Principal Component Analysis Score Plot Research Articles

Rapid discrimination of beer based on quantitative aroma determination using colorimetric sensor array

In this study, 6 beers from Tsingtao Brewery were analyzed by using colorimetric GC–MS and sensor array (CSA). First, forty volatile compounds of six beers, including 16 esters, 10 alcohols, 4 acids and 4 aldehydes, were identified by GC–MS. Beers from the same category were grouped using principal component analysis (PCA) score plot and hierarchical clustering analysis (HCA) dendrogram. Discrimination of the beers was subsequently implemented using a 4 × 4 CSA combined with multivariate analysis. A linear discriminant analysis (LDA) model achieved a 100% recognition rates of the 6 beers. In addition, a partial least square (PLS) model could be used to quantitatively determine ethyl octanoate, phenethyl acetate, isoamyl alcohol and octanoic acid, with correlation coefficients over 0.85 for both the calibration curves of the training and prediction sets. Hence, CSA could be used for rapid and non-destructive determination of beer quality.

Aroma Profile, Microbial and Chemical Quality of Ensiled Green Forages Mixtures of Winter Cereals and Italian Ryegrass

The objective of this study was to evaluate the aroma profile, microbial and chemical quality of winter cereals (triticale, oats, barley and wheat) and Italian ryegrass (Lolium multiflorum Lam., IRG) plus winter cereal mixture silages detected with an electronic nose. Four commercial mixtures (mixture A (40% of two cultivars of winter triticale + 30% of two cultivars of winter oats + 20% of winter barley + 10% of winter wheat), mixture B (50% of two cultivars of winter triticale + 40% of winter barley + 10% of winter wheat), mixture C (55% of three types of Italian ryegrass + 45% of two cultivars of winter oat), mixture D (40% of three types of Italian ryegrass + 30% of two cultivars of winter oat + 15% of two cultivars of winter triticale + 10% of winter barley + 5% of winter wheat)) were harvested, wilted and ensiled in laboratory-scale silos (n = 80) without additives. Both the principal component analysis (PCA) score plot for aroma profile and linear discriminant analysis (LDA) classification revealed that mixture D had different aroma profile than other mixture silages. The difference was caused by the presence of high ethanol and LA in mixture D. Ethyl esters such as ethyl 3-methyl pentanoate, 2-methylpropanal, ethyl acetate, isoamyl acetate and ethyl-3-methylthiopropanoate were found at different retention indices in mixture D silage. The low LA and higher mold and yeast count in mixture C silage caused off odour due to the presence of 3-methylbutanoic acid, a simple alcohol with unpleasant camphor-like odor. At the end of 90 days fermentation winter cereal mixture silages (mixture A and B) had similar aroma pattern, and mixture C was also similar to winter cereal silages. However, mixture D had different aromatic pattern than other ensiled mixtures. Mixture C had higher (p < 0.05) mold and yeast (Log10 CFU (colony forming unit)/g) counts compared to mixture B. Mixture B and C had higher acetic acid (AA) content than mixture A and D. The lactic acid (LA) content was higher for mixture B than mixture C. In general, the electronic nose (EN) results revealed that the Italian ryegrass and winter cereal mixtures (mixture D) had better aroma profile as compared to winter cereal mixtures (mixture A and B). However, the cereal mixtures (mixture A and B) had better aroma quality than mixture C silage. Otherwise, the EN technology is suitable in finding off odor compounds of ensiled forages.

