Bentonite as a Refining Agent in Waste Cooking Oils Recycling: Flash Point, Density and Color Evaluation

Clarification of waste recycled cooking oil (WCO) is very important in order to refine the intermediate regenerated base resulting from the previous steps. Bentonite has been historically employed as a filling material for oil refining filters due to its easy availability and its cheap price. In the present communication our early results from the filtration of degummed WCO through a pad of bentonite are presented. In particular, the variation of density, flash point and color have been monitored prior and after the filtration process and compared with samples of non-filtered WCO. An early classification of the bentonite employed has been conducted on the basis of FT-IR and XRD measurements.

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Classification of Vegetable Oils by FT-IR

Applied Spectroscopy ◽

10.1366/0003702971941935 ◽

1997 ◽

Vol 51 (8) ◽

pp. 1118-1124 ◽

Cited By ~ 51

Author(s):

Donald B. Dahlberg ◽

Shawn M. Lee ◽

Seth J. Wenger ◽

Julie A. Vargo

Keyword(s):

Ir Spectra ◽

Principal Component ◽

Fast Detection ◽

Signal Correction ◽

Ft Ir ◽

Ir Detection ◽

The Fourier Transform ◽

Cooking Oils ◽

Cis And Trans

The Fourier transform infrared (FT-IR) spectra of 27 brands of 10 types of cooking oils and margarines were measured without temperature control. Attempts to predict the vegetable source and physical properties of these oils failed until wavelength selection and multiplicative signal correction (MSC) were applied to the FT-IR spectra. After pretreatment of the data, principal component analysis (PCA) was totally successful at oil identification, and partial least-squares (PLS) models were able to predict both the refractive indices [standard error of estimation (SEE) 0.0002] and the viscosities (SEE 0.52 cP) of the oils. These models were based predominately on the FT-IR detection of the cis and trans double-bond content of the oils, as well as small amounts of defining impurities in sesame oils. Efforts to use selected wavelengths to discriminate oil sources were only partially successful. These results show the potential utility of FT-IR in the fast detection of substitution or adulteration of products like cooking oils.

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Response Surface Analysis of density and flash point in recycled Waste Cooking Oils

Chemical Data Collections ◽

10.1016/j.cdc.2019.100329 ◽

2020 ◽

Vol 25 ◽

pp. 100329 ◽

Cited By ~ 2

Author(s):

Alberto Mannu ◽

Monica Ferro ◽

Greta Colombo Dugoni ◽

Sebastiano Garroni ◽

Alessandro Taras ◽

...

Keyword(s):

Response Surface ◽

Surface Analysis ◽

Flash Point ◽

Response Surface Analysis ◽

Waste Cooking Oils ◽

Cooking Oils ◽

Recycled Waste

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Disturbance Analysis in the Classification of Objects Obtained from Urban LiDAR Point Clouds with Convolutional Neural Networks

Remote Sensing ◽

10.3390/rs13112135 ◽

2021 ◽

Vol 13 (11) ◽

pp. 2135

Author(s):

Jesús Balado ◽

Pedro Arias ◽

Henrique Lorenzo ◽

Adrián Meijide-Rodríguez

Keyword(s):

Laser Scanning ◽

Ambient Noise ◽

Point Clouds ◽

Density Variation ◽

Density Reduction ◽

Manual Intervention ◽

Point Density ◽

Disturbance Analysis ◽

Street Furniture

Mobile Laser Scanning (MLS) systems have proven their usefulness in the rapid and accurate acquisition of the urban environment. From the generated point clouds, street furniture can be extracted and classified without manual intervention. However, this process of acquisition and classification is not error-free, caused mainly by disturbances. This paper analyses the effect of three disturbances (point density variation, ambient noise, and occlusions) on the classification of urban objects in point clouds. From point clouds acquired in real case studies, synthetic disturbances are generated and added. The point density reduction is generated by downsampling in a voxel-wise distribution. The ambient noise is generated as random points within the bounding box of the object, and the occlusion is generated by eliminating points contained in a sphere. Samples with disturbances are classified by a pre-trained Convolutional Neural Network (CNN). The results showed different behaviours for each disturbance: density reduction affected objects depending on the object shape and dimensions, ambient noise depending on the volume of the object, while occlusions depended on their size and location. Finally, the CNN was re-trained with a percentage of synthetic samples with disturbances. An improvement in the performance of 10–40% was reported except for occlusions with a radius larger than 1 m.

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Quantification and Classification of Diclofenac Sodium Content in Dispersed Commercially Available Tablets by Attenuated Total Reflection Infrared Spectroscopy and Multivariate Data Analysis

Pharmaceuticals ◽

10.3390/ph14050440 ◽

2021 ◽

Vol 14 (5) ◽

pp. 440

Author(s):

Eirini Siozou ◽

Vasilios Sakkas ◽

Nikolaos Kourkoumelis

Keyword(s):

Infrared Spectroscopy ◽

Low Cost ◽

Diclofenac Sodium ◽

Reference Method ◽

Sodium Content ◽

Attenuated Total Reflection ◽

Accuracy And Precision ◽

Significant Difference ◽

Ft Ir

A new methodology, based on Fourier transform infrared spectroscopy equipped with an attenuated total reflectance accessory (ATR FT-IR), was developed for the determination of diclofenac sodium (DS) in dispersed commercially available tablets using chemometric tools such as partial least squares (PLS) coupled with discriminant analysis (PLS-DA). The results of PLS-DA depicted a perfect classification of the tablets into three different groups based on their DS concentrations, while the developed model with PLS had a sufficiently low root mean square error (RMSE) for the prediction of the samples’ concentration (~5%) and therefore can be practically used for any tablet with an unknown concentration of DS. Comparison with ultraviolet/visible (UV/Vis) spectrophotometry as the reference method revealed no significant difference between the two methods. The proposed methodology exhibited satisfactory results in terms of both accuracy and precision while being rapid, simple and of low cost.

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Variation of the Chemical Composition of Waste Cooking Oils upon Bentonite Filtration

Resources ◽

10.3390/resources8020108 ◽

2019 ◽

Vol 8 (2) ◽

pp. 108 ◽

Cited By ~ 10

Author(s):

Alberto Mannu ◽

Gina Vlahopoulou ◽

Paolo Urgeghe ◽

Monica Ferro ◽

Alessandra Del Caro ◽

...

Keyword(s):

Particle Size ◽

Chemical Composition ◽

Solid Phase ◽

Volatile Fraction ◽

X Ray Diffraction ◽

Specific Chemical ◽

Waste Cooking Oils ◽

Main Components ◽

Cooking Oils ◽

Chemical Groups

The chemical composition and the color of samples of waste cooking oils (WCOs) were determined prior to and after filtration on two different pads of bentonite differing in particle size. The volatile fraction was monitored by headspace solid-phase microextraction (HS-SPME) coupled with gas-chromatography, while the variation of the composition of the main components was analyzed by 1H NMR. Both techniques allowed the detection of some decomposition products, such as polymers, terpenes, and derivatives of the Maillard process. The analysis of the chemical composition prior to and after bentonite treatment revealed a tendency for the clays to retain specific chemical groups (such as carboxylic acids or double bonds), independent of their particle size. A pair comparison test was conducted in order to detect the sensory differences of the intensity of aroma between the WCO treated with the two different bentonites. In addition, characterization of the bentonite by means of powder X-ray diffraction (XRD) and thermogravimetric measurements (TG) was performed.

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