Accurate grading of brain gliomas by soft independent modeling of class analogy based on non‐negative matrix factorization of proton magnetic resonance spectra

Hydrogen magnetic resonance spectroscopy (¹H‐MRS) is a non‐invasive technique which provides a ‘frequency‐signal intensity’ spectrum of biochemical compounds of tissues in the body. Although this method is currently used in human brain studies, accurate classification of in‐vivo ¹H‐MRS is a challenging task in the diagnosis of brain tumors. Problems such as overlapping metabolite peaks, incomplete information on background component and low signal‐to‐noise ratio disturb classification results of this spectroscopic method. This study presents an alternative approach to the soft independent modeling of class analogy (SIMCA) technique, using non‐negative matrix factorization (NMF) for dimensionality reduction. In the adopted strategy, the performance of SIMCA was improved by application of a robust algorithm for classification in the presence of noisy measurements. Total of 219 spectra from two databases were taken by water‐suppressed short echo‐time ¹H‐MRS, acquired from different subjects with different stages of glial brain tumors (Grade II (26 cases), grade III (24 cases), grade IV (41 cases), as well as 25 healthy cases). The SIMCA was performed using two approaches: (i) principal component analysis (PCA) and (ii) non‐negative matrix factorization (NMF), as a modified approach. Square prediction error was considered to assess the class membership of the external validation set. Finally, several figures of merit such as the correct classification rate (CCR), sensitivity and specificity were calculated. Results of SIMCA based on NMF showed significant improvement in percentage of correctly classified samples, 91.4% versus 83.5% for PCA‐based model in an independent test set. Copyright © 2015 John Wiley & Sons, Ltd.     

ST‐PLS: a multi‐directional nearest shrunken centroid type classifier via PLS

The nearest shrunken centroid (NSC) Classifier is successfully applied for class prediction in a wide range of studies based on microarray data. The contribution from seemingly irrelevant variables to the classifier is minimized by the so‐called soft‐thresholding property of the approach. In this paper, we first show that for the two‐class prediction problem, the NSC Classifier is similar to a one‐component discriminant partial least squares (PLS) model with soft‐shrinkage of the loading weights. Then we introduce the soft‐threshold‐PLS (ST‐PLS) as a general discriminant‐PLS model with soft‐thresholding of the loading weights of multiple latent components. This method is especially suited for classification and variable selection when the number of variables is large compared to the number of samples, which is typical for gene expression data. A characteristic feature of ST‐PLS is the ability to identify important variables in multiple directions in the variable space. Both the ST‐PLS and the NSC classifiers are applied to four real data sets. The results indicate that ST‐PLS performs better than the shrunken centroid approach if there are several directions in the variable space which are important for classification, and there are strong dependencies between subsets of variables. Copyright © 2007 John Wiley & Sons, Ltd.     

Monitoring of water quality in South Paris district by clustering and SIMCA classification

With the aim of obtaining a monitoring tool to assess the quality of water, a multivariate statistical procedure based on cluster analysis (CA) coupled with soft independent modelling class analogy (SIMCA) algorithm, providing an effective classification method, is proposed. The experimental data set, carried out throughout the year 2004, was composed of analytical parameters from 68 water sources in a vast southwest area of Paris. Nine variables carrying the most useful information were selected and investigated (nitrate, sulphate, chloride, turbidity, conductivity, hardness, alkalinity, coliforms and Escherichia coli). Principal component analysis provided considerable data reduction, gathering in the first two principal components the majority of information representing about 92.2% of the total variance. CA grouped samples belonging to different sites, distinctly correlating them with chemical variables, and a classification model was built by SIMCA. This model was optimised and validated and then applied to a new data matrix, consisting of the parameters measured during the year 2005 from the same objects, providing a fast and accurate classification of all the samples. The most of the examined sources appeared unchanged during the 2-year period, but five sources resulted distributed in different classes, due to statistical significant changes of some characteristic analytical parameters.     

