Determination and Profiling of Secondary Metabolites in Aloe vera, Aloe arborescens and Aloe saponaria

Metabolomics is as an innovative technique for discriminating plant species. The objective of this study was to investigate the secondary metabolites of three different Aloe species, A. vera, A. arborescens, and A. saponaria profiled by 1 H-NMR analysis. Principal component analysis (PCA) derived from the 1 H-NMR spectra indicated a clear discrimination among the Aloe species, providing high predictability and good fitness of the PCA model (R2 = 0.928 and Q2 = 865). As observed in the PLS-DA score plot, discrimination was observed in the Aloe species with respect to primary metabolites including sugar and organic acid and secondary metabolites such as phenylpropanoids and carotenoids. A. vera was characterized by high levels of malate. On the other hand, as compared to the other Aloe species, A. arborescens was characterized by higher levels of aloenin and sugar metabolites such as sucrose and glucose. Furthermore, the secondary metabolites were quantitatively analyzed by HPLC, and the amounts of carotenoids including zeaxanthin, α- and β-carotene, and phenylpropanoids in A. arborescens were found to be significantly higher than those in the other Aloe species. In conclusion, we demonstrated that 1 H-NMR-based metabolomics with chemometric analysis can be used for the facile discrimination of Aloe species.

Discrimination of Four Cinnamomum Species with Physico-Functional Properties and Chemometric Techniques: Application of PCA and MDA Models

Discrimination of highly valued and non-hepatotoxic Cinnamomum species (C. verum) from hepatotoxic (C. burmannii, C. loureiroi, and C. cassia) is essential for preventing food adulteration and safety problems. In this study, we developed a new method for the discrimination of four Cinnamomum species using physico-functional properties and chemometric techniques. The data were analyzed through principal component analysis (PCA) and multiclass discriminant analysis (MDA). The results showed that the cumulative variability of the first three principal components was 81.70%. The PCA score plot indicated a clear separation of the different Cinnamomum species. The training set was used to build the discriminant MDA model. The testing set was verified by this model. The prediction rate of 100% proved that the model was valid and reliable. Therefore, physico-functional properties coupled with chemometric techniques constitute a practical approach for discrimination of Cinnamomum species to prevent food fraud.

Volatile Organic Compound Profiles Associated with Microbial Development in Feedlot Pellets Inoculated with Bacillus amyloliquefaciens H57 Probiotic

Mould and bacterial contamination releases microbial volatile organic compounds (mVOCs), causing changes in the odour profile of a feed. Bacillus amyloliquefaciens strain H57 (H57) has the potential ability to inhibit microbial growth in animal feeds. This study tested the hypothesis that H57 influences the odour profile of stored feedlot pellets by impeding the production of mVOCs. The emission of volatile organic compounds (VOCs) of un-inoculated pellets and those inoculated with H57, stored either at ambient temperature (mean 22 °C) or at 5 °C, was monitored at 0, 1, 2, and 3 months by gas chromatography–mass spectrometry. Forty VOCs were identified in all the pellet samples analysed, 24 of which were potentially of microbial and 16 of non-microbial origin. A score plot of the principal component analysis (PCA) showed that the VOC profiles of the pellets stored at ambient temperature changed more rapidly over the 3 months than those stored at 5 °C, and that change was greater in the un-inoculated pellets when compared to the inoculated ones. The bi-plot and correlation loading plots of the PCA indicated that the separation of the un-inoculated pellets from the other treatments over the 3 months was primarily due to nine mVOCs. These mVOCs have been previously identified in grains spoiled by fungi, and could be considered potential markers of the types of fungi that H57 can protect pellets against. These data indicate the ability of H57 to maintain the odour profile and freshness of concentrated feed pellets. This protective influence can be detected as early as 3 months into ambient temperature storage.

Evaluation of Apium graveolens from different geographical origins based on TLC-fingerprint and chemometrics

Apium graveolens (celery) has various roles both in the food and medicine sectors. It grows very well in the tropical and subtropical areas of Africa and Asia, including Indonesia. This Apiaceae member contains a number of phytoconstituents, and geographical origin is known to significantly determine the type and concentration of phytochemicals in plant material. This study was carried out to validate and develop thin layer chromatography (TLC)-based fingerprinting combined with chemometrics, i.e., Principal Component Analysis (PCA) and Cluster Analysis (CA), to evaluate the quality of celery harvested from thirteen different geographical origins in Indonesia. The mobile phase was first optimized with a simplex axial design, resulting in 2-propanol, toluene, and dichloromethane (1:6:1) as the optimum mobile phase for a stable and precise TLC system in the celery sample analysis. When analyzed with chemometrics, the TLC-fingerprints could discriminate celeries from various origins. The PCA score plot of the first two principal components (PCs) and CA clearly distinguished the samples’ properties and classified them into four clusters. Samples grouped into one cluster were concluded to have comparable quality, while those in different clusters had different qualities.

Chemical Profiling of Volatile Compounds in Brazilian Green Propolis and Its Application to Geographical Discrimination

In this paper, a novel approach was set up to analyze and discriminate propolis from different regions based on GC-MS and multivariate statistical analysis. A number of Chinese and Brazilian green propolis samples were dealt with based on this method, and a set of data were processed with partial least squares-discriminant analysis (PLS-DA). A clear differences between the two groups were shown in score plot. The chemical markers for the differentiation were selected through loading plot. Based on the comparison between the reference and/or NIST database and mass fragments in the publication, chemical markers were tentatively identified. Lauric acid, 2(3H)-naphthalenone, spathulenol and benzenebutanoic acid were taken as chemical markers based on the above strategy. This research could provide some valuable information to the quality control of propolis from different origins.

