Soft Independent Modelling Of Class Analogy Analysis Research Articles

The quality of EVOOs through the determination of VOCs coupled with SIMCA analysis

Determining the quality of the oil is essential to discriminate EVOOs from oils labeled as such but not having the chemical and physical characteristics to be EVOOs. The purpose of this study was to determine a metric based on multivariate Soft Independent Modeling of Class Analogy (SIMCA), to identify “Premium quality” versus “Standard quality”, 792 EVOO samples throughout VOCs and sensory analysis data. The result of SIMCA showed a correct classification of the “Premium quality” samples and only few samples were excluded from the SIMCA model (95.4 % sensitivity). The combination of VOCs collected by PTR-ToF-MS and SIMCA analysis allowed us to perfectly distinguish EVOO oils from non-EVOO oils. The proposed method could allow a secure, quick and reliable control of the EVOO quality within the global market.

Fertility detection of unincubated chicken eggs by hyperspectral transmission imaging in the Vis-SWNIR region

Detection of infertile eggs prior to incubation can lead to an increase in the hatchability rate and prevent the wastage of billions of non-fertile eggs ended up by failed incubation. In this study, the feasibility of a line-scan hyperspectral imaging system in the visible and short-wavelength near-infrared region was assessed for early detection of non-fertile eggs on day 0 before incubation. A total of 227 white-shell eggs including 131 fertile and 96 infertile eggs were collected from a flock with similar conditions in terms of hen age, feeding, and management. Hyperspectral images of eggs were captured on day 0 before incubation in a transmittance mode of illumination and then the eggs were incubated in a commercial incubator. The edge detection method was used to segment the egg, including both the white and yolk, from the background, and the image spectral information was extracted from the egg region. After applying various pretreatment methods, different classifiers including soft independent modeling of class analogy (SIMCA), linear discriminant analysis (LDA), quadratic discriminant analysis (QDA), and artificial neural networks (ANN) classifiers were utilized to extract the predictive models. Following the acceptable results of SIMCA analysis accomplished by 1st derivative pretreatment (accuracy of 86.67%), the discrimination power plot was used to select the most informative wavebands. The results showed that by using fewer variables in effective wavebands better performance (precision and accuracy of 92.59% and 93.33%, respectively) could be obtained in comparison with the ANN classifier based on the whole spectral data (precision and accuracy of 89.29% and 91.11%, respectively). This study revealed the potential application of hyperspectral transmittance imaging in the Vis-SWNIR region to discern the fertile and infertile eggs before starting the incubation process.

Precision Nomothetic Medicine in Depression Research: A New Depression Model, and New Endophenotype Classes and Pathway Phenotypes, and A Digital Self.

Machine learning approaches, such as soft independent modeling of class analogy (SIMCA) and pathway analysis, were introduced in depression research in the 1990s (Maes et al.) to construct neuroimmune endophenotype classes. The goal of this paper is to examine the promise of precision psychiatry to use information about a depressed person’s own pan-omics, environmental, and lifestyle data, or to tailor preventative measures and medical treatments to endophenotype subgroups of depressed patients in order to achieve the best clinical outcome for each individual. Three steps are emerging in precision medicine: (1) the optimization and refining of classical models and constructing digital twins; (2) the use of precision medicine to construct endophenotype classes and pathway phenotypes, and (3) constructing a digital self of each patient. The root cause of why precision psychiatry cannot develop into true sciences is that there is no correct (cross-validated and reliable) model of clinical depression as a serious medical disorder discriminating it from a normal emotional distress response including sadness, grief and demoralization. Here, we explain how we used (un)supervised machine learning such as partial least squares path analysis, SIMCA and factor analysis to construct (a) a new precision depression model; (b) a new endophenotype class, namely major dysmood disorder (MDMD), which is a nosological class defined by severe symptoms and neuro-oxidative toxicity; and a new pathway phenotype, namely the reoccurrence of illness (ROI) index, which is a latent vector extracted from staging characteristics (number of depression and manic episodes and suicide attempts), and (c) an ideocratic profile with personalized scores based on all MDMD features.

