Quantification of p -Cymene and Heptane in a Solvent-Based Green Process of Polystyrene Recycling

Abstract

The development of an economically viable technology for recycling polystyrene (PS) is of crucial importance. Most postconsumer PS feedstocks cannot be handled by mechanical recycling due to severe contamination. A solvent-based process was developed by Polystyvert and uses p-cymene as a solvent to solubilize PS and heptane as an antisolvent to precipitate it. This leads to the production of recycled PS resin of high purity. However, the optimization of this process requires the knowledge of p-cymene, heptane, and PS concentrations at different stages, where the samples of interest can be a viscoelastic paste or a solid (pellets). Here, we present an infrared spectroscopy (IR) method that can rapidly quantify the content of these samples at the plant level. Partial least squares statistical modeling on the first derivative IR spectra allowed the simultaneous quantification of the components with a limit of quantification of 0.5% and 1% for p-cymene and heptane. To illustrate the usefulness of this method, samples from a pilot PS-recycling plant were tested to improve the washing efficiency of the process.

Graphical Abstract

This is a visual representation of the abstract.

Keywords

Polystyrene dissolution/precipitation recycling quantitative methods infrared spectroscopy pilot plant scale multivariate models green process.

Introduction

Plastics are the workhorse material of the modern economy because of their tunable and unrivaled properties. Combined with their low cost, they are an ideal choice for a wide range of applications that include packaging, transport, construction, healthcare, clothing, and electronics.^1,2 The production of plastic resin has increased from 2 to 380 t/year in the last 60 years.³ On the downside, our current linear economic model, where we simply dispose of manufactured products at the end of their life (which can be very short for single-use applications), generates large-scale pollution that causes harmful effects on human health and on the environment.^4–6 The approximate total amount of plastic waste generated in 2015 was 6300 Mt, of which only 9% was recycled and 12% was incinerated.^3,7

The increasing awareness about the impact of plastic pollution on ecosystems stresses the need for a transition toward a circular economy that promotes an intelligent management by reusing, recycling, and even upcycling end-of-life plastics.^1,8–10 The implementation of such a circular market has been estimated to be worth $4.5 trillion by 2030.¹¹ Depending on the waste stream quality, the type of polymer, as well as the required purity of the recycled resin, different recycling solutions exist and are under continuous development. These include (i) separation and mechanical deconstruction, which often reduces the molecular weight and the performance of the recycled polymer; (ii) chemical depolymerization, where the polymer is converted to small molecules by chemical or enzymatic reactions; and (iii) dissolution/precipitation methods, where the polymer is purified by treatment in appropriate solvents and antisolvents.⁹ For styrene-based polymers, the dissolution/precipitation approach appears as the best option considering the yield, the reachable degree of purity of the recycled resin, and the overall cost, because the polymer chains are not altered during the process.^12–15 It is worth noting that among the recycling technologies, this method has the lowest environmental impact in terms of CO₂ emissions.¹² Some key challenges include solvent management, the reduction of the solvent volume used for purification, and the energetic cost associated with drying the recycled resin.

This paper focuses on the optimization of a versatile recycling process of dissolution/precipitation for polystyrene (PS), developed by Polystyvert Inc.¹⁶ As illustrated in Fig. 1, the first step consists of the dissolution of contaminated PS materials coming from various streams such as construction, food, electronics, etc. The “green” solvent used in this process is p-cymene (Cy), which is a fragrant compound derived from the essential oil of pine. Once the PS is solubilized, a first filtration removes the macroscopic solid contaminants such as other plastics, paper, etc. After a possible purification step¹⁷ used to remove fine insoluble impurities such as pigments or lime, the PS is selectively precipitated using heptane (H) as an antisolvent. The obtained precipitated PS “paste,” which consists of PS swollen by the solvents, is then washed with H in order to extract both the residual Cy and the soluble impurities. After a devolatilization step, the PS is extruded to obtain recycled PS pellets that can be directly used, while the contaminated solvents are purified by distillation for reuse purpose.

Figure 1.

