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26 results for “FTIR spectroscopy”
AdaptFerm: Bioprocess Monitoring Using FTIR spectroscopy: Insights into Substrate Effects and Domain Adaptation
<h2> </h2> <h2><strong>1. Introduction</strong></h2> <p>The AdaptFerm dataset is designed to support the development of a monitoring framework for lactic acid production fermentation using Fourier Transform Infrared (FTIR) spectroscopy. Its primary goal is to facilitate the control strategies for continuous fermentation processes to maximize the lactic acid production. The AdaptFerm encompasses data from two distinct batch fermentation environments: one employing simple sugar (glucose) as the substrate and the other utilizing complex sugars derived from bio-waste. The study focuses on developing accurate predictive models for glucose and lactic acid concentrations, with an emphasis on applying classical machine learning techniques and enhancing domain generalization capabilities.</p> <h2><strong>2. Prediction Model for Different Substrate Environments</strong></h2> <p>The chemical composition of substrates are presented in <a title="Digital model of biochemical reactions in lactic acid bacterial fermentation of simple glucose and biowaste substrates" href="https://doi.org/10.1016/j.heliyon.2024.e38791" target="_blank" rel="noopener">Table 1 [1]</a>. The dataset is utilized to train and test models within the same substrate domain. For instance, data from a single fermentation environment (e.g., glucose substrate) is used for both training and testing phases. The applied machine learning models showed accurate prediction within the same domain <a title="Digital model of biochemical reactions in lactic acid bacterial fermentation of simple glucose and biowaste substrates" href="https://doi.org/10.1016/j.heliyon.2024.e38791" target="_blank" rel="noopener">[1]</a>. For more details on the methods applied, please refer to the following link: <a title="Digital model of biochemical reactions in lactic acid bacterial fermentation of simple glucose and biowaste substrates" href="https://doi.org/10.1016/j.heliyon.2024.e38791" target="_blank" rel="noopener">https://doi.org/10.1016/j.heliyon.2024.e38791</a>. In this study, the MIR results correspond to the AdaptFerm dataset. The spectra of the glucose and biowaste hydrolysate fermentation process are presented in <a title="Digital model of biochemical reactions in lactic acid bacterial fermentation of simple glucose and biowaste substrates" href="https://doi.org/10.1016/j.heliyon.2024.e38791" target="_blank" rel="noopener">Figure 3</a> and <a title="Digital model of biochemical reactions in lactic acid bacterial fermentation of simple glucose and biowaste substrates" href="https://doi.org/10.1016/j.heliyon.2024.e38791" target="_blank" rel="noopener">Figure 4</a>.</p> <h2><strong>3. Domain Adaptation</strong></h2> <p>The dataset was also used to address the challenge posed by shifts in FTIR data when substrates change. Transitioning from simple sugar (glucose) to complex sugar (bio-waste) causes significant variations in the FTIR spectra, making it difficult for models trained on glucose fermentation data to maintain prediction accuracy in the complex sugar fermentation environment. This results in reduced robustness and performance when applied to out-of-distribution data. To address these challenges, we explore methods that improve the generalization ability and robustness of models in such scenarios without using labels from complex sugar fermentation <a title="Domain-Invariant Monitoring for Lactic Acid Production: Transfer Learning from Glucose to Bio-Waste Using Machine Learning Interpretation" href="https://dx.doi.org/10.2139/ssrn.5012080" target="_blank" rel="noopener">[2]</a>. It shows the application of machine learning interpretation to find domain invariant features for glucose and lactic acid. For more details on the methods applied, please refer to the following link: <a title="Domain-Invariant Monitoring for Lactic Acid Production: Transfer Learning from Glucose to Bio-Waste Using Machine Learning Interpretation" href="https://dx.doi.org/10.2139/ssrn.5012080" target="_blank" rel="noopener">https://dx.doi.org/10.2139/ssrn.5012080</a>. The code is available at <a title="ShapFS" href="https://github.com/shl-shawn/ShapFS" target="_blank" rel="noopener">https://github.com/shl-shawn/ShapFS</a>.</p> <h2><strong>4. Real-World Use Cases</strong></h2> <h3><strong>4.1. Regression Task</strong></h3> <p>AdaptFerm serves as a benchmark for machine learning model applications in fermentation processes, specifically for predicting glucose and lactic acid concentrations, measured in g/L (grams per liter), while considering issues of out-of-distribution generalization.