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

IF 1.9 4区 农林科学 Q3 CHEMISTRY, APPLIED Journal of the American Oil Chemists Society Pub Date : 2023-09-12 DOI:10.1002/aocs.12744
Gean Bezerra da Costa, David Douglas Sousa de Fernandes, Germano Véras, Paulo Henrique Gonçalves Dias Diniz, Amanda Duarte Gondim
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Abstract

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.

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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
大规模工业化生产的生物燃料主要是生物柴油和乙醇,它们是从更环保、更可持续的角度取代传统化石燃料的最经济可行和最广泛实施的解决方案。在这种情况下,有机会利用乙醇生产完全可再生的生物柴油,而不是主要来自化石资源的甲醇。本文利用近红外光谱同时对生物柴油/柴油(B7)混合物的合成路线和石油原料进行了鉴别。数据驱动的类比软独立建模(DD-SIMCA)正确鉴定了所有 B7 乙酯(目标)样品,将其纳入接受区域,同时剔除了所有非目标样品,这意味着效率达到了 100%。此外,考虑到棉籽、向日葵和大豆是油料原料,偏最小二乘判别分析结合连续投影算法的区间选择(iSPA-PLS-DA)能正确判别所有 B7 乙酯样品。此外,当模型中包含来自同样三种油料的甲基 B7 样品时,只有一个棉籽 B7 乙酯样品的判别是错误的。作为优势,拟议的分析方法有助于实现联合国可持续发展目标(SDG)#7(负担得起的清洁能源),并符合绿色分析化学的原则。
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来源期刊
CiteScore
4.10
自引率
5.00%
发文量
95
审稿时长
2.4 months
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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