Correlating Comprehensive Two-dimensional Gas Chromatography Volatile Profiles of Chocolate with Sensory Analysis

The identification of key components relevant to sensory perception of quality from commercial chocolate samples was accomplished after chemometric processing of GC×GC-MS (Comprehensive Two-dimensional Gas Chromatography with Mass Spectrometric Detection) profiles corresponding to HS-SPME (Headspace Solid Phase Microextraction) extracts of the samples. Descriptive sensory evaluation of samples was carried out using Optimized Descriptive Profile (ODP) procedures, where sensory attributes of 24 commercial chocolate samples were used to classify them in two classes (low and high chocolate flavor). 2D Fisher Ratio analysis was applied to four-way chromatographic data tensors (1st dimension retention time 1tR × 2nd dimension retention time 2tR × m/z × sample), to identify the crucial areas on the chromatograms that resulted on ODP class separation on Principal Component Analysis (PCA) scores plot. Comparing the relevant sections of the chromatograms to the analysis of the corresponding mass spectra, it was possible to assess that most of the information regarding the sample main sensory attributes can be related to only 14 compounds (2,5-dimethylpyrazine, 2,6-dimethyl-4-heptanol, 1-octen-3-ol, trimethylpyrazine, β-pinene, o-cimene, 2-ethyl-3,5-dimethylpyrazine, tetramethylpyrazine, benzaldehyde, 1,3,5-trimethylbenzene, 6-methyl-5-hepten-2-one, limonene, benzeneethanol and 1,1-dimethylbutylbenzene) among the complex blend of volatiles found on these extremely complex samples.

UHPLC/MS and NMR-Based Metabolomic Analysis of Dried Water Extract of Citrus-Type Crude Drugs.

Citrus-type crude drugs (CCDs) are commonly used to formulate decoctions in Kampo formula (traditional Japanese medicine). Our previous study reported metabolomic analyses for differentiation of the methanol extracts of Citrus-type crude drugs (CCDs) using ultra-HPLC (UHPLC)/MS, and 13C- and 1H-NMR. The present study expanded the scope of its application by analyzing four CCD water extracts (Kijitsu, Tohi, Chimpi, and Kippi); these CCDs are usually used as decoction ingredients in the Kampo formula. A principal component analysis score plot of processed UPLC/MS and NMR analysis data indicated that the CCD water extracts could be classified into three groups. The loading plots showed that naringin and neohesperidin were the distinguishing components. Three primary metabolites, α-glucose, β-glucose, and sucrose were identified as distinguishing compounds by NMR spectroscopy. During the preparation of CCD dry extracts, some compounds volatilized or decomposed. Consequently, fewer compounds were detected than in our previous studies using methanol extract. However, these results suggested that the combined NMR- and LC/MS-based metabolomics can discriminate crude drugs in dried water extracts of CCDs.

Defining Eucalyptus urophylla with its hybrid and the rules of genetic recombination using near infrared spectroscopy

Understanding the rules of genetic recombination in controlled pollination directly related to the selection of parental genotypes and the utilization of heterosis, and genotype identification is a primary study of the genetic rules. The aims of this study were to investigate the ability of near infrared (NIR) spectroscopy to accurately and efficiently discriminate pure species and hybrids within the genus of Eucalyptus, to evaluate the transmission of genetic pedigree in control pollination, and reveal the genetic variation within the genotypes studied. NIR spectra were collected both from fresh leaves and dried, milled leaves of seedlings from pure species E. urophylla and E. grandis, and their F1 hybrids. Principal component analysis (PCA) scores plots of NIR spectra from fresh leaves and dry, milled powder from pure species showed clear segregation, although the species clusters were scattered, suggesting different base genetics and high genetic variation within families of the two pure species. Classification using soft independent modelling of class analogy of the NIR spectra of dried leaves was significantly better than using spectra acquired on fresh leaves, meaning the water content had an effect on the analysis. The projections and orthogonal distance between hybrids and parents, as calculated using PCA models, demonstrated the visualized spectral distance between each hybrid and the parents was very different. Clouds of individuals within a hybrid clusters varied from tightly packed to scattered, which reflected the genetic additive effects inherited from female and male parents were different, and their genetic variation was also different after genetic recombination. The varying response values for partial least squares discriminant analysis prediction verified the conclusions shown by projections and orthogonal distance. The results of this study demonstrate the potential of using NIR spectroscopy to rapidly discriminate taxon. The application of NIR spectroscopy to non-destructively confirm taxonomic identity will greatly facilitate the evaluation of the genetic basis and genetic variation available within breeding populations and for accessing the levels of contamination by non-target pollen in control pollination.

