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A Beginner’s Guide to FTIR Analysis: Interpreting & Analyzing Results

July 16, 2018 by Jennifer Mathias Leave a Comment

How to Interpret FTIR Spectra

But when it comes to understanding and analyzing FTIR results, how we interpret FTIR spectra is a common question.

Your FTIR analysis service should be providing you with clear, detailed, and actionable insights and recommendations in an easy to understand format—something Innovatech Labs is dedicated to.

With that said, we’ve developed this helpful guide to interpreting FTIR spectra to help our customers—and anyone else who’s interested—know how to analyze FTIR data.

What is FTIR Analysis Used For?

FTIR analysis is used to identify molecular compounds. It works by measuring the absorbance of infrared radiation by a sample. The resulting spectrum can then be used to identify the functional groups present in the compound. 

FTIR testing is often used in fields such as chemistry and pharmaceuticals, where it can be used to identify unknown compounds or to confirm the identity of known compounds. It can also be used to determine the purity of a compound, as well as its physical and chemical properties. 

In recent years, FTIR instruments have become increasingly portable, making them more accessible and affordable for scientists and students alike. As a result, it is likely that we will see even more applications for this versatile tool in the future.

The Analysis

Contact Us for FTIR Analysis

Essentially, by applying infrared radiation (IR) to samples of materials, FTIR analysis measures a sample’s absorbance of infrared light at various wavelengths to determine the material’s molecular composition and structure. The Fourier transform spectrometer works to convert the raw data from the broad-band light source to actually obtain the absorbance level at each wavelength.

FTIR spectroscopy can be used on solid, liquid, and gaseous samples. Usually, the amount of material required for a viable analysis is very small and most analyses can be done relatively quickly with little sample preparation.

How to Read FTIR Analysis Results Graphs

The x-axis: the infrared spectrum.

The x-axis—or horizontal axis—represents the infrared spectrum, which plots the intensity of infrared spectra. The peaks, which are also called absorbance bands, correspond with the various vibrations of the sample’s atoms when it’s exposed to the infrared region of the electromagnetic spectrum. For mid-range IR, the wave number on the infrared spectrum is plotted between 4,000 to 400 cm-1.

FTIR Graph Pointing Out X-Axis

The Y-Axis: Absorbance or Frequency

The y-axis—or vertical axis—represents the amount of infrared light transmitted or absorbed by the sample material being analyzed.

FTIR Graph Pointing Out Y-Axis

The Absorbance Bands

Typically, absorbance bands are grouped within two types: Group frequencies and molecular fingerprint frequencies.

Group frequencies are characteristic of small groups of atoms or functional groups such as CH₂, OH, and C=O. These types of bands are typically seen above 1,500cm-1 in the infrared spectrum (See top spectrum in the graph below) and they’re usually unique to a specific functional group, making them a reliable means of identifying functional groups in a molecule.

As for fingerprint frequencies, these are highly characteristic of the molecule as a whole; they tell what is going on within the molecule. These types of absorbances are typically seen below 1500cm-1 in the infrared spectrum (See bottom graph of figure below); however, some functional groups will absorb in this region as well. As a result, this region of the spectrum is less reliable for identification, but the absence of a band is often more indicative than the presence of a band in this region.

Absorbance Bands on a FTIR Graph

How to Interpret FTIR Spectra

Once the initial testing and spectrum collection is complete, interpretation of FTIR spectra comes next.

Typically, interpreting FTIR spectra starts at the high frequency end to identify the functional groups present. The fingerprint regions are then studied to positively identify the compound. Thankfully, there are vast libraries of infrared spectra available, allowing our team to compare unknown materials to ensure quick and accurate identification.

Interpreting a FTIR Spectra Graph

Still Curious About FTIR Analysis?

FTIR analysis services are incredibly helpful and versatile quality control and troubleshooting tools for manufacturers and researchers across industries. If you have more questions about the technique or are wondering if it may be a fit for your testing needs, contact us for a quote . Our team is ready to help.

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Chapter 6: Structural Identification of Organic Compounds: IR and NMR Spectroscopy

6.3 IR Spectrum and Characteristic Absorption Bands

With a basic understanding of IR theory, we will now take a look at the actual output from IR spectroscopy experiments and learn how to get structural information from the IR spectrum. Below is the IR spectrum for 2-hexanone.


Notes for interpreting IR spectra:

  • The vertical axis is ‘% transmittance’, which indicates how strongly light was absorbed at each frequency. The solid line traces the values of % transmittance for every wavelength passed through the sample. At the high end of the axis, 100% transmittance means no absorption occurred at that frequency. Lower values of % transmittance mean that some of the energy is absorbed by the compound and gives downward spikes. The spikes are called absorption bands  in the IR spectrum. A molecule has a variety of covalent bonds, and each bond has different vibration modes, so the IR spectrum of a compound usually shows multiple absorption bands.

ftir spectrum band assignment

Please note that the direction of the horizontal axis (wavenumber) in IR spectra decreases from left to right. The larger wavenumbers (shorter wavelengths) are associated with higher frequencies and higher energy. 

Stretching Vibrations

Generally, stretching vibrations require more energy and show absorption bands in the higher wavenumber/frequency region. The characteristics of stretching vibration bands associated with the bonds in some common functional groups are summarized in Table 6.1 .

