consists of data for seven process inputs and one process output. The total process data were equally divided into a training set and a validation set using the Kennard–Stone maximal intra distance criterion. The training set was used to develop multiple linear regression, principal component regression and back propagation neural network models for the debutanizer column. Performances of the developed models were assessed by simulation with the validation data set. Results show that the neural network model designed using Levenberg–Marquardt algorithm is capable of estimating the product quality with nearly 95% accuracy. The performance of the neural network model reported in this article is found to be better than the performances of least square support vector regression and standard support vector regression models reported in the literature earlier.

Preparation of comprehensive data from huge data sets for predictive soft sensors

Soft sensors predict values of process variables that are difficult to measure in real time. Predictive ability of adaptive soft sensors depends on databases. However, there is no way to construct initial databases from huge data sets that are measured in plants and that are data rich, but information poor. Therefore, we propose a method to select comprehensive data from huge data sets to build soft sensors with high predictive ability. A genetic algorithm and the Kennard–Stone algorithm are modified for data selection considering predictive ability of regression models and data distribution. Through the analyses of numerical simulation data and real industrial data, we confirm that initial databases could be appropriately constructed from huge data sets and predictive accuracy of soft sensors subsequently increased.

Determination of total organic carbon and soluble solids contents in Tanreqing injection intermediates with NIR spectroscopy and chemometrics

Near infrared spectroscopy combined with chemometrics was investigated for the fast determination of total organic carbon (TOC) and soluble solids contents (SSC) of Tanreqing injection intermediates. The NIR spectra were collected in transflective mode, and the TOC and SSC reference values were determined with Multi N/C UV HS analyzer and loss on drying method. The samples were divided into calibration sets and validation sets using the Kennard–Stone (KS) algorithm. The Dixon test, leverage and studentized residual test were studied for the sample outlier analysis. The selection of wavebands, spectra pretreated method and the number of latent variables were optimized to obtain better results. The quantitative calibration models were established with 3 different PLS regression algorithms, named linear PLS, non-linear PLS and concentration weighted PLS, and the net result was defined as the average of the predicted values of the different calibration models. The overall results indicated that the presented method is more powerful than single multivariable regression method, characterized by higher mean recovery rate (MRR) of the validation set, and can be used for the rapid determination of TOC and SSC values of Tanreqing injection intermediates, which are two important quality indicators for the process monitoring.

Prediction of binding peptides to class I Major Histocompatibility Complex using modified scoring matrices and data splitting strategies

Predicting peptides that can bind to MHC class I molecules is an important step in the vaccine design process. Computational approaches have potential to provide good predictive models that save both time and cost of the process. Position Specific Scoring Matrix (PSSM) is a reliable approach when dealing with amino acid sequences. PSSM formation involves carefully selecting its constructing data and parameters. In this work, we apply three different data splitting strategies and propose alternative values for the embedded PSSM parameters. The basic principle of data splitting is to choose train data that is able to represent the whole data. We propose using the Kennard–Stone algorithm to highlight the importance of choosing the data constituting the PSSM. Furthermore, this work proposes modifications to PSSM parameters and studies the model behavior in response to each change. The model is applied to experimental data for the Major Histocompatibility Complex of class I. Performance of modified parameters show either comparable or better results to conventional parameters. Moreover, Kennard–Stone data splitting algorithm contributed to significant model performance enhancement.

Quantitative Structure Activity Relationship Analysis of Selected Chalcone Derivatives as Mycobacterium tuberculosis Inhibitors

In order to gain further insights into the structural requirements for anti-tuberculosis activity by chalcone derivatives of 1,3-diphenylprop-2-ene-1-one, quantitative structure activity relationship (QSAR) was performed using genetic function approximation (GFA). Geometry optimization was achieved at the density functional theory (DFT) level using Becke’s three-parameter Lee-Yang-Parr hybrid functional (B3LYP) in combination with the 6-31G* basis set. Subsequently, quantum chemical and molecular descriptors were generated and divided into training and test sets by Kennard Stone algorithm. Internal and external validations as well as Y-randomization tests were employed in model validation. Five predictive models were generated by GFA. The generated models showed that constitutional indices, 2D autocorrelations and radial distribution function (RDF) descriptors were important contributors to anti-tuberculosis activity of 1,3-diphenylprop-2-ene-1-one derivatives. Based on validation results, model 4 was chosen as the best of the five models.

Online near-infrared analysis coupled with MWPLS and SiPLS models for the multi-ingredient and multi-phase extraction of licorice (Gancao).

