Validation and Collaborative Studies of Headspace Gas Chromatography-Mass Spectrometry (HS GC-MS) for Determination of 1,4-Dioxane in Cosmetic Samples.

BACKGROUND: 1,4-Dioxane (1,4-D) is a by-product of the synthesis of surfactants, typically found in some cosmetics products such as shampoo, toothpaste, and soap. The presence of 1,4-D in cosmetics products is limited to certain amount since 1,4-D is classified as a probable human carcinogen. OBJECTIVE: This present study was intended to validate static headspace gas chromatography-mass spectrometry (HS GC-MS) for the determination of 1,4-D in cosmetics products. METHODS: The condition of headspace and GC-MS was optimized to get the best condition for analysis of 1,4-D using 1,4-Dioxane-d8 (1,4-D-d8) as internal standard (IS). The developed method was validated by evaluating the key performance characteristics, including specificity, linearity, limit of detection (LoD), limit of quantification (LoQ), accuracy, and precision. RESULTS: The results showed that HS GC-MS was specific since the peaks of the selected ion monitoring (SIM) mode could be separated and confirmed at m/z 88 and m/z 96 for 1,4-D and 1,4-D-d8, respectively. The method was linear over the concentration range of 0.1287-1.2875 µg/mL, with R2 > 0.999 and RSD residuals < 2.0. A collaborative study were conducted on this method, with ten participating laboratories from four countries. The outcome of this study was found to be accurate and precise, as evidenced by the excellent recoveries ranged from 94.6-102.1% and with good reproducibility with RSD values ranged from 0.2-1.1%. The collaborative studies exhibited that all data reported by ten participating laboratories in four countries were inliers without any extreme values observed either in mean or RSD values. CONCLUSION: This HS GC-MS is found to be fit and suitable for the determination of trace level of 1,4-D in cosmetics products. HIGHLIGHTS: HS GC-MS method could be proposed as a standard method for quantitative analysis of 1,4-D in cosmetics products since the collaborative studies indicated that the developed method meet the requirement in "Guidelines for Collaborative Study Procedures to Validate Characteristics of a Method of Analysis".

The combination of Fourier-transform infrared spectroscopy with pattern recognition techniques for classification and discrimination of red snapper fish oils.

Fish oils are good sources for essential fatty acids such as omega-3 and omega-6 fatty acids needed to human growth. Indonesia is rich in fish species and among this, red snapper fish (Lutjanus sp.) can be extracted to get red snapper fish oils (RSFOs). The aim of this study was to classify and discriminate RSFO from different origins using Fourier-transform infrared (FTIR) spectra and pattern recognition techniques. All of the RSFO's FTIR spectra were very similar. The FTIR vibrations showed the presence of triglycerides as the main composition in fish oils. Principal component analysis (PCA) could separate the RSFO according to sample origin. Supervised pattern recognition of partial least square-discriminant analysis (PLS-DA) and sparse PLS-DA (sPLS-DA) successfully discriminated and classified different Lutjanus species of fish oils obtained from different origins. The vibration of functional groups at 1711, 1653, 1745, and 3012 per cm were considered for their important contributions in discriminating of Lutjanus species (variable importance in projection, variable importance in the projection score >1). Fish oils obtained from the same species were classified into the same class indicating similar chemical compositions. Among the three pattern recognition techniques used, sPLS-DA offers the best model for the discrimination and classification of Lutjanus fish oils. It can be concluded that FTIR spectroscopy in combination with the pattern recognition technique is the potential to be used for of fish oil authentication to verify the quality of the fish oils. It can be further developed as a rapid and effective method for fish oil authentication.

Untargeted metabolomics using liquid chromatography-high resolution mass spectrometry and chemometrics for analysis of non-halal meats adulteration in beef meat.

