Functional approach and agro‐climatic information to improve the estimation of olive oil fatty acid content from near‐infrared data

The comparison of fatty acid content in Camellia oleifera (L.) Kuntze oil and olive oil was done by GC. C. oleifera oil contained more C18:1n9c, C18:2n6c, C20:1 and C22:1n9 than olive oil. Olive oil did not contain C21:0 and C24:1 but more C8:0, C16:0, C16:1, C18:0, C18:3n6, C18:3n3, C20:2, C22:0, C23:0 and C24:0 than C. oleifera oil, C. oleifera oil did not contain C20:2. C. oleifera oil and olive oil are beneficial for human health for their unsaturated fatty acids content and C. oleifera oil is healthy than olive oil. DOI: http://dx.doi.org/10.3329/bjb.v44i1.22741 Bangladesh J. Bot. 44(1): 155-157, 2015 (March)

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Chemical characterization and storage stability of extra virgin olive oil extracted from Derik Halhalı cultivar

Croatian Journal of Food Science and Technology ◽

10.17508/cjfst.2019.11.1.08 ◽

2019 ◽

Vol 11 (1) ◽

pp. 52-58

Author(s):

Esmael Ghanbari Shendi ◽

Dilek Sivri Özay ◽

Mücahit Taha Özkaya ◽

Nim,eti Feyza Üstünel

Keyword(s):

Fatty Acid ◽

Free Fatty Acid ◽

Olive Oil ◽

Free Fatty Acid Content ◽

Acid Content ◽

Fatty Acid Content ◽

Virgin Olive Oil ◽

Extra Virgin Olive Oil ◽

Processing Unit ◽

Uv Absorbance

Turkish olive cultivar known as “Halhalı” that is locally grown in Mardin (Derik) province, situated in the southeast Anatolia, was used for virgin olive oil (VOO) production. Halhalı olive was processed in the “Mobile Olive Oil Processing Unit” (TEM Oliomio 500-2GV, Italy) designed as the first mobile olive mill in Turkey. Some chemical and physical properties (colour, UV absorbance values, free fatty acid content, peroxide value, phenolic and tocopherol profiles) were determined and monitored during one year of storing in the dark at room temperature once in every three months. Results showed that up to the eighth month of storage, free fatty acid content, peroxide and UV-absorption values (K232 and K232 values) did not exceed the limits reported by International Olive Council (IOC) and olive oils were categorized as Extra Virgin Olive Oil (EVOO). Colour changed from green to yellow and UV absorbance values altered during storage. Total phenol and vitamin E (α- tocopherol) contents decreased by 18% and 16.38%, respectively. Luteolin and apigenin were the most abundant phenolics and their contents decreased up to 22% and 28% during storing, respectively.

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51 Feedstuff fatty acid content, variation, techniques and implications on practical animal nutrition

Journal of Animal Science ◽

10.1093/jas/skz122.047 ◽

2019 ◽

Vol 97 (Supplement_2) ◽

pp. 25-26

Author(s):

Cliff Ocker

Keyword(s):

Fatty Acid ◽

Acid Content ◽

Near Infrared ◽

Animal Health ◽

Fatty Acid Content ◽

Infrared Light ◽

Near Infrared Reflectance ◽

Wet Chemistry ◽

Peak Identification ◽

The Mean

Abstract Fatty acid nutrition in ruminants, dairy cattle primarily, has increased as a point of emphasis with nutrition formulation in the past decade, as the diet fatty acid profile and metabolism has been found to impact milk fat concentration and animal health. Both the fatty acid supply and rumen degradation warrant further investigation in dairy diets for improved formulation strategies in the future. As supply and degradation are better understood, improved formulation approaches will be possible now that routine feed fatty acid measures have become more practical with the development of near-infrared reflectance spectroscopy (NIR) models at commercial feed analysis laboratories. NIR model development techniques vary; however, the general approach is to calibrate against a wide ranging database of feedstuff wet chemistry measures using a partial least squares approach. Models relate spectral observations (i.e. reflectance at a specific near-infrared light wavelength) to wet chemistry observations. NIR models should reflect both the mean and variation observed in the wet chemistry database. The NIR models developed by Rock River Laboratory, and resulting feed library database information presented in Table 1, were developed by calibrating against feedstuff chemistry performed at the Lock laboratory with Michigan State University. Wet chemistry fatty acid determination by analytical laboratories, using gas chromatography techniques against known fatty acid standards, deserves further discussion to agree upon peak identification schemes. Differences in peak identification from one laboratory to the another will result in different total fatty acid measures and NIR models. The fatty acid content and profile coefficients of variation, determined from the mean and standard deviations presented in Table 1, range from less than 10 to over 100% of the mean with an average CV of 27%. This suggests substantial variation is present in commercial feeds, and opportunities may exist to better account for variation and fatty acid supply in dairy diets.

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Automated Flow Injection Spectrophotometric Determination of Olive Oil Free Fatty Acid Content and Comparison with FT-IR Method

Spectroscopy of Biological Molecules: Modern Trends ◽

10.1007/978-94-011-5622-6_225 ◽

1997 ◽

pp. 497-498

Author(s):

Panayotis G. Nouros ◽

Constantinos A. Georgiou ◽

Moschos G. Polissiou

Keyword(s):

Fatty Acid ◽

Free Fatty Acid ◽

Olive Oil ◽

Spectrophotometric Determination ◽

Flow Injection ◽

Free Fatty Acid Content ◽

Acid Content ◽

Fatty Acid Content ◽

Ft Ir

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Determination of Fatty Acid Content of Rice during Storage Based on Feature Fusion of Olfactory Visualization Sensor Data and Near-Infrared Spectra

Sensors ◽

10.3390/s21093266 ◽

2021 ◽

Vol 21 (9) ◽

pp. 3266

Author(s):

Hongping Lu ◽

Hui Jiang ◽

Quansheng Chen

Keyword(s):

Fatty Acid ◽

Acid Content ◽

Near Infrared ◽

Feature Fusion ◽

Fatty Acid Content ◽

Sensor Data ◽

Detection Accuracy ◽

Model Based ◽

Single Technique ◽

Fusion Feature

This study innovatively proposes a feature fusion technique to determine fatty acid content during rice storage. Firstly, a self-developed olfactory visualization sensor was used to capture the odor information of rice samples at different storage periods and a portable spectroscopy system was employed to collect the near-infrared (NIR) spectra during rice storage. Then, principal component analysis (PCA) was performed on the pre-processed olfactory visualization sensor data and the NIR spectra, and the number of the best principal components (PCs) based on the single technique model was optimized during the backpropagation neural network (BPNN) modeling. Finally, the optimal PCs were fused at the feature level, and a BPNN detection model based on the fusion feature was established to achieve rapid measurement of fatty acid content during rice storage. The experimental results showed that the best BPNN model based on the fusion feature had a good predictive performance where the correlation coefficient (RP) was 0.9265, and the root mean square error (RMSEP) was 1.1005 mg/100 g. The overall results demonstrate that the detection accuracy and generalization performance of the feature fusion model are an improvement on the single-technique data model; and the results of this study can provide a new technical method for high-precision monitoring of grain storage quality.

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