Classification of biodiesel using NIR spectrometry and multivariate techniques

This article describes the classification of biodiesel samples using NIR spectroscopy and chemometric techniques. A total of 108 spectra of biodiesel samples were taken (being three samples each of four types of oil, cottonseed, sunflower, soybean and canola), from nine manufacturers. The measurements for each of the three samples were in the spectral region between 12,500 and 4000 cm⁻¹. The data were preprocessed by selecting a spectral range of 5000-4500 cm⁻¹, and then a Savitzky-Golay second-order polynomial was used with 21 data points to obtain second derivative spectra. Characterization of the biodiesel was done using chemometric models based on hierarchical cluster analysis (HCA), principal component analysis (PCA) and soft independent modeling of class analogy (SIMCA) elaborated for each group of biodiesel samples (cotton, sunflower, soybean and canola). For the HCA and PCA, the formation of clusters for each group of biodiesel was observed, and SIMCA models were built using 18 spectral measurements for each type of biodiesel (training set), and nine spectral measurements to construct a classification set (except for the canola oil which used eight spectra). The SIMCA classifications obtained 100% accurate identifications. Using this strategy, it was feasible to classify biodiesel quickly and nondestructively without the need for various analytical determinations.     

Comparison of chemometric methods for brand classification of cigarettes by near-infrared spectroscopy

The combinations of NIR spectroscopy and three classification algorithms, i.e., multi-class support vector machine (BSVM), k-nearest neighbor (KNN) and soft independent modeling of class analogies (SIMCA), for discriminating different brands of cigarettes, were explored. The influence of the training set size on the relative performance of each algorithm was also investigated. A NIR spectral dataset involving the classification of cigarettes of three brands was used for illustration. Three performance criteria based on “correctly classified rate (CCR)”, i.e., “Average CCR”, “95 percentile of CCR” and “S.D. of CCR”, were defined to compare different algorithms. It was revealed that BSVM is significantly better than KNN or SIMCA in the statistical sense, especially in cases where the training set is relatively small. The results suggest that NIR spectroscopy together with BSVM could be an alternative to traditional methods for discriminating different brands of cigarettes.     

Chemometric characterisation of Basque and French ciders according to their polyphenolic profiles

Polyphenolic compositions of Basque and French ciders were determined by HPLC-DAD following thiolysis, in order to characterise and differentiate these beverages and then develop a classification system capable of confirming the authenticities of both kinds of cider. A data set consisting of 165 cider samples and 27 measured features was evaluated using multivariate chemometric techniques, such as cluster analysis and principal component analysis, in order to perform a preliminary study of data structure. Supervised pattern recognition techniques such as linear discriminant analysis (LDA), K-nearest neighbours (KNN), soft independent modelling of class analogy (SIMCA), and multilayer feed-forward artificial neural networks (MLF-ANN) attained classification rules for the two categories using the chemical data, which produced satisfactory results. Authentication systems obtained by combining two of these techniques were proposed. We found that SIMCA and LDA or KNN models achieved 100% hit-rates, since LDA and KNN permit the detection of every Basque cider and SIMCA provides a model for Basque cider that excludes all French ciders. Polyphenolic profiles of the ciders provided enough information to be able to develop classification rules for identifying ciders according to their geographical origin (Basque or French regions). Chemical and organoleptic differences between these two types of cider are probably due to the original and distinctive cidermaking technologies used for their elaboration. Using polyphenic profiles, about 80% of French ciders could be distinguished according to their region of origin (Brittany or Normandy). Although their polyphenolic profiles did not provide enough information to achieve an authentication system for Breton and Norman ciders.Abbreviations AVI Avicularin - CQA Caffeoylquinic acid - CAF Caffeic acid - CAT (+)-catechin - CT-1, -2, -3 Unknown flavan-3-ols - DPn Average degree of polymerization of procyanidins - EC (–)-epicatechin - HCA-7 Ferulic acid - HCA-1, -2 ,-3, 4, -5, -6 Unknown hydroxycinnamic acids - HYP Hyperin - IQC Isoquercitrin - PC Total procyanidins - PCM p-Coumaric acid - PCQ p-Coumaroylquinic acid - PL Phloretin - PLG Phloridzin - PLXG phloretin-2-O-xyloglucoside - PPO Polyphenoloxidase - QCE Quercetin - QCI Quercitrin - RUT Rutin - CA Cluster analysis - KNN K-nearest neighbours - LDA Linear discriminant analysis - MLF-ANN Multilayer feed-forward-artificial neural network - PCA Principal component analysis - PC1 First principal component - PC2 Second principal component - PC3 Third principal component - RMSE Root medium square error - SD Standard deviation - SIMCA Soft independent modelling of class analogy - DAD Diode array detector - HPLC High Performance Liquid Chromatography - ND Not detected     

