Gean Bezerra da Costa, David Douglas Sousa de Fernandes, Germano Véras, Paulo Henrique Gonçalves Dias Diniz, Amanda Duarte Gondim
{"title":"将近红外光谱与 DD-SIMCA 鉴定和 iSPA-PLS-DA 结合起来，鉴别生物柴油/柴油混合物中生物柴油的乙基路线和油类原料","authors":"Gean Bezerra da Costa, David Douglas Sousa de Fernandes, Germano Véras, Paulo Henrique Gonçalves Dias Diniz, Amanda Duarte Gondim","doi":"10.1002/aocs.12744","DOIUrl":null,"url":null,"abstract":"<p>The main biofuels produced on an industrial large scale are biodiesel and ethanol, which are the most economically viable and widely implemented solutions to replace conventional fossil fuels from a greener and more sustainable perspective. In such a scenario, there is an opportunity to produce fully renewable biodiesel using ethanol instead of methanol, which is mainly derived from fossil resources. In this paper, near-infrared (NIR) spectroscopy was used to discriminate biodiesel/diesel (B7) blends regarding the synthesis route and oil feedstock of biodiesels simultaneously. Data-Driven Soft Independent Modeling of Class Analogy (DD-SIMCA) authenticated correctly all ethyl B7 (target) samples into the acceptance area, while rejected all non-target samples, implying in an efficiency of 100%. Additionally, Partial Least Squares-Discriminant Analysis coupled with interval selection by the Successive Projections Algorithm (iSPA-PLS-DA) discriminated all ethyl B7 samples correctly, considering cottonseed, sunflower, and soybean as oil feedstocks. Moreover, only one ethyl cottonseed B7 sample was incorrectly discriminated when methyl B7 samples from the same three oil feedstocks were included in the model. As advantages, the proposed analytical methodology contributes to the United Nations' Sustainable Development Goal (SDG) #7 (affordable and clean energy) as well as aligns with the principles of Green Analytical Chemistry.</p>","PeriodicalId":17182,"journal":{"name":"Journal of the American Oil Chemists Society","volume":"101 2","pages":"187-196"},"PeriodicalIF":1.9000,"publicationDate":"2023-09-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Combining NIR spectroscopy with DD-SIMCA for authentication and iSPA-PLS-DA for discrimination of ethyl route and oil feedstocks of biodiesels in biodiesel/diesel blends\",\"authors\":\"Gean Bezerra da Costa, David Douglas Sousa de Fernandes, Germano Véras, Paulo Henrique Gonçalves Dias Diniz, Amanda Duarte Gondim\",\"doi\":\"10.1002/aocs.12744\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p>The main biofuels produced on an industrial large scale are biodiesel and ethanol, which are the most economically viable and widely implemented solutions to replace conventional fossil fuels from a greener and more sustainable perspective. In such a scenario, there is an opportunity to produce fully renewable biodiesel using ethanol instead of methanol, which is mainly derived from fossil resources. In this paper, near-infrared (NIR) spectroscopy was used to discriminate biodiesel/diesel (B7) blends regarding the synthesis route and oil feedstock of biodiesels simultaneously. Data-Driven Soft Independent Modeling of Class Analogy (DD-SIMCA) authenticated correctly all ethyl B7 (target) samples into the acceptance area, while rejected all non-target samples, implying in an efficiency of 100%. Additionally, Partial Least Squares-Discriminant Analysis coupled with interval selection by the Successive Projections Algorithm (iSPA-PLS-DA) discriminated all ethyl B7 samples correctly, considering cottonseed, sunflower, and soybean as oil feedstocks. Moreover, only one ethyl cottonseed B7 sample was incorrectly discriminated when methyl B7 samples from the same three oil feedstocks were included in the model. 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Combining NIR spectroscopy with DD-SIMCA for authentication and iSPA-PLS-DA for discrimination of ethyl route and oil feedstocks of biodiesels in biodiesel/diesel blends
The main biofuels produced on an industrial large scale are biodiesel and ethanol, which are the most economically viable and widely implemented solutions to replace conventional fossil fuels from a greener and more sustainable perspective. In such a scenario, there is an opportunity to produce fully renewable biodiesel using ethanol instead of methanol, which is mainly derived from fossil resources. In this paper, near-infrared (NIR) spectroscopy was used to discriminate biodiesel/diesel (B7) blends regarding the synthesis route and oil feedstock of biodiesels simultaneously. Data-Driven Soft Independent Modeling of Class Analogy (DD-SIMCA) authenticated correctly all ethyl B7 (target) samples into the acceptance area, while rejected all non-target samples, implying in an efficiency of 100%. Additionally, Partial Least Squares-Discriminant Analysis coupled with interval selection by the Successive Projections Algorithm (iSPA-PLS-DA) discriminated all ethyl B7 samples correctly, considering cottonseed, sunflower, and soybean as oil feedstocks. Moreover, only one ethyl cottonseed B7 sample was incorrectly discriminated when methyl B7 samples from the same three oil feedstocks were included in the model. As advantages, the proposed analytical methodology contributes to the United Nations' Sustainable Development Goal (SDG) #7 (affordable and clean energy) as well as aligns with the principles of Green Analytical Chemistry.
期刊介绍：
The Journal of the American Oil Chemists’ Society (JAOCS) is an international peer-reviewed journal that publishes significant original scientific research and technological advances on fats, oils, oilseed proteins, and related materials through original research articles, invited reviews, short communications, and letters to the editor. We seek to publish reports that will significantly advance scientific understanding through hypothesis driven research, innovations, and important new information pertaining to analysis, properties, processing, products, and applications of these food and industrial resources. Breakthroughs in food science and technology, biotechnology (including genomics, biomechanisms, biocatalysis and bioprocessing), and industrial products and applications are particularly appropriate.
JAOCS also considers reports on the lipid composition of new, unique, and traditional sources of lipids that definitively address a research hypothesis and advances scientific understanding. However, the genus and species of the source must be verified by appropriate means of classification. In addition, the GPS location of the harvested materials and seed or vegetative samples should be deposited in an accredited germplasm repository. Compositional data suitable for Original Research Articles must embody replicated estimate of tissue constituents, such as oil, protein, carbohydrate, fatty acid, phospholipid, tocopherol, sterol, and carotenoid compositions. Other components unique to the specific plant or animal source may be reported. Furthermore, lipid composition papers should incorporate elements of yeartoyear, environmental, and/ or cultivar variations through use of appropriate statistical analyses.
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