Weed species discrimination based on SIMCA analysis of plant canopy spectral data

Adoption of a site-specific weed management system (SSWMS) can contribute to sustainable agriculture. Weed classification is a crucial step in SSWMS that could lead to saving herbicides by preventing repeated chemical applications. In this study, the feasibility of visible and near infrared spectroscopy to discriminate three problematic weeds was evaluated. A greenhouse experiment was conducted to classify three common weed species: water-hemp (Amaranthus rudis), kochia (Kochia scoparia), and lamb's-quarters (Chenopodium album). Soft independent modelling of class analogy (SIMCA) method was used to classify these weed species based on canopy spectral reflectance. Five different pre-processing methods were evaluated to remove the irrelevant information from spectral reflectance. Analysis of data indicated that the second derivative pre-processing method applied to NIR (920–2500 nm) spectra was the best to discriminate three weed species with 100% accuracy for 63 test samples. The SIMCA model on NIR wavebands exhibited the highest discrimination power ratio. The results showed the model distance value for most developed classes in NIR range was more than three, which indicated its superior ability to discriminate weed species with low risk of misclassification. Furthermore, the discrimination power of different wavelengths obtained from the best models indicated that 640, 676, and 730 nm from the red and red-edge region, and 1078, 1435, 1490, and 1615 nm from the NIR region were the best wavelengths for weed discrimination.

Identification of fresh and expired ground roasted robusta coffee using UV-visible spectroscopy and chemometrics

The freshness of ground roasted coffee escapes extremely fast. For this reason, the evaluation of conservation state of ground roasted coffee must be taken into account for acceptability of coffee. Unfortunately, it is difficult to discriminate the fresh and expired ground roasted coffee physically by our naked eyes. Thus, it is desired to develop an analytical method to evaluate the fresh and expired ground roasted coffee using reliable methods. The objective of this research was to evaluate the potential of UV-visible spectroscopy and chemometrics method for classification of fresh and expired ground roasted robusta coffee. A number of 200 samples of robusta fresh coffee and 200 samples of robusta expired coffee was used. The spectral data were pre-treated using standard normal variate (SNV), moving average smoothing (window: 9) and Savitzky-Golay 2nd derivative (order: 2; window: 11). The analysis data was done statistically using multivariate chemometric techniques, including principal component analysis (PCA) and soft independent modeling of class analogy (SIMCA) in the spectral range of 230-400 nm. PCA with PC1 = 94% and PC2 = 4% showed clear clustering of samples (p ≤ 0.05). UV-visible spectroscopy with SIMCA analysis allowed to classify between fresh and expired ground roasted robusta coffee with a correct classification rate of 100%.

Early detection of freezing damage in sweet lemons using Vis/SWNIR spectroscopy

Three modes of measurement, reflectance, half-transmittance, and full-transmittance were examined to detect the freeze-damaged sweet lemons within the range 400–1100 nm. Soft independent modelling of class analogy (SIMCA), principal components analysis combined with artificial neural networks (PCA-ANN), and support vector machines (SVM) methods were conducted on the whole spectral information to detect freezing damage in the sweet lemons subjected to different laboratory simulated freeze conditions. Among the measurement modes, it was found that the half-transmittance outperformed the reflectance and full-transmittance by using all classifiers, such that the corresponding classification accuracy was 100% by using the PCA-ANN algorithm. The discrimination power plot of the SIMCA analysis obtained from the half-transmittance mode was then used to attain the effective features. To compare the performance of the features obtained from SIMCA analysis, a sensitivity analysis was carried out to extract the new informative wavelengths. For each feature selection procedure, twelve wavelength variables in the vicinity of four main wavelengths were found to be superior; then they were used to build new classification models. The test set validation results of ANN and SVM techniques revealed that SIMCA-based features led to better classification accuracies in comparison with the features obtained from sensitivity analysis. Among the ANN and SVM classifiers, the best performance was obtained by the ANN with the total accuracy of 96.3%, by using SIMCA-based features. The findings of this study can be useful for developing an online sweet lemon sorting system to detect the freezing damage.