Unsymmetrical covariance processing of COSY or TOCSY and HSQC NMR data to obtain the equivalent of HSQC‐COSY or HSQC‐TOCSY spectra

Artifacts observed in the indirect covariance NMR spectrum of HSQC‐TOCSY data have recently been analyzed and a method for their elimination proposed. More recently, unsymmetrical covariance processing has been applied HSQC and HMBC spectral data to afford long‐range carbon‐carbon correlation information equivalent to that obtained from n, 1‐, 1, n‐ and m,n‐ADEQUATE spectra. We now wish to describe the results obtained through the application of unsymmetrical covariance processing of HSQC and COSY or TOCSY data, which affords the equivalent of HSQC‐COSY and HSQC‐TOCSY data in a fraction of the time required to record these spectra directly and with considerably higher sensitivity.     

Comparison of PLS1-DA, PLS2-DA and SIMCA for classification by origin of crude petroleum oils by MIR and virgin olive oils by NIR for different spectral regions

This study compares results obtained with several chemometric methods: SIMCA, PLS2-DA, PLS2-DA with SIMCA, and PLS1-DA in two infrared spectroscopic applications. The results were optimized by selecting spectral ranges containing discriminant information. In the first application, mid-infrared spectra of crude petroleum oils were classified according to their geographical origins. In the second application, near-infrared spectra of French virgin olive oils were classified in five registered designations of origins (RDOs). The PLS-DA discrimination was better than SIMCA in classification performance for both applications. In both cases, the PLS1-DA classifications give 100% good results. The encountered difficulties with SIMCA analyses were explained by the criteria of spectral variance. As a matter of fact, when the ratio between inter-spectral variance and intra-spectral variance was close to the F_c (Fisher criterion) threshold, SIMCA analysis gave poor results. The discrimination power of the variable range selection procedure was estimated from the number of correctly classified samples.     

A new technique for differentiating between 2J(C,H) and 3/4J(C,H) connectivities

We present a new pulse sequence that yields two simultaneously detected types of long‐range correlation spectra. The one spectrum is to show all ⁿJ(C,H) connectivities and the other is to show exclusively ²J(C,H) connectivities. The method is demonstrated by using strychnine as a test sample. A comparison with HMBC shows that the ²J(C,H)/ⁿJ(C,H) experiment supplies a ⁿJ(C,H) spectrum that is of equal standard with regard to sensitivity and spectral information. The additional ²J(C,H) spectrum allows the disentanglement of ²J(C,H) and ⁿJ(C,H) signals (n > 2) in HMBC type spectra, which greatly simplifies signal assignment and structure elucidation in general. Copyright © 2003 John Wiley & Sons, Ltd.     

