Identification Of Different Varieties Research Articles

Identification of Different Varieties of Oil Peony Seeds Combining ICP-MS with Chemometrics and Assessment of Associated Health Risk.

Peony seed is an excellent oil crop, and peony seed oil is rich in unsaturated fatty acids needed by the human body. In this study, inductively coupled plasma mass spectrometry (ICP-MS), fingerprint, and chemometrics, the correlation between the content of inorganic elements in oil peony seeds, their origins, and varieties were investigated. Meanwhile, estimated daily intake (EDI), target hazard quotient (THQ), hazard index (HI), and carcinogenic risks (CR) were combined to evaluate the comprehensive health risks of heavy metals in peony seed oil. The results showed that the difference in the content of inorganic elements could identify the varieties of oil peony seeds. Sr, K, Ca, V, Al, Fe, Cu, Ba, As, Ga, Co, and Rb were the characteristic inorganic elements that played a role in identification. In addition, The THQs and HIs (< 1) for non-carcinogenic elements indicated no risk. The CRs indicated that the carcinogenic harm was negligible. The study concluded that three varieties of peony seed oil would not pose any health hazard. It provided an effective comprehensive method for the identification of oil peony seeds and predicted the potential health risks of edible peony seed oil, providing a reference for the development and consumption of peony seed oil food.

Detection of Strawberry Quality Based on Near Infrared Spectroscopy

In recent years, the food quality and safety testing technology in China has been developing continuously. Strawberry is a popular fruit. The detection of strawberry quality includes the detection of its maturity, sugar content, appearance and other parameters. Through the application of effective detection technology, it can provide effective model support for the internal quality, maturity and identification of different varieties of strawberries. In this paper, near-infrared spectroscopy detection technology is studied. First, this technology and its advantages and disadvantages are introduced, and the application of near-infrared spectroscopy based strawberry quality detection technology is explored.

Identification of the Quality of Tea Leaves by Using Artificial Intelligence Techniques: A Review

This paper summarizes the outcome of the survey carried out for quality identification of a tea leaf and eventually price prediction. Quality identification can allow to categorizing leaf in different grades, which helps the buyer and seller to acquire suitable quality to their need. Price prediction is an important feature, which can bring certainty at price and farmers can be benefitted more for their reasonable good quality. Additionally, if the leaf disease is identified at the initial stage that would also allow farmers to timely resolve the concerned issues and save their corps. In the field of agriculture, this has been always a research area to identify and predict the quality of tea leaves. Various artificial intelligence techniques are hot topics in the field of recognition and their effective combination cannot only solve the problem but also enhance recognition accuracy. Therefore, there is an imminent need for a detailed survey on compiling techniques used for the identification of different varieties of tea plants. In this research, we aim to propose a review of the various techniques which can be utilized for determining the quality and price prediction. The survey is hybrid in nature with a combination of different artificial techniques, which is a suitable approach to target effective tea leaf identification. Further for the classification of tea leaf images, various algorithms can be combined as well to obtain better results and different algorithms can be used for feature extraction on the basis of texture extraction, color extraction, and shape extraction.

Morphological Characterization of Pollen in Some Varieties of Walnut (Juglans regia)

ABSTRACT In this study carried out to determine the pollen characteristics of walnut, Bilecik, Chandler, Fernette, Fernor, Kaman 1, Pedro, Şebin, and Şen 1 varieties were used as material. As a result of scanning electron microscopy (SEM) examination it was stated that the pollen shapes were oblate spheroidal for all the walnut varieties. The pollen of the varieties exhibited diversity in pollen length (37.54–40.52 µm), width (41.10–45.02 µm), pori diameter (2.45–3.61 µm), distance between pori (11.28–14.41 µm) and spinules (0.54–0.73 µm), pore (16.40–23.00) and spinules (20.50–43.50) number, spinule width (0.18–0.26 µm) and length (0.09–0.28 µm). Results showed that in particular, the Fernor had the largest pore diameter, so their number per unit area was low. In contrast, Bilecik had the smallest pore diameter. Distance between pori of this variety was the least. Therefore, a large number of pori was observed on the surface. In addition, spinules sizes were determined to be larger in Şen 1, but smaller in Chandler variety than the others. In particular, these morphological properties of pollen can be used for identification of different varieties. Also, the pollen shape and exine patterning of the varieties studied were illustrated by SEM images.