Schematic representation of the main steps in the Polystyvert-patented¹⁶ dissolution/precipitation PS-recycling process; Cy and H are used as the solvent and antisolvent, respectively.

As a result, knowing the amounts of Cy, H, and PS at different strategic points is essential to optimize the recycling process. For example, when washing the PS paste with H, it is necessary to remove as much Cy as possible to reduce the energy consumption and duration of the devolatilization step. Meanwhile, it is also important to minimize the amount of H used to minimize the cost of the process, both from an economic and environmental standpoint. Quantifying the residual solvents in the recycled pellets is also essential because it is a direct measure of the devolatilization process efficiency. Therefore, it is necessary to implement a rapid, direct, and easy quantification technique for residual solvents throughout the process.

While mass spectrometry provides outstanding levels of sensitivity and selectivity, it is expensive and cannot readily be implemented for routine analysis and process optimization at a production plant. Gas chromatography is a more affordable alternative, but a long elution time makes it less practical for the quality control aspect of the intended application.

Thermogravimetric analysis was also considered but the evaporation of H and Cy was poorly separated both at slow and fast scanning rates. By comparison with these various techniques, infrared spectroscopy (IR) offers several advantages such as rapid measurements and data analysis, low cost, robustness, and easy operation by nonqualified operators at the plant level.^18,19 In this paper, we develop and validate calibration models allowing the rapid and accurate quantification of Cy and H in PS by IR spectroscopy. We also apply the model at the pilot plant level to representative pastes and pellets produced during the dissolution/precipitation recycling of PS waste material.

Experimental

Materials and Methods

High-performance liquid chromatography grade H (≥96%), Cy (99%), 1,2-dichloroethane (DCE; anhydrous, 99.8%), and PS (M_w ∼ 350,000, M_n ∼ 170 000) were purchased from Sigma–Aldrich and used as received. Calibration standards containing either Cy, H, or both, were prepared according to the following procedures. In all cases, approximately 600 mg of PS was first solubilized in an appropriate mass of DCE in order to obtain solutions of 30 wt% PS. For the Cy-only model, 26 calibration standards were prepared by adding weighted amounts of Cy to the PS solution to reach Cy concentrations ranging from 0 wt% to 16 wt% relative to the total mass (excluding DCE). Similarly, for the H-only model, 26 calibration standards containing from 0 wt% to 40 wt% of H relative to the total mass (excluding DCE) were prepared. Finally, for the mixed Cy + H model, selected masses of both Cy and H were added to the 30 wt% solutions of PS in DCE. A total of 67 solutions were prepared and used either as calibration or validation standards. Partial least squares (PLS) regression models were built using approximately 75% of the samples as the calibration set and the remaining 25% of the samples as an independent validation set to evaluate the model performance.

To test the applicability of the models in real-life conditions, PS samples were extracted at different stages of the recycling process in a pilot-scale plant. This plant can produce about 5 kg of recycled resin per hour. It consists of several modules of dissolution, purification, precipitation, washing, and extrusion. A weighted amount of each sample was dissolved in an appropriate mass of DCE to approximate the PS concentration used in the calibration standards. Three replicates were used for each sample.

For convenience, the models were built using either a spectrometer located in the university laboratory or one located at the pilot plant. Efforts were made to make the sampling and measurement parameters as similar as possible between the two instruments, but no attempt was made to transfer calibration models from one instrument to the other. Rather, samples were systematically quantified by measuring their spectrum with the same instrument that was used to create the calibration model. More specifically, the single-solvent (Cy-only and H-only) models were created using a Vertex 70 Fourier transform infrared (FT-IR) spectrometer (Bruker Optics) equipped with a deuterated L-alanine doped tri-glycine sulfate detector and a single reflection ZnSe attenuated total reflection (ATR) accessory (MIRacle, Pike Technologies), while the mixed-solvent (Cy + H) model was created using a Thermo Scientific Nicolet iS10 FT-IR spectrometer equipped with a deuterated triglycine sulfate detector and a single reflection diamond ATR accessory. To minimize the risk of solvent evaporation during the analysis, a large (at least 1 mm thick) droplet of the highly viscous solution was deposited on the ATR element. Spectra were recorded with a 4 cm⁻¹ resolution by averaging scans for 1 min for the samples and 2 min for the backgrounds. This acquisition time was selected as a compromise between the need for a good signal-to-noise ratio and the avoidance of a bias due to problematic solvent evaporation.