BackgroundThis study aims to analyze the active pharmaceutical ingredients (APIs) of licorice (Radix Glycyrrhizae; gancao), including glycyrrhizic acid, liquiritin, isoliquiritin and total flavonoids, in multi-ingredient and multi-phase extraction by online near-infrared technology with fiber optic probes and chemometric analysis.MethodsHigh-performance liquid chromatography and ultraviolet spectrophotometry determined the APIs content in different extraction phases by online near-infrared analysis, which included sample set selection by the Kennard–Stone algorithm, optimization of spectral pretreatment methods (i.e., orthogonal signal correction and wavelet denoising spectral correction), and model calibration by the partial least-squares algorithm, moving-window partial least-squares algorithm and synergy interval partial least-squares (SiPLS) algorithm. The relative errors and F values were used to assess the models in different extraction phases.ResultsThe root-mean-square error of correction, root-mean-square error of cross-validation and root-mean-square error of prediction of APIs in the SiPLS model was less than 0.07. The F values of glycyrrhizic acid, liquiritin, isoliquiritin and total flavonoids were 10,765, 32,431, 649 and 6080, respectively, which were larger than 6.90 (P < 0.01).ConclusionThe study demonstrated the feasibility of online NIR analysis in the multi-ingredient and multi-phase extraction of APIs from licorice.Electronic supplementary materialThe online version of this article (doi:10.1186/s13020-015-0069-2) contains supplementary material, which is available to authorized users.

Using near infrared spectroscopy to classify soybean oil according to expiration date

A rapid and non-destructive methodology is proposed for the screening of edible vegetable oils according to conservation state expiration date employing near infrared (NIR) spectroscopy and chemometric tools. A total of fifty samples of soybean vegetable oil, of different brands andlots, were used in this study; these included thirty expired and twenty non-expired samples. The oil oxidation was measured by peroxide index. NIR spectra were employed in raw form and preprocessed by offset baseline correction and Savitzky–Golay derivative procedure, followed by PCA exploratory analysis, which showed that NIR spectra would be suitable for the classification task of soybean oil samples. The classification models were based in SPA–LDA (Linear Discriminant Analysis coupled with Successive Projection Algorithm) and PLS–DA (Discriminant Analysis by Partial Least Squares). The set of samples (50) was partitioned into two groups of training (35 samples: 15 non-expired and 20 expired) and test samples (15 samples 5 non-expired and 10 expired) using sample-selection approaches: (i) Kennard–Stone, (ii) Duplex, and (iii) Random, in order to evaluate the robustness of the models. The obtained results for the independent test set (in terms of correct classification rate) were 96% and 98% for SPA–LDA and PLS–DA, respectively, indicating that the NIR spectra can be used as an alternative to evaluate the degree of oxidation of soybean oil samples.

Estimation of multivariate detection limits of four quality parameters in licorice using MEMS–NIR spectrometry coupled with two sampling accessories

In this work, multivariate detection limits (MDL) estimator was obtained based on the micro-electro-mechanical systems–near infrared (MEMS–NIR) technology coupled with two sampling accessories to assess the detection capability of four quality parameters (glycyrrhizic acid, liquiritin, liquiritigenin and isoliquiritin) in licorice from different geographical regions. 112 licorice samples were divided into two parts (calibration set and prediction set) using Kennard–Stone (KS) method. Four quality parameters were measured using high-performance liquid chromatography (HPLC) method according to Chinese pharmacopoeia and previous studies. The MEMS–NIR spectra were acquired from fiber optic probe (FOP) and integrating sphere, then the partial least squares (PLS) model was obtained using the optimum processing method. Chemometrics indicators have been utilized to assess the PLS model performance. Model assessment using chemometrics indicators is based on relative mean prediction error of all concentration levels, which indicated relatively low sensitivity for low-content analytes (below 1000 parts per million (ppm)). Therefore, MDL estimator was introduced with alpha error and beta error based on good prediction characteristic of low concentration levels. The result suggested that MEMS–NIR technology coupled with fiber optic probe (FOP) and integrating sphere was able to detect minor analytes. The result further demonstrated that integrating sphere mode (i.e., MDL0.05,0.05, 0.22%) was more robust than FOP mode (i.e., MDL0.05,0.05, 0.48%). In conclusion, this research proposed that MDL method was helpful to determine the detection capabilities of low-content analytes using MEMS–NIR technology and successful to compare two sampling accessories.