Parallel vector analysis based soft resolution of spectral data from pH‐metric titration using symmetry constraint

In this study, a soft method is proposed to calculate concentration and spectral profiles for the two‐way spectral data from dissociation equilibria of polyprotic acids (H_nA). This method has four main distinct steps: (i) a fixed size moving window evolving factor analysis (FSMWEFA) was used to identify the local rank map, (ii) WFA was applied to calculate the concentration profiles of H_nA and Aⁿ⁻ (selection of the window for application of WFA was performed using EFA), (iii) PVA was used to calculate H_{n − 1}A to HA spectral profiles, and (iv) a symmetry constraint, in addition to the non‐negativity constraint, was utilized to obtain the unique concentration and spectral profiles from different acceptable sets of profiles. In the absence of any selective region in the spectral data, the proposed soft method resulted in unique solution without rotational ambiguity. This study is the first application of symmetry constraint on concentration profiles. The rotational ambiguity drastically decreased on considering the constraint of symmetry of the H_{n − 1}A and HA concentration profiles, in addition to non‐negativity of profiles. Simulated examples were used to confirm these approaches. Effect of closeness of dissociation constants on the estimated values of constants was investigated. The results showed that when the difference between pK_a values is more than 1.2, the obtained errors in the estimation of pK_a values are less than about 6.5%. The considered real data were from pH‐metric titration of fluorescein. The obtained spectral and concentration profiles and the estimated pK_a values for fluorescein were in good agreement with the previously reported data. Copyright © 2009 John Wiley & Sons, Ltd.     

The Use of UV Spectroscopy and SIMCA for the Authentication of Indonesian Honeys According to Botanical,Entomological and Geographical Origins

As a functional food, honey is a food product that is exposed to the risk of food fraud. To mitigate this, the establishment of an authentication system for honey is very important in order to protect both producers and consumers from possible economic losses. This research presents a simple analytical method for the authentication and classification of Indonesian honeys according to their botanical, entomological, and geographical origins using ultraviolet (UV) spectroscopy and SIMCA (soft independent modeling of class analogy). The spectral data of a total of 1040 samples, representing six types of Indonesian honey of different botanical, entomological, and geographical origins, were acquired using a benchtop UV-visible spectrometer (190–400 nm). Three different pre-processing algorithms were simultaneously evaluated; namely an 11-point moving average smoothing, mean normalization, and Savitzky–Golay first derivative with 11 points and second-order polynomial fitting (ordo 2), in order to improve the original spectral data. Chemometrics methods, including exploratory analysis of PCA and SIMCA classification method, was used to classify the honey samples. A clear separation of the six different Indonesian honeys, based on botanical, entomological, and geographical origins, was obtained using PCA calculated from pre-processed spectra from 250–400 nm. The SIMCA classification method provided satisfactory results in classifying honey samples according to their botanical, entomological, and geographical origins and achieved 100% accuracy, sensitivity, and specificity. Several wavelengths were identified (266, 270, 280, 290, 300, 335, and 360 nm) as the most sensitive for discriminating between the different Indonesian honey samples.     

Multi-class classification with probabilistic discriminant partial least squares (p-DPLS)

This work describes multi-classification based on binary probabilistic discriminant partial least squares (p-DPLS) models, developed with the strategy one-against-one and the principle of winner-takes-all. The multi-classification problem is split into binary classification problems with p-DPLS models. The results of these models are combined to obtain the final classification result. The classification criterion uses the specific characteristics of an object (position in the multivariate space and prediction uncertainty) to estimate the reliability of the classification, so that the object is assigned to the class with the highest reliability. This new methodology is tested with the well-known Iris data set and a data set of Italian olive oils. When compared with CART and SIMCA, the proposed method has better average performance of classification, besides giving a statistic that evaluates the reliability of classification. For the olive oil set the average percentage of correct classification for the training set was close to 84% with p-DPLS against 75% with CART and 100% with SIMCA, while for the test set the average was close to 94% with p-DPLS as against 50% with CART and 62% with SIMCA.     