ATQ Analyst 9 (Thermo Nicolet Corporation) was used for spectral processing and multivariate analysis. The performance of the multivariate models was evaluated in terms of root mean square error of calibration (RMSEC), root mean square error of prediction (RMSEP), root mean square error of cross-validation (RMSECV), and determination coefficient (R²).^20,21 The RMSEC and RMSEP values indicate how well the model fits the calibration and the validation data sets, respectively, while the RMSECV evaluates the predictive ability of the model established on part of a data set to predict the rest of the data. Good models are characterized by low RMSEC, RMSEP, and RMSECV values and by a high R² value, but also by a small difference between the RMSEP and RMSECV values. A bias value could also reveal a systematic difference between the computed values and the true values, but a significant bias was never reported by TQ Analyst for any compound in the calibration models tested. The choice of the most relevant spectral regions is also a determinant parameter in the performance of the calibration model. Spectral regions providing the highest variation for the different components are listed in the Supplemental Material. Spectral pretreatments are sometimes required to reduce systematic noise, baseline variations, light scattering, pathlength differences, etc.^21–23 In our case, using the first derivative of the spectra, as opposed to the normal spectra, provided better RMSEC values due to reduced peak overlap and baseline distortions. Mean centering was applied to the spectra and a spectrum outlier diagnostic was applied to identify and eliminate outliers (only in the case of the Cy-only model).

Results and Discussion

Attenuated total reflection (ATR) was selected as the sampling method of choice because it can be readily used by operators at the plant level. Ideally, the PS samples would be analyzed directly in their solid (for pellets) or viscoelastic (for pastes) state to allow for a faster and more sensitive quantification of the Cy and H content. However, this direct sample presentation method makes the analytical outcome prone to random or systematic errors if there is heterogeneity in the bulk distribution of the solvents because ATR only probes the surface of the sample, with a penetration depth on the order of 0.6–2.3 µm over the spectral range of interest when using a diamond ATR element. Notably, a partial evaporation of the Cy or H at the surface of the samples would result in a systematic underestimation of their true content in the bulk sample. In view of this, we decided to dissolve the samples in a common solvent for all three components (PS, Cy, and H) to ensure a homogeneous distribution and unbiased results. This solvent must be minimally volatile during analysis, and it should interfere as little as possible with the characteristic bands of the compounds of interest. Several candidates were considered and DCE was selected as a good compromise in terms of volatility and spectral interference (see Fig. 2a). A concentration of 30 wt% of PS in DCE was used throughout as a compromise between the need for sensitivity, which improves at higher PS concentration, and a sufficiently low viscosity to enable homogenization of the sample solutions.

Figure 2.

(a) Infrared spectra of the pure components Cy, H, PS, and DCE. Shaded areas indicate the spectral regions used for multivariate analysis. (b) Infrared spectra of representative compositions in the selected spectral regions. The numerical values in the legend indicate the approximate Cy and H percentage in the mixture.

An investigation of the pure component spectra, shown in Fig. 2a, was conducted to evaluate the possibility of a univariate analysis of the Cy and H contents in PS using isolated bands of sufficient intensity. For simple systems, the Beer–Lambert law can be used directly to quantify the species of interest. In practice, the spectral information is often obscured, even for moderately complex systems, due to the overlap of bands originating from different components. This was particularly the case for H: although it presents very intense C–H stretching bands between 2800 and 3000 cm⁻¹, the overlap with the C–H stretching bands of both PS and Cy led to strong nonlinearity for univariate calibrations. On the other hand, good candidate bands can be identified for Cy. A spectrally isolated band is also needed for PS to serve as an internal standard because, while its concentration is well controlled in the calibration standards, it is unknown for samples collected at the pilot plant. The best univariate calibration was obtained using the intensity ratio of the 813 cm⁻¹ Cy band (shifted to 818 cm⁻¹ in DCE) and 1601 cm⁻¹ PS bands but, as can be seen in Fig. S1 (Supplemental Material), a nonlinear trend was obtained (see the nonrandom residuals). Univariate analysis of Cy in PS thus provides a quick general guidance for the recycling process optimization, but it cannot provide accurate results. In particular, the presence of a large H content provoked systematic errors due to the partial overlap of the baseline points. Recording spectra with a better spectral resolution led to worse calibration results due to a lower signal-to-noise ratio and the higher prevalence of water vapor rovibrational lines interfering with the PS band.

