Independent Modeling Of Class Analogy Research Articles

Untargeted metabolomics based on nuclear magnetic resonance spectroscopy and multivariate classification techniques for identifying metabolites associated with breast cancer patients

In this study, multivariate classification techniques combined with proton nuclear magnetic resonance (1HNMR) spectroscopy is proposed to identify breast cancer biomarkers that can precisely distinguish between healthy control and breast cancer (BC) patients. In this regard, first optimizing the metabolite extraction procedure was performed using Box-Behnken design (BBD). Then, data-driven soft independent modeling of class analogy (DD-SIMCA) model and partial least squares-discriminant analysis (PLS-DA) were successfully utilized for separating healthy from BC patient's classes. On this matter, both DD-SIMCA and PLS-DA models could successfully distinguish the healthy class from the BC class with the model's sensitivity and specificity of 100%. Variable importance in projection (VIP) method was implemented to detect significant metabolites. Based on significant variables, 13 significant metabolites (e.g., lactic acid, cysteine, and glucose) were detected as the influential factors for this discrimination. Also, a heat map revealing the trend in metabolites levels was depicted and altered metabolic pathways were detected.

Benchtop NIR data standardization on handheld spectrometers to identify paracetamol tablets

AbstractNear‐infrared spectroscopy (NIRS) allows innovative applications in terms of quality control, for example, in raw materials verification, in process analytical technology (PAT), and in discrimination between genuine and falsified medicines. The development of small and cheap handheld devices is expanding in the field, while trying to keep similar performances as benchtop instruments have. Considering traceability and quality control of drug compounds, this work is intended to identify 13 different paracetamol tablets on the Belgian market by using NIRS. The performances of a Fourier‐transform near‐infrared spectroscopy (FT‐NIR) benchtop and two handheld NIR spectrometers were investigated comparatively. All spectra were collected through the blister in the reflectance diffuse mode. NIR spectral fingerprints were pretreated and analyzed using principal component analysis (PCA) and classified with soft independent modeling of class analogy (SIMCA). The performances of the spectrometers were evaluated after standardization of the reference benchtop database to the handheld spectrometers. The instrumental response of the benchtop spectrometer was standardized towards the handheld device using the piecewise direct standardization (PDS) algorithm. These investigations permitted the advantages and limitations of NIR data standardization on predictive models to be pointed out. The SIMCA models based on the spectral data of the benchtop (B), handheld 1 (H1), and handheld 2 (H2) instruments had an accuracy of 99.2%, 97.7%, and 96.2%, respectively. The same accuracy was obtained with the models of the transferred benchtop spectra for the H1 and H2 devices. However, adding handheld spectra to the transferred benchtop spectra resulted in models with improved accuracy: from 97.7% to 98.5% for the H1 instrument and from 96.2% to 97.7% for the H2 instrument. The proposed strategy highlights the potential of using a reference database collected by NIRS for pharmaceutical analyses by handheld NIR spectrometers in terms of traceability.

Current role and future perspectives of chemometrics in spectroscopic and chromatographic analysis of traditional Chinese medicines

Recently, Traditional Chinese Medicines (TCMs), having a rich history in China for their use in maintaining health and treating disease, have gained popularity worldwide. However, their modernization and globalization are impeded due to their “multi-component, multi-pathway, and multi-target” properties. Chemometrics, a comprehensive product of statistics, computers, and information, is an interfacial discipline that extracts practical information from large chemical and biochemical datasets, beneficial to overcome TCMs restrictions. This review summarized key research findings on the basis and application of TCMs according to their components, authenticity, processing conditions, geographical origin, pharmacological activity, and metabolomics based on recent studies. Here, we discussed the benefits and shortcomings of cluster analysis, principal component analysis, soft independent modeling of class analogy, artificial neural networks, support vector machine, partial least squares-discriminant analysis, data fusion and calibration, and the appropriate application of these methods in different fields of TCMs. This review aimed to provide a basic understanding of the role and perspectives of chemometrics in the spectroscopic and chromatographic analysis of TCMs.

