Spectroscopy Tech Innovations & Applications

A Look at Inorganic Analyses, Proficiency Tests, and Contamination and Error, Part I

What Does Your Proficiency Testing (PT) Prove? 

Applications of Raman Spectroscopy in Solvent Distillation and Exchange 

Is Traceability the Glue for ALCOA, ALCOA+, or ALCOA++?

2022 Review of Spectroscopic Instrumentation

Spray paint is often used by vandals for creating graffiti, as well as for criminals to leave signs, messages, and blots to conceal the left traces at the scene of their efforts. Rajinder Singh and his colleagues in the Department of Forensic Science at Punjabi University (Punjab, India) have used attenuated total reflectance Fourier transform infrared (ATR-FT-IR) spectroscopy for nondestructive analysis of 20 red spray paints of different manufacturers, which could possibly be encountered at a crime scene, particularly in case of vandalism. Singh spoke to 

about the findings, and the paper that resulted from their efforts (1).

What inspired you to research the ATR-FT-IR method for the purpose of your analysis?


These days, spray paints are often used in criminal offenses. The most obvious cases are vandalism and graffiti where spray paints could be utilized to destroy or blot different types of objects. Additionally, in many criminal attempts, criminals leave any kind of messages or signs using spray paint at the scene of the crime. However, these marks can sometimes be the only available valued evidence deserving a precise analytical method for the identification and for discrimination purposes. In forensic casework conditions, the samples are often encountered in only trace levels; thus, the trace amount of available sample make their analysis very difficult. The traditional methods are destructive to the sample which makes the preservation and further analysis of sample not possible. The technique used in this present work is completely non-destructive, rapid, reliable, and eco-friendly in nature. Therefore, following ATR-FT-IR analysis, samples can be preserved and further analyzed using other analytical methods, if needed. Henceforth, the non-destructive nature of this method inspired us to move ahead with the analysis of various trace evidence such as spray paints. ATR FT-IR spectroscopy, in combination with chemometric tools, yielded valuable results leading to a high degree of discrimination between samples.

What benefits did you anticipate by using ATR-FT-IR spectroscopy over other analytical techniques for your intended analysis?


Generally, forensic examination of paint samples includes a comparison of their chemical content. In this aspect, various techniques such as optical microscopic analysis, scanning electron microscopy with energy dispersive X-ray analysis (SEM–EDX), micro-spectrometry in the ultraviolet (UV) and visible (vis) spectral ranges, pyrolysis gas chromatography-mass spectrometry (GC–MS), micro-X-ray fluorescence (μ-XRF), and so forth, are used for the determination of its chemical composition. These methods are routinely employed in the analysis of paint traces. The limitation that exists with these methods is that they are destructive in nature, time-consuming, and sometimes require various chemical processes for extraction and sample preparation. In forensic casework conditions, samples are often encountered that have been applied to different substrates. Unfortunately, it is often not possible to separate the paint traces entirely from the substrate, and this is because of the obvious fact that the paint sticks very firmly to the base material. Therefore, it usually is necessary to try and examine the paint traces directly so that the substrate has a diminished influence on the obtained results. Therefore, in situ non-destructive analysis is highly preferred and desirable for these examinations. ATR FT-IR spectroscopy meets all the requirements needed for the analysis of paint traces. Samples can be analyzed non-destructively in a rapid manner without any sample preparation, and directly measured from the different substrates.

Briefly discuss your findings and their implications. Were you surprised by the results?


ATR FT-IR spectroscopy is rapid and non-destructive; is able of analyzing small quantities of samples, and provides rapid analysis time. The results obtained showed good reproducibility and repeatability. The chemometric tools assist significantly in the interpretation of results in an objective manner. Principal component analysis (PCA) successfully conveyed 100% of discriminating power for the differentiation of different brands of red spray paints, which is highly significant. To check the reliability of the PCA model, the blind validation test was also carried out and 100% accurate classification of unknown samples was observed in this study. Furthermore, the results of the substrate study suggested that it is possible to analyze the type of spray paints on various substrates; however, the accuracy of discrimination depends mainly on the paint coat, material type, and thickness of the substrates. We observed that paint on floor, gloves, metal, plastic, leather shoes, tile, wood, and hair substrates all demonstrated significant chemical peaks; and all were present allowing comparative studies between neat spray paint and spray paint deposited on these various substrates. We, the authors, were then able to obtain the results as we expected in our hypothesis.

What are the biggest challenges that you have encountered in developing this method? What options or alternative developments are available to overcome these challenges or to improve your approach?


The biggest challenge faced in the current work was to analyze the spray paints directly on a variety of substrates, such as paper, fabric, and cemented wall using the ATR FT-IR spectroscopy technique. Sampling can be rather complicated, and thus, could severely hamper the comparative study. We are trying our best to improve our methodologies for the extraction of the paint sample from porous surface substrates.

What sort of feedback did you receive from this paper and the study results?


This is a good approachable methodology for the rapid, eco-friendly, and non-destructive analysis of spray paints on various fabric substrates.

Are there any additional analytical approaches or methods where your findings might be beneficial?


This technique can be used as a complementary tool for other available analytical methods. The findings of this approach will definitely provide a complement to Raman spectroscopy.

Are you aware of the various research work that has been done for forensic analysis of automotive paints using FT-IR? 


There is an article recently published in 

(2). There have also been several papers written describing the forensic analysis of automotive paints using ATR-FT-IR.

What are your next steps regarding this or related research?


In the future, we foresee undergoing further investigations on the applicability of ATR FTIR spectroscopy for the identification and discrimination of automotive paints and architectural paints of different brands and manufacturers under simulated forensic casework conditions.

(1) S. Sharma, R. Chophi, C. Kaur, and R. Singh, 

(2) J.M. Duarte, N.G.S.S. Sales, J.W.B. Braga, C. Bridge, M. Maric, M.H. Sousa, and J.A. Gomes, 

Rajinder Singh Chandel, earned his Master's degree in Forensic Science with a Gold medal from the Department of Forensic Science, Punjabi University, Patiala, India in 2001. He carried out his PhD research work on “Hair Characterization of Schedule-1 Felids of Wildlife (Protection) Act-1972” in collaboration with the Wildlife Institute of India (WII), Dehradun, which led him to attain his PhD in 2008 from Punjabi University. He has been actively involved in the research related to the forensic characterization of body fluids, trace cosmetics evidence, inks, paints, diatoms, grasses, poisonous plants, hair of various species protected under Wildlife (Protection) Act-1972, and other evidentiary materials of biological origin using morphological, chromatographic, spectroscopic, and DNA-based techniques.

