scholarly journals Fatty Acids-Based Quality Index to Differentiate Worldwide Commercial Pistachio Cultivars

Author(s):  
Mahnaz Esteki ◽  
Parvin Ahmadi ◽  
Yvan Vander Heyden ◽  
Jesus Simal-Gandara

The fatty-acid profiles of five main commercial pistachio cultivars, including Ahmad-Aghaei, Akbari, Chrok, Kalle-Ghouchi and Ohadi, were determined by gas chromatography: palmitic (C16:0), palmitoleic (C16:1), stearic (C18:0), oleic (C18:1), linoleic (C18:2), linolenic (C18:3) arachidic (C20:0) and gondoic (C20:1) acid. Based on the oleic to linoleic acid (O/L) ratio, a quality index was determined for these five cultivars: Ohadi (2.40) < Ahmad-Aghaei (2.60) < Kale-Ghouchi (2.94) < Chrok (3.05) < Akbari (3.66). Principal component analysis (PCA) of the fatty-acid data yielded three significant PCs, which together account for 80.0% of the total variance in the data set. A linear discriminant analysis (LDA) model evaluated with cross validation correctly classified almost all samples: the average percent accuracy for the prediction set was 98.0%. The high predictive power for the prediction set shows the ability to indicate the cultivar of an unknown sample based on its fatty-acid chromatographic fingerprint.

Molecules ◽  
2018 ◽  
Vol 24 (1) ◽  
pp. 58 ◽  
Author(s):  
Mahnaz Esteki ◽  
Parvin Ahmadi ◽  
Yvan Vander Heyden ◽  
Jesus Simal-Gandara

The fatty acid profiles of five main commercial pistachio cultivars, including Ahmad-Aghaei, Akbari, Chrok, Kalle-Ghouchi, and Ohadi, were determined by gas chromatography: palmitic (C16:0), palmitoleic (C16:1), stearic (C18:0), oleic (C18:1), linoleic (C18:2), linolenic (C18:3), arachidic (C20:0), and gondoic (C20:1) acid. Based on the oleic to linoleic acid (O/L) ratio, a quality index was determined for these five cultivars: Ohadi (2.40) < Ahmad-Aghaei (2.60) < Kale-Ghouchi (2.94) < Chrok (3.05) < Akbari (3.66). Principal component analysis (PCA) of the fatty acid data yielded three significant PCs, which together account for 80.0% of the total variance in the dataset. A linear discriminant analysis (LDA) model that was evaluated with cross-validation correctly classified almost all of the samples: the average percent accuracy for the prediction set was 98.0%. The high predictive power for the prediction set shows the ability to indicate the cultivar of an unknown sample based on its fatty acid chromatographic fingerprint.


2000 ◽  
Vol 66 (2) ◽  
pp. 694-699 ◽  
Author(s):  
Peter A. Noble ◽  
Jonas S. Almeida ◽  
Charles R. Lovell

ABSTRACT The microbial community compositions of surface and subsurface marine sediments and sediments lining burrows of marine polychaetes and hemichordates from the North Inlet estuary (near Georgetown, S.C.) were analyzed by comparing ester-linked phospholipid fatty acid (PLFA) profiles with a back-propagating neural network (NN). The NNs were trained to relate PLFA inputs to sediment type outputs (e.g., surface, subsurface, and burrow lining) and worm species (e.g., Notomastus lobatus, Balanoglossus aurantiacus, andBranchyoasychus americana). Sensitivity analysis was used to determine which of the 60 PLFAs significantly contributed to training the NN. The NN architecture was optimized by changing the number of hidden neurons and calculating the cross-validation error between predicted and actual outputs of training and test data. The optimal NN architecture was found to be four hidden neurons with 60-input neurons representing the 60 PLFAs, and four output neurons coding for both sediment types and worm species. Comparison of cross-validation results using NNs and linear discriminant analysis (LDA) revealed that NNs had significantly fewer incorrect classifications (2.7%) than LDA (8.4%). For the NN cross-validation, both sediment type and worm species had 3 incorrect classifications out of 112. For the LDA cross-validation, sediment type and worm species had 7 and 12 incorrect classifications out of 112, respectively. Sensitivity analysis of the trained NNs revealed that 17 fatty acids explained 50% of variability in the data set. These PLFAs were highly different among sediments and burrow types, indicating significant differences in the microbiota.


CORD ◽  
2012 ◽  
Vol 28 (1) ◽  
pp. 5
Author(s):  
J.M.N. Marikkar

A study was carried out to distinguish coconut oil from coconut pairing oil by the application of principal component analysis (PCA) to fatty acid compositional and iodine value data. Five samples of ordinary coconut oil extracted from five different batches of copra and five samples of coconut pairing oil obtained from five batches of dried coconut pairings were employed. Fatty acid composition and iodine values of oil samples were determined individually and the data were analyzed statistically. PCA analysis showed that lauric and oleic acid contents and iodine value data are the most influencing parameters to discriminate coconut oil from coconut pairing oil. Hence, the application of PCA to fatty acid compositional and iodine value data was successful in distinguishing coconut oil from coconut pairing oil.