Table 6.1 Characteristic IR Frequencies of Stretching Vibrations

The information in Table 6.1 can be summarized in the diagram for easier identification   (Figure 6.3b) , in which the IR spectrum is divided into several regions, with the characteristic band of certain groups labelled.


The absorption bands in IR spectra have different intensities that can usually be referred to as strong (s), medium (m), weak (w), broad and sharp. The intensity of an absorption band depends on the polarity of the bond, and a bond with higher polarity will show a more intense absorption band. The intensity also depends on the number of bonds responsible for the absorption, and an absorption band with more bonds involved has a higher intensity.

The polar O-H bond (in alcohol and carboxylic acid) usually shows strong and broad absorption bands that are easy to identify. The broad shape of the absorption band results from the hydrogen bonding of the OH groups between molecules. The OH bond of an alcohol group usually has absorption in the range of 3200–3600 cm -1 , while the OH bond of the carboxylic acid group occurs at about 2500–3300 cm -1 (Figure 6.4a and Figure 6.4c).

The polarity of the N-H bond (in amine and amide) is weaker than the OH bond, so the absorption band of N-H is not as intense or as broad as O-H, and the position is in the 3300–3500 cm -1 region.

The C-H bond stretching of all hydrocarbons occurs in the range of 2800–3300 cm -1 , and the exact location can be used to distinguish between alkane, alkene and alkyne. Specifically:

  • ≡C-H (sp C-H) bond of terminal alkyne gives absorption at about 3300 cm -1
  • =C-H (sp 2 C-H) bond of alkene gives absorption at about 3000-3100 cm -1
  • -C-H (sp 3 C-H) bond of alkane gives absorption at about ~2900 cm -1 (see the example of the IR spectrum of 2-hexanone in Figure 6.3a; the C-H absorption band at about 2900 cm -1 )

A special note should be made for the C-H bond stretching of an aldehyde group that shows two absorption bands: one at ~2800 cm -1   and the other at ~ 2700 cm -1 . It is therefore relatively easy to identify the aldehyde group (together with the C=O stretching at about 1700 cm -1 ) since essentially no other absorptions occur at these wavenumbers (see the example of the IR spectrum of butanal in  Figure 6.4d ).

The stretching vibration of triple bonds C≡C and C≡N have absorption bands of about 2100–2200 cm -1 . The band intensity is in a medium to weak level. The alkynes can generally be identified with the characteristic weak but sharp IR absorbance bands in the range of 2100–2250 cm -1   due to stretching of the C≡C triple bond, and terminal alkynes can be identified by their absorbance at about 3300 cm -1 due to stretching of sp C-H.

As mentioned earlier, the C=O stretching has a strong absorption band in the 1650–1750 cm -1  region. Other double bonds like C=C and C=N have absorptions in lower frequency regions of about 1550–1650 cm -1 . The C=C stretching of an alkene only shows one band at ~1600 cm -1   (Figure 6.4b) , while a benzene ring is indicated by two sharp absorption bands: one at ~1600 cm -1  and one at 1500–1430 cm -1  (see the example of the IR spectrum of ethyl benzene in  Figure 6.4e ) .

You will notice in Figure 6.3a and 6.3b that a region with the lower frequency 400–1400 cm -1 in the IR spectrum is called the fi ngerprint region . Similar to a human fingerprint, the pattern of absorbance bands in the fingerprint region is characteristic of the compound as a whole. Even if two different molecules have the same functional groups, their IR spectra will not be identical, and such a difference will be reflected in the bands in the fingerprint region. Therefore, the IR from an unknown sample can be compared to a database of IR spectra of known standards in order to confirm the identification of the unknown sample.

Organic Chemistry I Copyright © 2021 by Xin Liu is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License , except where otherwise noted.

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Review article, application of imaging and spectroscopy techniques for grading of bovine embryos - a review.

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  • Department of Biomedical Sciences, Ontario Veterinary College, University of Guelph, Guelph, ON, Canada

Although embryo transfers have grown considerably in the cattle industry, the selection of embryos required for successful pregnancies remains a challenging task. Visual inspection of 7th-day embryos using a stereomicroscope, followed by classification based on morphological features is the most commonly practiced procedure. However, there are inaccuracies and inconsistencies in the manual grading of bovine embryos. The objective of this review was to evaluate the potential of imaging and spectroscopic techniques in the selection of bovine embryos. Digital analysis of microscopic images through extracting visual features in the embryo region, and classification using machine learning methods have yielded about 88–96% success in pregnancies. The Raman spectral pattern provides valuable information regarding developmental stages and quality of the embryo. The Raman spectroscopy approach has also been successfully used to determine various parameters of bovine oocytes. Besides, Fourier Transform Infrared (FTIR) spectroscopy has the ability to assess embryo quality through analyzing embryo composition, including nucleic acid and amides present. Hyperspectral Imaging has also been used to characterize metabolite production during embryo growth. Although the time-lapse imaging approach is beneficial for morphokinetics evaluation of embryo development, optimized protocols are required for successful implementation in bovine embryo transfers. Most imaging and spectroscopic findings are still only at an experimental stage. Further research is warranted to improve the repeatability and practicality to implement in commercial facilities.

