Fourier transform infrared spectroscopy (FTIR) and multivariate analysis for identification of different vegetable oils used in biodiesel production

Acute Oral Toxicity and Genotoxicity Test and Evaluation of Cinnamomum camphora Seed Kernel Oil

Cinnamomum camphora seed kernel oil (CCSKO) is one of the important natural medium chain triglycerides (MCT) resources, with more than 95.00% of medium chain fatty acids found in the world, and has various physiological effects. However, CCSKO has not been generally recognized as a safe oil or new food resource yet. The acute oral toxicity test and a standard battery of genotoxicity tests (mammalian erythrocyte micronucleus test, Ames test, and in vitro mammalian cell TK gene mutation test) of CCSKO as a new edible plant oil were used in the study. The results of the acute oral toxicity test showed that CCSKO was preliminary non-toxic, with an LD₅₀ value higher than 21.5 g/kg body weight. In the mammalian erythrocyte micronucleus test, there was no concentration-response relationship between the dose of CCSKO and micronucleus value in polychromatic erythrocytes compared to the negative control group. No genotoxicity was observed in the Ames test in the presence or absence of S9 at 5000 μg/mL. In vitro mammalian cell TK gene mutation test showed that CCSKO did not induce in vitro mammalian cell TK gene mutation in the presence or absence of S9 at 5000 μg/mL. These results indicated that CCSKO is a non-toxic natural medium-chain oil.

Investigations on Storage and Oxidative Stability of Biodiesel from Different Feedstocks Using the Rancimat Method, Infrared Spectroscopy, and Chemometry

Biodiesel can be altered when exposed to air, light, temperature, and humidity. Other factors, such as microbial or inorganic agents, also interfere with the quality of the product. In the present work, the Rancimat method and mid-infrared spectroscopy associated with chemometry, were used to identify the oxidation process of biodiesel from different feedstocks and to evaluate the antioxidant activity of butylated hydroxytoluene. The study was carried out in four steps: preparation of biodiesel samples with and without the antioxidant agent, degradation of the samples under the effect of light and heating at 70 °C, measurements of the induction period, obtention of infrared spectra, and multivariate analysis. The Fourier transform mid-infrared spectroscopy was used in combination with multivariate analysis, using techniques such as principal component analysis (PCA) and hierarchical clustering analysis (HCA). The Rancimat results showed that babassu biodiesel has a higher resistance to oxidative degradation, while chicken biodiesel is the most susceptible to degradation; on the other hand, the antioxidant activity was more effective with chicken biodiesel, demonstrating that the antioxidant effect depends on the feedstock used in the production of biodiesel. The oxidative stability of babassu oil-, corn oil-, and chicken fat-based biodiesels decreased during storage both in the presence of light and at high temperature. Prior to PCA, all spectra were pre-processed with a combination of Savitzky-Golay smoothing filter with a 7-point window, baseline correction, and mean-centered data. The use of mid-infrared spectroscopy associated with PCA revealed the first two components to explain the greater variability of data, representing over 75% of total variation for all analyzed systems. In addition, it was able to separate the biodiesel samples according to the fatty acid profile of its feedstock, as well as the type of degradation to which it was subjected, the same being confirmed by HCA.

Potential Effects on Human Safety and Health from Infrasound and Audible Frequencies Generated by Vibrations of Diesel Engines Using Biofuel Blends at the Workplaces of Sustainable Engineering Systems

Employees involved in various occupational environments that include vibration machines and any kind of vehicles are adversely subjected to multiple source noise. Thus, the corresponding noise frequencies (and mainly the infrasound ones) present high interest, especially from the viewpoint of sustainability, due to the potential effects on human safety and health (H_S&H) in sustainable engineering projects. Moreover, the occupational safety and health (OSH) visualization (a fact of unveiling the social dimension of sustainability) of occupational workplaces (by evaluating the infrasound and audible noise frequencies generated by diesel engines) could help a safety officer to lessen crucial risk factors in the OSH field and also to protect, more efficiently, the employees by taking the most essential safety measures. This study (i) suggests a technique to determine the infrasound and audible sound frequencies produced due to vibrations of diesel engines, by using biofuels (i.e., sustainable utilization of resources), in order to evaluate potential effects on human safety and health at the workplaces of sustainable engineering projects, and (ii) it ultimately aims to contribute to the improvement of the three “sustainability pillars” (economy, social, and environmental). Therefore, it provides experimental results of the frequency of the noise (regarding the infrasound and audible spectrum) that a diesel motor generates by vibration, in the frame of using different engine rpms (850, 1150, and 2000) and a variety of biofuel mixtures (B20-D80, B40-D60, B60-D40, and B80-D20). The article shows that the fuel blend meaningfully affects the generated noise, and more particularly, the usage of biofuel blends coming from mixing diesel oil with biodiesel (a fact of the emerging environmental dimension of sustainability) can produce various noise frequencies, which are determined in the infrasound and audible spectra (~10–23 Hz). The suggested technique, by ameliorating the OSH situation, doubtless will help enterprises to achieve the finest allocation of limited financial resources (a fact corresponding to the economic dimension of sustainability), allowing financial managers to have more available budget for implementing other risk-reduction projects.