Detection of soybean oil adulteration in cow ghee (clarified milk fat): an ultrafast study using flash gas chromatography electronic nose coupled with multivariate chemometrics

Cow ghee is one of the expensive edible fats in the dairy sector. Ghee is often adulterated with low-priced edible oils, like soybean oil, owing to its high market demand. The existing adulteration detection methods are time-consuming, requiring sample preparation and expertise in these fields. The possibility of detecting soybean oil adulteration (from 10% to 100%) in pure cow ghee was investigated in this study. The fingerprint information of volatile compounds was collected using a flash gas chromatography electronic nose (FGCEN) instrument. The classification results were studied using the pattern recognition chemometric models principal component analysis (PCA), soft independent modelling of class analogy (SIMCA), and discriminant function analysis (DFA). The most powerful fingerprint odor of all the samples identified from FGCEN analysis was acetaldehyde (Z)-4-heptenal, 2-propanol, ethyl propanoate, and pentan-2-one. The odor analysis investigation was accomplished with an average analysis time of 90 s. A clear differentiation of all the samples with an excellent classification accuracy of more than 99% was achieved with the PCA and DFA chemometric methods. However, the results of the SIMCA model showed that SIMCA could only be used to detect ghee adulteration at higher concentration levels (30% to 100%). The validation study shows good agreement between FGCEN and gas chromatography-mass spectrometry methods. The methodology demonstrated coupled with PCA and DFA methods for adulteration detection in ghee using FGCEN apparatus has been an efficient and convenient technique. This study explored the capability of the FGCEN instrument to tackle the adulteration problems in ghee. © 2022 Society of Chemical Industry.

Aquaphotomics Research of Cold Stress in Soybean Cultivars with Different Stress Tolerance Ability: Early Detection of Cold Stress Response.

The development of non-destructive methods for early detection of cold stress of plants and the identification of cold-tolerant cultivars is highly needed in crop breeding programs. Current methods are either destructive, time-consuming or imprecise. In this study, soybean leaves’ spectra were acquired in the near infrared (NIR) range (588–1025 nm) from five cultivars genetically engineered to have different levels of cold stress tolerance. The spectra were acquired at the optimal growing temperature 27 °C and when the temperature was decreased to 22 °C. In this paper, we report the results of the aquaphotomics analysis performed with the objective of understanding the role of the water molecular system in the early cold stress response of all cultivars. The raw spectra and the results of Principal Component Analysis, Soft Independent Modeling of Class Analogies and aquagrams showed consistent evidence of huge differences in the NIR spectral profiles of all cultivars under normal and mild cold stress conditions. The SIMCA discrimination between the plants before and after stress was achieved with 100% accuracy. The interpretation of spectral patterns before and after cold stress revealed major changes in the water molecular structure of the soybean leaves, altered carbohydrate and oxidative metabolism. Specific water molecular structures in the leaves of soybean cultivars were found to be highly sensitive to the temperature, showing their crucial role in the cold stress response. The results also indicated the existence of differences in the cold stress response of different cultivars, which will be a topic of further research.

Data-Driven Soft Independent Modeling of Class Analogy in Paper Spray Ionization Mass Spectrometry-Based Metabolomics for Rapid Detection of Prostate Cancer.

Sensitive, rapid, and meaningful diagnostic tools for prostate cancer (PC) screening are urgently needed. Paper spray ionization mass spectrometry (PSI-MS) is an emerging rapid technology for detecting biomarker and disease diagnoses. Due to lack of chromatography and difficulties in employing tandem MS, PSI-MS-based untargeted metabolomics often suffers from increased ion suppression and subsequent feature detection, affecting chemometric methods for disease classification. This study first evaluated the data-driven soft independent modeling of class analogy (DD-SIMCA) model to analyze PSI-MS-based global metabolomics of a urine data matrix to classify PC. The efficiency of DD-SIMCA was analyzed based on the sensitivity and specificity parameters that showed 100% correct classification of the training set, based on only PC and test set samples, based on normal and PC. This analytical methodology is easy to interpret and efficient and does not require any prior information from the healthy individual. This new application of DD-SIMCA in PSI-MS-based metabolomics for PC disease classification could also be extended to other diseases and opens a rapid strategy to discriminate against health problems.