Chandel has been involved in regular teaching and research in the area of forensic biology and forensic chemistry since 2003 at Punjabi University. He has published 68 research papers in various national and international journals and has contributed reference mtDNA sequences for the identification of highly endangered and protected cats of India in NCBI (GenBank). He has been involved in many training courses on wildlife forensics and illegal wildlife trade at LNJN National Institute of Criminology and Forensic Science, Ministry of Home Affairs, Govt. of India, New Delhi, and has also been a resource for many conferences, symposia, and workshops of national and international repute.

Articles

Spray paint is often used by vandals for creating graffiti, as well as for criminals to leave signs, messages, and blots to conceal the left traces at the scene of their efforts. Rajinder Singh and his colleagues in the Department of Forensic Science at Punjabi University (Punjab, India) have used attenuated total reflectance Fourier transform infrared (ATR-FT-IR) spectroscopy for nondestructive analysis of 20 red spray paints of different manufacturers, which could possibly be encountered at a crime scene, particularly in case of vandalism. Singh spoke to Spectroscopy about the findings, and the paper that resulted from their efforts.

Chemometric Analysis on ATR-FT-IR Spectra of Spray Paint Samples for Forensic Purposes 

Seven common mistakes in the analysis of Raman spectra can lead to overestimating the performance of a model.

Raman spectroscopy is an inelastic scattering process on a quantified energy system where vibrational energy levels are typically measured. The technique provides an indirect measurement of vibrational energy levels within the sample and hence can characterize the biochemical composition of a sample. Normally, Raman spectroscopy is applied in a label-free manner, in contrast to using fluorescence labels, surface-enhanced Raman spectroscopy (SERS) labels, or Raman tags. The unlabeled Raman measurement provides a characteristic and phenotypical molecular fingerprint of the sample. In combination with artificial intelligence (AI) methods, such as chemometrics and machine learning, this fingerprint can be translated into high-level information. Because of the weakness of the Raman effect, AI-based data analysis pipelines are constructed, which aim at correcting, standardizing, and purifying the Raman data before the translating of high-level information can take place.

The data analysis pipeline for Raman spectra contains a sequence of algorithms to correct, standardize, and purify the measured Raman data. The data analysis pipeline starts with correcting cosmic spikes originating from high-energy cosmic particles or their secondary particles. Sometimes, other artifacts, such as fixed pattern noise, are removed as well. The next step deals with the wavelength, wavenumber, and intensity calibration. The first two calibration steps aim to generate a stable wavenumber axis with respect to the excitation wavelength. Generating a stable wavenumber is necessary because in Raman spectroscopy a relative measurement with respect to the excitation wavelength is performed. The intensity calibration aims at generat- ing setup-independent Raman spectra by correcting the spectral transfer function of optical components, such as lenses, filters, dispersive elements, and the quantum efficiency of the detector. After this step, multiple preprocessing methods are applied, which further standardize the data. This preprocessing block typically contains a baseline correction, because the Raman effect overlaps energetically with fluorescence. Thereafter, a denoising and a normalization step are applied to make spectra from different measurement days comparable. Denoising is not always applied, but the mixed Poisson–Gaussian noise needs to be considered in the selection of the algorithm. Finally, a feature extraction or dimension reduction (such as principal component analysis, or PCA) is applied. The selected features are further analyzed using classical machine learning or chemometric models. These last two steps can be combined with deep learning methods. Finally, these machine learning or deep learning models included in the whole data analysis pipeline are evaluated to judge the generalization performance of the models; that is, its performance on a new data set. Finally, the models are interpreted with visualization approaches.

The above described data analysis pipeline is described elsewhere (1) and sometimes there are changes in the ordering of the algorithms based on researchers’ idea of their data. Nevertheless, here we want to focus on specific problems or mistakes that should be avoided. These mistakes are shown in Figure 1 and these potential mistakes are explained in more detail below.

FIGURE 1: A visualization of the mistakes within the data analysis pipeline for Raman spectra. The mistakes are indicated in the arrow with respect to the location within the data analysis pipeline. The mistakes may arise from the sample size planning, which should determine enough independent observations. Thereafter, a wavenumber, wavelength, and intensity calibration are performed. The baseline correction is important, but the normalization should be done after the baseline correction. The complexity of the model is linked to the number of independent observations and the model should not be too complex. Thereafter, an unbiased estimation of the model generalizability is needed, which requires a cross-validation (or test validation) over independent measurements. Finally, the multiple testing problem needs to be corrected for and the correct statistical tests need to be applied.

Mistake #1: Absence of Independent Samples

The first critical issue is sample size planning, which is important because a sufficient quantity of independent data is needed to training and test data-driven AI models. It is advisable to measure at least three independent replicates in cell studies (five is preferable) and roughly 20–100 patients for diagnostic studies (2), which is especially important for the model evaluation, as discussed further in this tutorial (see mistake #6).

Generally, it is advisable to measure a wavenumber standard like 4-acetamidophenol with a high number of peaks in the wavenumber region of interest. These measurements can be used to construct a new wavenumber axis for each measurement day, which is subsequently interpolated to a common and fixed wavenumber axis (3). If this wavenumber (wavelength) calibration is not performed, systematic drifts in the measurement system overlap with possible sample related changes. An optimal spectrometer calibration is focus of ongoing research, but a white light measurement should be done every week or when the setup is modified. If these spectra (wavenumber standard or white light spectra) are not used for calibration, they can act as quality control measurements.

Mistake #3: Over-Optimized Preprocessing 

Because Raman spectra overlap with fluorescence and the fluorescence background can be 2–3 orders more intense than the Raman bands, a baseline correction or other approach, like performing the derivative, is important (4). Nevertheless, it is hard so select good parameters of the baseline correction and a grid search or optimization should be performed. To avoid overfitting, it is preferable to utilize spectral markers as the merit of such optimization rather than the performance of the model.

Mistake #4: Performing Spectral Normalization Before Background Correction 

Sometimes, it is seen that spectral normalization is performed before the background correction, resulting in a strong bias of the normalized Raman spectra. The fluorescence background and its intensity are coded within the normalization constant and hence every model might be biased. This issue should be avoided. Baseline correction needs to be done before normalization is performed.

Mistake #5: Selecting an Unsuitable Model

Depending on the number of independent measurements, the model and its complexity should be selected. For large independent data sets, modeling with a highly parameterized model such as a deep learning model can and should be applied. If the data set contains only a few independent observations, low-parameterized models should be used, such as linear models.