Rapid identification of the variety of maize seeds based on near-infrared spectroscopy coupled with locally linear embedding.

Maize is the main cereal crop in China. In the process of maize planting, the selection of suitable maize varieties is an important link to achieving a high yield. Because the appearance of maize seeds is very similar, it is difficult to accurately identify their species with the naked eye. In order to realize the rapid identification of different varieties of maize seeds, this paper proposes a rapid identification method of maize varieties based on near-infrared (NIR) spectroscopy coupled with locally linear embedding (LLE) and a support vector machine (SVM). The NIR data, preprocessed by multiple scattering correction (MSC), were dimensionally reduced by LLE, a principal component analysis (PCA), and isometric mapping (Isomap), and combined with SVM to establish a maize variety identification model. The results show that the LLE-SVM model has the best performance, whose classification accuracy and kappa coefficient of the test set can reach 100% and 1.00. The classification accuracy and kappa coefficient of the LLE-SVM model are better than the PCA-SVM model and Isomap-SVM model. Therefore, LLE can reduce the complexity of the model and improve the accuracy of the model. It can be used for the rapid identification of maize varieties and provide a new idea for the classification and identification of other agricultural products.

Rapid Identification of Different Cinnamon Using Coated Direct Inlet Probe Coupled with Atmospheric-Pressure Chemical Ionization Mass Spectrometry

This study demonstrates a rapid method for the identification of volatile and endogenous compounds in cinnamon through a coated direct intracavity probe (CDIP) coupled to the atmospheric pressure chemical ionization (APCI) source for mass spectrometry direct injection. Sixty-seven molecular ions were screened from quadrupole-time-of-flight (Q-ToF) mass spectrometer data as fingerprint ions of four varieties of cinnamons. Electronic nose and gas chromatography-mass spectrometry (GC-MS) as comparisons were used to analyze the cinnamons. The principal component analysis (PCA) results showed that both GC-MS and CDIP-APCI-Q-ToF method could be used to identify different kinds of cinnamons effectively. Part of the fingerprint ions obtained via CDIP-APCI-Q-ToF could be matched to the compounds detected by GC-MS. These findings indicated that CDIP-APCI-Q-ToF direct injection considerably shortened sample analysis time and achieved a faster, more efficient, and sensitive identification of different varieties of cinnamons.

Identification of Anoectochilusroxburghibased on transfer learning

Different varieties of Anoectochilusroxburghi(A. roxburghi) have different medicinal values. However, the identification and the clinical efficacy of A. roxburghi was seriously affe cted by the phenomena of doping and adulteration. Thus, accurate identification of different va rieties of A.roxburghiis important. However, traditional identification methods for A.roxburghii dentification are usually time-consuming, laborious or have low accuracy. Thus, an A.roxburgh i leaf image recognition method based on transfer learning is proposed. The model use convolu tion neural network to extract leaf features of A.roxburghautomatically. Then, the knowledge, l earned by a model from a large-scale image data set ImageNet, is transferred to realize the task of A.roxburghi image recognition with the help of transfer learning.Low training cost and high precision classification of leaf strains is realized in this way. Finally, 368 leaf images of 6 speci es of A.roxburghi are collected for recognition, including dayuanye, xiaoyuanye,and etc.. To te st the transfer effect, the Resnet50 transfer learning model is compared with the the other four models. The comparison results show that the accuracy of Resnet50 transfer learning model rea ches 100% in a quicker speed, which illustrates that the model has good robustness and better p erformance of realizing the accurate recognition and classification of the leaves of A.roxburghi. Moreover, data augmentation helps to reduce the over-fit phenomenon of the transfer learning model.