Origin Discrimination of Osmanthus fragrans var. thunbergii Flowers using GC-MS and UPLC-PDA Combined with Multivariable Analysis Methods.

Osmanthus fragrans flowers are used as folk medicine and additives for teas, beverages and foods. The metabolites of O. fragrans flowers from different geographical origins were inconsistent in some extent. Chromatography and mass spectrometry combined with multivariable analysis methods provides an approach for discriminating the origin of O. fragrans flowers. To discriminate the Osmanthus fragrans var. thunbergii flowers from different origins with the identified metabolites. GC-MS and UPLC-PDA were conducted to analyse the metabolites in O. fragrans var. thunbergii flowers (in total 150 samples). Principal component analysis (PCA), soft independent modelling of class analogy analysis (SIMCA) and random forest (RF) analysis were applied to group the GC-MS and UPLC-PDA data. GC-MS identified 32 compounds common to all samples while UPLC-PDA/QTOF-MS identified 16 common compounds. PCA of the UPLC-PDA data generated a better clustering than PCA of the GC-MS data. Ten metabolites (six from GC-MS and four from UPLC-PDA) were selected as effective compounds for discrimination by PCA loadings. SIMCA and RF analysis were used to build classification models, and the RF model, based on the four effective compounds (caffeic acid derivative, acteoside, ligustroside and compound 15), yielded better results with the classification rate of 100% in the calibration set and 97.8% in the prediction set. GC-MS and UPLC-PDA combined with multivariable analysis methods can discriminate the origin of Osmanthus fragrans var. thunbergii flowers. Copyright © 2017 John Wiley & Sons, Ltd.

Selecting optimal wavelengths for detection of insect infested tomatoes based on SIMCA-aided CFS algorithm

Selection of effective wavelengths is a decisive step for online multispectral imaging systems. In this study, a new approach was utilized to distinguish the most informative wavelengths for detection of insect infestation in tomatoes within 400–1100nm. Soft independent modeling of class analogy (SIMCA) was first conducted in the entire spectral region after applying different pretreatment procedures. Following satisfactory results obtained from 1st derivative preprocessing (accuracy of 90%), the most effective wavebands for detection of infestation were attained by discrimination power plot of SIMCA analysis. Transmission differences between all possible pairs of wavelengths (T(λ1)–T(λ2)) in the obtained informative wavebands were then calculated to substitute the 1st derivative spectra. Afterward, correlation-based feature selection (CFS) algorithm was used to find the best pairs of wavelengths. To compare the performance of SIMCA-aided CFS procedure, CFS was also conducted on the raw spectral data. Seven spectral difference features and six wavelength features were found superior by CFS. To classify tomatoes, three different machine learning techniques including Bayesian networks (BNs), artificial neural networks (ANNs), and support vector machines (SVMs) were implemented. The test set validation results of all machine learning techniques revealed that the spectral difference features outperformed the raw spectra features, indicating the superiority of SIMCA-aided CFS procedure for detection of optimal wavelengths. Among different machine learning techniques, the best performance obtained by ANN based on spectral difference features with a classification accuracy of 95.0%. The results of this study can be adapted for developing an online tomato sorting system for detection of infestations.

Aging of target lipid parameters in fingermark residue using GC/MS: Effects of influence factors and perspectives for dating purposes