Unravelling the beehive air volatiles profile as analysed via solid-phase microextraction (SPME) and chemometrics

ObjectiveBeehive air therapy is recognized as a potential remedy for treating asthma, bronchitis, lung fibrosis, and respiratory tract infections. Developed countries in which beehive air therapy is currently authorized include Germany, Hungary, Slovenia, and Austria. However, scientific proof of its efficacy is lacking which warrants further chemical and biological analyses as a proof of concept. In this study, beehive air volatile profile was determined for the first time along with its individual components (bees, venom, honey, and beeswax). MethodsVolatile compounds were collected from beehive air using solid phase micro-extraction (SPME) coupled to gas chromatography-mass spectrometry (GC–MS). Antimicrobial assay of the air released from 4 beehive products was further performed against Staphylococcus aureus, Klebsiella pneumoniae, Acinetobacter baumannii, and multi drug-resistant Staphylococcus aureus (MRSA) using the in vitro agar-well diffusion and microtiter plate assays.Results and conclusions: A total of 56 volatile compounds were identified from beehive air, venom, bee insect and wax air including 6 fatty acids, 6 alcohols, 10 aldehydes, 5 esters, 1 ether, 9 hydrocarbons, 1 phenol, 7 ketones, 1 nitrogenous compound and 10 terpenes. The most abundant constituents were short-chain fatty acids (26.32%) while the lowest were the nitrogenous compounds (0.82%). The principal component analysis (PCA) scores plot of the UPLC/MS dataset showed the similarity of the beehive air to the insect bee's aroma profile. With regards to antimicrobial assay, beehive air and venom exerted the strongest antimicrobial activity among the examined bee products against S. aureus, K. pneumoniae, A. baumannii, and MRSA in agar-well diffusion assay but failing to exert an effect using microtiter plate assay as in case of bee venom against the aforementioned bacteria.

Data Fusion of UV-Vis and FTIR Spectra Combined with Principal Component Analysis for Distinguishing of <i>Andrographis paniculata</i> Extracts Based on Cultivation Ages and Solvent Extraction

Andrographis paniculata is one of the medicinal plants used for the treatment of antidiabetic. Cultivation ages and solvent extraction affected metabolites' composition and concentration that directly cause the plant's efficacies. This research aimed to distinguish A. paniculata based on cultivation ages and solvent extraction using data fusion of UV-Vis and FTIR spectra combined with principal component analysis (PCA). A. paniculata with 2, 3, and 4 months post-planting were extracted by water, 50% ethanol, 70% ethanol, and ethanol. In each extract, we measured UV-Vis and FTIR spectra. Then, we used the data fusion from both spectra. We used UV-Vis and FTIR absorbance from 200–400 nm and 1800–400 cm–1, respectively. Each extract gives a similar pattern of UV-Vis and FTIR spectra, only differ in their intensities. PCA score plot in two and three-dimensional showed A. paniculata extracts could be distinguished based on cultivation ages and solvent extraction with a total variance of 86 and 92%, respectively. Furthermore, this study confirms the data fusion of UV-Vis and FTIR spectra could distinguished A. paniculata extracts combined with chemometrics based on cultivation ages and solvent extraction.

Clustering aged bitumens through multivariate statistical analyses using phase angle master curve

Bitumen aging has long been recognised as one of the primary factors that affect pavement’s durability. The amount of rheology-based indexes proposed to track the evolution of bitumen aging level underlines their potential for this purpose. Among these phase angle is a powerful tool since it is highly sensitive to changes that occur in bitumen. Hence, this work aims to estimate the aging level of 23 neat bitumens based solely on the evaluation of their phase angle master curve through the application of two multivariate statistical approaches: Principal Component Analysis (PCA) and Hierarchical Cluster Analysis (HCA). PCA score plot displays correctly the studied bitumens according to their aging level, going from left to right, where a marked inflection point is detected along the path taken by the studied bitumens when they age. The groups of bitumens located on each side of this inflection point coincide with the two major clusters obtained through HCA. All the bitumens classified as being in the imminence of the cracking zone through the well-known Glover Rowe (G-R) cracking indicator integrate the HCA cluster after the inflection point. The originality of this study lies in the possibility of indicating the aging state and also cracking susceptibility of bitumen based exclusively on the assessment of the phase angle master curve. HIGHLIGHTS Using solely phase angle master curves, PCA score plot projects correctly the studied bitumens according to their aging level, where a marked inflection point is detected. The bitumens projected at each side of the inflection point coincide with the two major clusters obtained through HCA. All the bitumens classified as being in the damage onset or cracking zone according to the G-R method belong to the HCA cluster after the inflection point. The inflection point observed on the PCA score plot appears to be a transition from which the studied bitumens are more susceptible to cracking.