Learning a Fully Connected U-Net for Spectrum Reconstruction of Fourier Transform Imaging Spectrometers

Fourier transform imaging spectrometers (FTISs) are widely used in global hyperspectral remote sensing due to the advantages of high stability, high throughput, and high spectral resolution. Spectrum reconstruction (SpecR) is a classic problem of FTISs determining the acquired data quality and application potential. However, the state-of-the-art SpecR algorithms were restricted by the length of maximum optical path difference (MOPD) of FTISs and apodization processing, resulting in a decrease in spectral resolution; thus, the applications of FTISs were limited. In this study, a deep learning SpecR method, which directly learned an end-to-end mapping between the interference/spectrum information with limited MOPD and without apodization processing, was proposed. The mapping was represented as a fully connected U-Net (FCUN) that takes the interference fringes as the input and outputs the highly precise spectral curves. We trained the proposed FCUN model using the real spectra and simulated pulse spectra, as well as the corresponding simulated interference curves, and achieved good results. Additionally, the performance of the proposed FCUN on real interference and spectral datasets was explored. The FCUN could obtain similar spectral values compared with the state-of-the-art fast Fourier transform (FFT)-based method with only 150 and 200 points in the interferograms. The proposed method could be able to enhance the resolution of the reconstructed spectra in the case of insufficient MOPD. Moreover, the FCUN performed well in visual quality using noisy interferograms and gained nearly 70% to 80% relative improvement over FFT for the coefficient of mean relative error (MRE). All the results based on simulated and real satellite datasets showed that the reconstructed spectra of the FCUN were more consistent with the ideal spectrum compared with that of the traditional method, with higher PSNR and lower values of spectral angle (SA) and relative spectral quadratic error (RQE).

An improved Method for Determining Transient Fuel Dilution of Oil in an Internal-Combustion Engine Using Laser-Induced Florescence and Multivariate Least Square Calibration

An optical diagnostic, based on laser-induced fluorescence (LIF), has been developed for on-engine measurements of real-time fuel dilution of engine oil or fuel in oil (FIO). Fuel dilution of oil is broadly relevant to advancing engine technology including durability, calibration, and catalyst-system management, and believed to promote destructive stochastic pre-ignition (SPI) during high-load engine operations. While standard (e.g., ASTM D3524-90) methods are not capable of real-time transient measurements, the LIF technique resolves transient dilution on the minutes time scale. We have expanded on our original FIO instrument development by introducing an improved analysis based on multivariate least square chemometrics analysis. The measurement uses a fuel dye (180-1300 parts per million, by mass) and monitors for its presence in the oil using 532 nm excitation and LIF. While the original FIO instrument utilized a two-color ratio method for analysis, the improved chemometric analysis uses the fully resolved LIF dye spectra to provide better predictive FIO accuracy (>92%) over a wide FIO range (1.5-14%) typical of engine application. We also investigate the effect of oil temperature on the LIF signal. Limited engine applications for demonstrating and validating the improved FIO instrument are shown, and the related data used to quantify practical detection limit and sensitivity. The improved analysis is insensitive to laser power fluctuation and change in detector integration time, providing an excellent FIO sensitivity (1-2%) and detection limit (0.01 %FIO) over a wide range of loads and injection timings, illustrating this updated approach to be a promising tool for advancing engine technology.

The application of parallel processing in the selection of spectral variables in beer quality control

Parallel data analysis was investigated to improve performance in variable selection and to develop predictive models for beer quality control. A set of spectral near infrared (NIR) data from 60 beer samples and its primitive extracts as the original concentration was used. The dataset was distributed to Raspberry Pi 3 Model B devices connected to a network that was running a Machine Learning service. With more than 4 devices acting in parallel, it was possible to reduce time in 57% to find the best linear regression coefficient (0.999) with the lower RMSECV (0.216) if compared to a singular desktop computer. Thus, parallel processing can significantly reduce the time to indicate the best model fitted during the variable's selection.