</p> <h3><strong>4.2. Domain Adaptation Regression Task</strong></h3> <p>The dataset is also suitable for evaluating different domain adaptation methods. In particular, the glucose substrate fermentation data can be used as the source domain, while the complex sugar fermentation data from bio-waste serves as the target domain. For semi-supervised domain adaptation approaches, it is recommended to use the initial data points (i.e., those collected at the beginning of the fermentation process) from the target domain, as the dataset is organized chronologically by collection day. These approaches aim to improve the robustness of models by transferring knowledge across domains and mitigating the effects of out-of-distribution data.</p> <h3><strong>4.3. Anomaly Detection</strong></h3> <p>The dataset can be used to train anomaly detection models to identify outliers or deviations from normal fermentation behavior. This could be valuable in industrial bioprocessing, where early detection of issues like contamination or process failure is crucial. Techniques like Isolation Forests, One-Class SVM, or Autoencoders could be applied to identify unusual patterns in FTIR spectra.</p> <h3><strong>4.4. Classification Task</strong></h3> <p>Although the main task is regression, the dataset could also be used in classification tasks by discretizing the concentrations of glucose and lactic acid into categories (e.g., low, medium, high). This would allow for the application of classification algorithms like Support Vector Machines (SVM), Random Forests, or Neural Networks for predicting the fermentation phase or identifying specific operational conditions.</p> <h3><strong>4.5. Transfer Learning</strong></h3> <p>Given the nature of the domain adaptation approach in this dataset, transfer learning models can be explored. Models pre-trained on glucose fermentation data can be fine-tuned on complex sugar fermentation data, enabling quicker model convergence and improved performance in data-scarce environments.</p> <h3><strong>4.6. Multi-Task Learning</strong></h3> <p>In a multi-task learning scenario, models could simultaneously predict both glucose and lactic acid concentrations from the same FTIR data. This could help in improving model accuracy by leveraging shared representations across the two tasks.</p> <h3><strong>4.7. Feature Selection</strong></h3> <p>The FTIR spectral data contains a large number of features (wavelengths), and feature selection techniques such as Recursive Feature Elimination (RFE), Lasso regression, or mutual information could be applied to identify the most relevant wavelengths for predicting glucose and lactic acid concentrations, improving model performance and interpretability.</p> <h2><strong>5. Dataset Structure and Meta Information</strong></h2> <p>The dataset is organized into four Excel files, corresponding to two main fermentation domains (different substrates) and two key process variables:</p> <p><strong>a) Simple Sugar Substrate</strong><br>This domain contains data for the fermentation process using glucose as the substrate to produce lactic acid. It includes two files—one for glucose concentrations and one for lactic acid concentrations. Both are measured in g/L.</p> <p><strong>b) Complex Sugar Substrate</strong><br>This doman contains data for the fermentation process using bio-waste as the substrate to produce lactic acid. Similar to the previous domain, it includes two files—one for glucose concentrations and one for lactic acid concentrations. Both are measured in g/L.</p> <p>Each file is structured as follows:</p> <ul> <li>The first column contains the<strong> </strong>sample ID, which serves as the timeline of measurements (Sample ID 1 represents the first measurement in the fermentation process).</li> <li>From the second column onwards, the FTIR data is provided, covering the spectral range from 549.6 cm-1 to 3999.6 cm-1 comprising 3,579 features.</li> <li>The final column contains the ground truth data, the chemical measurements of fermentation variables such as glucose and lactic acid concentrations, both measured in g/L.</li> </ul> <h2><strong>6. Conclusion</strong></h2> <p>The AdaptFerm features FTIR spectra data from two distinct fermentation environments: simple sugar (glucose) and complex sugar (bio-waste). The dataset is designed to be used in regression tasks, including domain adaptation, and can be applied in machine learning model development for fermentation process monitoring, with a focus on enhancing model robustness and handling out-of-distribution data. This dataset provides a valuable resource for exploring<strong> </strong>domain shift and improving the robustness of machine learning models in bioengineering and fermentation processes. It enables further research into domain generalization techniques and offers a wide range of possibilities for machine learning applications.</p> <h2>References</h2> <p> [1] Arman Arefi, Barbara Sturm, Majharulislam Babor, Michael Horf, Thomas Hoffmann, Marina Höhne, Kathleen Friedrich, Linda Schroedter, Joachim Venus, Agata Olszewska-Widdrat, Digital model of biochemical reactions in lactic acid bacterial fermentation of simple glucose and biowaste substrates, Heliyon, Volume 10, Issue 19, 2024, e38791, ISSN 2405-8440, DOI: 10.1016/j.heliyon.2024.e38791, <a href="https://doi.org/10.1016/j.heliyon.2024.e38791" target="_blank" rel="noopener">https://doi.org/10.1016/j.heliyon.2024.e38791</a>.</p> <p>[2] Majharulislam Babor, Shanghua Liu, Arman Arefi, Agata Olszewska-Widdrat, Barbara Sturm, Joachim Venus, and Marina M.-C. Höhne, Domain-Invariant Monitoring for Lactic Acid Production: Transfer Learning from Glucose to Bio-Waste Using Machine Learning Interpretation. Available at <a href="https://dx.doi.org/10.2139/ssrn.5012080" target="_blank" rel="noopener">http://dx.doi.org/10.2139/ssrn.5012080.</a></p>