Discrimination of Vitis vinifera varieties using DNA fingerprinting and NMR coupled with chemometrics and their impact on the efficacy of fluoxetine and indomethacin in vivo

The grapes, Vitis vinifera, belong to family Vitaceae. Some fruit juices substantially decrease the oral absorption of certain drugs undergoing intestinal uptake transport. Green seedless “Thompson” type (GT) and reddish black seeded “Magic black” type (BM) of V. vinifera L. were discriminated using deoxyribonucleic acid (DNA) fingerprinting and nuclear magnetic resonance (NMR) coupled with chemometrics. Their effects on the bioavailability of fluoxetine and indomethacin were assessed in vivo. Practical applications Random amplification of polymorphic DNA (RAPD) analysis of GT and BM revealed that primers OPA-02 and OPB-10 indicate a taxonomical relationship between them as they produced high similarity coefficients estimated by 80 and 66.67%, respectively. OPC-03, OPB-13, and OPC-07 primers help in their discrimination based on their minimal levels of similarity coefficients estimated by zero percentages accompanied by the high levels of polymorphism. Principal component analysis score plot obtained from processing of proton nuclear magnetic resonance (1H NMR) showed very good discrimination between GT and BM in PC1 that accounts for most of the variance. Oral administration of 100 mg/kg of GT and BM separately produce no significant change in immobility period of rats dropped individually into a glass cylinder. Concomitant administration of either 100 mg/kg of GT or BM with 20 mg/kg of fluoxetine significantly decreased the efficacy of antidepressant reference drug (fluoxetine) by decreasing time before immobility showing a percentage of depression inhibition estimated by 17.2 and 29.1%, respectively, that obviously decreased when compared to fluoxetine alone. Regarding anti-inflammatory activity, both GT and BM exhibited a notable activity by inhibiting the increase in paw edema and inflammation induced by carrageen in rats where BM is more potent approaching indomethacin. Administration of GT or BM with indomethacin reduces its efficacy revealing 27.5 and 26.1% in edema percentage after 1 hr, respectively, that decreased minimally to be 19.1% and 15.6% after 4 hr, respectively, instead of 7% in case of indomethacin alone. Thus, it can be concluded that DNA and NMR fingerprinting could successfully differentiate between Vitis varieties. Grape juice decreased the efficacy of fluoxetine and indomethacin. Further studies should be conducted to declare the effect of Vitis varieties on the bioavailability of additional drugs that treat chronic diseases as diabetes and hypertension.

Urine metabolomics analysis based on ultra performance liquid chromatography-high resolution mass spectrometry combined with osmolality calibration sample concentration variability