1 Introduction

The embryo transfer technology in bovine was introduced commercially in 1970’s. Currently, the selection of embryos to be used for in-vivo produced multiple ovulation and embryo transfer (MOET) or in vitro produced (IVP) is carried out by specialized embryologists. In many places, there is no specified embryologist available always for evaluation and grading of embryos. The selection of embryos for transfer is a critical step in the successful breeding of cattle. The morphological features that are most commonly followed to grade embryos are explained by the International Embryo Technology Society (IETS). IETS defines the criteria for the quality of embryos and developmental stages through a numerical grading system: Category I- Stage of development: 1-unfertilized oocyte or a 1-cell embryo; 2–4 cell embryo; 3- morula (mass of at least 16 cells); 4-compact morula; 5-early blastocyst; 6-blastocyst; 7-expanded blastocyst; 8-hatched blastocyst; 9-expanding hatched blastocyst Category II- Quality of embryo based on morphological features: 1-Excellent or good; 2-Fair; 3-poor; 4-dead or degenerating.

1.1 Grading methodology

Bó and Mapletoft ( 1 ) explained the process to assess bovine embryos using IETS protocols. The assessment is generally carried out using a stereomicroscope (50-100X) while the embryo is on a dish. It is recommended to roll the embryo in order to view the zona pellucida at different angles. The mean diameter of the bovine embryo ranges from 150 to 190 μm, and thickness of zona pellucida ranges from 12 to 15 μm. The diameter of the embryo is consistent from single cell to blastocyst stages. The desired morphological features for an ideal embryo are: compact and spherical; blastomeres - similar size, even color and texture; cytoplasm - should not be granular or vesiculated; perivitelline space - clear and no cellular debris; zona pellucida - uniform, neither cracked nor collapsed, no debris on its surface.

1.2 Current success rate with bovine embryo transfers

There is a direct relationship between embryo quality and post-embryo transfer gestational success ( 2 , 3 ). In other words, embryos that are morphologically distinguished to be a higher rank (grade 1) have greater success rates during gestation. The selection of an ideal embryo for transfer, and its consequent gestational success has been an ongoing challenge. The current selection process through manual methods have yielded only a 30–50% success rate in pregnancy even with good to excellent grade embryos ( 4 , 5 ). Further, most of the time it is difficult to have data with transfer of all quality embryos, and mostly it is limited to excellent to good grade.

1.3 Challenges in manual selection of bovine embryos

Although there are several guidelines available for embryo classification, great subjectivity exists in the real-time decision-making processes which leads to grading variations. The manual method is subjective and biased based on the experience of the evaluator and other environmental factors. Farin et al. ( 6 ) proved that a given embryo was assigned to different grades when analyzed by different examiners. Furthermore, it was also observed that consecutive evaluations by the same evaluator yielded different grades for the same embryo (no repeatability). While evaluating the stage of development and quality of 40 bovine embryos, Farin et al. ( 6 ) studied the agreement between six evaluators. Although 89% agreement was achieved on the stage of embryo development, only 69% agreement was obtained among the examiners regarding quality ranking. The variable success rate of transferred embryos in MOET and IVP is one of the limitations of bovine embryo technology, as it requires a surplus of recipient cows which increases the overall cost per calf and associated farm logistics. Thus, it is necessary to develop an objective, automated, accurate, and fast method to determine the viability of embryos to ensure success in their subsequent implantation and gestation.

The current developments in machine learning techniques provide opportunities to analyze images and videos to make objective decisions for embryo grading. These techniques utilize visual variables present in morphokinetic imaging, alongside biological features of a developing embryo to determine the success of an embryo to complete its development and gestation period. The objective of this review is to evaluate the status and challenges in the development of image or spectroscopy based tools to grade and assess bovine embryo quality using machine learning models for automatic prediction of embryo quality to improve the outcome of pregnancy success rate in embryo transfer technology program.

In the last decade, enormous amount of work has gone into morphokinetic research using time-lapse imaging technique in analyzing human embryos ( 7 – 9 ). However, this technology is still in infancy for bovine embryos. So far, optical microscopy, Raman spectroscopy, Fourier Transform Infrared (FTIR) spectroscopy and near infrared (NIR) hyperspectral imaging have been tested for their potential to assess bovine embryo quality.

2 Optical microscopic imaging

The optical microscope uses visible light region of the electromagnetic spectrum (transmission or reflection) and appropriate lenses to magnify the small objects. In general, these microscopes are less expensive and simple to operate when compared to other techniques. The microscopic images can be automatically analyzed to obtain useful information for decision making.

2.1 Image segmentation

Image segmentation is a process in which the region of interest (ROI) from any image is separated from the background to obtain useful information. The selection of segmentation methods and procedures must be accurate in order to avoid background noise or losing valuable information from the ROI. The selection of a suitable segmentation technique requires preliminary experimentation with target images ( 10 ). Melo et al. ( 11 ) developed algorithms for the automated segmentation of bovine embryos. After preprocessing, a thresholding technique was implemented based on statistical features. The images of 30 embryos (early cleavage, morula, and blastocyst stages) were segmented and accuracy was then compared with gold-standard reference images. They achieved 91–93%, 93–96 and 92% segmentation accuracy for early cleavage, morula, and blastocyst stages of embryos, respectively.