The inner mechanics of rhodopsin guanylyl cyclase during cGMP-formation revealed by real-time FTIR spectroscopy
<p>Enzymerhodopsins represent a recently discovered class of rhodopsins which includes histidine kinase rhodopsin, rhodopsin phosphodiesterases and rhodopsin guanylyl cyclases (RGCs). The regulatory influence of the rhodopsin domain on the enzyme activity is only partially understood and holds the key for a deeper understanding of intra-molecular signaling pathways. Here we present a UV-Vis and FTIR study about the light-induced dynamics of a RGC from the fungus <em>Catenaria anguillulae</em>, which provides insights into the catalytic process. After the spectroscopic characterization of the late rhodopsin photoproducts, we analyzed truncated variants and revealed the involvement of the cytosolic N-terminus in the structural rearrangements upon photo-activation of the protein. We tracked the catalytic reaction of RGC and the free GC domain independently by UV-light induced release of GTP from the photolabile NPE-GTP substrate. Our results show substrate binding to the dark-adapted RGC and GC alike and reveal differences between the constructs attributable to the regulatory influence of the rhodopsin on the conformation of the binding pocket. By monitoring the phosphate rearrangement during cGMP and pyrophosphate formation in light-activated RGC, we were able to confirm the M state as the active state of the protein. The described setup and experimental design enable real-time monitoring of substrate turnover in light-activated enzymes on a molecular scale, thus opening the pathway to a deeper understanding of enzyme activity and protein-protein interactions.</p>
Identification of glucose-6 phosphate dehydrogenase deficient patients by ATR-FTIR spectroscopy using partial least squares discriminant analysis in aqueous blood samples
<p>Dataset of associated MIR spectra collected in the titled study</p> <p>Baseline: spectrum of air</p> <p>Water: spectrum of deionised water</p> <p>P#_M/F_GD/P/C/T/U_WB/PL/RBC_D/AQ_R: sample spectrum with each section representing the following</p> <ul> <li>P#: patient number 1-30</li> <li>M (male) or F(Female)</li> <li>GD (G6PD deficient), P (Partially G6PD deficient), C & T & ET(Control), U(Unknown) <ul> <li>P10, P28, P29, & P30 were determined later to be P, C, C, P respectively</li> </ul> </li> <li>WB (whole blood), PL (plasma), RBC (red blood cells)</li> <li>D (dried sample spectrum), AQ (aqueous sample spectrum)</li> </ul>
Passive Open-Path Fouriertransform Infrared spectroscopy (OP-FTIR) measurements in the Arctic environment of Ny Ålesund / Svalbard 2023
<p>In the Polar MOSES campaign 2023, a ground-based remote sensing method – OP-FTIR spectroscopy- which is proven to be a flexible long-path technique for the characterization of larger areas was used. This method is able to simultaneously detect various volatile atmospheric compounds relevant for environmental assessment with a single rapid measurement. Many greenhouse gas molecules (e.g., CO<sub>2</sub>, H<sub>2</sub>O, CH<sub>4</sub>) have unique signatures (absorption or emission bands) in the measured spectral IR range. IR spectroscopy allows spatial and non-invasive characterization of emissions and is a useful method to capture emissions along the coastline, both seaward and landward.</p> <p>The passive technique was applied in a mobile mode to obtain IR spectra at different relevant locations. In order to investigate the near surface atmospheric composition, the mobile passive OP-FTIR spectroscopic scanning measurements was executed with Bruker RAPID devices to cover marine and terrestrial areas. The RAPID passive detection systems was deployed for 360° remote sensing of threats at distances up to 5000 m. Initially, the survey's primary objective was to qualitatively interpret spectra, enabling the identification of emission sources and the characterization of the heterogeneous emission pattern. This was achieved through the measurements in different directions to allow the direct comparisons of the various compartments under investigation and their potential interactions.</p>
The inner mechanics of rhodopsin guanylyl cyclase during cGMP-formation revealed by real-time FTIR spectroscopy
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FTIR-Plastics: a Fourier Transform Infrared Spectroscopy dataset for the six most prevalent industrial plastic polymers.