Geographical identification of strawberries based on stable isotope ratio and multi-elemental analysis coupled with multivariate statistical analysis: A Slovenian case study

The geographical classification and authentication of strawberries were attempted using discriminant and class-modelling methods applied to stable isotopes of light elements and elemental composition. The work involved creating a database of 92 authentic Slovenian strawberry samples and 32 imported samples. All samples were harvested between 2018 and 2020. A good geographical classification of Slovenian and non-Slovenian strawberries was obtained despite different production years using discriminant approaches. However, for verifying compliance with a given specification (geographical indications), a class-modelling approach was used to build an unbiased verification model. Class models generated by data-driven soft independent modelling of class analogy (DD-SIMCA) had high sensitivity (96% to 97%) and good specificity (81% to 91%) on a yearly basis, while a more generalised model combining total yearly data gave a lower specificity (63%). Of the 33 commercially available samples (test samples) with declared Slovenian origin, 39% were from outside of Slovenia.

Spectral pattern study of citrus black rot caused by Alternaria alternata and selecting optimal wavelengths for decay detection.

Fungal decay is one of the most common diseases that affect postharvest operations and sales of citrus. Sometimes, fungal disease develops and spreads inside the fruit and in the advanced stages of the disease, it appears apparent, so the use of efficient and reliable methods for early detection of the disease is very important. In this study, early detection of citrus black rot disease caused by Alternaria genus fungus was examined using spectroscopy. Jaffa oranges were inoculated with Alternaria alternata. The samples were inspected by spectroscopy (200–1100 nm) in the 1st, 2nd, and 3rd weeks after inoculation. The classification of healthy and infected samples and selection of most important wavelengths were conducted by soft independent modeling of class analogy (SIMCA). The most important wavelengths in the detection of healthy and infected samples of the 1st week were 507, 933, 937, and 950 nm with a classification accuracy of 60%. The most important wavelengths of the 2nd week were 522 and 787 nm with a classification accuracy of 60%. Also, wavelengths of 546, 660, 691, and 839 were found to be effective in the 3rd week with a classification accuracy of 100%.

Characterization of high value Italian chickpeas (Cicer arietinum L.) by means of ICP-OES multi-elemental analysis coupled with chemometrics

Sixty samples of chickpea ( Cicer arietinum L.) harvested in the Italian territories of Cicerale (Campania), Valentano (Lazio) and Navelli (Abruzzo) in 2019 were characterized by determination of the content of ten elements (Ca, K, P, Mg, Mo, Cu, Fe, Mn, Zn and Sr) with Inductively Coupled Plasma Optical Emission Spectrometry (ICP-OES). Classification of the samples was performed using both discriminant (Linear Discriminant Analysis, LDA) and class-modelling (Soft Independent Modelling of Class Analogies, SIMCA) methods, after the application of Analysis of Variance (ANOVA) to assess the significance of the detected elements. Both discriminant and class models were calibrated on 33 samples and eventually applied on a prediction set of 27 samples to evaluate the classification ability and class-modelling efficiency, respectively. LDA led to 100% classification rate on the external set, whereas the class models developed using SIMCA exhibited good sensitivity (external samples accepted by the respective classes were 88% for Cicerale, 90% for Valentano and 100% for Navelli) and 100% specificity (all the extraneous samples were correctly rejected by each class-model). • 3 chickpea varieties analysed by Inductively Coupled Plasma Optical Emission Spectrometry. • The aim is to discriminate chickpeas according to three diverse origins. • Classification was carried out by LDA and SIMCA. • LDA correctly classified 100% of the test samples (external validation). • SIMCA exhibited good sensitivities and 100% specificity for all classes (external validation).