Raman Spectroscopy Enables Non-Invasive Identification of Peanut Genotypes and Value-Added Traits

Identification of specific genotypes can be accomplished by visual recognition of their distinct phenotypical appearance, as well as DNA analysis. Visual identification (ID) of species is subjective and usually requires substantial taxonomic expertise. Genotyping and sequencing are destructive, time- and labor-consuming. In this study, we investigate the potential use of Raman spectroscopy (RS) as a label-free, non-invasive and non-destructive analytical technique for the fast and accurate identification of peanut genotypes. We show that chemometric analysis of peanut leaflet spectra provides accurate identification of different varieties. This same analysis can be used for prediction of nematode resistance and oleic-linoleic oil (O/L) ratio. Raman-based analysis of seeds provides accurate genotype identification in 95% of samples. Additionally, we present data on the identification of carbohydrates, proteins, fiber and other nutrients obtained from spectroscopic signatures of peanut seeds. These results demonstrate that RS allows for fast, accurate and non-invasive screening and selection of plants which can be used for precision breeding.

Development of Film-Based Electrochemical Integrated Microfluidic Chip for Identification of Food-Borne Pathogens

Food safety is a global issue directly related with human life and foodborne diseases take a major crisis on health. In this reason, a reliable and cost-effective device is required as a solution to the prevention and recognition of problems concerned with health and safety. Conventional and standard bacterial detection methods such as culture and colony counting methods, immunology-based methods and polymerase chain reaction based methods, may take up to several hours or even a few days to yield an answer. Obviously this is inadequate, and recently many researchers are focusing towards the progress of rapid methods. Here, a fully integrated electrochemical microfluidic chip was developed for identification of food-poisoning bacteria, consisting of three compartments such as film-based gene amplification, three-dimensional micro mixer for homogeneous mixing, the detection electrodes for obtaining electrochemical signal. Just after loading genomic DNA into a developed microfluidic chip, which is extracted from cells such as Esterichia coli. O157:H7, Salmonella enteritidis, Staphylococcus aureus, Clostridium perfringens and Listeria monocytogenes, gene is amplified during PCR procedure in film-based reaction chamber followed by mixing amplified PCR product with electroactive reagent. And then electrochemical signal was produced on electrode surface. If there is no corresponding target pathogen, no electrochemical signal was produced. Furthermore, we can detect one or multiple pathogens at the same time by reason that 5 kinds of foodborne pathogens are amplified in parallel and identify the target pathogens simultaneously. With just one-step including sample injection, identification of different varieties of food-borne pathogens was successfully performed in a microfluidic chip within an hour. Owing to its simplicity and rapidity, the developed microfluidic device holds great promise for identification of several species in facility-limited environment or POCT applications.

Morphological diversity of mango germplasm from the upper Athi River region of Eastern Kenya: An analysis based on non- fruit descriptors

Phenotypic variation in plants can be evaluated by morphological characterization using visual attributes. Fruits have been the major descriptors for identification of different varieties of fruit crops. However, even in their absence, farmers, breeders and interested stakeholders require to distinguish between different mango varieties. This study aimed at determining diversity in mango germplasm from the Upper Athi River (UAR) and providing useful alternative descriptors for the identification of different mango varieties in the absence of fruits. A total of 20 International Plant Genetic Resources Institute (IPGRI) descriptors for mango were selected for use in the visual assessment of 98 mango accessions from 15 sites of the UAR region of eastern Kenya. Purposive sampling was used to identify farmers growing diverse varieties of mangoes. Evaluation of the descriptors was performed on-site and the data collected were then subjected to multivariate analysis including Principal Component Analysis (PCA) and Cluster analysis, one- way analysis of variance (ANOVA) and Chi square tests. Results classified the accessions into two major groups corresponding to indigenous and exotic varieties. The PCA showed the first six principal components accounting for 75.12% of the total variance. A strong and highly significant correlation was observed between the color of fully grown leaves, leaf blade width, leaf blade length and petiole length and also between the leaf attitude, color of young leaf, stem circumference, tree height, leaf margin, growth habit and fragrance. Useful descriptors for morphological evaluation were 14 out of the selected 20; however, ANOVA and Chi square test revealed that diversity in the accessions was majorly as a result of variations in color of young leaves, leaf attitude, leaf texture, growth habit, leaf blade length, leaf blade width and petiole length traits. These results reveal that mango germplasm in the UAR has significant diversity and that other morphological traits apart from fruits can be useful in morphological characterization of mango. Keywords: Mango, morphological characterization, Principal Component Analysis, IPGRI, eastern Kenya