OBJECTIVE: The adulteration of raw beef (BMr) with dog meat (DMr) and pork (PMr) becomes a serious problem because it is associated with halal status, quality, and safety of meats. This research aimed to develop an effective authentication method to detect non-halal meats (dog meat and pork) in beef using metabolomics approach. METHODS: Liquid chromatography-high resolution mass spectrometry (LC-HRMS) using untargeted approach combined with chemometrics was applied for analysis non-halal meats in BMr. RESULTS: The untargeted metabolomics approach successfully identified various metabolites in BMr DMr, PMr, and their mixtures. The discrimination and classification between authentic BMr and those adulterated with DMr and PMr were successfully determined using partial least square-discriminant analysis (PLS-DA) with high accuracy. All BMr samples containing non-halal meats could be differentiated from authentic BMr. A number of discriminating metabolites with potential as biomarkers to discriminate BMr in the mixtures with DMr and PMr could be identified from the analysis of variable importance for projection value. Partial least square (PLS) and orthogonal PLS (OPLS) regression using discriminating metabolites showed high accuracy (R2>0.990) and high precision (both RMSEC and RMSEE <5%) in predicting the concentration of DMr and PMr present in beef indicating that the discriminating metabolites were good predictors. The developed untargeted LC-HRMS metabolomics and chemometrics successfully identified non-halal meats adulteration (DMr and PMr) in beef with high sensitivity up to 0.1% (w/w). CONCLUSION: A combination of LC-HRMS untargeted metabolomic and chemometrics promises to be an effective analytical technique for halal authenticity testing of meats. This method could be further standardized and proposed as a method for halal authentication of meats.

Analysis of dog meat adulteration in beef meatballs using non-targeted UHPLC-Orbitrap HRMS metabolomics and chemometrics for halal authentication study.

Due to the different price and high quality, halal meat such as beef can be adulterated with non-halal meat with low price to get an economical price. The objective of this research was to develop an analytical method for halal authentication testing of beef meatballs (BM) from dog meat (DM) using a non-targeted metabolomics approach employing liquid chromatography-high-resolution mass spectrometry (LC-HRMS) and chemometrics. The differentiation of authentic BM from that adulterated with DM was successfully performed using partial least square-discriminant analysis (PLS-DA) with high accuracy (R2X = 0.980, and R2Y = 0.980) and good predictivity (Q2 = 0.517). In addition, partial least square (PLS) and orthogonal PLS (OPLS) were successfully used to predict the DM added (% w/w) in BM with high accuracy (R2 > 0.990). A number of metabolites, potential for biomarker candidates, were identified to differentiate BM and that adulterated with DM. It showed that the combination of a non-targeted LC-HRMS Orbitrap metabolomics and chemometrics could detect up to 0.1% w/w of DM adulteration. The developed method was successfully applied for analysis of commercial meatball samples (n = 28). Moreover, pathway analysis revealed that beta-alanine, histidine, and ether lipid metabolism were significantly affected by dog meat adulteration. In summary, this developed method has great potential to be developed and used as an alternative method for analysis of non-halal meats in halal meat products.

Cytotoxicity Activity, Metabolite Profiling, and Isolation Compound from Crude Hexane Extract of Cleome rutidospermae.

OBJECTIVE: This study isolated the chemical compounds and evaluated the cytotoxic activity of the crude hexane extract of Cleome rutidospermae herb (CRH). METHODS: The isolate was purified using silica gel, column chromatography, and preparative thin layer chromatography (PTLC). Furthermore, the structure of the compounds was identified by spectroscopic methods using 1D, 2D NMR, and mass spectrometry. The cytotoxic activity of CRH at a concentration of 20 ug/mL was also tested against MCF-7, A549, KB, KB-VIN, and MDA-MB-231 cancer cells using the sulforhodamine B (SRB) method. RESULTS: The CRH contained compounds of unsaturated fatty acid, saturated fatty acid, lipid, glycerol, ω-3 fatty acid, and cholesterol. Two compounds were obtained from the plant, and their structures were identified as (1) Stigmasta-5,22-dien-3-ol (STML) and (2) 1,2-Benzene dicarboxylic acid, 1,2-bis (2-Ethylhexyl) esters (DEHP). These compounds were reported in this plant for the first time. In comparison, CRH had % growth inhibition in the proliferation of MCF-7 cells up to 28.1%, with cancer cells A549, KB, KB-VIN, and MDA-MB-231 by >50% Compared to the negative DMSO of 0.20%, while the positive control could inhibit the growth of all cancer cells (100%). CONCLUSION: Our findings suggested that crude herb from the plant CRH was the potential for breast cancer treatment.