A validated HPLC‐fluorescence method with a semi‐micro column for routine determination of homocysteine,cysteine and cysteamine,and the relation between the thiol derivatives in normal human plasma

A semi‐micro column HPLC‐fluorescence method for routine determination of thiol derivatives such as homocysteine (Hcy), cysteine (Cys) and cysteamine (CA) is described. The thiol derivatives labeled with ammonium‐7‐fluorobenzo‐2‐oxa‐1,3‐diazole‐4‐sulfonate (SBD‐F) were isocratically separated within 12 min on a semi‐micro ODS column (Daisopak‐SP‐120‐5‐ODS‐BP) with a mixture of 25 mm acetate buffer (pH 2.00) and CH₃CN as a mobile phase. The purity and similarity of SBD‐thiols by a multi‐wavelength fluorescence detector were more than 92.3 and 96.7%. The detection limits of Hcy, Cys and CA at a signal‐to‐noise ratio of 3 were 0.16, 0.47 and 0.03 µm , respectively. Furthermore validation parameters such as accuracy, precision and robustness of the proposed method showed satisfactory results. Almost 850 plasma sample injections (range 572–1076, n = 3) for a column could be performed without differences in retention time and peak heights of labels. As an application of the proposed method, the determination of thiol derivatives in normal human plasma (n = 103) was demonstrated. The correlation coefficients between Hcy vs Cys and Hcy vs CA were 0.38 and −0.35, respectively. Copyright © 2009 John Wiley & Sons, Ltd.     

HMBC‐1,n‐ADEQUATE spectra calculated from HMBC and 1,n‐ADEQUATE spectra

Unsymmetrical and generalized indirect covariance processing methods provide a means of mathematically combining pairs of 2D NMR spectra that share a common frequency domain to facilitate the extraction of correlation information. Previous reports have focused on the combination of HSQC spectra with 1,1‐, 1,n‐, and inverted ¹J_CC 1,n‐ADEQUATE spectra to afford carbon–carbon correlation spectra that allow the extraction of direct (¹J_CC), long‐range (ⁿJ_CC, where n ≥ 2), and ¹J_CC‐edited long‐range correlation data, respectively. Covariance processing of HMBC and 1,1‐ADEQUATE spectra has also recently been reported, allowing convenient, high‐sensitivity access to ⁿJ_CC correlation data equivalent to the much lower sensitivity n,1‐ADEQUATE experiment. Furthermore, HMBC‐1,1‐ADEQUATE correlations are observed in the F₁ frequency domain at the intrinsic chemical shift of the ¹³C resonance in question rather than at the double‐quantum frequency of the pair of correlated carbons, as visualized by the n,1, and m,n‐ADEQUATE experiments, greatly simplifying data interpretation. In an extension of previous work, the covariance processing of HMBC and 1,n‐ADEQUATE spectra is now reported. The resulting HMBC‐1,n‐ADEQUATE spectrum affords long‐range carbon–carbon correlation data equivalent to the very low sensitivity m,n‐ADEQUATE experiment. In addition to the significantly higher sensitivity of the covariance calculated spectrum, correlations in the HMBC‐1,n‐ADEQUATE spectrum are again detected at the intrinsic ¹³C chemical shifts of the correlated carbons rather than at the double‐quantum frequency of the pair of correlated carbons. HMBC‐1,n‐ADEQUATE spectra can provide correlations ranging from diagonal (⁰J_CC or diagonal correlations) to ⁴J_CC under normal circumstances to as much as ⁶J_CC in rare instances. The experiment affords the potential means of establishing the structures of severely proton‐deficient molecules. Copyright © 2013 John Wiley & Sons, Ltd.     