尿液是代谢组学研究中主要关注的体液样本之一。尿液样本中的代谢物浓度受饮食、疾病等因素影响变异较大,这极大阻碍了高质量组学数据的获取和可靠生物标志物的鉴定。研究为克服尿液样本的浓度变异性,在原始数据采集前,根据样本渗透压的大小,针对性地调整进样量或者稀释样本,从而确保代谢组学分析样本的渗透压与进样量的乘积相当,再经超高效液相色谱-高分辨质谱技术(UPLC-HRMS)分析,采用总离子丰度或总有用峰面积(MSTUS)对数据集进行归一化处理。研究利用临床样本及其梯度稀释的溶液,对该方法与现有研究普遍使用的方法进行了比较,随后通过先天性肾积水患者及健康志愿者的尿液样本做了进一步的方法学验证。数据集经校正后,峰面积RSD<30%的提取峰数量增加,主成分分析结果较校正前有更高的组内聚集和组间分群效应,正交偏最小二乘判别分析的统计模型更不易过拟合。与肌酐比较,渗透压值与质谱信号间呈现了更好的线性关系。以上结果表明,数据采集前通过样本渗透压进行校正,能有效消除因样本本身代谢物浓度变化引起的组内差异,提高方法的重复性和统计模型的可靠度。以渗透压为基准的校正策略,比肌酐校正法适用范围更广,结果也更准确。研究可对后续各类来源的尿液代谢组学研究提供数据归一化的指导和参考。

Metabolomics for Biomarker Discovery: Key Signatory Metabolic Profiles for the Identification and Discrimination of Oat Cultivars.

The first step in crop introduction—or breeding programmes—requires cultivar identification and characterisation. Rapid identification methods would therefore greatly improve registration, breeding, seed, trade and inspection processes. Metabolomics has proven to be indispensable in interrogating cellular biochemistry and phenotyping. Furthermore, metabolic fingerprints are chemical maps that can provide detailed insights into the molecular composition of a biological system under consideration. Here, metabolomics was applied to unravel differential metabolic profiles of various oat (Avena sativa) cultivars (Magnifico, Dunnart, Pallinup, Overberg and SWK001) and to identify signatory biomarkers for cultivar identification. The respective cultivars were grown under controlled conditions up to the 3-week maturity stage, and leaves and roots were harvested for each cultivar. Metabolites were extracted using 80% methanol, and extracts were analysed on an ultra-high performance liquid chromatography (UHPLC) system coupled to a quadrupole time-of-flight (qTOF) high-definition mass spectrometer analytical platform. The generated data were processed and analysed using multivariate statistical methods. Principal component analysis (PCA) models were computed for both leaf and root data, with PCA score plots indicating cultivar-related clustering of the samples and pointing to underlying differential metabolic profiles of these cultivars. Further multivariate analyses were performed to profile differential signatory markers, which included carboxylic acids, amino acids, fatty acids, phenolic compounds (hydroxycinnamic and hydroxybenzoic acids, and associated derivatives) and flavonoids, among the respective cultivars. Based on the key signatory metabolic markers, the cultivars were successfully distinguished from one another in profiles derived from both leaves and roots. The study demonstrates that metabolomics can be used as a rapid phenotyping tool for cultivar differentiation.

Gender discrimination of Populus tomentosa barks by HPLC fingerprint combined with multivariate statistics.

A high‐performance liquid chromatography (HPLC) fingerprint method with multivariate statistical analyses was applied to discriminate the male and female barks of Populus tomentosa for the first time. The samples of 11 male and 13 female barks of mature P. tomentosa were collected in Beijing. The chemical fingerprint of methanol extract was established by HPLC method with diode array detector (DAD). The principal component analysis (PCA), hierarchical clustering analysis (HCA), and supervised orthogonal partial least squares discriminant analysis (OPLS‐DA) were applied to discriminate male and female barks based on the area of common peaks identified in HPLC fingerprints. A clear grouping trend (R 2 X, 0.83; Q 2, 0.595) among the male and female samples was exhibited by PCA score plot. Two groups were clearly divided into male and female samples by HCA. Both male and female samples were well discriminated with OPLS‐DA (R 2 X, 0.775; Q 2, 0.795). Seven potential chemical markers were screened by variable importance in projection (VIP values >1.0) of OPLS‐DA model and four of them were identified as micranthoside, siebolside B, sakuranin, and isosakuranin. The HPLC fingerprint combined with multivariate statistical analyses could be used to discriminate the gender of barks of P. tomentosa and revealed the differences in chemical components, which enriched the basic studies on dioecious plant.