Despite the recurrence of fingermark dating issues and the research conducted on fingermark composition and aging, no dating methodology has yet been developed and validated. In order to further evaluate the possibility of developing dating methodologies based on the fingermark composition, this research proposed an in-depth study of the aging of target lipid parameters found in fingermark residue and exposed to different influence factors. The selected analytical technique was gas chromatography coupled with mass spectrometry (GC/MS). The effects of donor, substrate and enhancement techniques on the selected parameters were firstly evaluated. These factors were called known factors, as their value could be obtained in real caseworks. Using principal component analysis (PCA) and univariate exponential regression, this study highlighted the fact that the effects of these factors were larger than the aging effects, thus preventing the observation of relevant aging patterns. From a fingermark dating perspective, the specific value of these known factors should thus be included in aging models newly built for each case. Then, the effects of deposition moment, pressure, temperature and lighting were also evaluated. These factors were called unknown factors, as their specific value would never be precisely obtained in caseworks. Aging models should thus be particularly robust to their effects and for this reason, different chemometric tools were tested: PCA, univariate exponential regression and partial least square regression (PLSR). While the first two models allowed observing interesting aging patterns regardless of the value of the applied influence factors, PLSR gave poorer results, as large deviations were obtained. Finally, in order to evaluate the potential of such modelling in realistic situations, blind analyses were carried out on eight test fingermarks. The age of five of them was correctly estimated using soft independent modelling of class analogy analysis (SIMCA) based on PCA classes, univariate exponential linear regression and PLSR. Furthermore, a probabilistic approach using the calculation of likelihood ratios (LR) through the construction of a Bayesian network was also tested. While the age of all test fingermarks were correctly evaluated when the storage conditions were known, the results were not significant when these conditions were unknown. Thus, this model clearly highlighted the impact of storage conditions on correct age evaluation.This research showed that reproducible aging modelling could be obtained based on fingermark residue exposed to influence factors, as well as promising age estimations. However, the proposed models are still not applicable in practice. Further studies should be conducted concerning the impact of influence factors (in particular, storage conditions) in order to precisely evaluate in which conditions significant evaluations could be obtained. Furthermore, these models should be properly validated before any application in real caseworks could be envisaged.

Development and application of a new low cost electronic nose for the ripeness monitoring of banana using computational techniques (PCA, LDA, SIMCA and SVM)

Potential application of a metal oxide semiconductor based electronic nose (e-nose) as a non-destructive instrument for monitoring the change in volatile production of banana during the ripening process was studied. The proposed e-nose does not need any advanced or expensive laboratory equipment and proved to be reliable in recording meaningful differences between ripening stages. Principal Component Analysis (PCA), Linear Discriminant Analysis (LDA), Soft Independent Modelling of Class Analogy (SIMCA) and Support Vector Machines (SVM) techniques were used for this purpose. Results showed that the proposed e-nose can distinguish between different ripening stages. The e-nose was able to detect a clear difference in the aroma fingerprint of banana when using SVM analysis compared with PCA and LDA, SIMCA analysis. Using SVM analysis, it was possible to differentiate and to classify the different banana ripening stages, and this method was able to classify 98.66% of the total samples in each respective group. Sensor array capabilities in the classification of ripening stages using loading analysis and SVM and SIMCA were also investigated, which leads to develop the application of a specific e-nose system by applying the most effective sensors or ignoring the redundant sensors.  

Application of near Infrared Spectroscopy for Discrimination of Similar Rare Woods in the Chinese Market

Red sanders ( Pterocarpus santalinus L.F.) is one of the most precious woods in the Chinese market, and its price rises every year. Some similar rare woods are traded as red sanders to reap high profits. Therefore, a fast and reliable discrimination method to distinguish true red sanders from similar rare woods is needed. To investigate the feasibility of using near infrared spectroscopy for wood discrimination under laboratory and field conditions, samples of red sanders, bois de rose and African padauk from various wood furniture manufacturers were analysed. Three discrimination models, based on soft independent modelling of class analogy (SIMCA), were developed, with two-thirds of the wood samples used as the calibration set and a third of the wood samples used as the validation set. The results show that SIMCA modelling and analysis methods can discriminate between these three similar species of rare woods. Furthermore, three models were able to distinguish between six other species of rare woods: Burma tulipwood ( Dalbergia oliveri Gamble), Neang nuon ( Dalbergia bariensis), Siam rosewood ( Dalbergia cochinchinensis Pierre ex Laness), cocobolo ( Dalbergia retusa Hesml), Ambila ( Pterocarpuserinaceus Poir.) and Burma padauk ( Pterocarpus macarocarpus Kurz).