Detection Method of Falsified Medicines by Using a Low-Cost Raman Scattering Spectrometer Combined with Soft Independent Modeling of Class Analogy and Partial Least Squares Discriminant Analysis

There are many reports of falsified medicines that may cause harm to patients. A rapid and simple method of identifying falsified medicines that could be used in the field is required. Although Raman scattering spectroscopy has become popular as a non-destructive analysis, few validation experiments on falsified medicines that are actually distributed on the market have been conducted. In this study, we validated a discriminant analysis using an ultra-compact, portable, and low-cost Raman scattering spectrometer combined with multivariate analysis. The medicines were three types of erectile dysfunction therapeutic tablet and one type of antifungal tablet: tadalafil (Cialis), vardenafil hydrochloride (Levitra), sildenafil citrate (Viagra), and fluconazole (Diflucan), which is sometimes advertised as female Viagra. For each medicine, the authentic standard product and products obtained by personal import via the internet (genuine or falsified) were used. Discriminant analyses were performed on the Raman spectra combined with soft independent modeling of class analogy (SIMCA) and partial least squares discriminant analysis (PLS-DA). It was possible to identify all falsified samples by SIMCA using the standard product model for all four products. Using the PLS-DA using the PLS models of the four standard products, falsified Levitra and Diflucan samples were classified correctly, although some falsified Cialis and all Viagra samples also belonged to the standard class. In this study, SIMCA might be more suitable than PLS-DA for identifying falsified medicines. A spectroscopic module that combines the low-cost Raman scattering spectroscopy with SIMCA might contribute to the rapid identification of falsified medicines in the field.

Improvement of lipase biochemical properties via a two-step immobilization method: Adsorption onto silicon dioxide nanoparticles and entrapment in a polyvinyl alcohol/alginate hydrogel

In this study, the factors affecting lipase adsorption onto SiO₂ nanoparticles including SiO₂ nanoparticles amounts (8, 19 and 30 mg/mL), lipase concentrations (30, 90 and 150 μg/mL), adsorption temperatures (5, 20 and 35 °C) and adsorption times (1, 12.5 and 24 h) were optimized using central composite design. The optimal conditions were determined as a SiO₂ nanoparticles amount of 8.5-14 mg/ml, a lipase concentration of 106-116 μg/mL, an adsorption temperature of 20 °C and an adsorption time of 12.5 h, which resulted in a specific activity and immobilization efficiency of 20,000 (U/g protein) and 60 %, respectively. The lipase adsorbed under optimal conditions (SiO₂-lipase) was entrapped in a PVA/Alg hydrogel, successfully. FESEM and FTIR confirmed the two-step method of lipase immobilization. The entrapped SiO₂-lipase retained 76.5 % of its initial activity after 30 days of storage at 4 °C while adsorbed and free lipase retained only 43.4 % and 13.7 %, respectively. SiO₂-lipase activity decreased to 34.43 % after 10 cycles of use, while the entrapped SiO₂-lipase retained about 64.59 % of its initial activity. Compared to free lipase, the K_m values increased and decreased for SiO₂-lipase and entrapped SiO₂-lipase, respectively. V_max value increased for both SiO₂-lipase and entrapped SiO₂-lipase.

Biodiesel production from palm olein: A sustainable bioresource for Nigeria

Dangerous environmental consequences and market unpredictability of fossil fuels have necessitated the need for sustainable large-scale production of biofuel in Nigeria. Unrefined palm oil (UPO) is a significant product of commercially available oil palm plants in the country. This study experimentally investigates the production of biodiesel from refined, bleached and deodorised (RBD) palm olein extracted from UPO obtained from batch reactors. The transesterification process of the RBD palm olein with methanol and in the presence of potassium hydroxide (KOH) catalyst produced biodiesel with a 62.5% yield, thus confirming its feasibility for mass production. The derived biodiesel has properties equivalent to ASTM D792 standard for biodiesel fuels.

