Industrial Hydrogenation Process Monitoring Using Ultra-compact Near-Infrared Spectrometer and Chemometrics

Pereira, Juliana Mendes Garcia; Sánchez, Jorge; Lima, Patrícia Casarin de; Possebon, Gabriela; Tanamati, Augusto; Tanamati, Ailey Aparecida Coelho; Bona, Evandro

doi:10.1007/s12161-017-0989-9

Cited by 12 publications

(7 citation statements)

References 37 publications

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“…Hence, the spectra were reduced to the region between 1,100 and 1,500 nm. After, it was pre‐processed by Savitzky‐Golay smoothing (first order, 15 points), and first derivative (second order 15 points; Pereira et al., ). After spectra pre‐processing (Figure b), it could be observed some peaks and valleys in the spectra, differentiating samples.…”

Section: Resultsmentioning

confidence: 99%

Portable NIR Spectrometer for Prediction of Palm Oil Acidity

Kaufmann

Favero

Vasconcelos

et al. 2019

Journal of Food Science

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Palm oil is widely used in the food industry, and its quality is associated with the free fatty acids (FFA) content. Determination of FFA in oil is time‐consuming, requires chemicals and generates residues. There is a trend of applying process analytical technologies (PAT) for fast and nondestructive determination of oil parameters. Portable near‐infrared (NIR) spectrometers are cheaper than bench top equipment, and have been used for several tasks in the food processing industry, as it provides fast and reliable data for inline measurements. This study investigated the use of NIR spectra using a portable equipment, combined with both unsupervised and supervised multivariate analyses for identification of palm oil samples with different levels of FFA. Soft independent modeling of class analogy , k‐Nearest Neighbors, and linear discriminant analysis models were able to correctly identify 100% of the studied samples with selected wavelengths from NIR spectra. Calibration models were performed for acidity prediction, achieving R2 = 0.97, with root mean square error of prediction = 4.37 for partial least squares model using most relevant wavelengths. These results demonstrate the feasibility of applying a low‐cost portable NIR spectrophotometer to predict quality parameters of palm oil. Practical Application This work presents results that show the feasibility of using a low‐cost portable near‐infrared spectrophotometer for the classification of raw palm oil samples according to free fatty acids contents. Regression models are presented as a fast and nondestructive alternative to classify samples for acidity, which is an important quality parameter and that directly affects the market value of crude palm oil.

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Section: Resultsmentioning

confidence: 99%

Portable NIR Spectrometer for Prediction of Palm Oil Acidity

Kaufmann

Favero

Vasconcelos

et al. 2019

Journal of Food Science

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“…SOM-type ANN routine was developed and used according to the algorithm described in Haykin (2001) and was carried out in Matlab software (MathWorks). For more specific details regarding the theoretical and mathematical foundations of the SOM algorithm, readers can consult the references of Cremasco et al (2016), , Galvan, Effting, et al (2020), Pereira et al (2018), andSanchez et al (2019). SOM setup contained a hexagonal topology between 3 × 3 and 20 × 20 neurons, with 7000 epochs to ensure the convergence of the mean quantization error.…”

Section: Chemometrics Approachmentioning

confidence: 99%

An overview of research of essential oils by self‐organizing maps: A novel approach for meta‐analysis study

Galvan

Effting

Neto

et al. 2021

Comp Rev Food Sci Food Safe

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Essential oils (EOs) are commercially important products, sources of compounds with antioxidant and antimicrobial activities considered indispensable for several fields, such as the food industry, cosmetics, perfumes, pharmaceuticals, sanitary and agricultural industries. In this context, this systematic review and meta‐analysis, a novel approach will be presented using chemometric tools to verify and recognize patterns of antioxidant, antibacterial, and antifungal activities of EOs according to their geographic, botanical, chemical, and microbiological distribution. Scientific papers were selected following the Preferred Reporting Items for Systematic Review and Meta‐Analyses statement flow diagram, and the data were evaluated by the self‐organizing map and hierarchical cluster analysis. Overall, this novel approach allowed us to draw an overview of antioxidants and antimicrobials activities of EOs reported in 2019, through 585 articles evaluated, obtaining a dataset with more than 10,000 data, distributed in more than 80 countries, 290 plant genera, 150 chemical compounds, 30 genera of bacteria, and 10 genera of fungi. The networks for geographic, botanical, chemical, and microbiological distribution indicated that Brazil, Asia, the botanical genus Thymus, species Thymus vulgaris L. “thyme,” the Lamiaceae family, limonene, and the oxygenated monoterpene class were the most representative in the dataset, while the species Escherichia coli and Candida albicans were the most used to assess the antimicrobial activity of EOs. This work can be seen as a guide for the processing of metadata using a novel approach with non‐conventional statistical methods. However, this preliminary approach with EOs can be extended to other sources or areas of food science.

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“…PLS models were obtained by direct implementation of the Gamma 3.0 PLS routine, which works with a statistically modified PLS (SIMPLS) algorithm, , applying the spectral range from δ −2.00 to 10.00 ppm.…”

Section: Experimental Partmentioning

confidence: 99%

Calibration Transfer of Partial Least Squares Regression Models between Desktop Nuclear Magnetic Resonance Spectrometers

et al. 2020

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Low-field proton nuclear magnetic resonance (LF-1H NMR) devices based on permanent magnets are a promising analytical tool to be extensively applied to the process analytical chemistry scenario. To enhance its analytical applicability in samples where the spectral resolution is compromised, multivariate regression methods are required. However, building a robust calibration model, such as partial least squares (PLS) regression, is a laborious task because (1) the number of measurements required during the calibration process is large and (2) the procedure must be repeated when the instrument is changed or after a certain period due to the long-term stability of the instrument. Thus, the present work describes the application of calibration transfer methodologies (direct standardization (DS), piece-wise direct standardization (PDS), and double-window piece-wise direct standardization (DWPDS)) on LF-1H NMR to exempt the necessity of a recalibration procedure when moving from the original spectrometer to a second one with the same, lower, or higher magnetic field. These calibration transfer methodologies were tested with PLS models built on a 60 MHz (for the proton Larmor frequency) spectrometer to predict the specific gravity (SG), distillation temperature (T50%), and final boiling point (FBP) of commercial gasoline. The results showed that the DWPDS method applying only 2 to 7 transference samples enables the transference of all PLS models built on the primary instrument (60 MHz) to other (43, 60, and 80 MHz) different instruments, reaching the same RMSEP values as the primary instrument: 1.2 kg/m3 for SG, 5.1 °C for FBP, and 1.1 °C for T50%.

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Industrial Hydrogenation Process Monitoring Using Ultra-compact Near-Infrared Spectrometer and Chemometrics

Cited by 12 publications

References 37 publications

Portable NIR Spectrometer for Prediction of Palm Oil Acidity

Portable NIR Spectrometer for Prediction of Palm Oil Acidity

An overview of research of essential oils by self‐organizing maps: A novel approach for meta‐analysis study

Calibration Transfer of Partial Least Squares Regression Models between Desktop Nuclear Magnetic Resonance Spectrometers

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