Another mistake that is often seen relates to the model evaluation (cross-validation or training validation). It is paramount that the different data sets within the validation be independent. To fulfill this requirement, biological replicates or patients, which act as independent measurements, need to be in either a training, validation, or test data subset. If this requirement is violated, the model and its performance is highly overestimated. In a previous study, we could show that a model with 60% classification accuracy, which was evaluated reliably (replicate-out cross-validation), was overestimated to nearly 100% classification accuracy with normal cross-validation (5). The main problem that can arise is an information leakage between the data sets that leads to a bias in the model performance estimation.

The last mistake that is often encountered is when a statistical analysis is performed on band intensities. If multiple Raman intensities are tested, then a Bonferroni correction that accounts for the alpha-error cumulation is needed. This issue occurs when 100 tests are performed, roughly five are positive by chance alone if an error probability of 95% is used. The second point is that a 

-test is normally not applicable because the assumptions are not meet. Therefore, nonparameterized alternative tests like the Mann-Whitney-Wilcoxon U test should be used.

These seven mistakes are often seen in publications about Raman spectroscopy and should be avoided, because they affect the final performance estimate of the model. These biased performance estimates need to be interpreted with caution, especially when Raman spectroscopy together with AI-based modeling is used in real world applications such as clinical diagnostics.

The financial support given from the European Union (EU), the Thüringer Ministerium für Wirtschaft, Wissenschaft und Digitale Gesellschaft, the Thüringer Aufbaubank, the Federal Ministry of Education and Research, Germany (BMBF), the German Science Foundation, the Fonds der Chemischen Industrie, and the Carl-Zeiss Foundation are greatly acknowledged.

(1) S. Guo, O. Ryabchykov, N. Ali, R. Houhou, and T. Bocklitz, in 

Reference Module in Chemistry, Molecular Sciences, and Chemical Engineering

 (Elsevier, Amsterdam, The Netherlands, 2020). https://doi.org/10.1016/B978-0-12-409547-2.14600-1.

(2) N. Ali, S. Girnus, P. Roesch, J. Popp, and T.W. Bocklitz, 

, 12485–12492 (2018). https://doi.org/10.1021/acs.analchem.8b02167.

(3) T.W. Bocklitz, T. Dörfer, R. Heinke, M. Schmitt, and J. Popp, 

Spectrochim. Acta. A. Mol. Biomol. Spectrosc.

, 544–549 (2015). https://doi.org/10.1016/j.saa.2015.04.079.

, 2396–2404 (2016). https://doi.org/10.1039/C6AN00041J.

(5) S. Guo, T. Bocklitz, U. Neugebauer, and J. Popp, 

 are with the Leibniz Institute of Photonic Technology in Jena, Germany. 

 are with the Leibniz Institute of Photonic Technology in Jena, Germany, and with the Institute of Physical Chemistry and Abbe Center of Photonics at Friedrich-Schiller University in Jena, Germany. Direct correspondence to: 

Errors and Mistakes to Avoid when Analyzing Raman Spectra

A set of food samples with different percentage compositions of fats, proteins, and carbohydrates were digested in a single microwave digestion batch and analyzed to determine elemental concentrations using inductively coupled plasma–mass spectrometry (ICP-MS) and ICP-optical emission spectroscopy (OES) using the U.S. Food and Drug Administration (FDA) Elemental Analysis Manual (EAM) 4.7 (for ICP-MS) and FDA EAM 4.4 (for ICP-OES). Built-in software tools were used to streamline the analytical workflow, which is especially useful for new or less experienced users running these methods. Processed foods often contain high concentrations of some elements and trace amounts of others, so a wide analytical range is required. The ICP-MS method used to analyze the varied samples in this work was new. To ensure optimum setup of the ICP-MS method, semi-quantitative results were used to assess the solid content of the food digests before the sample preparation dilution was finalized. The same food samples were analyzed using ICP-MS and ICP-OES and the two methods were compared.

Agricultural food samples contain a wide variety of trace and minor elements, which can vary depending on several factors, including the geographical location, soil type, agricultural practices, and crop type. Along with the elements that have been monitored for decades, newly emerging contaminants may also be present in agricultural soil and other chemicals and additives that enter the food chain. Important emerging contaminants include the technology critical elements (TCEs), such as rare earth elements (REEs) that are used in advanced materials, renewable energy, batteries, consumer electronics, and life science applications. The mining, refining, use, and disposal of these elements may cause environmental contamination, which can ultimately lead to food contamination.

Snack foods contain multiple ingredients and are often highly processed, with each ingredient and process representing a potential source of contamination. Although cases are rare, especially if manufacturers adhere to Current Good Manufacturing Practices (cGMP) (1), processed foods can become contaminated at any point during the production, packaging, and distribution process. Typically, the chemicals that are controlled in foodstuffs include organic contaminants, such as pesticide residue, and inorganic contaminants. According to the general standard for food and feed outlined in the Codex Alimentarius (2) published by the Food and Agricultural Organization (FAO) and World Health Organization (WHO), the most concerning elements include arsenic (As), cadmium (Cd), lead (Pb), and mercury (Hg). The maximum levels (MLs) of each of these elements depend on the type of food, as shown in Table I. Codex MLs ensure food does not contain contaminants at levels which could threaten human health. Many countries base legislation on Codex standards and related texts.

To ensure foods are safe and comply with United States regulations and laws, the U.S. Food and Drug Administration (FDA) develops and publishes works that describe analytical methods that manufacturers or importers can use in their laboratories. Examples include the FDA Elemental Analysis Manual (EAM) 4.4 for inductively coupled plasma–optical emission spectroscopy (ICP-OES) and EAM 4.7 for ICP–mass spectrometry (MS) (3,4). Both ICP-OES and ICP-MS are well-established techniques, and because of their multielement and high sample throughput capabilities, both are widely used in the routine measurement of trace elements in a wide range of samples, including foods and beverages (5–11). EAM 4.4 and 4.7 outline how to determine multiple elements in food digests prepared using microwave-assisted acid decomposition. The methods also outline a series of quality control (QC) tests to ensure instrument performance and data accuracy.

To assist busy analysts who run the EAM methods using ICP-OES or ICP-MS, software tools can speed up method development, improve the usability of both techniques, and provide confidence in the results. Many laboratories that analyze unknown samples or handle a wide variety of samples use semi-quantitative analysis to gain a better understanding of the sample before developing the quantitative method. Knowing more about the samples is useful for all analysts, but software tools that reduce some of the uncertainty of using ICP techniques are especially useful for new users.