<p><span><span>Two datasets are presented: FTIR-Plastics-C4 and FTIR-Plastics-C8, comprising 6,000 spectra obtained through Fourier Transform Infrared Spectroscopy (FTIR) applied to the six most used synthetic polymers: Polyethylene Terephthalate (PET), High-Density Polyethylene (HDPE), Polyvinyl Chloride (PVC), Low-Density Polyethylene (LDPE), Polypropylene (PP), and Polystyrene (PS). The key feature of the datasets lies in the FTIR analysis, which reports the percentage transmittance as the intensity measure as a function of the wavelength of an Infrared light source, expressed as wavenumber (with units in cm</span></span><sup><span><span>-1</span></span></sup><span><span>). FTIR analysis was performed using a Jasco FTIR PRO 4x spectrophotometer with a wavenumber resolution setting of 8 cm</span></span><sup><span><span>-1</span></span></sup><span><span> for FTIR-Plastics-C8 and 4 cm</span></span><sup><span><span>-1</span></span></sup><span><span> for FTIR-Plastics-C4, both employing a configuration of 32 scans and a range from 4000 to 400 cm</span></span><sup><span><span>-1</span></span></sup><span><span>. The datasets are presented in CSV (comma-separated values) format, including the following information (per each column):</span></span></p> <ul> <li> <p><span><span><strong>IDE</strong></span></span><span><span>: unique identifier of the sample.</span></span></p> </li> <li> <p><span><span><strong>Polymer: </strong></span></span><span><span>type of synthetic polymer (PET, HDPE, PVC, LDPE, PP, or PS).</span></span></p> </li> <li> <p><span><span><strong>Technique: </strong></span></span><span><span>Type of technique used (FTIR).</span></span></p> </li> <li> <p><span><span><strong>Sample: </strong></span></span><span><span>polymer sample number.</span></span></p> </li> <li> <p><span><span><strong>BR</strong></span></span><span><span>: scanning configuration (32).</span></span></p> </li> <li> <p><span><span><strong>RST</strong></span></span><span><span>: resolution configuration (8 or 4 cm</span></span><sup><span><span>-1</span></span></sup><span><span>).</span></span></p> </li> <li> <p><span><span><strong>Data (x) y Data(y): </strong></span></span><span><span>1884 pairs of columns for FTIR-Plastics-C8 and 3751 pairs of columns for FTIR-Plastics-C4, representing values on the "x" axis (wavenumber) and the "y" axis values associated with molecular vibration intensities, indicating the transmittance (%), which differentiates each polymer.</span></span></p> </li> </ul> <p><span><span>Additionally, the files generated by the Jasco spectrophotometer for each polymer are provided, which were standardized by adding a header with the following structure:</span></span></p> <ul> <li> <p><span><span>TITLE SAMPLE NAME: referring to the name of the analyzed polymer.</span></span></p> </li> <li> <p><span><span>DATA TYPE: specifying the characterization technique.</span></span></p> </li> <li> <p><span><span>MEASUREMENT INFORMATION: equipment used for data collection.</span></span></p> </li> <li> <p><span><span>MODEL NAME: name of the equipment used.</span></span></p> </li> <li> <p><span><span>SERIAL No: serial number assigned to the equipment used.</span></span></p> </li> <li> <p><span><span>ACCESSORY: complementary device integrated into the equipment.</span></span></p> </li> <li> <p><span><span>LIGHT SOURCE: standardized light source related to the DLATGS detector.</span></span></p> </li> <li> <p><span><span>RESOLUTION: parameters are used to distinguish the wavenumber in the analyzed materials.</span></span></p> </li> <li> <p><span><span>XUNIT/HORIZONTAL AXIS: referring to the unit’s title assigned on the x-axis.</span></span></p> </li> <li> <p><span><span>YUNITS/VERTICAL AXIS: referring to the unit’s title designated on the y-axis.</span></span></p> </li> <li> <p><span><span>FIRSTX: initial value set for the x-axis.</span></span></p> </li> <li> <p><span><span>FIRSTY: initial value set for the y-axis.</span></span></p> </li> <li> <p><span><span>LASTX: final value set for the x-axis.</span></span></p> </li> <li> <p><span><span>LASTY: final value set for the y-axis.</span></span></p> </li> <li> <p><span><span>NPOINTS: total data points in the file.</span></span></p> </li> </ul> <p><span><span>Data collection was carried out meticulously, following specific steps to ensure the accuracy and reliability of the results. The calibration certificates issued by the supplier (calibration_certificate.pdf) corresponding to the equipment used in the experiments and data collection that give rise to these databases are attached.</span></span></p>