A multivariate model is thus necessary to quantify H in PS and to provide better predictive power for Cy in PS. Multivariate data analysis methods include classical least squares, multiple linear regression, principal component regression, and PLS regression.^21,23–27 PLS is arguably the most common approach for IR because bands associated with the components to quantify are often severely overlapped. Indeed, PLS models provided better determination coefficients than the other methods for Cy and H in PS. The calibration range for the standards was based on the expected Cy and H mass percentages relative to the total mass, Cy_wt% and H_wt%, at the various stages of the dissolution/precipitation recycling process. Figure 2b shows spectra obtained for mixtures representative of those that could be obtained. This includes samples that mimic a PS solution in Cy before precipitation (18% of Cy and 0% of H), pastes, and pellets. The precipitated PS paste may contain up to 40% of H and 10% of Cy. Washing the paste with H gradually reduces the Cy content. Finally, the devolatilization process reduces the residual solvent content as low as possible in the final pellets. In other words, there are steps during the process where either Cy or H is the dominant solvent present and other steps where both solvents are present simultaneously.

Two separate calibration models were first prepared for Cy-only and H-only conditions. Figure 3 shows the correlation between the predicted and real concentrations of the prepared samples. A linear regression is observed with R² values of 0.9998 and >0.9999 for the Cy-only and H-only models, respectively, indicating high prediction capability when a single solvent is present in the PS samples. Relatively low RMSEC and RMSEP values were obtained (0.016 and 0.0166 for Cy and 0.0041 and 0.0255 for H).

Figure 3.

Performance of the separate PLS models for the quantification of either Cy-only or H-only in PS.

These calibration models were then used to determine the residual solvent in samples representative of the recycling process, that is, the precipitated paste, the washed paste, and the pellets. To evaluate the washing step efficiency, two different washing conditions, W₁ and W₂, were tested. The Cy-only model was used for the precipitated paste and pellets, where it should be the dominant solvent, while the H-only model was used for the washed pastes. The results are presented in Table I in terms of wt% of the relevant solvent and percentage of spectrum fit, a parameter that indicates how well the experimental sample spectrum is reconstructed based on the model. An acceptable spectrum fit value above 97% was found only for the pellets, in which a minute quantity of H is indeed expected to be present. The Cy content is lower in the washed pellets obtained from W₂ (1.1%) than from its W₁ counterpart (1.8%), consistent with more drastic washing conditions for W₂. On the other hand, the Cy-only model completely fails to estimate the Cy content in the precipitated paste, where the spectrum fit is very poor (54%) and the sum of the percentages of Cy, PS, and DCE is far from 100%. Observation of the paste spectra indicates that this is due to the presence of H that makes the Cy-only model inapplicable. Similarly, the H-only model fails to properly describe the washed pastes, with spectrum fit values typically <70%, due to the unaccounted presence of Cy. Such single-solvent models are therefore not reliable except for the pellets, leading to the need for a model combining both Cy and H.

Table I.

Determination of the Cy or H content in PS samples collected at specific steps of the recycling process as determined using the individual cymene or H PLS models.