2019 ◽  
Vol 14 (1) ◽  
pp. 20
Author(s):  
Supriyadi Supriyadi ◽  
Widyatmani Sih Dewi ◽  
Desmiasari Nugrahani ◽  
Adila Azza Rahmah ◽  
Haryuni Haryuni ◽  
...  

Increased rice needs in an extensive use of paddy fields in the Jatipurno, Wonogiri. Managing rice fields can reduce soil quality. Proper management can improve soil quality, Jatipurno has management such as organic, semi-organic and inorganic paddy field management which have a real effect on soil quality. Assessment of soil quality is measured by physical, chemical and biological indicators, where each factor has a different effect. The chemical indicators are often used as the main indicators for determining soil quality, whereas every parameter has the opportunity to be the main indicator. So, biological indicators can play indicators. The main indicators are obtained from the correlation test (p-values &le; 0,05 - &lt; 0,01) and Principal Component Analysis with high value, eigenvalues &gt; 1 have the potential to be used as Minimum Data Sets. The result is biological can be able to use as the Minimum Data Set such as microbial carbon biomass, respiration, and total bacterial colonies. The Soil Quality Index (SQI) of various paddy management practices shows very low to low soil quality values. The management of organic rice systems shows better Soil Quality Index with a score of 0,20 compared to other management. The practice of organic rice management shows that it can improve soil quality.


2020 ◽  
pp. 118-118
Author(s):  
Predrag Mitrovic ◽  
Branislav Stefanovic ◽  
Mina Radovanovic ◽  
Nebojsa Radovanovic ◽  
Dubravka Rajic ◽  
...  

Introduction/Objective. The aim of this study was to analyze the usefulness and accuracy of artificial neural network in the prognosis of infarcted patients with operation. Methods. The thirteen predictor variables per patient were defined as data set. All patients were divided in two groups randomly: training group of 1090 patients and test group of 1090 patients. Evaluation of neural network performance was organized by using of the original data, as well as its complementary test data, containing patient data not used for training the network. Generating a file of comparative results, program compared actual with predict outcome for each patient. Results. All results were compared with 2x2 contingency table constructed from sensitivity, specificity, accuracy and positive-negative prediction. Network was able to predict outcome with accuracy of 96.2%, sensitivity of 78.4%, specificity 100%, positive predictivity 100% and negative predictivity 96%. There was not efficient for prognosis of infarcted patients with operation using linear discriminant analysis (accuracy 68.3%, sensitivity 66.4%, and positive predictivity 30.2%). Conclusion. This study suggest that neural network was better for almost all parameters in outcome prognosis of infarcted patients with operation.


2020 ◽  
Vol 222 (1) ◽  
pp. 305-326
Author(s):  
Hui Wang ◽  
Gary Egbert ◽  
Yusong Yao ◽  
Jiulong Cheng

SUMMARY Ten years (2008–2017) of continuous measurements from 40 electric and 36 magnetic sites collected in China for earthquake prediction research represent a unique EM array data set, which can be used to explore the challenging problem of very long-period MT data acquisition, to study source characteristics, and ultimately to learn about electric conductivity of Earth's mantle beneath East Asia. In this study, we focus on basic noise and signal characteristics in this data set, and on estimation of the MT impedances. We report a novel method to fix the numerous timing errors in the electric data caused by limitations in instrumentation and data acquisition. Then, we use multivariate array analysis to study signal and noise characteristics for periods from 250 s to 3.5 × 105 s (4 d). Signal-to-noise ratios (SNR) are above 30 dB in magnetic fields for the first two dominant modes, which correspond roughly to N–S and E–W quasi-uniform sources. SNRs for electric fields are lower, especially at very long periods, and especially for N–S electric components. There are clear peaks in signal strength at the daily variation (DV) periods, but source structure becomes more complex, and significant biases in MT impedance tensors are more often seen at these periods. The MT quasi-impedance, computed using the closest magnetic site for each electric site, is estimated by robust remote reference techniques (RR) and by using linear combinations of PCA (principal component analysis) modes that best approximate a uniform or plane-wave source (PW). For almost all sites, smooth impedances are obtained for periods up to 104 s using either approach. This result, and a more detailed analysis of impedances estimates obtained with shorter-wavelength (gradient) sources extracted from the array, suggests that source effects in MT impedances are minimal for periods below 104 s, at least at the latitude of China. At many sites curves can be extended a decade further, to 105 s, but here results are improved by carefully omitting DV bands and (at a few sites) with the PW approach. For longer periods (&gt;105 s) SNR is very low in electric field channels at most sites, making estimation challenging. However, at a few sites, even some near big cities (e.g. including a site within 40 km of Beijing) smooth impedance components related to N–S magnetic sources (Zxx and Zyx) are obtained to periods to 3.5 × 105 s (4 d). This result suggests that cultural noise may not be the main impediment to collecting very long-period MT data.