Analysis of Pork in Beef Sausages Using LC-Orbitrap HRMS Untargeted Metabolomics Combined with Chemometrics for Halal Authentication Study.

Beef sausage (BS) is one of the most favored meat products due to its nutrition and good taste. However, for economic purposes, BS is often adulterated with pork by unethical players. Pork consumption is strictly prohibited for religions including Islam and Judaism. Therefore, advanced detection methods are highly required to warrant the halal authenticity of BS. This research aimed to develop a liquid chromatography-high-resolution mass spectrometry (LC-HRMS) method to determine the halal authenticity of BS using an untargeted metabolomics approach. LC-HRMS was capable of detecting various metabolites in BS and BS containing pork. The presence of pork in BS could be differentiated using principal component analysis (PCA) and partial least squares-discriminant analysis (PLS-DA) with high accuracy. PLS-DA perfectly classified authentic BS and BS containing pork in all concentration levels of pork with R2X = (0.821), R2Y(= 0.984), and Q2 = (0.795). The level of pork in BS was successfully predicted through partial least squares (PLS) and orthogonal PLS (OPLS) chemometrics. Both models gave high R2 (>0.99) actual and predicted values as well as few errors, indicating good accuracy and precision. Identification of discriminating metabolites' potential as biomarker candidates through variable importance for projections (VIP) value revealed metabolites of 2-arachidonyl-sn-glycero-3-phosphoethanolamine, 3-hydroxyoctanoylcarnitine, 8Z,11Z,14Z-eicosatrienoic acid, D-(+)-galactose, oleamide, 3-hydroxyhexadecanoylcarnitine, arachidonic acid, and α-eleostearic acid as good indicators to detect pork. It can be concluded that LC-HRMS metabolomics combined with PCA, PLS-DA, PLS, and OPLS was successfully used to detect pork adulteration in beef sausages. The results imply that LC-HRMS untargeted metabolomics in combination with chemometrics is a promising alternative as an analytical technique to detect pork in sausage products. Further analysis of larger samples is required to warrant the reproducibility.

Application of Chromatographic and Spectroscopic-Based Methods for Analysis of Omega-3 (ω-3 FAs) and Omega-6 (ω-6 FAs) Fatty Acids in Marine Natural Products.

Omega-3 fatty acids v(ω-3 FAs) such as EPA (eicosapentaenoic acid) and DHA (docosahexaenoic acid) and omega-6 fatty acids (ω-6 FAs) such as linoleic acid and arachidonic acid are important fatty acids responsible for positive effects on human health. The main sources of ω-3 FAs and ω-6 FAs are marine-based products, especially fish oils. Some food, supplements, and pharmaceutical products would include fish oils as a source of ω-3 FAs and ω-6 FAs; therefore, the quality assurance of these products is highly required. Some analytical methods mainly based on spectroscopic and chromatographic techniques have been reported. Molecular spectroscopy such as Infrared and Raman parallel to chemometrics has been successfully applied for quantitative analysis of individual and total ω-3 FAs and ω-6 FAs. This spectroscopic technique is typically applied as the alternative method to official methods applying chromatographic methods. Due to the capability to provide the separation of ω-3 FAs and ω-6 FAs from other components in the products, gas and liquid chromatography along with sophisticated detectors such as mass spectrometers are ideal analytical methods offering sensitive and specific results that are suitable for routine quality control.

UHPLC-Q-Orbitrap HR-MS-Based Metabolomics for Profiling the Sida rhombifolia Metabolites with Different Plant Organs and Cultivation Ages.