Acquiring a full ICP-MS or ICP-OES spectrum adds only a few seconds to the quantitative measurement, but the extra data collected can provide valuable additional information on the samples. As a technique, ICP-MS can acquire semi-quantitative results for as many as 78 elements in each sample, which provide a more complete overview of the sample composition and confirmation of the quantitative results. The additional information provides certainty and confidence in the results, preventing the need for remeasurements, potentially saving time and resources over the course of the analytical sequence.

Processed foods often contain high concentrations of calcium (Ca), iron (Fe), potassium (K), magnesium (Mg), sodium (Na), phosphorus (P), manganese (Mn), copper (Cu), and zinc (Zn), whereas trace elements such as As, chromium (Cr), Cd, Pb, nickel (Ni), molybdenum (Mo), selenium (Se), and Hg are required analytes in most regulated methods, so a wide analytical range is required.

A set of food samples with different percentage compositions of fats, proteins, and carbohydrates were measured in this study, including pepperoni, rice noodles, frozen dinner, and frozen pizza bought from a supermarket in North Carolina (Figure 1).

FIGURE 1: Nutritional composition plot for foods and standard reference materials (SRM) analyzed in this study.

The food samples were digested as received, except the frozen pizza and frozen dinner, which were homogenized in a blender before sampling. The samples were prepared for analysis according to the digestion procedure outlined in the EAM method using a MARS 6 iWave closed-vessel microwave digestion system (CEM Corporation). After accurately weighing the samples (approximately 0.5 g of food or SRM) into 75 mL PFA MARSXpress vessel liners, 8 mL of nitric acid (HNO

) were added to the vessel liners. Duplicates of the samples, standard reference materials (SRMs), and spiked samples were then digested in a single batch using the heating program shown in Table II. Each digestion batch can accommodate up to 40 varied food sample matrices, with a single program being used for all sample types. Finally, 0.5 mL concentrated hydrochloric acid (HCl) was added to the digests, followed by deionized water (DIW) to a final weight of 100 g.

Calibration standards for both ICP-MS and ICP-OES methods were prepared in 1% HNO

 and 0.5% HCl using standard solutions (environmental calibration standard, multielement calibration standard-1, and 1000 μg/mL single calibration standard for Hg, Agilent Technologies). For ICP-MS analysis, most elements were calibrated from 0.1 to 25 ppb, and Cu, Zn, and Mn were calibrated up to 250 ppb, whereas Hg was calibrated from 0.01 to 2.5 ppb. For ICP-OES analysis, standards were prepared from 10 ppb to 10 ppm for all elements, with a 100-ppm standard for Ca, K, Mg, Na, and P.

For ICP-MS analysis, an internal standard (ISTD) solution containing 2 ppm scandium (Sc), germanium (Ge), rhodium (Rh), indium (In), terbium (Tb), lutetium (Lu), and bismuth (Bi) was prepared in a solution consisting of 1% HNO

, 0.5% HCl, and 10% isopropanol (IPA). The IPA was added to ensure a consistent level of carbon (C) in the digests and calibration standards. This approach alleviated the concern of signal variability that could occur for some poorly ionized analytes (such as As and Se) because of ionization enhancement in the presence of C. The ISTD solution was added automatically to the sample and standard solution at a ratio of approximately 1:16 using a T-connector fitted to the sample uptake line that directly lead to the nebulizer.

The ICP-OES ISTD consisted of a 2 mg/L solution of yttrium (Y) in 2% HNO

, which was added in-line at a dilution of ~1:8. The sample peristaltic pump tubing was white-white (1.03 mm i.d.), and the internal standard was orange-green (0.38 mm i.d.). The standard sample introduction system was used, consisting of a glass-concentric nebulizer and a double-pass, glass cyclonic spray chamber. An independent check solution (ICS) and standard blank were analyzed after calibration, and a check standard and standard blank were analyzed every 10 samples thereafter.

The analytical sequences of calibration standards, samples, and QC solutions for both techniques are shown in Figure 2. Each EAM method specified a minimum number of QC samples to be analyzed with each batch of sample. These QC samples include reference materials (RM), fortified analytical portions (FAP), fortified analytical solutions (FAS), method blanks (MBK), fortified method blanks (FMB), and the number of replicates to be included in each analytical run.

FIGURE 2: Analytical sequence for (a) ICP-MS and (b) ICP-OES.

The accuracy of the semi-quantitative analysis of complex sample matrices by ICP-MS is vastly improved if all elements are acquired using helium collision cell mode to control polyatomic interferences. Helium collision cell mode is widely used to remove polyatomic interferences in multielement quantitative analysis. Because helium is a nonreactive gas, no new interferences are formed in the cell, whereas common matrix-based polyatomic interferences are removed using kinetic energy discrimination (KED). This makes helium mode ideally suited to the quantitative and semi-quantitative analysis of samples with complex and variable major element compositions (12). In this study, a single quadrupole ICP-MS instrument (7850, Agilent Technologies) fitted with an octopole-based collision–reaction cell (Octopole Reaction System CRC) and Ultra High Matrix Introduction (UHMI) aerosol dilution technology was used.

Although polyatomic ions were successfully removed using helium cell mode, some other spectral overlaps required a different approach. The REEs have relatively low second ionization potentials, so they can form a small percentage of doubly charged ions (M

) in the plasma along with the normal single-charged (M

) ions. Doubly charged ions appear at half their true mass in the ICP-MS mass spectrum because the quadrupole mass filter separates ions based on their mass-to-charge ratio (

). Therefore, REEs, such as neodymium (Nd), samarium (Sm), gadolinium (Gd), and dysprosium (Dy), which have isotopes at masses 150, 156, and 160, will form M

 75, 78, and 80, respectively. If the REEs are present at a high enough concentration in a sample, these REE

 ions can interfere with the measurement of the normal analytical isotopes of As and Se. The EAM 4.7 ICP-MS method recommends that analysts measure 

Se. When analyzing unknown or new sample types, the REE content can be quickly and simply assessed using the IntelliQuant semi-quantitative analysis function of ICP-MS MassHunter software (version 4.6 and later). If a high concentration of REEs is expected or identified following the semi-quantitative analysis, a method wizard within the software can set up an automated function to correct for the contribution that REE

 ions make to the signals measured for As and Se.