Identification of different varieties of Rhei Radix et Rhizoma based on chemical analysis

A HPLC method was established to determine the contents of the five anthraquinones and rhaponticin in the different varieties of Rhei Radix et Rhizoma. The difference existed in different varieties. The results showed that rhein and rhaponticin were marker substances which could be used to distinguish palm leaf groups rhubarb and wave leaf groups rhubarb. Authentic rhubarb didn't contain rhaponticin. Falsify rhubarb contains trace amounts of rhein. Rheum tanguticum contains abundant rhein. The ratio value of the content of rhein to chrysophanol could be used to distinguish R. tanguticum from the other two authentic varieties (R. palmatum and R. officinale). The content of rhaponticin varied largely in different varieties of wave leaf groups rhubarb.

Discrimination of instant coffee by pattern recognition of chemical oscillation fingerprints

A practical and promising method has been developed to identify the different varieties of instant coffee based on the chemical oscillation fingerprints combined with pattern recognition. Chemical oscillation fingerprints originate from the complex and characteristic interaction of the redox constituents and their relative contents in different kinds of biological samples in the chemical oscillation reaction system. Chemometric pattern recognition methods such as principal component analysis (PCA) and cluster analysis were applied to enhance the authenticity of identification of different varieties of instant coffee. The method can fully utilize diversified fingerprint characteristics of the coffee sample. It has the advantages of no pretreatment, simple operation, low cost, rapid analysis, accurate and reliable identification results, and so on. Therefore, nonlinear chemical fingerprints can provide a novel strategy to identify instant coffee, enabling a simple and accurate evaluation of coffee sample quality in a fast way.

Identification of different varieties of sesame oil using near-infrared hyperspectral imaging and chemometrics algorithms.

This study investigated the feasibility of using near infrared hyperspectral imaging (NIR-HSI) technique for non-destructive identification of sesame oil. Hyperspectral images of four varieties of sesame oil were obtained in the spectral region of 874–1734 nm. Reflectance values were extracted from each region of interest (ROI) of each sample. Competitive adaptive reweighted sampling (CARS), successive projections algorithm (SPA) and x-loading weights (x-LW) were carried out to identify the most significant wavelengths. Based on the sixty-four, seven and five wavelengths suggested by CARS, SPA and x-LW, respectively, two classified models (least squares-support vector machine, LS-SVM and linear discriminant analysis,LDA) were established. Among the established models, CARS-LS-SVM and CARS-LDA models performed well with the highest classification rate (100%) in both calibration and prediction sets. SPA-LS-SVM and SPA-LDA models obtained better results (95.59% and 98.53% of classification rate in prediction set) with only seven wavelengths (938, 1160, 1214, 1406, 1656, 1659 and 1663 nm). The x-LW-LS-SVM and x-LW-LDA models also obtained satisfactory results (>80% of classification rate in prediction set) with the only five wavelengths (921, 925, 995, 1453 and 1663 nm). The results showed that NIR-HSI technique could be used to identify the varieties of sesame oil rapidly and non-destructively, and CARS, SPA and x-LW were effective wavelengths selection methods.

Identification of Different Varieties of Oyster Juice Based on the Comparison of Free Amino Acids

To identify different varieties of oyster juice, a study was conducted to compare the free amino acids (FAAs) determined by automatic amino-acid analyzer in Southern oyster juice, Pacific oyster juice and Pearl oyster juice. In 18 amino acids, glutamic acids, glycine, alanine were considered to be the main flavor amino acids in oyster juice according to the method of Taste Activity Value (TAV). Serine, glycine, alanine, arginine, threonine and histidine proved to be the characteristic amino acids in three oyster juice with different Brix degrees. The standard characteristic amino acids content model chart was established, and it was worked as a characteristic index to identify different varieties of oyster juice. According to the result calculated by included angle cosine, the similarity between the characteristic amino acids content model chart of Southern oyster juice and that of a bottle of market oyster juice turned to be 0.9693.