Comparative metabolic study of Citrus sinensis leaves cultivars based on GC–MS and their cytotoxic activity

Citrus sinensis L. Osbeck, (family Rutaceae), known as sweet orange, is a fruit-bearing shrub that widely cultivated all over the world due to its nutritive value, nutraceutical attributes and economic importance. In the present study, a comparative metabolic profile study of the essential oils of the leaves of nine cultivars of Citrus sinensis cultivated in Egypt was carried out based on GC–MS analysis coupled to chemometrics. A total of 47 compounds were identified, where monoterpenes hydrocarbons (61.39 %–78.26 %) represented the main recognized class in the essential oils. Sabinene (8.25 %–28.81 %), 2-carene (11.25 %–16.72 %) and cis-β-ocimene (10.22 %–13.93 %) were the major components identified in different cultivars. Chemometric analyses comprising Principal Component Analysis (PCA) and Hierarchical Cluster Analysis (HCA) were experienced for their ability to discriminate between closely related cultivars. Both PCA score plot and HCA dendrogram could successfully segregate different cultivars based on their metabolic profiles. The MTT assay was used to evaluate the cytotoxicity of essential oils of Citrus sinensis leaves cultivars on different cell lines; MCF-7, HepG-2, HeLa, were all essential oils showed moderate cytotoxic activity. The MCF-7 cell lines were the most resistant cells with IC50 range from 243 to 343 μg/mL, while HeLa cells were the most sensitive cell lines IC50 range from 203 to 283 μg/mL. This may be attributed to the synergistic effects of different chemical components.

Fast diagnosis of men's fertility using Raman spectroscopy combined with chemometric methods: An experimental study.

BackgroundIdiopathic infertile men suffer from unexplained male infertility; they are infertile despite having a normal semen analysis, a normal history, and physical examination, and when female infertility factor has been ruled out.ObjectiveThe present study aimed to develop a metabolic fingerprinting methodology using Raman spectroscopy combined with Chemometrics to detect idiopathic infertile men vs. fertile ones by seminal plasma.Materials and Methods In this experimental study, the seminal plasma of 26 men including 13 fertile and 13 with unexplained infertility who reffered to, Avicenna Infertility Clinic, 2018, Tehran, Iran, have been investigated. The seminal metabolomic fingerprinting was evaluated using Raman spectrometer from 100 to 4250 cm-1. The principal component analysis and discriminate analysis methods were used.ResultsThe total of 26 samples were divided into 20 training and 6 test sets. The Principal component analysis score plot of the training set showed that the data were perfectly divided into two sides of the plot, which statistically approves the direct effect of semen metabolome changes on the Raman spectra. A classification model was constructed by linear discriminant analysis using the training set and evaluated by the test group which resulted in completely correct classification. While three of the six test samples appeared in the fertile group, the rest appeared in the infertile as expected.ConclusionMetabolic fingerprinting of seminal plasma using Raman spectroscopy combined with chemometric classification methods accurately discriminated between the idiopathic infertile men and the fertile ones and predicted their fertility type.

1H-NMR metabolomics-based classification of Japanese sake and comparative metabolome analysis by gas chromatography–mass spectrometry

Six categories of Japanese sake have been established by the National Tax Agency of Japan. In this system, the rice polishing ratio and the addition of alcohol are the main criteria for classification. The most common nuclear magnetic resonance (NMR) spectrometry method is 1H-NMR, and has higher throughput than gas chromatography-mass spectrometry (GC-MS) or liquid chromatography-mass spectrometry (LC-MS) analysis due to its short measurement time, easy sample preparation, and high reproducibility. However, owing to the production of dominant ethanol signals, metabolome analyses have not been used for classifying Japanese sake using 1H-NMR. In this study, a technique to selectively suppress ethanol signals was used to classify Japanese sake by 1H-NMR, and a model was constructed to predict the rice polishing ratio. The results were compared to those obtained by GC-MS. The suppression of ethanol signals enabled the detection of trace components by 1H-NMR. In a principal component analysis (PCA) score plot of 1H-NMR spectra with ethanol signal suppression, PC1 was associated with both the addition of alcohol and the rice polishing ratio. Additionally, the separation of samples observed was similar when PCA score plots of 1H-NMR and GC-MS data were compared. Similarly, to predict the rice polishing ratio using partial least squares regression analysis, a model was constructed using 1H-NMR data, and showed nearly similar values for precision and predictive performance with the model constructed using GC-MS data. These results suggest that metabolomic analyses of Japanese sake based on 1H-NMR spectral patterns may be useful for classification.