Plant organs and cultivation ages can result in different compositions and concentration levels of plant metabolites. The metabolite profile of plants can be determined using liquid chromatography. This study determined the metabolite profiles of leaves, stems, and roots of Sida rhombifolia at different cultivation ages at 3, 4, and 5 months post-planting (MPP) using liquid chromatography-mass spectrometry/mass spectrometry (LC/MS/MS). The results identified that 41 metabolites in S. rhombifolia extract for all plant organs and cultivation ages. We successfully identified approximately 36 (leaves), 22 (stems), and 18 (roots) compounds in all extract. Using principal component analysis (PCA) with peak area as the variable, we clustered all sample extracts based on plant organs and cultivation ages. As a result of PCA, S. rhombifolia extracts were grouped according to plant organs and cultivation ages. In conclusion, a clear difference in the composition and concentration levels of metabolites was observed in the leaves, stems, and roots of S. rhombifolia harvested at 3-, 4-, and 5-MPP.

Detection of Pork in Beef Meatballs Using LC-HRMS Based Untargeted Metabolomics and Chemometrics for Halal Authentication.

Adulteration of high-quality meat products using lower-priced meats, such as pork, is a crucial issue that could harm consumers. The consumption of pork is strictly forbidden in certain religions, such as Islam and Judaism. Therefore, the objective of this research was to develop untargeted metabolomics using liquid chromatography-high resolution mass spectrometry (LC-HRMS) combined with chemometrics for analysis of pork in beef meatballs for halal authentication. We investigated the use of non-targeted LC-HRMS as a method to detect such food adulteration. As a proof of concept using six technical replicates of pooled samples from beef and pork meat, we could show that metabolomics using LC-HRMS could be used for high-throughput screening of metabolites in meatballs made from beef and pork. Chemometrics of principal component analysis (PCA) was successfully used to differentiate beef meatballs and pork meatball samples. Partial least square-discriminant analysis (PLS-DA) clearly discriminated between halal and non-halal beef meatball samples with 100% accuracy. Orthogonal projection to latent structures-discriminant analysis (OPLS-DA) perfectly discriminated and classified meatballs made from beef, pork, and a mixture of beef-pork with a good level of fitness (R2X = 0.88, R2Y = 0.71) and good predictivity (Q2 = 0.55). Partial least square (PLS) and orthogonal PLS (OPLS) were successfully applied to predict the concentration of pork present in beef meatballs with high accuracy (R2 = 0.99) and high precision. Thirty-five potential metabolite markers were identified through VIP (variable important for projections) analysis. Metabolites of 1-(1Z-hexadecenyl)-sn-glycero-3-phosphocholine, acetyl-l-carnitine, dl-carnitine, anserine, hypoxanthine, linoleic acid, and prolylleucine had important roles for predicting pork in beef meatballs through S-line plot analysis. It can be concluded that a combination of untargeted metabolomics using LC-HRMS and chemometrics is promising to be developed as a standard analytical method for halal authentication of highly processed meat products.

Volatile compounds, texture, and color characterization of meatballs made from beef, rat, wild boar, and their mixtures.

The purpose of this research was to characterize the volatile compounds, texture, and color profile of meatballs made from beef, rat, wild boar, and their combinations. Volatile compounds were analyzed using SPME/GC-MS and multivariate data analysis (PCA, PLS-DA). Additionally, several textural features such as hardness, gumminess, chewiness, cohesiveness, and colour (L, a∗, b∗, C, and h) were also analyzed. The findings revealed that texture and color characteristics can only be used to differentiate meatballs based on their raw meat materials when meat adulterants are used in high concentrations (≥50%). PLS-DA analysis of volatile data revealed distinct groupings among various types of meatballs, including meatballs adulterated with rat or wild boar meat at the lowest percentage used in this study (20%). By using VIP and correlation coefficient, the strongest markers in beef, rat, and wild boar meatballs were identified as (Z)-2-amino-5-methyl-benzoic acid, 2-heptenal, and cyclobutanol, respectively. Nonanal was consistently found as a significant marker in the meatballs made from a mixture of beef-rat and beef-wild boar at different ratios. This study demonstrated that the volatile profile of meat is more reliable than physicochemical profiles for developing an analytical tool for quickly identifying undesired meat in meat-derived products.