Principal Component Analysis on Soil Fertility Parameters in Vegetable Growing Locations of Kottayam District of Kerala

Present study utilizes Principal Component Analysis (PCA) of 13 soil testing variables obtained from 28 vegetable growing locations of Kottayam district and there were a total of 718 samples for analysis. Thirteen Principal Components (PCs) were generated and five PCs could explain the major share of variance (80%). Score plot was drawn based on PCA and the results indicated that none of the variables was predominant in Bharananganam, Kadanadu, Kozhuvanal, Kidangoor and Pallikkathode and also these panchayats had positive scores on both F1 and F2 when factor analysis was conducted. Boron (B), Copper (Cu) and Zinc (Zn) were predominant in Akalakkunnam, Kadalpalamattom, Meeaachil, Melukavu, Poonjar and Ramapuram panchayats. Elikulam, Erumeli, Karoor, Mundakkayam, Mutholi, Poonjar south, Thalapalm and Vakathanom were those panchayats where the contribution of Magnesium (Mg), Potassium (K) and pH was more. All other elements viz, Oxidisable Organic Carbon (OC), Sulphur (S), Phosphorus (P), Calcium (Ca), Manganese (Mn) and Iron (Fe) had significant importance in Ayarkkunnam, Aymanam, Chempu, Kaduthuruthy, Kurichi, Manjoor, Maravanthuruth, Puthuppally and Thalayazham panchayats.

Polyphenolic Profile of Orange Wines

Objectives The objective of this study is to describe the polyphenolic profile of orange wine and compare it with red wine, additionally this study explores the potential effect of iron content in the orange wine characteristic color. Methods Nine orange wines and 1 red wine were analyzed in triplicate. The skin-contact maceration of the orange wines ranged from 3 days to 6 months. Three orange wines were made following the Georgian traditional method (using Quevri and 6 months skin-contact maceration), one orange wine went through 6 months of skin-contact maceration in oak barrels. Analysis consisted of color density (CD) and hue tint (HT) by measuring absorbance at 420,520, and 700 nm; identification and quantification of the polyphenols gallic acid, vanillic acid, caffeic acid, caftaric acid, 4-caffeoylquinic acid, r-coumaric acid, myricetin, catechin, epicatechin, kaempferol, b-carotene using LC-MS/MS; and iron content using ICP-OES. PCA of polyphenols was also performed. Results According to the PCA score plot of polyphenols, the three orange wines made following the Georgian traditional method were grouped together with the orange wine that went through 6 months skin-contact maceration in oak barrels closely located in the plot. While orange wines with skin-maceration times of 7 and 3 days were grouped together and clearly separated from those with a 6 months skin-contact maceration. The red wine sample was clearly separated from all the orange wine samples in the score plot. No correlation between color density, hue tint and iron content was observed since iron was not detected in any sample. Conclusions It seems to be a correlation between the length of the skin-contact maceration and the polyphenolic profile of orange wine. Funding Sources CSUN.

Data Fusion of UV-Vis and FTIR Spectra Combined with Principal Component Analysis for Distinguishing of Andrographis paniculata 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.

Fundamental characterization of wheat gluten

Vital wheat gluten plays an important role in the food industry, especially in baking to help standardize dough properties and improve bread volume. However, a fundamental characterization of a wide variety of vital gluten samples is not available so far. This would be necessary to relate compositional characteristics to the production process. Therefore, we analyzed the content of crude protein, starch, lipids and ash, oil and water absorption capacity, particle size distribution, gluten protein composition and spectroscopic properties of 39 vital gluten samples from 6 different suppliers. Principle component analysis of all analytical parameters revealed that the samples from one specialized vital gluten manufacturer had a different composition and a greater variability compared to all other samples from wheat starch producers. While the composition of vital gluten samples from the same manufacturer was similar and the score plot showed a cluster formation for samples from three suppliers, the variability over all samples was comparatively low. The samples from the other suppliers were too similar altogether so that it was hardly possible to identify clear differences, also related to functionality.

ELECTRONIC NOSE (E-NOSE) DESIGN FOR ARDUINO NANO-BASED HALAL HARAM IDENTIFICATION

It has been successfully designed an Electronic Nose (e-Nose) instrumentation system consisting of 6 MQ gas sensors, namely, MQ2, MQ4, MQ5, MQ7, MQ9, MQ135. The E-nose system is used to identify halal-haram food. This E-Nose system uses an Arduino Nano microcontroller. The Graphic User Interface (GUI) system is built with Visual Studio 2008. Then, the data responses will be evaluated by using 2 patterns recognition methods called Principle Component Analysis (PCA). The classification results can be explained by the value of the score plot on the PCA of the data. PC1 accounts for 19% of the variance, and PC2 accounts for 5% of the variance, data obtained is stored and displayed on personal computers in Excel format. Each sample was tested for up to ten repetitions. The data obtained from the six sensors in the e-nose was processed using Minitab 18 and it was necessary to obtain classification data on lard, pig oil, and sample B, which were fried crackers using pork oil.