2.2 Machine learning techniques for classification of embryos

The machine learning (ML) techniques develop algorithms to learn from dataset and used for further classification or prediction of unknown data without additional programming ( 12 ). The learning or training process in ML techniques may be broadly classified into supervised, unsupervised and reinforcement. In supervised learning, the learning for a model is from “labeled training data” that helps in making classification or prediction about the future data. In this learning process, the labeling of data is done to guide the machine to look for the pattern. The examples for the supervised learning tools are Artificial Neural Network (ANN), Decision Trees, Random Forest, Support Vector Machines k-Nearest Neighbor, Logistic Regression, Naïve Bayes, Linear Discriminant Analysis, Quadratic Discriminant Analysis and so on. Deep learning (DL) is a subset of ANN which involves a representation-learning through utilization of the deep ANN with multiple neuron layers. Convolutional neural network (CNN) is the commonly used DL for analyzing images. Unsupervised ML uses unlabeled data or unknown data structure for training. It analyzes the data structure to obtain meaningful information without the help of a known outcome variable. The examples for unsupervised ML tools are k-means Clustering, Independent Component Analysis (ICA), Principle Component Analysis (PCA) and so on. In reinforcement learning, the specific task-oriented algorithms are developed to learn and achieve a complex goal.

de Souza Ciniciato et al. ( 13 ) designed a smartphone-based user interface to classify bovine embryos in accordance with IETS grades. A smartphone was attached to the eyepiece lens of a stereomicroscope to perform the real-time evaluation. An ANN model with 482 bovine blastocyst images was developed by Rocha et al. ( 14 ) to use in the smartphone based classification of embryos. They also developed an online server 1 that can be used through a phone or computer to perform embryo evaluation.

In another study, Matos et al. ( 15 ) developed digital image-based classification system for bovine blastocysts. Morphological features of the embryos were used to build ANN classifier. They tested 56 embryos (15% of the total images) using the developed model. The system took 2.5 s per image to complete the tasks (image uploading, preprocessing, feature extraction and classification), and provide a grade in accordance with the IETS classification. The accuracy of the ANN system was 84, 20 and 89% for grade 1, grade 2 and grade 3, respectively. They observed poor classification in grade 2 as they were misclassified as grade 1 or grade 3. They recommended using other imaging techniques or ML approaches to builda robust model and improve the classification efficiency.

Another automated embryo classification model was developed with 482 images of in vitro produced bovine blastocysts by Rocha et al. ( 16 ). An inverted microscope (32X magnification) was used to obtain images (one embryo per image). The image dataset had 113 grade − 1, 175 grade − 2 and 194 grade − 3 embryos as confirmed by three experienced embryologists as per the IETS standards. The developed ANN model classified the embryos with an accuracy of 76 to 88%.

Thompson ( 17 ) developed a prediction model for embryo quality to determine the success in the embryo transfer program. They used 476 inverted microscopic images of day 7 embryos (MOET pregnant −266; MOET non-pregnant-100; JIVET (Juvenile animals) pregnant-62; JIVET non-pregnant-63) from different Australian breeds - Angus, Poll Hereford, Wagyu and Composite cattle. A model was developed using the top 10 contributing features from the microscopic images. The pregnancy or non-pregnancy was confirmed on day 60. The model yielded up to 88% for MOET embryos and 96% for JIVET embryos.

The implementation of advanced image enhancement, image preprocessing, image analysis and ML-based classification models have the potential to build robust and objective and accurate grading systems with existing optical microscopes.

3 Time-lapse imaging

In situ monitoring of a developing embryo inside an incubator using video or time-lapse imaging is another approach for determining embryo quality. In this method, the embryo does not experience any culture-related stresses. Advancements in video analysis provide a plethora of opportunities to accurately evaluate embryo development using morphokinetic observations ( 18 , 19 ).

The first time-lapse monitoring for bovine embryo was carried out by Grisart et al. ( 20 ) in 1994 using an inverted microscope ( 21 ). They monitored 130 embryos from one-cell stage to blastocyst stage for 8 days and observed timing of cleavage, duration of each cell cycle, time to blastocyst stage. It was reported that faster the embryos cleaved during the early stages, the higher its ability to develop to blastocyst stage.

Sugimura et al. ( 5 ) used time-lapse cinematography (TLC) to observe numerous prognostic factors including timing of the 1 st cleavage, number of blastomeres at the end of 1st cleavage, presence of multiple fragments at the end of 1st cleavage, number of blastomeres at the onset of the lag-phase, and oxygen consumption at the blastocyst stage using a real-time cultured cell monitoring system (CCM-MULTI, Astec, Japan). Around 673 images were taken during a 168 h culture period at 15 min intervals (4X magnification) and analyzed. This approach yielded 79% accuracy in pregnancy prediction.

The differences in the early stages of embryo development between sex-sorted and un-sorted (control) bovine sperm were investigated by Steele et al. ( 22 ) using morphokinetic investigation with the help of time-lapse imaging and video microscopy techniques. In this study, cryopreserved semen from Holstein Friesian bulls were used for in vitro fertilization. A time-lapse video-microscopy was used to record and analyze the development of 360 embryos. It was reported that the embryos derived from sexed-sperm showed increased incidences of arrest at the 4-cell stage. The embryos derived from sexed-sperm were at a higher risk for shrinkage/fusion of blastomeres with subsequent lysis resulting in reduced blastocyst rates ( Figure 1 ). Further, embryos derived from sex-sorted sperm had a lower chance of cleavage and reduced survival times than that of conventional sperm. However, there was no difference in total time required for completion of development of embryos in both sex-sorted and conventional sperms. It was concluded that sexed-sperm kinetics were more discordant than conventional sperm kinetics, and time-lapse video-microscopy is a useful tool for consistently monitoring embryos.