Enhancement of biochemical aspects of lipase adsorbed on halloysite nanotubes and entrapped in a polyvinyl alcohol/alginate hydrogel: strategies to reuse the most stable lipase

Entrapment of halloysite nanotubes (HNTs) loaded with enzyme, into a polymer matrix (PVA/Alg), is a way to produce an environment surrounding the adsorbed enzyme molecules which improves the enzyme properties such as storage and operational stability. Hence, in this study, we optimised the factors affecting lipase adsorption onto halloysite nanotubes including halloysite amounts (5, 42.5 and 80 mg), lipase concentrations (30, 90 and 150 µg/ml), temperatures (5, 20 and 35 °C) and adsorption times (30, 165 and 300 min). The optimal conditions were determined as an halloysite amount of 50 to 80 mg, a lipase concentration of 30 to 57 μg/ml, an adsorption temperature of 20 °C and an adsorption time of 165 min, which resulted in a specific activity and adsorption efficiency of 15,000 (U/g protein) and 70%, respectively. Then, lipase adsorbed under optimal conditions was entrapped in a PVA/Alg hydrogel. The formation mechanism of immobilized lipase was investigated by FESEM and FTIR. Subsequent entrapment of adsorbed lipase improved the lipase storage and operational stability. K_m, V_max, K_cat and K_cat/K_m values showed an increase in the entrapped HNT-lipase performance in comparison with the free and adsorbed lipase.

High Resolution and Fast Processing of Spectral Reconstruction in Fourier Transform Imaging Spectroscopy

High-resolution spectrum estimation has continually attracted great attention in spectrum reconstruction based on Fourier transform imaging spectroscopy (FTIS). In this paper, a parallel solution for interference data processing using high-resolution spectrum estimation is proposed to reconstruct the spectrum in a fast high-resolution way. In batch processing, we use high-performance parallel-computing on the graphics processing unit (GPU) for higher efficiency and lower operation time. In addition, a parallel processing mechanism is designed for our parallel algorithm to obtain higher performance. At the same time, other solving algorithms for the modern spectrum estimation model are introduced for discussion and comparison. We compare traditional high-resolution solving algorithms running on the central processing unit (CPU) and the parallel algorithm on the GPU for processing the interferogram. The experimental results illustrate that runtime is reduced by about 70% using our parallel solution, and the GPU has a great advantage in processing large data and accelerating applications.

Diagnosis of malignant pleural mesothelioma from pleural fluid by Fourier transform-infrared spectroscopy coupled with chemometrics

This study was conducted to differentiate malignant pleural mesothelioma (MPM) from lung cancer (LC) and benign pleural effusion (BPE) from pleural fluids using the diagnostic power of Fourier transform-infrared spectroscopy with attenuated total reflectance mode coupled with chemometrics. Infrared spectra of MPM (n  =  24), LC (n  =  20), and BPE (n  =  25) were collected, and hierarchical cluster analysis (HCA) and principal component analysis (PCA) were applied to their spectra. HCA results indicated that MPM was differentiated from LC with 100% sensitivity and 100% specificity and from BPE, with 100% sensitivity and 88% specificity, which were also confirmed by PCA score plots. PCA loading plots indicated that these separations originated mainly from lipids, proteins, and nucleic acids-related spectral bands. There was significantly higher lipid, protein, nucleic acid, and glucose contents in the MPM and LC. However, the significant changes in triglyceride and cholesterol ester content, protein and nucleic acid structure, a lower membrane fluidity, and higher membrane order were only observed in the MPM. To check the classification success of some test samples/each group, soft independent modeling of class analogies was performed and 96.2% overall classification success was obtained. This approach can provide a rapid and inexpensive methodology for the efficient differentiation of MPM from other pleural effusions.

Molecular Structural Changes in Alfalfa Detected by ATR-FTIR Spectroscopy in Response to Silencing of TT8 and HB12 Genes

This study investigated the spectral changes in alfalfa molecular structures induced by silencing of Transparent Testa 8 (TT8) and Homeobox 12 (HB12) genes with univariate and multivariate analyses. TT8-silenced (TT8i), HB12-silenced (HB12i) and wild type (WT) alfalfa were grown in a greenhouse under normal conditions and were harvested at early-to-mid vegetative stage. Samples were free-dried and grounded through 0.02 mm sieve for spectra collections with attenuated total reflectance Fourier transform infrared (ATR-FTIR) spectroscopy. Afterwards, both univariate and multivariate analyses were conducted on amide, carbohydrate and lipid regions. Univariate results showed that silencing of TT8 and HB12 genes affected peak heights of most total carbohydrate (TC) and structural carbohydrate (STC), and structural carbohydrate area (STCA) in carbohydrate regions; and β-sheet height, amide areas, and ratios of amide I/II and α-helix/β-sheet in amide region; and symmetric CH2 (SyCH2), asymmetric CH2 (AsCH2) and (a)symmetric CH2 and CH3 area (ASCCA) in the lipid region. Multivariate analysis showed that both hierarchy cluster analysis (HCA) and principal component analysis (PCA) clearly separated WT from transgenic plants in all carbohydrate regions and (a)symmetric CH2 and CH3 (ASCC) lipid region. In the amide region, PCA separated WT, TT8i and HB12i into different groups, while HCA clustered WT into a separate group. In conclusion, silencing of TT8 and HB12 affected intrinsic molecular structures of both amide and carbohydrate profiles in alfalfa, and multivariate analyses successfully distinguished gene-silenced alfalfa from its parental WT control.