Hybrid learning method based on feature clustering and scoring for enhanced COVID-19 breath analysis by an electronic nose.

Breath pattern analysis based on an electronic nose (e-nose), which is a noninvasive, fast, and low-cost method, has been continuously used for detecting human diseases, including the coronavirus disease 2019 (COVID-19). Nevertheless, having big data with several available features is not always beneficial because only a few of them will be relevant and useful to distinguish different breath samples (i.e., positive and negative COVID-19 samples). In this study, we develop a hybrid machine learning-based algorithm combining hierarchical agglomerative clustering analysis and permutation feature importance method to improve the data analysis of a portable e-nose for COVID-19 detection (GeNose C19). Utilizing this learning approach, we can obtain an effective and optimum feature combination, enabling the reduction by half of the number of employed sensors without downgrading the classification model performance. Based on the cross-validation test results on the training data, the hybrid algorithm can result in accuracy, sensitivity, and specificity values of (86 ± 3)%, (88 ± 6)%, and (84 ± 6)%, respectively. Meanwhile, for the testing data, a value of 87% is obtained for all the three metrics. These results exhibit the feasibility of using this hybrid filter-wrapper feature-selection method to pave the way for optimizing the GeNose C19 performance.

Advances in Fingerprint Analysis for Standardization and Quality Control of Herbal Medicines.

Herbal drugs or herbal medicines (HMs) have a long-standing history as natural remedies for preventing and curing diseases. HMs have garnered greater interest during the past decades due to their broad, synergistic actions on the physiological systems and relatively lower incidence of adverse events, compared to synthetic drugs. However, assuring reproducible quality, efficacy, and safety from herbal drugs remains a challenging task. HMs typically consist of many constituents whose presence and quantity may vary among different sources of materials. Fingerprint analysis has emerged as a very useful technique to assess the quality of herbal drug materials and formulations for establishing standardized herbal products. Rather than using a single or two marker(s), fingerprinting techniques take great consideration of the complexity of herbal drugs by evaluating the whole chemical profile and extracting a common pattern to be set as a criterion for assessing the individual material or formulation. In this review, we described and assessed various fingerprinting techniques reported to date, which are applicable to the standardization and quality control of HMs. We also evaluated the application of multivariate data analysis or chemometrics in assisting the analysis of the complex datasets from the determination of HMs. To ensure that these methods yield reliable results, we reviewed the validation status of the methods and provided perspectives on those. Finally, we concluded by highlighting major accomplishments and presenting a gap analysis between the existing techniques and what is needed to continue moving forward.

Untargeted metabolomics and proteomics approach using liquid chromatography-Orbitrap high resolution mass spectrometry to detect pork adulteration in Pangasius hypopthalmus meat.

Pangasius hypopthalmus is well known as a good source of protein. However, Pangasius hypopthalmus meat (PHM) can be adulterated with pork for economic concern, thus, analytical methods for authentication are required. Untargeted metabolomics and proteomics using liquid chromatography-high resolution mass spectrometry (LC-HRMS) and chemometrics of principal component analysis (PCA) and partial least square-discriminant analysis (PLS-DA) was successfully used to differentiate authentic and adulterated PHM with the good of fitness (R > 0.95) and good of predictivity (Q > 0.5). Metabolites of PC(o-18:0/18:2(9Z,12Z)) was found to be a potential marker for pork whereas DMPC (dimyristoylphosphatidylcholine) was a potential marker for PHM. Meanwhile, pork peptide marker of myoglobin (HPGDFGADAQGAMSK) and ß-hemoglobin (FFESFGDLSNADAVMGNPK) could be identified. Both metabolomics and proteomics using LC-HRMS could detect pork at the lowest concentration level (0.5% w/w). In conclusion, untargeted metabolomics and proteomics using LC-HRMS in combination with chemometrics could be used as powerful methods to detect pork adulteration in fish meat.