Prediction of Injured and Dead Inactivated Escherichia coli O157:H7 Cells after Heat and Pulsed Electric Field Treatment with Attenuated Total Reflectance Infrared Microspectroscopy Combined with Multivariate Analysis Technique

Attenuated total reflectance infrared microspectroscopy combined with soft independent modeling of class analogy (SIMCA) multivariate technique was used to differentiate between alive, dead, and injured Escherichia coli O157:H7 cells. E. coli O157:H7 cells were suspended in citrate phosphate buffer at pH 4.0 and 7.0 and treated by heat at 54 °C for 5, 10, 20, and 90 min or pulsed electric field (PEF) at 35 kV/cm for 10, 25, 50, and 60 pulses. The SIMCA analysis confirmed that major contribution to the discrimination of the untreated and treated E. coli cells were (1) the amide I band at 1,638 and 1,618 cm−1 corresponding to changes in β-pleated secondary protein structure (heat-treated cells at pH 7.0 and 4.0, and PEF-treated cells at pH 4.0), and (2) the bands at 1,078 and 993 cm−1 corresponding to changes in P = O (PO2−) stretching of phosphodiesters or lipopolysaccharides and C–O–C or C–O stretching of different polysaccharides (PEF-treated cells at pH 7.0). The use of partial least squares regression analysis allowed for correctly predicting the survivors of the thermal treatment. Injured cells could be estimated from the comparison of cell counts predicted in nonselective and selective plating media with sodium chloride and bile salts. The prediction results yielded inactivation values with a coefficient of determination (R2) of 0.83 or higher and a standard error of cross validation between 0.11 and 0.37 log cycles of inactivation.

FT-NIR and FT-MIR spectroscopy to discriminate competitors, non compliance and compliance grated Parmigiano Reggiano cheese

In this investigation the potential of infrared spectroscopy, coupled to different statistical methods, were used to estimate the authenticity of grated Protected Denomination of Origin (PDO) Parmigiano Reggiano cheese (P-R).The feasibility of the analytical approach in the prediction of cheese authenticity without the use of wet chemistry was evaluated. A total of 400 plastic-sealed grated cheese samples classified as: compliance P-R, competitors, non-compliance P-R (defected P-R), and P-R with rind content of >18%. PCA was conducted for an explorative spectra analysis. Soft independent modelling of class analogy (SIMCA) analysis and artificial neural networks (ANNs) were used to classify samples, according to different cheese categories. For both the spectroscopic techniques, PCA correctly discriminated compliance P-R from competitors, but not the P-R as a function of the rind percentage and months of ripening. SIMCA analysis accurately classified the compliance and competitors' P-R samples, while samples belonging to the classes of defected P-R and P-R with rind content >18% were not accurately classified. ANN was more efficient than SIMCA in the classification of all the cheese classes. The results showed that NIR and MIR combined with different statistical approaches can be suitable for a sensitive, non-destructive, rapid and inexpensive screening of grated P-R cheese authenticity.

Quantitative Determination of Quality Parameters and Authentication of Vodka Using near Infrared Spectroscopy

The objective of this study was to determine the potential of using near infrared (NIR) transmission spectroscopy to build calibration models for the quantitative characterisation and qualitative discrimination of Russian and non-Russian (foreign) vodkas. The results of partial least squares models based on the NIR spectra of 109 vodka samples showed that the major constituent alcoholic strength (root mean square error of prediction ( RMSEP) 0.25% vol) and the physical parameter relative density ( RMSEP 0.0003) could be successfully determined quantitatively. The method failed, however, in quantifying certain minor components such as anions, cations and sugars. For qualitative discrimination, soft independent modelling of class analogy analysis (SIMCA) and linear discriminant analysis (LDA) were applied to the sample set containing both the Russian and the foreign vodkas. Despite the correct assignment of unknown test samples to the respective vodka species, both modelling approaches, however, did not prove reliable enough for unambiguous authentication purposes.