Determining the adulteration of spices with Sudan I-II-II-IV dyes by UV-visible spectroscopy and multivariate classification techniques

We propose a very simple and fast method for detecting Sudan dyes (I, II, III and IV) in commercial spices, based on characterizing samples through their UV-visible spectra and using multivariate classification techniques to establish classification rules. We applied three classification techniques: K-Nearest Neighbour (KNN), Soft Independent Modelling of Class Analogy (SIMCA) and Partial Least Squares Discriminant Analysis (PLS-DA). A total of 27 commercial spice samples (turmeric, curry, hot paprika and mild paprika) were analysed by chromatography (HPLC-DAD) to check that they were free of Sudan dyes. These samples were then spiked with Sudan dyes (I, II, III and IV) up to a concentration of 5 mg L⁻¹. Our final data set consisted of 135 samples distributed in five classes: samples without Sudan dyes, samples spiked with Sudan I, samples spiked with Sudan II, samples spiked with Sudan III and samples spiked with Sudan IV.Classification results were good and satisfactory using the classification techniques mentioned above: 99.3%, 96.3% and 90.4% of correct classification with PLS-DA, KNN and SIMCA, respectively. It should be pointed out that with SIMCA, there are no real classification errors as no samples were assigned to the wrong class: they were just not assigned to any of the pre-defined classes.     

1H NMR spectral analysis and conformational behavior of n‐alkanes in different chemical environments

Alkyl chains are common structural units, for example in lipids, and their ¹H NMR spectral parameters offer valuable information about their conformational behavior in solvent environment. Even the spectra of short n‐alkanes are complex, which is obviously a reason why their accurate spectral analyses have not been reported before. The present study reports the quantum mechanical analysis of ¹H NMR spectra of n‐butane, n‐pentane, n‐hexane, and n‐heptane. The spectral parameters were used to characterize the conformational behavior of n‐alkanes. The temperature dependence analysis of coupling constants suggests that the enthalpy difference between the gauche (g) and trans (t) conformations (ΔH_g) of n‐butane in chloroform is 2.55–2.85 kJ mol^?1. The difference between the trans–gauche (tg) and all‐trans (tt) conformers of n‐pentane (ΔH_tg) seems to be 0.1–0.2 kJ mol^?1 higher. The coupling constant information shows that the t_n conformations become more favored with longer chains, although not only for energetic reasons but also partly because the g⁺g^‐ arrangements become sterically unfavorable, which decreases the number of favorable g_n‐type conformations. The analysis of the ¹H NMR spectra of n‐pentane and n‐hexane in solvents representing different chemical environments indicates that polar and spherical dimethyl sulfoxide favors clearly the g conformations, whereas n‐hexane‐d₁₄ favors slightly the extended t_n conformation. In addition to the intrinsic scientific importance for NMR spectral parameter prediction and molecular modeling in solution, the results provide some insights to behavior of hydrocarbon chains and their spectra in different chemical environments. Copyright © 2012 John Wiley & Sons, Ltd.     

Spectral,magnetic studies and molecular orbital calculations for (4,5-dimethyl-3-pyrazolyl)aldazine copper(II) complexes

Summary A new series of copper(II) complexes derived from (4,5-dimethyl-3-pyrazolyl) aldazine (DMPA), formulated as: (a) [LCuX₂]·nEtOH; (b) [LCu(H₂O)](ClO₄)₂; and (c) [LCuX]BF₄·nH₂O, where n = 1/2 or 1, X = Cl or Br and L = DMPA, have been synthesized. The formulations are based on elemental analyses and molar conductivity data. I.r., u.v.-vis., e.s.r. and magnetic data reveal that the complexes are trigonal bipyramidal, tetrahedral and stacked square-planar structures for (a), (b) and (c), respectively. The trigonal bipyramidal structure for class (a) compounds is confirmed using the atom superposition and electron delocalization-molecular orbital calculations, which agree with the experimental electronic spectral data. Variable temperature magnetic susceptibility data shows a ferromagnetic interaction within copper(II) complexes of class (c).     