Molecular Structural Changes in Alfalfa Detected by ATR-FTIR Spectroscopy in Response to Silencing of TT8 and HB12 Genes

This study investigated the spectral changes in alfalfa molecular structures induced by silencing of Transparent Testa 8 (TT8) and Homeobox 12 (HB12) genes with univariate and multivariate analyses. TT8-silenced (TT8i), HB12-silenced (HB12i) and wild type (WT) alfalfa were grown in a greenhouse under normal conditions and were harvested at early-to-mid vegetative stage. Samples were free-dried and grounded through 0.02 mm sieve for spectra collections with attenuated total reflectance Fourier transform infrared (ATR-FTIR) spectroscopy. Afterwards, both univariate and multivariate analyses were conducted on amide, carbohydrate and lipid regions. Univariate results showed that silencing of TT8 and HB12 genes affected peak heights of most total carbohydrate (TC) and structural carbohydrate (STC), and structural carbohydrate area (STCA) in carbohydrate regions; and β-sheet height, amide areas, and ratios of amide I/II and α-helix/β-sheet in amide region; and symmetric CH2 (SyCH2), asymmetric CH2 (AsCH2) and (a)symmetric CH2 and CH3 area (ASCCA) in the lipid region. Multivariate analysis showed that both hierarchy cluster analysis (HCA) and principal component analysis (PCA) clearly separated WT from transgenic plants in all carbohydrate regions and (a)symmetric CH2 and CH3 (ASCC) lipid region. In the amide region, PCA separated WT, TT8i and HB12i into different groups, while HCA clustered WT into a separate group. In conclusion, silencing of TT8 and HB12 affected intrinsic molecular structures of both amide and carbohydrate profiles in alfalfa, and multivariate analyses successfully distinguished gene-silenced alfalfa from its parental WT control.

Simple, fast, and accurate methodology for quantitative analysis using Fourier transform infrared spectroscopy, with bio-hybrid fuel cell examples

The standard methodologies for quantitative analysis (QA) of mixtures using Fourier transform infrared (FTIR) instruments have evolved until they are now more complicated than necessary for many users’ purposes. We present a simpler methodology, suitable for widespread adoption of FTIR QA as a standard laboratory technique across disciplines by occasional users.•

Algorithm is straightforward and intuitive, yet it is also fast, accurate, and robust.

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Relies on component spectra, minimization of errors, and local adaptive mesh refinement.

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Tested successfully on real mixtures of up to nine components.

We show that our methodology is robust to challenging experimental conditions such as similar substances, component percentages differing by three orders of magnitude, and imperfect (noisy) spectra. As examples, we analyze biological, chemical, and physical aspects of bio-hybrid fuel cells.

Structural discrimination of nanosilica particles and mixed-structure silica by multivariate analysis applied to SAXS profiles in combination with FT-IR spectroscopy

New methodology to quickly identify changes in the structural properties of mesoporous silica materials through simultaneous multivariate analyses applied to techniques with different principles as SAXS curves and FT-IR spectra.

Rapid and undamaged analysis of crude and processed Radix Scrophulariae by Fourier transform infrared spectroscopy coupled with soft independent modeling of class analogy

Objective:

The main objective of this work is to determine the feasibility of identification of crude and processed Radix Scrophulariae using the Fourier transform infrared spectroscopy couple with soft independent modeling of class analogy (FT-IR-SIMCA).

Materials and Methods:

A total of 50 different crude Radix Scrophulariae was used to product processed ones. The spectra were acquired by FT-IR spectroscopy using a diffuse reflectance fiber optic probe. For the multivariate analysis, SIMCA was used. Results showed that FT-IR-SIMCA was useful to discriminate the processed Radix Scrophulariae samples from crude samples. These samples could be successfully classified by SIMCA.

Results:

In all cases, the recognition and rejection rates were 97.8% and 100%, respectively. When testing with the blind sample that was picked out from the chosen samples, the accuracy was up to 90%.

Conclusion:

It means that the methodology is capable of accurately separating processed Radix Scrophulariae from crude samples.