In this study, the semi-quantitative data were calculated from a full mass quick scan spectrum acquired as part of the quantitative analysis, with only two seconds of additional measurement time. The semi-quantitative results provide valuable information about the elemental content of food samples, including:

The full elemental composition of each sample. The results can be displayed in a table of concentrations or as a periodic table heat map, enabling a quick overview and easy comparison of each sample’s composition (Figure 4).

Identifying the presence of unexpected major or trace elements not included in the quantitative analysis and identifying contamination during sample preparation. For example, the relatively high concentration of Ti identified in the powdered doughnut from the periodic table heat map shown in Figure 4 might be unexpected. It was likely because of TiO

 in the white frosting, but the full mass IntelliQuant spectrum can be used to unequivocally confirm the element from its isotope pattern.

An estimation of the total matrix solids (TMS) level for each sample run, or for a typical sample of a new sample batch.

The TMS function uses the semi-quantitative data to calculate the approximate solids levels of a sample.

FIGURE 4: Periodic table heat map view of ICP-MS semiquantitative data acquired for powdered doughnut, showing a high concentration of titanium.

The calculation excludes gas elements, such as argon (Ar), oxygen (O), and nitrogen (N), together with C, P, sulfur (S), and the halides, ensuring a more accurate result. To simplify method setup and ensure the best possible accuracy for uncalibrated elements, the semi-quantitative data (and TMS calculation) uses a mass response profile generated from the quantitative calibration standards. The TMS function is a great diagnostic tool in identifying possible causes of internal standard suppression. It is especially useful when dealing with unknown and potentially difficult food sample matrices by helping the analyst to decide if a sample needs to be diluted or a higher HMI setting is needed. The measured TMS levels for the samples analyzed in this study are shown in Table III.

Based on the TMS levels in these food sample types (Table III), the plasma mode of “HMI-4” was selected. HMI applies an additional flow of Ar gas to dilute the sample aerosol by a factor of four times. This “aerosol” dilution method saves the analyst from having to manually dilute samples, which saves time and reduces the chance of introducing errors or contaminants. When HMI is used, all related settings are auto-tuned for the matrix levels of the target sample types. For ease of setup, other instrument operating settings were optimized automatically using the auto-tune function of the instrument control software. Also contributing to the ease of method setup, all quantitative analytes and the quick scan measurement were acquired in helium mode. Instrument operating conditions are listed in Table IV.

For ICP-OES analysis, semi-quantitative data was acquired using a function of the Agilent ICP Expert software known as IntelliQuant Screening. The software automatically makes wavelength recom- mendations for analytes based on the elemental composition of a sample and any potential spectral interferences that may arise from the sample matrix. An example for a beef jerky sample is given in Figure 3. The data shows a Fe interference on Mn 259.372 nm, accounting for the high concentration of Mn measured using the 259.372 nm line. In the software (Figure 3), Mn 257.610 nm was given the highest confidence ranking (five stars) so was selected for the quantitative method. Having access to this type of wavelength information makes it easier for the analyst to have confidence in the results that they report.

FIGURE 3: Information for manganese (Mn) in a beef jerky sample. The ICP- OES IntelliQuant data automatically produces a star ranking system for wavelengths. This information enables the analyst to easily identify the best wavelengths and what wavelengths to avoid.

The ICP-OES sample analysis was conducted per FDA EAM-4.4 for 22 elements using an Agilent 5900 ICP-OES operating in synchronous vertical dual-view (SVDV) mode. SVDV mode allows for both the axial and radial light to reach the detector at the same time. Eliminating the need for separate plasma views shortens analysis times while maintaining low limits of detection (LOD) from axial view and the greater linear range for major cation elements from the radial view. The ICP-OES plasma parameters are shown in Table V.

Quality Control Criteria in EAM 4.4 and 4.7

To verify the sample digestion process, two sets of the three NIST SRMs were analyzed in duplicate using ICP-MS and ICP-OES. As shown in Table VI, the mean concentrations were in agreement with the certified concentrations, meeting the QC criteria requirements of the FDA EAM method of 80–120%. Because not all SRMs are certified for all analytes, blank cells indicate the absence of a certified or reference value. There are no certified values for As, Ni, and thallium (Tl), which are listed in EAM 4.7.

The detection limits in Table VII were calculated from three times the standard deviation of 10 measurements of the calibration blank. Per the EAM methods, minimum QC requirements were included in each analytical run. For ICP-OES, all FAP samples were spiked at 0.05 ppm, and the recoveries are shown in Table VII. For samples with higher naturally occurring concentrations, the 0.05 ppm spike results were not reported. For ICP-MS, the same samples were spiked with all elements at 1 or 50 ppb and measured using ICP-MS. For samples that had naturally occurring elemental concentrations below 5 ppb, a 1 ppb spike is reported. For samples with higher naturally occurring concentrations, the 50 ppb spike results are reported. The recoveries for all elements in the fortified food samples were within the EAM method QC criteria of ±20%, as shown in Table VII.

Dilution corrected quantitative results obtained using ICP-MS are given in Table VII (beef jerky and gummy bears), and Table VIII shows the dilution corrected quantitative results for frozen dinners, pepperoni, pizza, and rice noodles. In addition to the 12 elements specified in EAM 4.7, data is provided for aluminum (Al), vanadium (V), Fe, cobalt (Co), antimony (Sb), barium (Ba), thorium (Th), and uranium (U). Per the Codex elements of concern, the concentrations for As (4.4 to 89.2 μg/kg), Cd (6.5 to 39.4 μg/kg), Pb (6 to 9.5 μg/kg), and Hg (below detection limit) in all snack foods were below any of the Codex MLs (Table I). The high concentration of Al in the pizza sample, which is likely because of an Al food additive, agrees with the literature values (13).

Periodic Table View of Elements in Food Samples

ICP-MS and ICP-OES methods can both be set up to acquire and process semi-quantitative data in addition to quantitative data acquisition. The ICP-MS quick scan function used in this study acquires full mass-spectrum data for every sample with only 2 s of additional measurement time. Because the data is acquired in helium collision cell mode, analytes are free from common polyatomic ion overlaps, ensuring the quality of the results. For ICP-OES, the detector and comprehensive database of wavelengths allows the software to provide real-time sample insights based on each individual spectrum. In both cases, the results can be displayed in a table or as a color-coded periodic table heat map, showing the approximate concentration of all elements present in a sample.

Figure 4 shows an ICP-MS IntelliQuant heat map for powdered doughnuts. The color intensity indicates the concentration ranges of elements in the sample, with a darker color indicating a higher concentration of that element. The semi-quantitative data provides a complete picture of the elements present in the sample, as data can be reported for elements not included in the calibration standards, which is demonstrated by the high concentration of titanium (Ti), labeled as food additive “TiO ” on the packaging. The semi-quantitative result for Ti in the doughnut samples was ~90 ppm.