Use of Fly Ash for Heavy Metals Removal from Wastewater A Ftir Spectroscopy Study
<p>This material has presented on 2nd International Conference on Advanced Research in Engineering and Technology in October 25, 2023.</p>
FIGURE 1 in Fourier transform infrared spectroscopy (FTIR) characteristics of ancient amber artifacts of the Han Dynasty from Hunan, China
FIGURE 1. Possible ancient trade roadmap of both the Eurasian Steppe Silk Road and the Maritime Silk Road. 1, the present Capital, Beijing; 2, the ancient Capital city during the Han Dynasty, Xi'an, Shaanxi Province; 3, Nanyang, Henan Province, where unearthed artifacts of Han Dynasty made by Burmese amber (Chen et al., 2019); 4, Tomb Changchen M010, Changsha, Hunan Province; 5, Tomb Chentie M001, Chenzhou, Hunan Province.
FIGURE 3 in Fourier transform infrared spectroscopy (FTIR) characteristics of ancient amber artifacts of the Han Dynasty from Hunan, China
FIGURE 3. FTIR spectra of amber samples. A, SYS20220716-1. B, SYS20020716-2. C, SYS20220716-4. D, SYS20220716-5.
Impact of Fourier Transform Infrared Spectroscopy (FTIRS) in ICUs
ClinicalTrials.gov study NCT05872152. IPD Sharing: NO. Countries: 1. Publications: 5.
Endometriosis and ATR-FTIR Spectroscopy
ClinicalTrials.gov study NCT06426420. IPD Sharing: NO. Countries: 1. Publications: 0.
Data from: A high-throughput FTIR spectroscopy approach to assess adaptive variation in the chemical composition of pollen
The two factors defining male reproductive success in plants are pollen quantity and quality, but our knowledge about the importance of pollen quality is limited due to methodological constraints. Pollen quality in terms of chemical composition may be either genetically fixed for high performance independent of environmental conditions, or it may be plastic to maximize reproductive output under different environmental conditions. In this study, we validated a new approach for studying the role of chemical composition of pollen in adaptation to local climate. The approach is based on high-throughput Fourier infrared (FTIR) characterization and biochemical interpretation of pollen chemical composition in response to environmental conditions. The study covered three grass species, Poa alpina, Anthoxanthum odoratum, and Festuca ovina. For each species, plants were grown from seeds of three populations with wide geographic and climate variation. Each individual plant was divided into four genetically identical clones which were grown in different controlled environments (high and low levels of temperature and nutrients). In total, 389 samples were measured using a high-throughput FTIR spectrometer. The biochemical fingerprints of pollen were species and population specific, and plastic in response to different environmental conditions. The response was most pronounced for temperature, influencing the levels of proteins, lipids, and carbohydrates in pollen of all species. Furthermore, there is considerable variation in plasticity of the chemical composition of pollen among species and populations. The use of high-throughput FTIR spectroscopy provides fast, cheap, and simple assessment of the chemical composition of pollen. In combination with controlled-condition growth experiments and multivariate analyses, FTIR spectroscopy opens up for studies of the adaptive role of pollen that until now has been difficult with available methodology. The approach can easily be extended to other species and environmental conditions and has the potential to significantly increase our understanding of plant male function.