The Application of FTIR Spectroscopy and Chemometrics for the Authentication Analysis of Horse Milk.

Expensive milk such as horse's milk (HM) may be the target of adulteration by other milk such as goat's milk (GM) and cow's milk (CM). FTIR spectroscopy in combination with chemometrics of linear discriminant analysis (LDA) and multivariate calibrations of partial least square regression (PLSR) and principal component regression (PCR) was used for authentication of HM from GM and CM. Milk was directly subjected to attenuated total reflectance (ATR) spectral measurement at midinfrared regions (4000-650 cm-1). Results showed that LDA could make clear discrimination between HM and HM adulterated with CM and GM without any misclassification observed. PLSR using 2nd derivative spectra at 3200-2800 and 1300-1000 cm-1 provided the best model for the relationship between actual values of GM and FTIR predicted values than PCR. At this condition, R 2 values for calibration and validation models obtained were 0.9995 and 0.9612 with RMSEC and RMSEP values of 0.0093 and 0.0794. PLSR using normal FTIR spectra at 3800-3000 and 1500-1000 cm-1 offered R 2 for the relationship between actual values of CM and FTIR predicted values of >0.99 in calibration and validation models with low errors of RMSEC of 0.0164 and RMSEP of 0.0336 during authentication of HM from CM. Therefore, FTIR spectroscopy in combination with LDA and PLSR is an effective method for authentication of HM from GM and CM.

Metabolite Fingerprinting Based on 1H-NMR Spectroscopy and Liquid Chromatography for the Authentication of Herbal Products.

Herbal medicines (HMs) are regarded as one of the traditional medicines in health care to prevent and treat some diseases. Some herbal components such as turmeric and ginger are used as HMs, therefore the identification and confirmation of herbal use are very necessary. In addition, the adulteration practice, mainly motivated to gain economical profits, may occur by substituting the high price of HMs with lower-priced ones or by addition of certain chemical constituents known as Bahan Kimia Obat (chemical drug ingredients) in Indonesia. Some analytical methods based on spectroscopic and chromatographic methods are developed for the authenticity and confirmation of the HMs used. Some approaches are explored during HMs authentication including single-component analysis, fingerprinting profiles, and metabolomics studies. The absence of reference standards for certain chemical markers has led to exploring the fingerprinting approach as a tool for the authentication of HMs. During fingerprinting-based spectroscopic and chromatographic methods, the data obtained were big, therefore the use of chemometrics is a must. This review highlights the application of fingerprinting profiles using variables of spectral and chromatogram data for authentication in HMs. Indeed, some chemometrics techniques, mainly pattern recognition either unsupervised or supervised, were applied for this purpose.

Metabolite profiling of Andrographis paniculata leaves and stem extract using UHPLC-Orbitrap-MS/MS.

Andrographis paniculata is known as the king of bitter and it has been widely used as a medicinal plant. The properties of A. paniculata are generally determined by the metabolite composition, which may be influenced by several factors, one of which is the part of the plant extracted. The objectives of this research are to identify putatively the metabolite composition of the stem and the leaves extracts using LC-MS/MS and classify them using PCA. The stem and the leaves samples were separated and extracted using sonication with 70% ethanol. A total of 31 metabolite compounds has been putatively identified. All compounds were identified in the stem and the leaves extracts, which only differed in their intensity. These metabolite compounds were divided into diterpene lactones, flavonoids, and phenolic acid groups. By using the peak intensities of the 18 compounds identified, the leaves and stem extracts were grouped using PCA.

In vivo anti-inflammatory activities of Plantago major extract and fractions and analysis of their phytochemical components using a high-resolution mass spectrometry.