Figure 1 . Images of sexed and conventional early embryos ( 22 ). Day 2: (A) Unfertilized oocyte displaying cleavage failure after insemination. (B) Control 4-cell embryo after insemination. Day 3: (C) Sexed sperm derived embryo displaying blastomere lysis (arrow). (D) Control embryo revealing >8 blastomeres. Day 6: (E) Sexed sperm derived expanding blastocyst. (F) Conventional sperm derived expanded blastocyst with trophectoderm cells (black arrowhead) and inner cell mass (ICM, white arrowhead).

Magata ( 21 ) mentioned that continuous observation of bovine embryo with the help of time-lapse monitoring techniques will yield accurate quantification of cellular dynamics and cell cycle length. This method has the capability to predict ploidy status of embryos because morphokinetics patterns are different for normal and aneuploid embryos due to aberrant chromosome complement.

Magata et al. ( 23 ) investigated the growth potential of bovine embryos showing abnormal cleavage during early developmental stages (reverse cleavage-RC; direct cleavage-DC), irregular and unsmooth ruffling of the oolema membrane (ruffling) through time lapse imaging. For each embryo, in situ images were taken at 20 min intervals for 240 h. They observed that about 36% of embryos that developed into a blastocyst showed abnormal cleavage. The morphokinetic investigation explained that RC, DC, and ruffling embryos showed slower development than that of normal cleaved embryos. The Embryos with RC and DC showed impaired hatchability with increased collapse of the blastocyst cavity until hatching. Also, it was reported that RC and DC embryos had increased chromosomal aneuploidy.

Since limited morphokinetic knowledge is available for the bovine embryo development, more research is required in this field to develop accurate techniques for commercial applications.

4 Raman spectroscopy

Alongside the use of morphological features for the assessment of developmental stages and bovine embryo quality using color imaging or optical microscopy, there are several other techniques being researched to support objective embryo grading. The principle, merits and demerits of each technique that are used for embryo quality assessment are given in Table 1 . Cofactors other than morphological features such as timing of first embryonic cell divisions or metabolic profiles may be used to predict the ability of an embryo to establish pregnancy or as markers of embryonic viability ( 27 ). In general, the metabolomics profile of embryos is not yet established for bovine subjects.


Table 1 . Comparison of imaging and spectroscopic techniques.

Laser tweezer Raman spectroscopy (LTRS) was used to evaluate the differences in composition between the embryo culture media of in vitro fertilized (IVF) and intracytoplasmic sperm injection (ICSI) methods ( 28 ). The bovine embryos were produced through ICSI and IVF methods to the 2-, 4-, 8-, 16-,32-cell, and blastocyst stages with individual in vitro culturing, and the culture media for embryos at different developmental stages were analyzed using LTRS. It was reported that the composition of culture media between IVF- and ICSI-derived embryos differed in carbohydrates, lipids, DNA, and proteins. The wave bands had specific patterns at 1004 cm − 1 (phenylalanine) and 1,529 cm − 1 (-C=C-carotenoid) which were associated with metabolic activity of embryos ( Figure 2 ).


Figure 2 . Raman spectra of culture medium: a - intracytoplasmic sperm injection (ICSI) b - in vitro fertilized (IVF) c - difference spectra between ICSI and IVF ( 28 ).

Raman microscopy has the potential in determining the quality of bovine oocytes. This technique may be used as a screening tool for selection of oocytes for fertilization. Jimenez et al. ( 29 ) measured the biochemical changes in the cytoplasm of bovine oocytes during the in vitro maturation process using Raman microscopy. The markers for proteins, lipids, and carbohydrates were used to investigate the ooplasm at four stages. It was reported that lipid accumulation was higher in the first 6 h of culture, carbohydrates were decreased during the developmental stage, and protein content reached average levels characteristic of mature oocytes within the last 4 h of the maturation process. In another study, Rusciano et al. ( 30 ) investigated the vitrification-induced structural damage in bovine oocytes. Raman microspectroscopy was used to quantify the biochemical variations in zona pellucida and cytoplasm of vitrified in cryogenically preserved vitro-matured bovine oocytes at different post-warming times. It was found that vitrification induced a transformation of the protein’s secondary structure from α-helices to β -sheet form. It was also observed that lipids tend to assume a more packed configuration in the zona pellucida. A decrease in lipid unsaturation was measured in vitrified oocytes (may be due to oxidative damage).

5 Fourier transform infrared (FTIR) spectroscopy

Wiechec et al. ( 31 ) used Fourier transform infrared (FTIR) spectroscopy and focal plane array (FPA) imaging to determine the quality of bovine embryos based on nucleic acids and amides. Three different blastocysts were used for this study: (1) Blastocyst 1 (BL1-HA) - fertilized oocyte cultured with low concentration of hialuronian (HA). (2) Blastocyst 2 (BL2-SOF) - oocytes cultured in standard conditions and cultured in SOF medium. (3) Blastocyst 3 ((BL3-VERO) - oocytes cultured in standard conditions and Cleavage stage blastocyst) cultured in co-culture with VERO cells. The FTIR spectrum of the inner cell mass of the single blastocysts was collected for this study. The differences in DNA were analyzed between wavenumbers 1,240 and 950 cm − 1 and for protein amides was analyzed between wavenumbers 1800 and 1,400 cm − 1 using principal component analysis (PCA) and Hierarchical Cluster Analysis (HCA). The multivariate statistical analysis (Hierarchical Cluster Analysis – HCA and Principal Component Analysis – PCA) of single cells spectra showed a high similarity of cells forming the inner cell mass within a single blastocyst. It was reported that the difference between the three types of blastocysts was significant in amide bands, and the quantitative and qualitative composition of the protein can be used to examine blastocysts. They concluded that FTIR spectroscopy has potential in reproductive biology for quality estimates of bovine embryos at various developmental stages, however further research is warranted to validate these approaches.