Study on Electrochemical Fingerprints and Identification of Different Varieties of Rice and Mouldy Rice by B‐Z Oscillation Reaction System

AbstractThe electrochemical fingerprints of different varieties of rice and mouldy rice were studied by Belousov‐Zhabotinskii (B‐Z) oscillation reaction system with malonic acid as dissipative material under the condition of constant temperature. The results indicated that the chemical composition of rice may influence the kinetic parameters of the B‐Z oscillation reaction, which lead to the electrochemical fingerprint characteristic parameters and the intuitive information changes obviously. The varieties of rice and mouldy rice can be identified and evaluated by the difference of the electrochemical characteristic information.

Variety Identification of Rice Vinegars Using Visible and Near Infrared Spectroscopy and Multivariate Calibrations

Visible and near infrared spectroscopy was investigated to identify the varieties of rice vinegars based on back propagation neural network and least squares-support vector machine. Five varieties of rice vinegars were prepared. Partial least squares discriminant analysis was implemented for calibration and extraction of partial least squares factors. The factors were used as inputs of back propagation neural network and least squares-support vector machine. Finally, back propagation neural network and least squares-support vector machine models were achieved. The threshold value of prediction was set as ±0.1. An excellent precision and recognition ratio of 100% was achieved by both methods. Simultaneously, certain effective wavelengths were proposed by x-loading weights and regression coefficients. The performance of effective wavelengths was validated and an acceptable result was achieved. The results indicated that visible and near infrared spectroscopy could be used as a rapid and high precision method for the identification of different varieties of rice vinegars.

Isolation and Characterization of 11 New Microsatellite Loci in Erigeron breviscapus (Asteraceae), an Important Chinese Traditional Herb

Erigeron breviscapus (Vant.) Hand.-Mazz. (Asteraceae) is a species endemic to southwestern China and an important traditional Chinese herb for cardiovascular and cerebral vessel diseases. Applying a modified biotin-streptavidin capture method, 11 microsatellite loci were discovered. Polymorphism of each locus was assessed in 24 individuals collected from five wild populations. The number of alleles per locus ranged from 2 to 7, with an average of 4.273. The observed (HO) and expected (HE) heterozygosities varied from 0.250 to 0.958 and from 0.337 to 0.786, respectively. Over half of these loci were successfully amplified in two congeneric species. The developed microsatellite markers will be useful for future population genetics and conservation studies, as well as accurate identification of different varieties.

Varietal Identification in Paddy (Oryza sativa L.) and Moong (Phaseolus mungo L.) by Gel Electrophoresis of Soluble Seed Proteins

Abstract Varietal development and its identification is one of the most important aspects of seed industry and seed trade. With the increase in the number of varieties in each crop, it has become difficult to identify and characterize these varieties on the basis of morphological characters alone. It has led to the exploration of new stable characters including genetic makeup to be used as markers for varietal identification. The present study includes the identification of different varieties (10) of paddy and (6) of moong on the basis of their protein profile. Protein was extracted from dry seeds using Laemmli buffer. The extracted protein samples were run at 150-200 volts for 5 hours using Sodium Dodecyl Sulphate-Polyacrylamide Gel Electrophoresis (SDS-PAGE). After fixation and silver staining varieties were identified according to their differences in the banding pattern and staining intensities. It was observed that among paddy varieties 2 Basmati varieties were showing a clear distinction from the r...

Optical and structural properties of gemmological materials used in works of art and handicraft

Disorder phenomena related to formation conditions of natural and synthetic spinels, both stoichiometric (MgO·Al 2O 3) and those exhibiting different Al/Mg ratios, are investigated by photoluminescence spectroscopy. The same approach appears to be less adequate when dealing with more complex materials, such as beryls (3BeO·Al 2O 3·6SiO 2). On the other hand, Raman spectroscopy allows easy identification of different varieties of beryls and of their inclusions, together with that of widely used imitations. The above techniques are totally non-destructive and do not require any treatment of the samples. The apparatus developed at our laboratories allows investigation of irregularly shaped samples within a large range of sizes.