ATR-FTIR Spectroscopy, HPLC Chromatography, and Multivariate Analysis for Controlling Bee Pollen Quality in Some Algerian Regions.

Bee pollen collected by honeybees (Apis mellifera) is one of the bee products, and it is as valuable as honey, propolis, royal jelly, or beebread. Its quality varies according to its geographic location or plant sources. This study aimed to apply rapid, simple, and accurate analytical methods such as attenuated total reflectance Fourier transform infrared spectroscopy (ATR–FTIR) and high-performance liquid chromatography (HPLC) along with chemometrics analysis to construct a model aimed at discriminating between different pollen samples. In total, 33 samples were collected and analyzed using principal component analysis (PCA), hierarchical clustering analysis (HCA), and partial least squares regression (PLS) to assess the differences and similarities between them. The PCA score plot based on both HPLC and ATR–FTIR revealed the same discriminatory pattern, and the samples were divided into four major classes depending on their total content of polyphenols. The results revealed that spectral data obtained from ATR–FTIR acquired in the region (4000–500 cm–1) were further subjected to a standard normal variable (SNV) method that removes scattering effects from spectra. However, PCA, HCA, and PLS showed that the best PLS model was obtained with a regression coefficient (R2) of 0.9001, root-mean-square estimation error (RMSEE) of 0.0304, and root-mean-squared error cross-validation (RMSEcv) of 0.036. Discrimination between the three species has also been possible by combining the pre-processed ATR–FTIR spectra with PCA and PLS. Additionally, the HPLC chromatograms after pre-treatment (SNV) were subjected to unsupervised analysis (PCA–HCA) and supervised analysis (PLS). The PLS model confers good results by factors (R2 = 0.98, RMSEE = 8.22, and RMSEcv = 27.86). Prospects for devising bee pollen quality assessment methods include utilizing ATR–FTIR and HPLC in combination with multivariate methods for rapid authentication of the geographic location or plant sources of bee pollen.

A Multivariate Approach for the Determination of the Optimal Mixing Ratio of the Non-Strong Interacting Co-Amorphous System Carvedilol-Tryptophan.

Converting crystalline compounds into co-amorphous systems is an effective way to improve the solubility of poorly water-soluble drugs. It is, however, of critical importance for the physical stability of co-amorphous systems to find the optimal mixing ratio of the drug with the co-former. In this study, a novel approach for this challenge is presented, exemplified with the co-amorphous system carvedilol–tryptophan (CAR–TRP). Following X-ray powder diffraction (XRPD) and differential scanning calorimetry (DSC) of the ball-milled samples to confirm their amorphous form, Fourier-transform infrared spectroscopy (FTIR) and principal component analysis (PCA) were applied to investigate intermolecular interactions. A clear deviation from a purely additive spectrum of CAR and TRP was visualized in the PCA score plot, with a maximum at around 30% drug (mol/mol). This deviation was attributed to hydrogen bonds of CAR with TRP ether groups. The sample containing 30% drug (mol/mol) was also the most stable sample during a stability test. Using the combination of FTIR with PCA is an effective approach to investigate the optimal mixing ratio of non-strong interacting co-amorphous systems.

Use of 13C-NMR Chemical Shifts; Application of Principal Component Analysis for Categorizing Structurally Similar Methoxyflavones and Correlation Analysis between Chemical Shifts and Cytotoxicity.