The investigation of zona pellucida protein during fertilization and pre-implantation might yield valuable information about the quality of oocytes or embryo. Nara et al. ( 32 ) used FTIR to investigate the secondary structure of zona during fertilization. It was found that attenuated reflection-FTIR spectrum of intact bovine zona pellucida was different between before fertilization and after fertilization (blastocyst stage). The ß-structure content was increased during fertilization. It was reported that changes in zona architecture during fertilization was due to changes in the secondary structure of the zona protein.

6 Hyperspectral and multispectral imaging

Sutton-McDowall et al. ( 33 ) evaluated the effect oxygen concentration (7% = optimal vs. 20% = stressed) in incubators on metabolic heterogeneity of the bovine embryos using hyperspectral microscopy. The embryos were exposed to two oxygen concentrations for 5 days (metabolism measurements) or for 8 days (for embryo developmental competence), and imaged ( Figure 3 ). It was reported that exposure to 20% oxygen following fertilization reduced total, expanded and hatched blastocyst rates by 1.4, 1.9 and 2.8 fold, respectively, compared to a 7% oxygen environment.


Figure 3 . Images of Day 5 (cleaved) and on-time (morula) embryos cultured in 7 and 20% O2 and labeled with fluorescence probes ( 33 ). MCB, monochlorobimane (reduced glutathione); PF1, perfluoxy 1 (hydrogen peroxide) and MTR, Mitotracker Red CMXRos (active mitochondria).

Santos et al. ( 34 ) used hyperspectral imaging to characterize the metabolites produced by bovine embryos during development (on-time development or fast-development). The metabolic activity and DNA damage for the on-time developing embryo (Day 2: 2 cell; Day 4: 5–7 cell) and fast-developing embryo (Day 2: 3–7 cell; Day 4: 8–16 cell) were analyzed. The Hyperspectral microscopy was used to assess a broader range of endogenous fluorophores. They measured the DNA damage using ŸH2AX immunohistochemistry. It was found that fast-developing embryos showed lower abundance of endogenous fluorophores (lower metabolic activity) than that of on-time embryos on Day 2. The fast-developing embryos showed a ‘quiet’ metabolic pattern on Day 2 and Day 4 of development, than that of on-time embryos. Also no difference was found in the level of DNA damage between on-time and fast-developing embryos on either day of development.

7 Other techniques

A microfluidic chip method was developed by Szczepanska et al. ( 35 ) to evaluate bovine oocyte and embryo. In the preliminary trials, they used a miniature spectrophotometric system in the microfluidic chip, and reported that this custom-built lab-on-chip has the potential to determine IVF results and bovine pregnancy rate.

8 Prospects and future direction

1. In the last decade lots of work have been done on human embryos using morphokinetics or time-lapse imaging or other imaging approaches. The advancements in artificial intelligence techniques including machine learning has been efficiently incorporated to develop high accuracy classification models to predict the quality of human embryos. However, in general, the research on bovine embryo quality assessment is very limited. As first step, the successful approaches and tools in human study should be investigated for bovine embryos with appropriate modifications.

2. More research is warranted for bovine embryos using color /optical microscopy supported with ML based image analysis and classification tools. These techniques offer simple operational procedures, no damage to live cells, and easy to implement.

3. The potential of non-morphological features in bovine embryo quality assessment should be thoroughly studied with imaging or spectroscopic techniques such as Raman spectroscopy/ imaging, FTIR, NIR hyperspectral imaging and so on. Cofactors other than morphological features such as timing of first embryonic cell divisions or metabolic profiles may be used to predict the ability of an embryo to establish pregnancy or as markers of embryonic viability. In general, more metabolomics profile of embryos should be established for bovine subjects.

9 Conclusion

In the current manual grading of bovine embryos, only qualitative morphological features are used for decision making. However, while using image based grading systems, additional data such as proteomics, metabolomic profile, quantitative or constructed morphological features can be incorporated to obtain beneficial information about embryos ( 36 ). Researchers are currently in pursuit of developing an accurate, repeatable, and most importantly, objective method of determining the gestational success of bovine embryos post-transfer. More research is warranted on morphokinetics of bovine embryos, metabolomics profiles of bovine embryo culture media, and composition of bovine embryos including nucleic acids and amides to build a database for developing robust models to deploy image or spectroscopy based techniques in commercial facilities.

Author contributions

MS: Writing – original draft, Writing – review & editing. PM: Writing – review & editing.

The author(s) declare that no financial support was received for the research, authorship, and/or publication of this article.


The University of Guelph’s initiative on “Independent Research” course series for the undergraduate students is highly acknowledged.

Conflict of interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

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Keywords: embryo, morphokinetics, machine learning, morphological features, FTIR, Raman spectroscopy

Citation: Shivaani M and Madan P (2024) Application of imaging and spectroscopy techniques for grading of bovine embryos - a review. Front. Vet. Sci . 11:1364570. doi: 10.3389/fvets.2024.1364570

Received: 08 January 2024; Accepted: 18 April 2024; Published: 07 May 2024.