Posaconazole: Application of HSQC‐ADEQUATE from General Indirect Covariance Processing

Posaconazole is a structurally complex triazole antifungal agent that, by virtue of its structural complexity, provides a good test molecule for the evaluation of NMR structure elucidation methodologies. Although GHMBC and related long‐range ¹H–¹³C heteronuclear shift correlation techniques are extremely powerful, at the same time, when dealing with unknowns, they can be problematic in that there is no way to readily differentiate adjacent (² J_CH) correlations from longer range correlations, e.g., ³J_CH and ⁿJ_CH, n > 3. The 1,1‐ADEQUATE experiment, in contrast, provides unequivocal experimental access to adjacent carbon–carbon correlation information, albeit with a sensitivity penalty, as the experiment involves an adjacent ¹³C–¹³C out‐and‐back magnetization transfer. In part, the sensitivity penalty can be overcome by using unsymmetrical indirect covariance or general indirect covariance processing methods. The application of these methods through the coprocessing of multiplicity‐edited GHSQC and 1,1‐ADEQUATE data to generate an HSQC‐ADEQUATE correlation plot is demonstrated for posaconazole.     

Discrimination and classification techniques applied on Mallotus and Phyllanthus high performance liquid chromatography fingerprints

Mallotus and Phyllanthus genera, both containing several species commonly used as traditional medicines around the world, are the subjects of this discrimination and classification study. The objective of this study was to compare different discrimination and classification techniques to distinguish the two genera (Mallotus and Phyllanthus) on the one hand, and the six species (Mallotus apelta, Mallotus paniculatus, Phyllanthus emblica, Phyllanthus reticulatus, Phyllanthus urinaria L. and Phyllanthus amarus), on the other. Fingerprints of 36 samples from the 6 species were developed using reversed-phase high-performance liquid chromatography with ultraviolet detection (RP-HPLC-UV). After fingerprint data pretreatment, first an exploratory data analysis was performed using Principal Component Analysis (PCA), revealing two outlying samples, which were excluded from the calibration set used to develop the discrimination and classification models. Models were built by means of Linear Discriminant Analysis (LDA), Quadratic Discriminant Analysis (QDA), Classification and Regression Trees (CART) and Soft Independent Modeling of Class Analogy (SIMCA). Application of the models on the total data set (outliers included) confirmed a possible labeling issue for the outliers. LDA, QDA and CART, independently of the pretreatment, or SIMCA after “normalization and column centering (N_CC)” or after “Standard Normal Variate transformation and column centering (SNV_CC)” were found best to discriminate the two genera, while LDA after column centering (CC), N_CC or SNV_CC; QDA after SNV_CC; and SIMCA after N_CC or after SNV_CC best distinguished between the 6 species. As classification technique, SIMCA after N_CC or after SNV_CC results in the best overall sensitivity and specificity.     

Classification of cultivation area of ginseng by near infrared spectroscopy and ICP-AES

A rapid and nondestructive near infrared (NIR) method using soft independent modeling of class analogy (SIMCA) for the classification of cultivation area (Korea and China) was evaluated and confirmed. Raw, first, and second derivative NIR spectra were compared to develop a robust classification rule. The chemical properties of ginseng samples were also investigated to find out the differences between Korean samples and Chinese samples. These differences make NIR spectroscopic method viable. The average value of each Korean and Chinese ginseng sample for crude fiber, crude protein, starch, and 10 inorganic constituents were measured and compared with F-test and t-test. The inorganic constituents were also measured by induced coupled plasma-atomic emission spectroscopy (ICP-AES). It could be found that the amount of starch and ten inorganic elements for example Na, Mg, P, K, Ca, Mn, Fe, Ni, Cu and Zn in ginseng samples are considerably different based on cultivation area. SIMCA has been applied to the inorganic data to investigate the possibility of ICP-AES as classification tool. However, it was observed that the result was not equal to than NIR spectra data. The overall results showed the availability of NIR method using SIMCA would be adequate for classification of cultivation of ginseng, since NIR spectra includes useful and various information on chemical properties in spite of broad and overlapped bands.