Data from: Monitoring of plant-environment interactions by high throughput FTIR spectroscopy of pollen
Fourier transform infrared (FTIR) spectroscopy enables chemical analysis of pollen samples for plant phenotyping to study plant–environment interactions, such as influence of climate change or pathogens. However, current approach, such as microspectroscopy and attenuated total reflection spectroscopy, does not allow for high-throughput protocols. This study at hand suggests a new spectroscopic method for high-throughput characterization of pollen. Samples were measured as thin films of pollen fragments using a Bruker FTIR spectrometer with a high-throughput eXTension (HTS-XT) unit employing 384-well plates. In total, 146 pollen samples, belonging to 31 different pollen species of Fagaceae and Betulaceae and collected during three consecutive years (2012–2014) at locations in Croatia, Germany and Norway, were analysed. Critical steps in the sample preparation and measurement, such as variabilities between technical replicates, between microplates and between spectrometers, were studied. Measurement variations due to sample preparation, microplate holders and instrumentation were low, and thus allowed differentiation of samples with respect to phylogeny and biogeography. The spectral variability for a range of Fagales species (Fagus, Quercus, Betula, Corylus, Alnus and Ostrya) showed high-species-specific differences in pollen's chemical composition due to either location or year. Statistically significant inter-annual and locational differences in the pollen spectra indicate that pollen chemical composition has high phenotypic plasticity and is influenced by local climate conditions. The variations in composition are connected to lipids, proteins, carbohydrates and sporopollenins that play crucial roles in cold and desiccation tolerance, protection against UV radiation and as material and energy reserves. The results of this study demonstrate the value of high-throughput FTIR approach for the systematic collection of data on ecosystems. The novel FTIR approach offers fast, reliable and economical screening of large number of samples by semi-automated methodology. The high-throughput approach could provide crucial understanding on plant–climate interactions with respect to biochemical variation within genera, species and populations.
Figure 5 from: Salamah N, Cantika CD, Nurani LH, Guntarti A (2024) Authentication of citrus peel oils from different species and commercial products using FTIR Spectroscopy combined with chemometrics. Pharmacia 71: 1-7. https://doi.org/10.3897/pharmacia.71.e118789
Figure 5 PCA score plot of the sweet orange, lime, lemon peel oils, turpentine oil and commercial oil products A, B and C.
Figure 3 from: Salamah N, Cantika CD, Nurani LH, Guntarti A (2024) Authentication of citrus peel oils from different species and commercial products using FTIR Spectroscopy combined with chemometrics. Pharmacia 71: 1-7. https://doi.org/10.3897/pharmacia.71.e118789
Figure 3 ATR-FTIR spectra of three commercial oil products in the 3500–500 cm-1 region. (1: Product A, 2: Product B, 3: Product C).
Figure 4 from: Salamah N, Cantika CD, Nurani LH, Guntarti A (2024) Authentication of citrus peel oils from different species and commercial products using FTIR Spectroscopy combined with chemometrics. Pharmacia 71: 1-7. https://doi.org/10.3897/pharmacia.71.e118789
Figure 4 Correlation curves between the actual values (x-axis) and the predicted values (y-axis): (A) the model's calibration using the 1650–1450 cm-1 region (optimised wavenumbers), (B) internal validation and (C) external validation.
Figure 2 from: Salamah N, Cantika CD, Nurani LH, Guntarti A (2024) Authentication of citrus peel oils from different species and commercial products using FTIR Spectroscopy combined with chemometrics. Pharmacia 71: 1-7. https://doi.org/10.3897/pharmacia.71.e118789
Figure 2 ATR-FTIR spectra of sweet orange peel oil (MKJM) mixed with turpentine oil (MT) at different concentrations.
Fourier transform infrared (FTIR) spectroscopy as a screening tool for osteosarcopenia in community-dwelling older women
<p>Dataset generated by MATLAB to be analyzed and identify osteosarcopenic women. </p>
Stability and Control of RT-VCM employed for Moving Mirror Control in FTIR Spectroscopy within the Realms of Biomedical Applications using FOPID Controller
<p>Table. 2 Variation and Selection of non-integer order with lamda 0.2</p>
Data from: A high-throughput FTIR spectroscopy approach to assess adaptive variation in the chemical composition of pollen
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