Sample	Cy (wt%)	H (wt%)	Spectrum fit (%)
Precipitated paste	24.9 ± 0.6	N/A	54
Washed paste (W₁)	N/A	11.8 ± 0.4	66
Washed paste (W₂)	N/A	12.9 ± 0.1	70
Pellet (W₁)	1.8 ± 0.2	N/A	99
Pellet (W₂)	1.1 ± 0.2	N/A	97

A PLS calibration model was thus built to describe the four components (PS, Cy, H, and DCE) over the concentration range relevant to a PS dissolution/precipitation recycling process. A very good correlation (R² > 0.998) was found between the predicted and real concentration values for all components. Figure 4 shows the results for the two solvents of interest and Fig. S2 (Supplemental Material) for PS (after eliminating the DCE contribution). Compared to the previous single-solvent models, larger values of RMSEC (0.0297 and 0.156) and RMSEP (0.0865 and 0.147) were obtained for Cy and H, respectively. Regarding the robustness of the model, the RMSECV values obtained for Cy and H are 0.0781 and 0.189, respectively. A small difference between RMSEP and RMSECV values is normally an indication of the predictive ability of a model.

Figure 4.

Performance of the combined (Cy + H) PLS model for the quantification of Cy and H in PS.

The model was applied to the same series of recycled PS samples recovered at the pilot plant (Table II). The spectrum fit percentages are systematically improved with values ≥98.8%. This is particularly the case for the pastes for which the single-solvent models failed badly. This is easily understood when noting that the Cy and H contents were substantial in all types of pastes, leading to the breakdown of the single-solvent models. The Cy content decreases from a large value of 11.4% in the precipitated pastes to 3.1% or 2.5% after washing the paste with H. Meanwhile, the H content increases up to ∼22% for the washed paste W₂, slightly more than for W₁. The results obtained for the pellets are consistent, within quantification error, between the Cy-only model (Table I) and the Cy + H model (Table II). This is understood when noting that the H content in the pellets is indeed within the determination uncertainty of the Cy + H model, so the Cy-only model was indeed applicable. When considering the propagation of the errors on all components of the system, the limit of detection of the model is estimated to be 0.5% for Cy and 1% for H, even though the sample-to-sample replicability is better. These limits of detection are not competitive with mass spectrometry for an accurate determination of the residual solvent content in pellets. Nevertheless, IR spectroscopy allows quantification of large solvent contents, up to 60% for H and 20% for Cy, with relative errors on the order of 2-3% and reproducibility on the order of <0.6% for triplicate samples. It is therefore a useful tool for a rapid determination of the solvent content in pastes for quality control purposes, as well as for the optimization of the precipitation and washing steps in view of a more sustainable and economical PS-recycling process.

Table II.

Determination of the Cy and H content in PS samples collected at specific steps of the recycling process as determined using the combined PLS model.

Sample	Cy (wt%)	H (wt%)	Spectrum fit (%)
Precipitated paste	11.4 ± 0.2	15.7 ± 0.4	99.2
Washed paste (W₁)	3.1 ± 0.2	20.2 ± 0.6	98.8
Washed paste (W₂)	2.5 ± 0.1	21.8 ± 0.1	98.8
Pellet (W₁)	1.6 ± 0.3	0.5 ± 0.5	99.8
Pellet (W₂)	0.8 ± 0.4	0 ± 2	99.8

Conclusion

Infrared spectroscopy (IR) was applied as a rapid and reliable tool to determine the solvent content in PS at various stages of a dissolution/precipitation recycling process. The application of PLS regression allows quantifying simultaneously the mass percentages of the dissolution solvent, Cy, and the precipitation antisolvent, H, over a large concentration range and with limits of quantifications of ∼0.5%. In its current implementation, the method is not sufficiently sensitive to determine if the solvent content in the recycled pellets is low enough to respect the regulatory limits for commercial-grade applications. It may be possible to compress the pellets into a film form and to probe them by transmission IR spectroscopy, thereby substantially increasing the detection limit compared to the ATR sampling approach used here. Nevertheless, the broad quantification range, fast measurement turnaround time, demonstrated implementation at the plant level, and simplicity for application by minimally trained operators, make ATR-IR a sampling mode of choice for the day-to-day surveillance of a PS-recycling process. It can also be a valuable tool to process engineers seeking to optimize solvent usage during the different stages of the dissolution/precipitation process. While the model reported here is restricted to the specific combination of Cy and H in PS, the approach is applicable to other polymer/solvent/antisolvent combinations. Considering the environmental benefits of the dissolution/precipitation recycling process, it is predictable that other thermoplastic polymers will be recycled by this approach in the future and would benefit from a similar IR-based solvent/antisolvent management tool.