Reviewed by:

Copyright © 2024 Shivaani and Madan. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

*Correspondence: Pavneesh Madan, [email protected]

Spatial Variations of the Activity of 137 Cs and the Contents of Heavy Metals and Petroleum Products in the Polluted Soils of the City of Elektrostal

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  • Published: 15 June 2022
  • Volume 55 , pages 840–848, ( 2022 )

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ftir spectrum band assignment

  • D. N. Lipatov 1 ,
  • V. A. Varachenkov 1 ,
  • D. V. Manakhov 1 ,
  • M. M. Karpukhin 1 &
  • S. V. Mamikhin 1  

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The levels of specific activity of 137 Cs and the contents of mobile forms (1 M ammonium acetate extraction) of heavy metals (Zn, Cu, Ni, Co, Cr, Pb) and petroleum products were studied in the upper soil horizon of urban landscapes of the city of Elektrostal under conditions of local radioactive and chemical contamination were studied. In the soils within a short radius (0–100 m) around the heavy engineering plant, the specific activity of 137 Cs and the contents of mobile forms of Pb, Cu, and Zn were increased. The lognormal distribution law of 137 Cs was found in the upper (0–10 cm) soil layer; five years after the radiation accident, the specific activity of 137 Cs varied from 6 to 4238 Bq/kg. The coefficients of variation increased with an increase in the degree of soil contamination in the following sequence: Co < Ni < petroleum products < Cr < 137 Cs < Zn < Pb < Cu ranging from 50 to 435%. Statistically significant direct correlation was found between the specific activity of 137 Cs and the contents of mobile forms of Pb, Cu, and Zn in the upper horizon of urban soils, and this fact indicated the spatial conjugacy of local spots of radioactive and polymetallic contamination in the studied area. It was shown that the specific activity of 137 Cs, as well as the content of heavy metals and petroleum products in the upper layer (0–10 cm) of the soils disturbed in the course of decontamination, earthwork and reclamation is reduced.

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Contaminants migrate and accumulate in urban ecosystems under the impact of both natural and technogenic factors. The processes of technogenic migration of 137 Cs are most pronounced in radioactively contaminated territories. It was found in urboecological studies that the intensity of sedimentation of aerosol particles containing radionuclides and heavy metals is determined by the types of the surfaces of roofs, walls, roads, lawns, and parks and by their position within the urban wind field [ 12 , 26 ]. Traffic in the cities results in significant transport of dust and associated contaminants and radionuclides [ 15 , 24 ]. During decontamination measures in the areas of Chernobyl radioactive trace, not only the decrease in the level of contamination but also the possibility of secondary radioactive contamination because of the transportation of contaminated soil particles by wind or water, or anthropogenic transfer of transferring of ground were observed [ 5 , 6 ]. Rainstorm runoff and hydrological transport of dissolved and colloidal forms of 137 Cs can result in the accumulation of this radionuclide in meso- and microdepressions, where sedimentation takes place [ 10 , 16 ]. Different spatial distribution patterns of 137 Cs in soils of particular urban landscapes were found in the city of Ozersk near the nuclear fuel cycle works [ 17 ]. Natural character of 137 Cs migration in soils of Moscow forest-parks and a decrease in its specific activity in industrial areas have been revealed [ 10 ]. Determination of the mean level and parameters of spatial variations of 137 Cs in soils is one of primary tasks of radioecological monitoring of cities, including both unpolluted (background) and contaminated territories.

Emissions and discharges from numerous sources of contamination can cause the accumulation of a wide range of toxicants in urban soils: heavy metals (HMs), oil products (OPs), polycyclic aromatic hydrocarbons (PAHs), and other chemical substances. Soil contamination by several groups of toxicants is often observed in urban landscapes [ 20 , 23 ] because of the common contamination source or close pathways of the migration of different contaminants. A comprehensive analysis of contamination of urban soils by radionuclides and heavy metals has been performed in some studies [ 21 , 25 ]. The determination of possible spatial interrelationships between radioactive and chemical contaminations in urban soils is an important problem in urban ecology.

A radiation accident took place in the Elektrostal heavy engineering works (EHEW) in April 2013: a capacious source of 137 Cs entered the smelt furnace, and emission of radioactive aerosols from the aerating duct into the urban environment took place. The activity of molten source was estimated at about 1000–7000 Ci [ 14 ]. The area of contamination in the territory of the plant reached 7500 m 2 . However, radioactive aerosols affected a much larger area around the EHEW, including Krasnaya and Pervomaiskaya streets, and reached Lenin Prospect.

Geochemical evaluation of contamination of the upper soil horizon in the city of Elektrostal was carried out in 1989–1991. This survey indicated the anomalies of concentrations of wolfram, nickel, molybdenum, chromium, and other heavy metals related to accumulation of alloying constituent and impurities of non-ferrous metals in the emissions of steelmaking works [ 19 ].

The aim of our work was to determine the levels of specific activity of 137 Cs, concentrations of mobile forms of heavy metals (Zn, Cu, Ni, Co, Cr, and Pb) and oil products in the upper soil horizons in different urban landscapes of the city of Elektrostal under the conditions of local radioactive and chemical contamination.