Figure 5 shows an ICP-OES generated periodic table heat map with estimated concentrations of elements in a beef jerky sample. The color-coding used to represent the concentration range for the elements is user adjustable.

Like many analytical laboratories, food testing facilities are under pressure to increase productivity while ensuring data quality, compliance, and audit controls, which can be challenging for staff who may be expected to analyze more samples each day or operate instrumentation that is not familiar to them. It is useful if the instrumentation they use is as intuitive as possible and any reference or regulated methods can be implemented quickly, especially by new users.

In this article, we have shown how software tools available for ICP-MS and ICP-OES can help analysts to simplify method setup and reduce the potential for errors. Software functions, such as IntelliQuant, can also provide valuable information on sample composition and additional analytes beyond the regulated method requirements. These capabilities are demonstrated for the analysis of various foods that were prepared and analyzed in accordance with the U.S. FDA EAM methods 4.4 and 4.7. None of the snack foods were found to contain potentially harmful contaminant elements above Codex maximum levels of concern.

Current Good Manufacturing Practices (CGMPs) for Food and Dietary Supplements

 (FDA, Rockville, MD, 2021). https://www.fda.gov/food/guidance-regulation-food-and-dietary-supplements/current-good-manufacturing-practices-cgmps-food- and-dietary-supplements.

(2) Codex Alimentarius International Food Standards, “General Standard for Contaminants and Toxins in Food and Feed, CSX 193-1995,” amended 2019, pp. 45–54 (accessed September 2021). www.fao.org/fao-who-codex- alimentarius/sh-proxy/en/?lnk=1&u rl=https%253A%252F%252Fworksp ace.fao.org%252Fsites%252Fcodex %252FStandards%252FCXS%2B193- 1995%252FCXS_193e.pdf

U.S. Food and Drug Administration Elemental Analysis Manual, 4.4 Inductively Coupled Plasma-Atomic Emission Spec

trometric Determination of Elements in Food Using Microwave Assisted Digestion, Version 1.1 (FDA, Rockville, MD, 2010).

(4) P.J. Gray, W.R. Mindak, and J. Cheng, 

U.S. FDA Elemental Analysis Manual, 4.7 Inductively Coupled Plasma-Mass Spectrometric Determination of Arsenic, Cadmium, Chromium, Lead, Mercury, and Other Elements in Food Using Microwave Assisted Digestion, Version 1.2

(6) D. Link, P.J. Walter, and H. Kingston, 

. https://www.fda.gov/food/science-research-food/total-diet-study (accessed October 2021)

(10) S.A. Baker, D.K. Bradshaw, and N.J. Miller-Ihli, 

 are with Agilent Technologies. Direct correspondence to: 

Software tools for ICP-MS and ICP-OES can help analysts to simplify method setup and reduce the potential for errors.

Analysis of Elements in Snack Foods: A Closer Look at Pepperoni, Rice Noodles, Frozen Dinners, and Pizza

Phycobilisomes (PBSs) are the largest light-harvesting complex in cyanobacteria and red algae. To understand the energy transfer dynamics in phycobilisome, the cyanobacterium, 

Thermosynechococcus vulcanus NIES 2134 (T. 2134)

, with a phycocyanin (PC) trimer PC612 linking the rod and core, was selected. The energy transfer dynamics in PBS from 

 were studied via time-resolved fluorescence spectroscopy in sub-picosecond resolution. The energy transfer pathways and transfer rates were uncovered by deconvolution of the fluorescence decay curve. A fast time-component of 10 ps from PC612 trimer to the core and a slow time-component of 80 ps from rods to the core were recognized in the energy transfer in PBSs. The faster energy transfer rate of 10 ps enables PC612 trimer to modulate the energy transfer dynamics between rods and core. The findings help us understand the structure-induced energy transfer mechanisms in PBSs.

Cyanobacteria constitute a phylogenetically coherent group of evolutionarily ancient, morphologically diverse, and ecologically important phototrophic bacteria (1). Cyanobacteria are defined by their ability to carry out photosynthesis. They synthesize PBSs as light-harvesting antenna and feature high energy transfer efficiency up to 95% (2). Curiosity-driven research has led to the discovery of the intricate structural and functional organization of PBS and its relationship with high energy transfer efficiency (3–5).

The geometrical arrangement of a PBS is elegant in an antenna-like assembly, which is comprised of phycobiliproteins (PBPs), resulting in high efficiency and rate of energy transfer. PBPs have a central core, which is made of two to five allophycocyanin (APC) cylinders and surrounded by six to eight rods (6,7). All rods contain phycocyanin (PC) and occasionally other hypochromic variants of PBPs as well. This mega-structure architecture contains additional non-pigment-binding proteins called linker proteins (LPs) (8). These LPs have a critical role in determining the directionality of excitation energy transfer (6). PBPs are assembled from two types of subunits that form the αβ monomer. The PC monomer has three phycocyanobilin (PCB) chromophores: β155; α84; and β84 (9–11), whereas APC contains only two PCBs, α84 and β84 (12–15). Monomers further assemble into trimers, hexamers, and finally into rod or core cylinders, which further assemble to form the complete PBS.

To date, researchers have proposed different mechanisms of energy transfer between PBS sub-complexes in cyanobacteria. Several ultrafast energy transfer pathways have been identified in PBS from 

. Sauer and others suggested a fast 370 fs component between two different monomers of the PC trimers (16). Edington and others reported the presence of two components with excited state lifetimes of 280 fs and 1000 fs in the APC trimer (17). Zhang and others found an 18 ps component from terminal PC trimer to the APC core (18). However, the structural modulated mechanism of energy transfer in PBS had been hindered, and the energy transfer dynamics are still unclear.

In this study, thermophilic cyanobacterium 

, consisting of five APC core cylinders surrounded by six PC (PC620 hexamers) rods arranged around the core with a PC (PC612 trimer) subunit (identified to date only in 

) filling in the gap between rods and core (19,20), was selected to study the energy transfer within PBS. The steady state and time-resolved fluorescence spectra of PBS of 

 were measured, and the sequential energy flow among PBPs in PBS was examined. The fluorescence relative decay curves of the respective transfer components were estimated by deconvolution of time-resolved spectra. The energy transfer kinetics were analyzed in detail to explore the structure-induced mechanism of energy migration within a PBS antenna system.

 was cultured at 22 °C in constant low light illumination (40 w-white tubes, a light cycle of 16 h, and a dark cycle of 8 h) in a Bold 1NV: Erdshreiber (1:1) medium (UTEX). Contamination was inhibited by filtering the medium with 0.22 μm paper filters and the air supply with a cotton filter. PBSs from 

 were prepared, according to the methods described in our previous studies (3,4).