Novelty and Similarity: Detection Using Data Driven Soft Independent Modeling of Class Analogy

Novelty and Similarity: Detection Using Data Driven Soft Independent Modeling of Class Analogy

One-Class Classification Models for the Authentication of Analgesic Tablet Reference Medicine Using Differential Scanning Calorimetry and Visible-Near Infrared Spectroscopy

The aim of this study was to develop a fast, simple and accurate analytical method for the classification of reference tablet analgesic drugs containing dipyrone, orphenadrine and caffeine using differential scanning calorimetry (DSC) and visible-near infrared spectroscopy (VNIRS) associated with one-class chemometric classification algorithm. The training set is based on reference medicine with 15 samples as target class. Three different brands with 10 samples each and five reference medicine samples, obtaining 35 samples, were used as the test set. Chemometric models based on principal component analysis (PCA) and data-driven soft independent modelling of class analogy (DD-SIMCA) were used to obtain the results. Two DD-SIMCA models obtained 100% sensitivity, specificity, and accuracy using DSC and VNIRS, both with a significance level of 0.01. This method using one-class classification as a chemometric tool proved to be a good alternative for quality control of pharmaceutical samples.

Infrared spectroscopy combined with chemometrics as a convenient method to detect adulterations in cooking/stretching process in commercial cheese

Stretching and kneading of the curd during fresh Kashar cheese manufacturing must take place in hot water; dry cooking/stretching of the curd with the assistance of emulsifying salts is not allowed. However, some producers in recent years tend to label their processed cheese as “Kashar cheese” resulting in unfair economic gain and consumer deception. Near-infrared (NIR) diffuse reflectance and mid-infrared (MIR) attenuated total reflectance (ATR) spectroscopy were assessed for the fast and convenient identification of these two types of cheese. Soft independent modeling of class analogy (SIMCA) models of both NIR and MIR-ATR spectra were developed; the latter gave superior separation due to the greater inter-class distance originating from better-resolved peaks associated with phosphates (6.7 for MIR-ATR versus 3.2 for NIR). Information Theory determined two and three variables were enough for MIR and NIR spectral data classification, respectively; quadratic discriminant and support vector machine provided 100% accuracy for class prediction.

Encoding of Luminescent Ink Markers Using Low-Level Data Fusion and Chemometrics

The identification and analysis of documentary fraud is always a challenge for forensic science. Document analysis has proven to be an important branch of forensics in elucidating the authenticity of documents. The development and incorporation of luminescent inks in authentic documents have proved to be an excellent security feature. This paper purposes the use of a possible luminescent ink marker for anti-counterfeiting applications, aiming to create a document encoding process that is simple, robust, sensitive, and non-destructive. Since luminescent inks markers provide a visual, chemical, and spectral signature, and can be easily detected by using a UV lamp, the aid of unsupervised chemometric tools makes it possible to differentiate the luminescent markers inserted in the ink. Unsupervised models of principal component analysis (PCA) and K-mean were successful in correctly associating marked inks with their respective pure markers, while a supervised classification model based on partial least squares discriminant analysis (PLS-DA) correctly classified all samples from the prediction set and the blind test samples. For comparison, a soft independent modeling of class analogy (SIMCA) model was also built, which despite showing a misclassified sample it is also a strong candidate for future applications.

Non-invasive identification of textile fibres using near-infrared fibre optics reflectance spectroscopy and multivariate classification techniques

The identification of textile fibres from cultural property provides information about the object's technology. Today, microscopic examination remains the preferred method, and molecular spectroscopies (e.g. Fourier transform infrared (FTIR) and Raman spectroscopies) can complement it but may present some limitations. To avoid sampling, non-invasive fibre optics reflectance spectroscopy (FORS) in the near-infrared (NIR) range showed promising results for identifying textile fibres; but examining and interpreting numerous spectra with features that are not well defined is highly time-consuming. Multivariate classification techniques may overcome this problem and have already shown promising results for classifying textile fibres for the textile industry but have been seldom used in the heritage science field. In this work, we compare the performance of two classification techniques, principal component analysis–linear discrimination analysis (PCA-LDA) and soft independent modelling of class analogy (SIMCA), to identify cotton, wool, and silk fibres, and their mixtures in historical textiles using FORS in the NIR range (1000–1700 nm). We built our models analysing reference samples of single fibres and their mixtures, and after the model calculation and evaluation, we studied four historical textiles: three Persian carpets from the nineteenth and twentieth centuries and an Italian seventeenth-century tapestry. We cross-checked the results with Raman spectroscopy. The results highlight the advantages and disadvantages of both techniques for the non-invasive identification of the three fibre types in historical textiles and the influence their vicinity can have in the classification.