DETECTION AND DISCRIMINATION OF ENTEROBACTER SAKAZAKII (CRONOBACTER SPP.) BY MID‐INFRARED SPECTROSCOPY AND MULTIVARIATE STATISTICAL ANALYSES

ABSTRACTEnterobacter sakazakii can cause rare but life‐threatening diseases such as meningitis in infants and neonates. Fourier transform infrared (FT‐IR) spectroscopy was used to detect and discriminate between eight E. sakazakii strains, two Enterobacter cloacae strains, three Escherichia coli strains and two Klebsiella pneumoniae strains. FT‐IR vibrational combination bands reflect subtle compositional differences in the cell membranes of E. sakazakii strains, especially in the region between 1,200 and 900 cm−1which contains absorption bands from carbohydrates. Two multivariate statistical analyses including principal component analysis (PCA) and soft independent modeling of class analogy (SIMCA) were used for data analysis. E. sakazakii strains were clearly distinguishable from the other strains by PCA. Based upon SIMCA analysis, 90% of E. sakazakii, 88% of E. cloacae, 91% of E. coli and 91% of K. pneumoniae samples were correctly classified, suggesting that this technique could be used to detect E. sakazakii strains rapidly and accurately.PRACTICAL APPLICATIONSFourier transform infrared (FT‐IR) coupled with multivariate statistical analyses can be used to detect, discriminate and identify Enterobacter sakazakii strains that have been implicated in food safety incidents caused by contaminated infant formula. Compared with traditional microbiological plating methods, this new approach of using FT‐IR could be an alternative means for rapid and accurate detection of bacterial samples that are important in agricultural, food and medical areas.

Characterisation of sparkling cider by the yeast type used in taking foam on the basis of polypeptide content and foam characteristics

This paper describes the characterisation of sparkling ciders from Asturias, northern Spain, by means of the analysis of their protein content and their foam characteristics. A capillary sieving electrophoretic method was used in the protein analysis to determine molecular mass and the Bradford method to determine total protein content. The foam parameters were measured using the Bikerman method. Chemometric techniques, such as principal component analysis (PCA) and soft independent modelling of class analogy analysis (SIMCA), allowed the sparkling cider to be differentiated on the basis of the yeast strain used in their manufacture. As a result, feasible models for classifying sparkling ciders were computed (classification hits higher than 96%). Furthermore, the relationship between polypeptides and foam was demonstrated with a prediction equation of foam stability time, which was computed using the partial least square (PLS) regression technique. This mathematical equation confirmed that the polypeptides of high molecular mass are especially related to this foaming parameter.

ELECTRONIC NOSE CHEMICAL SENSOR FEASIBILITY STUDY FOR THE DIFFERENTIATION OF APPLE CULTIVARS

The ability to analytically differentiate and match intact apple (Malus domestica, Borkh) fruit and fruit juice extracts from different apple cultivars is of interest to the food industry. This study tested the feasibility of detecting the difference among volatile gases evolved from intact ‘McIntosh (Buhr),’ ‘Delicious,’ and ‘Gala’ apples and their extracted juice using a prototype 32-array polymeric detector chemical sensor. All data were first processed to obtain principal components. PCA analysis clearly separated whole ‘McIntosh,’ ‘Gala,’ and ‘Delicious’ samples from juiced on day 1. PCA analysis of day 2 samples showed clustering of whole vs. juiced for all three cultivars, although there was some overlap between the clusters. A soft independent modeling of class analogy (SIMCA) class discrimination of the sensor principal component data sets was then performed to determine the degree of difference. SIMCA analysis of the same samples showed a pronounced difference (SIMCA value >3.00) for only the ‘McIntosh’ samples. SIMCA values between 2.00 and 3.00 were found for the other two cultivars on day 1. For day 2 samples, no SIMCA values greater than 2.00 were found for any cultivar whole vs. juiced. PCA analysis showed clear separation between cultivars for day 1 whole samples. SIMCA analysis showed that there was a difference between ‘Delicious’ and ‘McIntosh’ and between ‘Delicious’ and ‘Gala.’ Neither PCA nor SIMCA showed good separation between day 2 whole cultivars, nor between juiced cultivars on either day. As a reference, the same sample headspace volatile gases were analyzed with a mass spectrometer. A hierarchical cluster analysis (HCA) of the principal components from the mass spectrometer data sets revealed five clusters that discriminated differences among intact apple and apple juice samples but did not discriminate between samples from different apple cultivars.