Determination of Steady-State and Time-Resolved Spectral Properties 

Absorption and steady-state fluorescence spectra were recorded using a spectrophotometer (Persee TU-1810c) and a spectrofluorometer (Hitachi FL- 4500), respectively. The spectral sensitivity of the fluorometer was corrected by the radiation profile of the standard lamp. Time-resolved fluorescence spectra (TRFS) were measured at 77 K by a synchroscan streak-camera (Hamamatsu C6860, time-resolution 700 fs) coupled to a polychromator, as shown in Figure 1. The silica cuvette-containing samples were placed in liquid nitrogen and were frozen in the dark. The light source was a regenerative Ti:sapphire amplifier system (Legend Elite USP HE+, Coherent), with an output pulse laser at the wavelength of 498 nm for excitation. The pulse duration was 35 fs, and the pulse repetition rate was 1 kHz. Spectral data were measured with a 0.2-nm interval and stored, and the TRFS were reconstructed afterwards.

FIGURE 1: Experimental setup for time-resolved spectroscopy measurements.

Analysis of the Time-Resolved Fluorescence Spectra

The fluorescence delay-associated spectra (FDAS) were calculated as follows: fluorescence decay curves were deconvoluted based on a global optimization method (21). After deconvolution, the time resolution was improved to approximately 2 ps. All the decays were re-analyzed using the same lifetime parameters. The amplitudes of these exponential components as a function of emission wavelength provided FDAS (22).

The fluorescence detected by the streak camera was transferred into the computer for deconvolution (23–25). The detected fluorescence intensity F

 represents the pump laser pulse. We considered the theoretical fluorescence intensity F

We applied a deconvolution procedure based on a global optimization method which described as follows to fit the isotropic fluorescence (21). The variable A denotes the amplitude of the fluorescence isotropic decay constant of τ

The steady-state spectra of the PBS from 

 were measured at room temperature and 77 K, respectively. The results obtained are displayed in Figure 2. The absorption spectrum of PBS from 

 at room temperature exhibited a peak at 622 nm and a shoulder at 573 nm, with a large half-width because of the absorption of the PCB chromophores. The fluorescence spectra of PBS from 

 were obtained with an excitation at 570 nm. The maximum fluorescence peak at room temperature was at 660 nm. Although the fluorescence spectrum at 77 K is much narrower compared to that at room temperature, it exhibited a peak at 679 nm with a shoulder approximately at 653 nm, which is because of the low temperature at 77 K that leads to the phycobiliprotein conforming again, resulting in the change of the electronic state of chromophoric group (26). This conformational change has a significant effect on the spectral properties of the chromophore, which may increase the rigidity of the chromophore in the PBS and the quantum yield of fluorescence.

FIGURE 2: The steady-state spectra of PBS from 

 PBS at room temperature. (Black line) Normalized fluorescence spectra of PBS from 

 at room temperature with excitation at 570 nm. (Blue line) Normalized fluorescence spectra of PBS from 

With picosecond time-resolved spectroscopy and the application of deconvolution techniques, we have studied the energy transfer paths, energy transfer time constants, and energy transfer dynamics in a complete PBS of thermophilic cyanobacterium 

. The results indicate that the energy transfer time constant between PC612 trimer and APC core is approximately 10 ps. The energy transfer time constant between PC620 rods to APC core is approximately 80 ps. The flexible PC612 trimer connects the end of the PC rod and the circumference of APC core cylinders, acts as the energy transfer bridge in the rod-core structural antenna system. The PC612 trimer exhibits a fast energy transfer route to the core and modulates the energy transfer dynamics between the rods and the core.

The large and complex structure complicated the energy transfer in PBSs and PBPs. In theory, energy transfer takes place among different chromophores, and more than half of the energy transfer with a time constant of less than 1 ps greatly hindered the direct observation in available methods. With the development of ultrafast techniques, the future research on the physical mechanism of the energy transfer in the initial photosynthesis process can go deep into the energy localization kinetics in its coherence condition of the excited state, which may bring us a more accurate understanding of the energy transfer dynamics in the photosynthesis antenna systems.

Fluorescence spectra in the steady state are combinations of emissions from fluorescence components. The energy transfer among the spectra are described on the base of their kinetics. Thus, the time-resolved fluorescence spectra of PBS from 

 were analyzed in detail. The time-resolved fluorescence spectrum was conducted at 77 K, at which the molecular vibration effect and solvent effect that affect the precise reflection of energy transfer process could be neglected, allowing large signal-to-noise (S/N) ratios to guarantee that the time-resolved fluorescence spectrum is close to the necessary assumption of the energy transfer in theoretical models (27).

Figure 3 shows the reconstructed three-dimensional (3D) time-resolved fluorescence spectra of PBS from 

 at 77 K, represented in color gradation. The time-resolved fluorescence spectra show the changes of spectra with time. As the different wavelengths of fluorescence spectra correspond to the different chromophores emissions in PBS, we can get the emission information of chromophores fluorescence in different positions of PBS with time. The expression helps us to understand the energy flow conceptually. With an excitation at 570 nm when it excites the chromophore in the PCB, the time-resolved fluorescence spectrum of PBS showed initially a spike of the PC fluorescence band at approximately 640–660 nm, and then with the APC core emission band from 680 nm to 690 nm that rose concomitantly with the decay of the PC emission. Changes in the relative intensities of fluorescence components directly show the energy transfer from PC rod to APC core in PBS.

FIGURE 3: Time-resolved fluorescence spectra of PBS 

 from at 77 K, with excitation at 570 nm. Fluorescence intensity is expressed as a gradation shown in the right side of the figure, and blue indicates a low intensity while red indicates a high intensity.

The changes in fluorescence spectrum were much more clearly observed, as shown in Figure 4. The fluorescence components in the time-resolved spectra were resolved by deconvolution of spectra attributing intensity changes to certain component bands. Gaussian band shape was assumed to be a function of wavenumber. To obtain the best fit, four components were used for simulation, and each spectrum was reasonably fitted only by changes in the relative intensities. The band around 645 nm (green line) is from PC612 emission, the band around 660 nm (blue line) is from PC620 emission, the band around 680 nm (pink line) is from the APC core emission, and the band longer than 700 nm (orange line) might originate from vibrational band of PC rod.