Palmer Amaranth (Amaranthus palmeri S. Watson) and Soybean (Glycine max L.) Classification in Greenhouse Using Hyperspectral Imaging and Chemometrics Methods

HighlightsHyperspectral image processing was used to classify Palmer amaranth and soybean species.Chemometrics methods (PCA, PLS-DA, and SIMCA) were used to extract features and establish classification models.Abstract. Herbicide-resistant weed species are one of the largest threats to modern agriculture, as ineffective weed control results in significant yield losses or increased costs through alternatives such as mechanical methods. Palmer amaranth (Amaranthus palmeri S. Watson) has been one of the most troublesome weeds. Its identification through the adoption of site-specific weed management systems will help farmers select more appropriate control options to reduce costs and improve efficacy, resulting in increased farm revenue. In this study, a pixel-wised method was evaluated for the classification of Palmer amaranth and soybean (Glycine max L.). A pushbroom hyperspectral imagery acquisition system was used to collect imagery from 224 spectral bands ranging from 400 to 1000 nm. Greenhouse experiments were conducted in three different runs. Greenhouse-grown plants were evaluated to generate predictive models from paired samples generated with 56 replications in each run. Data collection occurred weekly when Palmer amaranth plants were between approximately 2.5 and 12.7 cm tall. Partial least squares discriminant analysis (PLS-DA) and soft independent modeling of class analogy (SIMCA) were tested to classify Palmer amaranth and soybean. Half of the dataset (Palmer amaranth = 42, soybean = 42) was used to train the models, and the other half (Palmer amaranth = 42, soybean = 42) was used to test model performance. Preliminary results showed that the PLS-DA model with PLS factors as input had a cumulative variation (R2Y(cum)) of 60% and predictive ability (Q2Y(cum)) of 60%. The SIMCA model showed a cumulative variation of 85% and a predictive ability of 82%. Overall, this study illustrated the capability of hyperspectral imagery to classify Palmer amaranth and soybean, which will increase the efficiency of weed control in modern agriculture. Keywords: Chemometrics methods, Hyperspectral imaging, Palmer amaranth classification.

Handheld Spectral Sensing Devices Should Not Mislead Consumers as Far as Non-Authentic Food Is Concerned: A Case Study with Adulteration of Milk Powder.

With the rising trend of consumers being offered by start-up companies portable devices and applications for checking quality of purchased products, it appears of paramount importance to assess the reliability of miniaturized sensors embedded in such devices. Here, eight sensors were assessed for food fraud applications in skimmed milk powder. The performance was evaluated with dry- and wet-blended powders mimicking adulterated materials by addition of either ammonium sulfate, semicarbazide, or cornstarch in the range 0.5–10% of profit. The quality of the spectra was assessed for an adequate identification of the outliers prior to a deep assessment of performance for both non-targeted (soft independent modelling of class analogy, SIMCA) and targeted analyses (partial least square regression with orthogonal signal correction, OPLS). Here, we show that the sensors have generally difficulties in detecting adulterants at ca. 5% supplementation, and often fail in achieving adequate specificity and detection capability. This is a concern as they may mislead future users, particularly consumers, if they are intended to be developed for handheld devices available publicly in smartphone-based applications.

FINDIK YAĞININ FT-NIR, FT-MIR VE RAMAN SPEKTROMETRELERİ İLE BİRLİKTE ÇOK BİLEŞENLİ VERİ ANALİZLERİ KULLANILARAK DOĞRULANMASI

This research studied the authentication of hazelnut oil by portable FT-NIR, FT-MIR, and Raman spectrometers. Hazelnut oils were adulterated with vegetable oils at various concentrations (0-25%) (w/w). Collected spectra were analyzed using Principal Component Analysis (PCA) and Soft Independent Modelling of Class Analogy (SIMCA) to generate classification models to authenticate pure hazelnut oil and Partial Least Squares Regression (PLSR) to predict the fatty acids and adulterant levels. For confirmation, oil’s fatty acid profile was determined by gas chromatography. In all three instruments, SIMCA provided distinct clusters for pure and adulterated samples with interclass distance (ICD)3. All instruments showed excellent performance in predicting fatty acids and adulteration levels with rval>0.93 and standard error prediction (SEP)<1.75%. Specifically, the FT-MIR unit provided the best performances. Still, all the units can be used as an alternative to traditional methods. These units showed great potential for in-situ surveillance to detect hazelnut oil adulterations.