1994 ◽  
Vol 77 (5) ◽  
pp. 1326-1334 ◽  
Author(s):  
Franz Ulberth

Abstract Analysis of the fatty acid (FA) profile of milk fat (MF) by gas-liquid chromatography is widely used to detect adulteration with foreign fats. On the basis of the FA spectra of 352 genuine Austrian MF samples collected over a 4-year period, the effectiveness of concentration ranges of the major FA of MF and of certain FA ratios to identify non-MF/MF mixtures was tested. FA ratios proved useful for the detection of coconut fat in MF and admixture of vegetable oils rich in linoleic acid down to a level of 2%. This approach failed to identify non-MF/MF blends containing beef tallow, lard, olive oil, or palm oil at a level less than 10% commingling. Linear discriminant analysis applied to FA data was successful in distinguishing pure MFfrom adulterated MF. Computer-simulated data were used to derive the discriminant functions. Saturated and un-saturated FA with 18 C atoms were the most useful discriminating variables selected by a stepwise variable selection procedure. More than 95% of a data set composed of pure MF, and non-MF/MF blends containing 3% of either tallow, lard, olive oil, or palm oil were correctly classified. The validity of the classification rule was also tested by 206 gravimetrically prepared fat mixtures. Mixtures containing &gt;3% foreign fat were detected in all cases.


2010 ◽  
Vol 08 (06) ◽  
pp. 995-1011 ◽  
Author(s):  
HAO ZHENG ◽  
HONGWEI WU

Metagenomics is an emerging field in which the power of genomic analysis is applied to an entire microbial community, bypassing the need to isolate and culture individual microbial species. Assembling of metagenomic DNA fragments is very much like the overlap-layout-consensus procedure for assembling isolated genomes, but is augmented by an additional binning step to differentiate scaffolds, contigs and unassembled reads into various taxonomic groups. In this paper, we employed n-mer oligonucleotide frequencies as the features and developed a hierarchical classifier (PCAHIER) for binning short (≤ 1,000 bps) metagenomic fragments. The principal component analysis was used to reduce the high dimensionality of the feature space. The hierarchical classifier consists of four layers of local classifiers that are implemented based on the linear discriminant analysis. These local classifiers are responsible for binning prokaryotic DNA fragments into superkingdoms, of the same superkingdom into phyla, of the same phylum into genera, and of the same genus into species, respectively. We evaluated the performance of the PCAHIER by using our own simulated data sets as well as the widely used simHC synthetic metagenome data set from the IMG/M system. The effectiveness of the PCAHIER was demonstrated through comparisons against a non-hierarchical classifier, and two existing binning algorithms (TETRA and Phylopythia).


Author(s):  
Ioana Feher ◽  
Cornelia Veronica Floare-Avram ◽  
Florina-Dorina Covaciu ◽  
Olivian Marincas ◽  
Romulus Puscas ◽  
...  

Edible mushrooms have been recognized as highly nutritional food for a long time, due to their specific flavor, texture and also for therapeutic effects. This study proposes a new simple approach, based on FT-IR analysis, followed by statistical methods, in order to differentiate three wild mushrooms species from Romanian spontaneous flora, namely Armillaria mellea, Boletus edulis and Cantharellus cibarius. The preliminary data treatment consisted of data set reduction with principal component analysis (PCA), which provided scores for the next methods. Linear discriminant analysis (LDA) manage to 100% classify the three species and the cross validation step of the method returned 97.4% of correctly classified samples. Only one A. mellea sample overlapped on B. edulis group. When kNN was used in the same manner as LDA, the overall percent of correctly classified samples from the training step was 86.21%, while for holdout set the percent raised at 94.74%. The lowered values obtained for the training set was due to one C. cibarius sample, two B. edulis and five A. mellea, which were placed to other species. Anyway, for holdout sample set, only one sample from B. edulis was misclassified. The fuzzy c-means clustering (FCM) analysis successfully classified investigated mushroom samples according to their species, meaning that in every partition the predominant specie had the biggest DOMs, while samples belonging to other specie had lower DOMs.


Mljekarstvo ◽  
2021 ◽  
Vol 71 (2) ◽  
pp. 83-94
Author(s):  
Jasmina Vitas ◽  

Milk-based kombucha beverages were obtained conducting kombucha lead fermentation of milk. In order to discriminate the analysed samples and to detect similarities or dissimilarities among them in the space of experimentally determined variables, hierarchical cluster analysis (HCA) and principal component analysis (PCA) were applied. Linear discriminant analysis (LDA) was conducted on the raw data set in order to find a rule for allocating a new sample of unknown origin to the correct group of samples. In the space of the variables analysed by HCA, the dominant discriminating factor for the studied samples of kombucha beverages is the milk fat (MF) content, followed by total unsaturated fatty acids content (TUFA), monounsaturated fatty acids content (MUFA) and polyunsaturated fatty acids content (PUFA). The samples with 0.8 and 1.6% milk fat belong to the same cluster in the space of the analysed variables due to similarities in their AADPPH. It was determined by LDA that there was the biggest difference in quality between the groups of products with winter savoury and stinging nettle, while the highest similarity is between groups of products with wild thyme and peppermint regarding their pH values and antioxidant activity expressed as AADPPH.


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