Background and purpose: Plantago major has been applied as a herbal remedy for centuries. However, studies on anti-inflammatory activities and their chemical ingredients are limited. The objective of this study was to investigate the anti-inflammatory properties of P. major in three animal models and its phytochemical contents. Experimental approach: Dichloromethane extract (DCM) of P. major was fractionated with n-hexane to yield the soluble (SHF) and insoluble (IHF) fractions. The anti-inflammatory activities of DCM, SHF, and IHF were evaluated using rat's paw edema induced by carrageenan, thioglycolate-induced leukocyte emigration in the mice, and rheumatoid arthritis (RA) induced by complete Freund's adjuvants in rats. The chemical constituents were analyzed using a high-resolution mass spectrometer (HRMS). Findings / Results: The DCM, SHF, and IHF inhibited paw edema in the rats and reduced the leukocyte migration in the mice. At dose 560 mg/kg, the percentage of inhibitory was 47.33%, 55.51%, and 46.61% for the DCM, IHF, and SHF, respectively. In the RA animal model, IHF at 280 and 560 mg/kg reduced osteoclast formation and COX-2 expression compared to diclofenac. Some compounds namely oleic acid, linoleic acid, palmitic acid, and oleamide identified in the DCM, IHF, and SHF may be responsible for these activities. Conclusion and implications: This study showed that P. major has several in-vivo anti-inflammatory activities.

Metabolite Fingerprinting Using 1H-NMR Spectroscopy and Chemometrics for Classification of Three Curcuma Species from Different Origins.

Curcuma longa, Curcuma xanthorrhiza, and Curcuma manga have been widely used for herbal or traditional medicine purposes. It was reported that turmeric plants provided several biological activities such as antioxidant, anti-inflammatory, hepatoprotector, cardioprotector, and anticancer activities. Authentication of the Curcuma species is important to ensure its authenticity and to avoid adulteration practices. Plants from different origins will have different metabolite compositions because metabolites are affected by soil nutrition, climate, temperature, and humidity. 1H-NMR spectroscopy, principal component analysis (PCA), and orthogonal projections to latent structures-discriminant analysis (OPLS-DA) were used for authentication of C. longa, C. xanthorrhiza, and C. manga from seven different origins in Indonesia. From the 1H-NMR analysis it was obtained that 14 metabolites were responsible for generating classification model such as curcumin, demethoxycurcumin, alanine, methionine, threonine, lysine, alpha-glucose, beta-glucose, sucrose, alpha-fructose, beta-fructose, fumaric acid, tyrosine, and formate. Both PCA and OPLS-DA model demonstrated goodness of fit (R2 value more than 0.8) and good predictivity (Q2 value more than 0.45). All OPLS-DA models were validated by assessing the permutation test results with high value of original R2 and Q2. It can be concluded that metabolite fingerprinting using 1H-NMR spectroscopy and chemometrics provide a powerful tool for authentication of herbal and medicinal plants.

The Combination of Vibrational Spectroscopy and Chemometrics for Analysis of Milk Products Adulteration.

Milk products obtained from cow, goat, buffalo, sheep, and camel as well as fermented forms such as cheese, yogurt, kefir, and butter are in a category of the most nutritious foods due to their high contents of high protein contributing to total daily energy intake. For certain reasons, high price milk products may be adulterated with low-quality ones or with foreign substances such as melamine and formalin which are added into them; therefore, a comprehensive review on analytical methods capable of detecting milk adulteration is needed. The objective of this narrative review is to highlight the use of vibrational spectroscopies (near infrared, mid infrared, and Raman) combined with multivariate analysis for authentication of milk products. Articles, conference reports, and abstracts from several databases including Scopus, PubMed, Web of Science, and Google Scholar were used in this review. By selecting the correct conditions (spectral treatment, normal versus derivative spectra at wavenumbers region, and chemometrics techniques), vibrational spectroscopy is a rapid and powerful analytical technique for detection of milk adulteration. This review can give comprehensive information for selecting vibrational spectroscopic methods combined with chemometrics techniques for screening the adulteration practice of milk products.