FIGURE 4: (a–h) Deconvolution pattern of time-resolved fluorescence spectra. In each subfigure, black solid line represents the fluorescence spectra data; green, blue, fuscia, and orange lines represent the component bands; the red line represents the sum of component bands. Numbers in the upper righthand corner of each subfigure show the time in picoseconds after the excitation pulse.

The energy transfer dynamics in PBS can be analyzed in detail through the changes of relative intensity of fluorescence components. The intensity of the PC620 band gave rise to take dominate at 20 ps (Figure 4a), owing to the outermost location of PC620 hexamers in PBS. At the same time, the PC612 and APC emission were evident with its lower intensity, which indicated that the energy was converted to the core within 20 ps. After 40 ps, the intensity of the PC612 emission band quickly increased and became high (Figure 4b). It is obvious that the PC612 trimer is the energy acceptor from PC620 rods and the energy donor to APC core. After 80 ps, the intensity of PC612, PC620, and APC simultaneously increased (Figure 4c). After 120 ps, the PC612 emission decreased, and the decay of PC612 emission corresponded to the rises of PC620 and APC emission (Figure 4d). At this stage, the energy transfer from PC rods to PC612 was saturated because the number of PC612 trimers is much fewer than that of PC620 hexamers, resulting in the growth of PC620 and APC emissions. After 160 ps, the APC core emission continues rising with PC620 emission decay quickly (Figure 4e). The intensity of the PC620 emission kept decreasing and became relatively low when compared to APC emission (Figure 4f), which means most of the rod energy was transferred to the core. Both the PC620 and PC612 emissions kept decreasing after 200 ps (Figure 4g) and almost disappeared after 250 ps (Figure 4h). The energy transfer process in the PBSs is a dynamics process. The absorbed solar light can be transferred from the rods to the core, finally reaching the reaction center. Meanwhile, the rods and the core can also emit fluorescence when the chromophores in the rods and core obtain the energy. Thus, in the energy transfer process, part of the solar energy was transferred to the reaction center, and the rest was used for fluorescence emission via the chromophores. Although we cannot directly observe the energy transfer pathways in the PBSs, we can get the information of dynamic fluorescence emission in different positions of the PBSs by measuring the time-resolved fluorescence spectroscopy, which can help us reveal the energy transfer pathways and the related transfer rate. According to the time-resolved spectroscopy of 

, we can see the revolution and competition of the fluorescence peaks, and the energy can be transferred to the core within 20 ps.

To determine the constants of fluorescence decay in PBS, deconvolution was conducted in a global optimization method. The time-resolved fluorescence decay at different detected wavelengths was resolved by multiexponential deconvolution, and the Monte-Carlo method was adopted to process the experimental data, as shown in Figure S1.

FIGURE S1: The fluorescence intensity decay curves of PBS from 

The fluorescence decay of PBS from T. 2134 was well-fitted with three exponentials of 10, 80, and 1250 ps, and the amplitudes of each time component are listed in Table I. All the time constant, except for the longest one of the terminal fluorescent emission, can be considered as a function of the time constant of energy transfer. The component (A%) with a negative amplitude corresponds to the uprising stage of fluorescence, indicative of an energy acceptor, and the component with a positive pre-exponential corresponds to the downturn stage of fluorescence, indicative of an energy donor.

Based on the results of the deconvolution, we obtained the FDAS. The FDAS can reveal the energy transfer dynamics among PBPs in PBSs. In FDAS, the negative and positive factors indicate the kinetic increase and decrease in the populations in the excited state, respectively. The coupling of negative and positive bands was a clear indication of energy transfer from the pigments showing the positive band to those showing the negative band. Figure 5 shows the FDAS from PBS from 

. According to the FDAS results, these decay constants may be assigned as follows: (1) The fluorescence decay constant of 10 ps is because of the energy transfer time from PC612 to APC core, because its amplitude, shown in Figure 5, is positive in the blue side with a maximum around 650 nm and negative at the red side with a minimum around 680 nm. The negative amplitude represents the fluorescence rise term and indicates that energy transfer occurs from a donor to an acceptor. Therefore, from the fluorescence spectra of the PC612 and APC core, it is reasonable to assign decay time of 10 ps to the energy transfer time between PC612 and APC core (2). The fluorescence decay constant of 80 ps is because of the energy transfer time from PC rod to the APC core. because its amplitude, shown in Figure 5, is positive at the red side with a maximum around 660 nm, then quickly decrease and became negative at 690 nm. Holzwarth and others (28) also found the same energy transfer pathway with time of 80–120 ps from PC rod to APC, which is confirmed by our experiment and the analyses (3). The fluorescence decay constant of 1250 ps reflects the fluorescence emission of the APC core.

Figure S1 shows the fluorescence intensity decay curves of PBS from 

Conceptualization was done by Mingyuan Xie and Wenjun Li. The methodology was created by Mingyuan Xie and Zhanghe Zhen. The investigation was performed by Mingyuan Xie. The original draft was prepared by Mingyuan Xie. The review of the article was done by Fuli Zhao. Supervision was done by Song Qin and Fuli Zhao. All authors have read and agreed to the published version of the manuscript.

This research was funded by the National Natural Science Foundation of China, (grant number 11274397, 41176144, 41376139, and 41906109) and the Science and Technology Planning Project of Guangdong Province (grant number 2016B090918099).

The authors would like to thank the staff at the school of physics, Sun Yat-sen University, for their technical support on femtosecond laser system and streak camera performances.

The authors declare no conflict of interest.

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 (Moselio Schaechter, San Diego, 2009), pp. 107–124.

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(4) J.F. Ma, X. You, S. Sun, X.X. Wang, S. Qin, and S.F. Sui, 

(5) W.J. Li, Y. Pu, Z.H. Tang, F.L. Zhao, M.Y. Xie, and S. Qin, 

(9) T. Schirmer, W. Bode, R. Huber, W. Sidler, and H. Zuber, 

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Acta Scientiarum Naturalium Universitatis Sunyatseni

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Time-resolved fluorescence spectroscopy reveals much about the structure-induced energy transfer mechanisms of phycobilisomes, the light-harvesting antenna in cyanobacteria.

Time-resolved Fluorescence Spectroscopy Study of Energy Transfer Dynamics in Phycobilisome of Thermophilic Cyanobacterium Thermosynechococcus vulcanus NIES 2134

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