Supplemental Material

sj-docx-1-app-10.1177_27551857231179982 - Supplemental material for Quantification of p-Cymene and Heptane in a Solvent-Based Green Process of Polystyrene Recycling

Supplemental material, sj-docx-1-app-10.1177_27551857231179982 for Quantification of p-Cymene and Heptane in a Solvent-Based Green Process of Polystyrene Recycling by Zeinab Kara Ali, Jean-Mathieu Pin and Christian Pellerin in Applied Spectroscopy Practica

Footnotes

Acknowledgments

The authors thank the Polystyvert team for their contribution at different stages of this investigation.

Author Contributions

The manuscript was written through contributions of all authors. All authors have approved the final version of the manuscript.

Declaration of Conflicting Interests

The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The authors disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This research was supported by the Natural Science and Engineering Research Council of Canada (grant no. RGPIN-05098-2020) and cofinanced by MITACS (grant no. IT20169).

ORCID iD

Christian Pellerin

Supplemental Material

All supplemental material mentioned in the text is available in the online version of the journal.

References

Ellen MacArthur Foundation. The New Plastics Economy: Rethinking the Future of Plastics and Catalysing Action. https://ellenmacarthurfoundation.org/the-new-plastics-economy-rethinking-the-future-of-plastics-and-catalysing [accessed Mar 29 2023].

Thompson

R.C.

Moore

C.J.

vom Saal

F.S.

Swan

S.H.

. “Plastics, the Environment and Human Health: Current Consensus and Future Trends”. Philos. Trans. R. Soc. B: Biol. Sci. 2009. 364(1526): 2153–2166.

Geyer

Jambeck

J.R.

Law

K.L.

. “Production, Use, and Fate of All Plastics Ever Made”. Sci. Adv. 2017. 3(7): e1700782.

Mancia

Chenet

Bono

Geraci

M.L.

, et al. “Adverse Effects of Plastic Ingestion on the Mediterranean Small-Spotted Catshark (Scyliorhinus canicula)”. Mar. Environ. Res. 2020. 155: 104876.

Halden

R.U.

. “Plastics and Health Risks”. Annu. Rev. Public Health. 2010. 31: 179–194.

Waring

R.H.

Harris

R.M.

Mitchell

S.C.

. “Plastic Contamination of the Food Chain: A Threat to Human Health?”. Maturitas. 2018. 115: 64–68.

Tukker

. Plastics Waste: Feedstock Recycling, Chemical Recycling, and Incineration. Shawbury, UK: Rapra Technology Limited, 2002.

Mohanty

A.K.

Vivekanandhan

J.-M.

Misra

. “Composites From Renewable and Sustainable Resources: Challenges and Innovations”. Science. 2018. 362(6414): 536–542.

Korley

L.T.J.

Epps

T.H.

Helms

B.A.

Ryan

A.J.

. “Toward Polymer Upcycling: Adding Value and Tackling Circularity”. Science. 2021. 373(6550): 66–69.

10.

Fan

Zhang

Cui

Xiong

Maqsood

. “Conversion of Polystyrene Plastic Into Aviation Fuel through Microwave-Assisted Pyrolysis as Affected by Iron-Based Microwave Absorbents”. ACS Sustain. Chem. Eng. 2023. 11(3): 1054–1066.

11.

Billiet

Trenor

S.R.

. “100th Anniversary of Macromolecular Science Viewpoint: Needs for Plastics Packaging Circularity”. ACS Macro Lett. 2020. 9(9): 1376–1390.

12.

Vollmer

Jenks

M.J.F.

Roelands

M.C.P.

White

R.J.

, et al. “Beyond Mechanical Recycling: Giving New Life to Plastic Waste”. Angew. Chem. Int. Ed. Engl. 2020. 59(36): 15402–15423.

13.

Zhao

Y.B.

X.D.

H.G.