Feasibility of a rapid and non-destructive methodology for the study and discrimination of pine nuts using near-infrared hyperspectral analysis and chemometrics

Spanish pine nut is highly appreciated globally for its aroma and taste. Nevertheless, its market is affected by the growing presence of Chinese pine nuts, entailing mislabeling and counterfeits. In this study, near-infrared hyperspectral imaging (940–1625 nm) coupled to chemometrics, was applied, for the first time, to perform a spectral study (identification of chemical distribution and composition) of commercial pine nuts labeled on their package as Spanish and Chinese and to develop a single class-modelling classification model. Sixty-three pine nuts from both marketed origin labels and different qualities were analysed. Principal component analysis (PCA) and multivariate curve resolution (MCR) showed the chemical distribution of the major compounds (bands around 1170–1210 nm and 1485–1550 nm, associated with fats and fatty acids and water and proteins, respectively) of each marketed origin. Soft independent modelling of class analogies (SIMCA) classified the samples according to their labeling of origin, in a pixel-based and nut-based approach, obtaining 89–98% and 84–100% of correct prediction, respectively. This preliminary study demonstrated that the proposed methodology could be used as a fast, comprehensive and innovative quality control tool (for characterisation and classification) for the pine nut industry. • NIR-HSI was for first-time applied for studying and classifying pine nuts. • It allows studying internal pine nut composition and chemical distribution. • Satisfactory classification models were achieved according to two marketed origins. • This approach shows a comprehensive fast and reliable quality control for pine nuts. • NIR-HSI could enhance pine nut traceability and detection of compositional changes.

Potencial Use of Near Infrared Spectroscopy (NIRS) to Categorise Chorizo Sausages from Iberian Pigs According to Several Quality Standards

The ability of Near Infrared Spectroscopy (NIRS) to classify pre-sliced Iberian chorizo modified atmosphere packaged (MAP) according to the animal material used in their production (Black, Red, White) in their production in accordance with the official trade categories (which includes the handling system and the different inter-racial crossbreeds) without opening the package was assayed. Furthermore, various spectra pre-treatments and supervised classification chemometric tools; Partial least square-discriminant analysis (PLS-DA), soft independent modelling of class analogies (SIMCA) and linear discriminant analysis (LDA), were assessed. The highest sensitivity values in both calibration and external validation were achieved with SIMCA followed by PLS-DA approaches, while LDA had more provided values among sensitivity and specificity and between the different commercial categories in both sample sets, thus yielding the highest discriminant ability. These results could be a resource to support the traceability and authentication control of individual pre-sliced MAP Iberian chorizo according to the commercial category of the raw material in a non-destructive way.

Application of reverse engineering in the field of pharmaceutical tablets using Raman mapping and chemometrics

Raman micro-spectroscopy technique offers a combination of relatively high spatial resolution with identification of components or mixtures of components in different sample areas, e.g. on the surface or the cross-section of a sample. This study is focused on the analysis of the tablets from pharmaceutical development with different technological parameters: (1) the manufacturing technology, (2) the particle size of the input API (active pharmaceutical ingredient) and (3) the quantitative composition of the individual excipients.These three mentioned parameters represent the most frequently solved problems in the field of reverse engineering in pharmacy. The investigation aims to distinguish tablets with the above-described technological parameters with limited subjective steps by Raman microscopy. Furthermore, non-subjective methods of Raman data analysis using advanced statistical analysis have been proposed, namely Principal Component Analysis, Soft Independent Modelling of Class Analogy and Linear Discriminant Analysis. The methods successfully distinguished and identified even very small differences in the analysed tablets within our study and provided objective statistic evaluation of Raman maps. The information on component and particle size distribution including their small differences, which is the critical parameter in the development of the original and generic products, was obtained due to combination of these methods. Even though each of these chemometric methods evaluates the data set from a different perspective, their mutual application on the problem of Raman maps evaluation confirmed and specified results on level that would be unattainable with the use of only one them.