Visible/near infrared reflectance spectroscopy for predicting composition and tracing system of production of beef muscle

AbstractMuscle chemical analysis and muscle identification both were attempted by using visible and near infrared reflectance spectroscopy (NIRS). Seventy-eight beef muscles (m. longissimus dorsi) from Hereford cattle were used. The samples were scanned in a NIRS monochromator instrument (NIRSystems 6500, Silver Spring, MD, USA) in reflectance mode (log 1/R). Both intact and minced muscle presentation to the instrument were explored. Predictive equations were made using ISI software (Infrasoft International, Port Matilda, PA, USA) and muscle identification was performed by Principal Component Analysis (PCA) and Soft Independent Modelling of Class Analogy (SIMCA). The coefficient of determination in calibration (R2CAL) and standard error in cross validation (SECV) for the intact sample presentation were 009 (SECV: 15·6), 0·89 (SECV: 46·9), 0·48 (SECV: 23·9) for moisture (M), fat and crude protein (CP) on g/kg fresh weight basis respectively. R2CAL and SECV for minced sample presentation were 0·41 (SECV: 161), 0·92 (SECV: 43·4), 0·71 (SECV: 20·5) for M, fat and CP on g/kg fresh weight basis respectively. Qualitative analysis of optical information through PCA and SIMCA analysis showed differences in muscles resulting from two different feeding systems.

The Differentiation of Illicit Methaqualone Tablet Formulations Using Principal Component and Soft Independent Modeling of Class Analogy Analysis of Their Near-Infrared Reflectance Spectra

Abstract The viability of using principal component analysis (PCA) and soft independent modeling of class analogy analysis (SIMCA) of the near-infrared reflectance spectra of illicit methaqualone tablet formulations as an aid in sample differentiation was investigated. Near-infrared spectra of ten different illicit species occurring on the South African market was obtained through direct diffuse reflectance measurements made on the tablet surfaces using a bidirectional fiber-bundle probe. The learning set was analyzed using PCA and SIMCA and resolved in well-separated clusters thus making differentiation possible.

Classification of Lactobacillus reuteri by Restriction Endonuclease Analysis of Chromosomal DNA

A total of 14 field strains originally labeled Lactobacillus reuteri, 4 field strains of Lactobacillus fermentum, and 12 Lactobacillus type strains, as well as four additional reference strains, were classified by performing a restriction endonuclease analysis of chromosomal DNAs digested with Asp 718, Cla I, and Eco RI. The patterns were analyzed by using (i) Jaccard coefficients and the unweighted pair group algorithm with arithmetic averages and (ii) principal-component analysis (PCA) and soft independent modeling of class analogy (SIMCA). Grouping by using the Jaccard coefficients and the unweighted pair group algorithm with arithmetic averages resulted in six clusters, defined at a similarity level of 69%. Cluster 1 comprised 10 field strains of L. reuteri, Lactobacillus sp. strain DSM 20056, and L. reuteri DSM 20016T (T = type strain) and could be divided into four subclusters. Cluster 2 included two L. reuteri field strains. Cluster 3 included one field strain labeled L. reuteri and one field strain labeled L. fermentum. Cluster 4 could be divided into three subclusters and included two field strains labeled L. fermentum, one reference strain and the type strain of L. fermentum, and four additional type strains. Cluster 5 contained two Lactobacillus plantarum strains. Cluster 6 included two type strains. The two numerical methods gave the same general results, but the PCA-SIMCA method resulted in a more complete view of the relationships. The SIMCA analysis grouped L. reuteri DSM 20016T, Lactobacillus sp. strain DSM 20056, and, with one exception, all of the L. reuteri field strains as a “class.” Lactobacillus helveticus DSM 20075T and Lactobacillus casei subsp. pseudoplantarum DSM 20008T were the type strains most closely related to the L. reuteri SIMCA class. The L. reuteri strains isolated from rats could be separated from the L. reuteri strains isolated from humans and pigs by the PCA. DNA-DNA-hybridization showed that strains classified as L. reuteri on the basis of the results of the restriction enzyme analysis also exhibited >70% DNA-DNA-homology to the type strain of L. reuteri. The phenotype of L. reuteri (ability to ferment carbohydrates) coincided in most cases with the genotype; this seemed not to be the case for L. fermentum strains.