. “Solvent-Based Separation and Recycling of Waste Plastics: A Review”. Chemosphere. 2018. 209: 707–720.

14.

de Sousa Cunha

Mumbach

G.D.

Machado

R.A.F.

Bolzan

. “A Comprehensive Investigation of Waste Expanded Polystyrene Recycling by Dissolution Technique Combined with Nanoprecipitation”. Environ. Nanotechnol. Monit. Manag. 2021. 16: 100470.

15.

Mangalara

S.C.H.

Varughese

. “Green Recycling Approach to Obtain Nano- and Microparticles From Expanded Polystyrene Waste”. ACS Sustain. Chem. Eng. 2016. 4(11): 6095–6100.

16.

R. Côté. Processes for Recycling Polystyrene Waste. US Patent 20170298207A1. Filed 2015. Issued 2019.

17.

R. Côté. Processes for Recycling Polystyrene Waste and/or Polystyrene Copolymer Waste. US Patent 11407877. Filed 2019. Issued 2022.

18.

Violet

Mifleur

Vanoye

Nguyen

D.H.

, et al. “Online Monitoring by Infrared Spectroscopy using Multivariate Analysis: Background Theory and Application to Catalytic Dehydrogenative Coupling of Butanol to Butyl Butyrate”. React. Chem. Eng. 2019. 4(5): 909–918.

19.

Tate

J.D.

Chauvel

Guenard

R.D.

Harner

. “Process Monitoring by Mid- and Near-Infrared Fourier Transform Spectroscopy”. In: Griffiths

P.R.

Chalmers

J.M.

, editors. Handbook of Vibrational Spectroscopy. Chichester, UK: John Wiley and Sons Inc., 2001. Vol. 4, pp. 2737–2769.

20.

Chen

Xie

Zhang

Wang

, et al. “Quantification of Total Polysaccharides and Triterpenoids in Ganoderma lucidum and Ganoderma atrum by Near Infrared Spectroscopy and Chemometrics”. Food Chem. 2012. 135(1): 268–275.

21.

Gemperline

. Practical Guide to Chemometrics. Boca Raton, Florida: CRC Press, 2006. P. 541.

22.

Næs

Isaksson

Fearn

Davies

. A User-Friendly Guide to Multivariate Calibration and Classification. Chichester, UK: NIR Publications, 2002.

23.

Hasegawa

. Quantitative Infrared Spectroscopy for Understanding of a Condensed Matter. Tokyo: Springer, 2017.

24.

Pacheco-Londono

L.C.

Aparicio-Bolano

J.A.

Galan-Freyle

N.J.

Roman-Ospino

A.D.

, et al. “Classical Least Squares-Assisted Mid-Infrared (MIR) Laser Spectroscopy Detection of High Explosives on Fabrics”. Appl. Spectrosc. 2019. 73(1): 17–29.

25.

Horton

R.B.

Duranty

McConico

Vogt

. “Fourier Transform Infrared (FT-IR) Spectroscopy and Improved Principal Component Regression (PCR) for Quantification of Solid Analytes in Microalgae and Bacteria”. Appl. Spectrosc. 2011. 65(4): 442–453.

26.

Rohman

Sismindari

Erwanto

Man

. “Analysis of Pork Adulteration in Beef Meatball Using Fourier Transform Infrared (FTIR) Spectroscopy”. Meat Sci. 2011. 88(1): 91–95.

27.

Chen

Jiang

Zhao

. “Measurement of Total Flavone Content in Snow Lotus (Saussurea involucrate) Using Near Infrared Spectroscopy Combined with Interval PLS and Genetic Algorithm”. Spectrochim. Acta, Part A. 2010. 76: 50–55.

Supplementary Material

Please find the following supplemental material available below.

For Open Access articles published under a Creative Commons License, all supplemental material carries the same license as the article it is associated with.

For non-Open Access articles published, all supplemental material carries a non-exclusive license, and permission requests for re-use of supplemental material or any part of supplemental material shall be sent directly to the copyright owner as specified in the copyright notice associated with the article.

0.00 MB

0.04 MB