The 13C-NMR spectral data for the 15-carbon flavonoid skeleton in eleven methoxyflavones isolated from Kaempferia parviflora (Zingiberaceae) were processed by principal component analysis (PCA). Based on the PCA score plots, the methoxyflavones were categorized into three groups according to their structural features. The cytotoxicities of the methoxyflavones toward 3T3-L1 murine preadipocyte cells were evaluated by 3-(4,5-dimethylthiazole-2-yl)-5-(3-carboxymethoxyphenyl)-2-(4-sulfophenyl)-2H-tetrazolium (MTT) assay and found to differ according to structure. The relationship between the 13C-NMR chemical shifts of the methoxyflavones and their cytotoxicities was investigated using Pearson's correlation analysis. The 13C-NMR signal at C-10, a quaternary carbon, was correlated with cytotoxicity. Based on these results, a structural design which lowers the 13C-NMR chemical shift at C-10 would be important for the development of cytotoxic compounds. Although quantitative structure-activity and structure-property relationships are well established paradigms for predicting trends among a series of compounds, quantitative property-activity relationships have been relatively unstudied. This approach offers a new strategy for directing structure-activity relationship research.

Evaluating the effects of temperature and time on heterocyclic aromatic amine profiles in roasted pork using combined UHPLC-MS/MS and multivariate analysis

In this study, ultra-high-performance liquid chromatography–tandem mass spectrometry (UHPLC-MS/MS) combined with principal component analysis (PCA) were used to investigate the effects of process conditions on the profiles of carcinogenic and mutagenic heterocyclic aromatic amine (HAA) in the pork roasted at 175 °C, 200 °C, 225 °C and 250 °C for 10, 15, 20, 25, 30, 35 and 40 min. Twelve HAAs from four categories, including carboline (Norharman, Harman, and Phe-p-1), imidazopyridine (PhIP, 4′-OH-PhIP, DMIP, and 1,5,6-TMIP), imidazoquinoline (IQ, IQ [4,5-b], and MeIQ), and imidazoquinoxaline (MeIQx and 4,8-DiMeIQx), were detected, quantified and used to compose the HAA profiles in roasted pork. After being Analyzed by PCA, the distributions of HAA profiles from different temperature on the PCA score plot demonstrated that there are significant differences among the HAA profiles from different temperatures. The loading plot also showed that PhIP, 4′-OH-PhIP, IQ[4,5-b], and MeIQ were mainly responsible for the difference. The profiles from higher temperature distribute more scattered than the lower ones, illustrating that the time effects on the HAA profiles from higher temperature are stronger than the lower ones. Comparing the score and loading plots of different heating times, the diversities of the HAA profiles at different temperatures increased under prolonged heating because of the changingpyridines levels. The results of PCA that comparing the HAA from different categories displayed that the formation features of four categories HAAs were significantly differed because of their formation discrepancy under low temperatures and short–term roasting. Using HAA profiles as an entirety, these findings obtained in this study are more close to the real process of HAA formation in roasted pork, and make the complex effects of temperature and time on multiple HAA formations more simply to be concluded.

Principal Component Analysis Applications in COVID-19 Genome Sequence Studies.

RNA genomes from coronavirus have a length as long as 32 kilobases, and the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) that caused the outbreak of coronavirus disease 2019 (COVID-19) pandemic has long sequences which made the analysis difficult. Over 20,000 sequences have been submitted to GISAID, and the number is growing fast each day which increased the difficulties in data analysis; however, genome sequence analysis is critical in understanding the COVID-19 and preventing the spread of the disease. In this study, a principal component analysis (PCA) was applied to the aligned large size genome sequences and the numerical numbers were converted from the letters using a published method designed for protein sequence cluster analysis. The study initialized with a shortlist sequence testing, and the PCA score plot showed high tolerance with low-quality data, and the major virus sequences from humans were separated from the pangolin and bat samples. Our study also successfully built a model for a large number of sequences with more than 20,000 sequences which indicate the potential mutation directions for the COVID-19 which can be served as a pretreatment method for detailed studies such as decision tree-based methods. In summary, our study provided a fast tool to analyze the high-volume genome sequences such as the COVID-19 and successfully applied to more than 20,000 sequences which may provide mutation direction information for COVID-19 studies.