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D. N. Lipatov, V. A. Varachenkov, D. V. Manakhov, M. M. Karpukhin & S. V. Mamikhin

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Lipatov, D.N., Varachenkov, V.A., Manakhov, D.V. et al. Spatial Variations of the Activity of 137 Cs and the Contents of Heavy Metals and Petroleum Products in the Polluted Soils of the City of Elektrostal. Eurasian Soil Sc. 55 , 840–848 (2022). https://doi.org/10.1134/S1064229322060072

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Lab 3: Fourier Transform Infrared Spectroscopy (FTIR)

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  • W. R. Fawcett, John Berg, P. B. Kelley, Carlito B. Lebrilla, Gang-yu Liu, Delmar Larsen, Paul Hrvatin, David Goodin, and Brooke McMahon
  • University of California, Davis
  • Students should develop an understanding of molecular symmetry and vibrational modes.
  • Students should be able to use symmetry elements to predict which vibrational modes are IR active.
  • Student's should be able to use a solve the pollution mystery by determining the air pollutant and the source of the air pollutant.


The procedure of this lab does not follow the normal format. In this lab, you have been hired by an environmental testing company to monitor air pollutants. By working through a series of exercises, you will gain important information to determine the air pollutant in an air sample and therefore determine the source of the pollution. The air sample was taken in a location 20 m away from an open farm field where strawberries were growing. In the field on one side, there were cows grazing; on the other side, there was a natural gas pumping station. Across the street, there was a gas station with an auto repair shop specializing in air conditioner repair and a dry cleaner. This information narrows the list of suspected chemicals to six. Test your air sample using an infrared spectrometer and determine the culprit.

The FTIR / Vibrational spectroscopy experiment is an adaptation of "Pollution Police" by Profs. Jodye Selco and Janet Beery at the University of Redlands, which was presented at the Division of Chemical Education Regional ACS meeting in Ontario, CA 1999. Many students currently in CHE 115 have already taken CHE 124A which focuses on symmetry, molecular vibrations, and point groups. For students that have taken CHE 124A, use this lab as an opportunity to use your knowledge and assist your lab partners that have not taken the class yet.

There are resources available to further an understanding of FTIR. The theory of FTIR spectrometer operation is discussed in SHN Chapters 16 and 17. The group theory and vibrational quantum mechanics are discussed in McQuarrie and Simon (Chem 110B text) Chapters 12 and 13. Principles of Infrared Spectrometry and its application can be found in Skoog, Holler, and Crouch (the textbook for CHE 115) in chapters 16 and 17.

The FTIR exercise follows the format of a detective story involving solving a series of problems rather than the normal lab format. The experimental portion of the exercises are problems #9 and #10. The computer in room 3475 will be used to complete problems #2, 7, and 8. The computers are set up to run HyperChem, Gaussian, and Spartan.

Setting the Scene

You have been hired by an environmental testing company to monitor air pollutants. Air pollutants can be released from many types of sources. A few examples of sources are: factories, cars, or cattle. Sometimes, volatile chemicals can evaporate from agricultural fields. Since most air pollutant chemicals can absorb infrared light, we are able to detect them with an infrared spectrometer. Some of the molecules commonly found in the atmosphere include those in Table 3.1. Water and small amounts of carbon dioxide, methane, and sulfur trioxide are found in “clean” air samples. Large amounts of any of the chemicals other than water usually indicate atmospheric pollution.

When testing, a test sample is first obtained by taking an evacuated cell to the target location, opening a valve, and allowing the ambient air to fill the cell. An FTIR – Fourier Transform Infrared Spectrometer will be used to perform Infrared Spectrometry. The final output from the spectrometer called an infrared spectrum ( Figure 3.1 ), is a plot of the intensity of light reaching the detector divided by the initial intensity of light, as a function of frequency (%Transmittance= I/I o vs. frequency). The goal of this project is to gain a better understanding of group theory and to identify atmospheric pollutants from their infrared spectra.

For the molecules listen in Table 4.1, examine the three-dimensional ball-and-stick models for these molecule s and compare them with the two-dimensional representations of the molecules in Figure 3.2. In the drawings in Figure 3.2, straight lines represent bonds that lie in the plane of the paper; two lines between a pair of atoms represent a double bond; a filled arrowhead indicates a single bond that is angled out of the plane of the paper toward you; and the dashed arrowhead indicates a bond angled into the plane of the paper away from you. Carbon dioxide is an example of a linear molecule; water, ethylene, sulfur trioxide, and benzene are planar mol ecules.

Problem #1: Center-of-mass and Coordinate Axes

In order to accomplish our goal of identifying the chemical pollutant you are investigating from its infrared spectrum, we must know which infrared frequencies, if any, the molecule absorbs. To decide which molecules absorb infrared radiation and at which frequencies, we will need to examine various properties of the molecules themselves, including their centers of mass, their Cartesian coordinate axes, their vibrations, and their symmetries. Specifically, we will compare the actions of the symmetry operations of a molecule on its Cartesian coordinate axes with the actions of the symmetry operations on its vibrations. When a molecule absorbs infrared radiation of a given frequency, this energy causes the molecule to vibrate in a specific way; the atoms bounce against each other much like balls connected by a spring. The vibrational motions of a molecule that absorb infrared radiation are the ones that exhibit the same behavior as do the Cartesian coordinate axes of the molecule when the atoms of the molecule are permuted in certain ways. This is a result of the orthogonal interaction between the electromagnetic field of the light and the electric field of the molecule itself. Therefore, you will begin by drawing in a three-dimensional coordinate system for each of the molecules in the set assigned (see Table 3.2). The convention for molecules is that the origin of the axis system is placed at the center of mass of the molecule. (This is the weighted average of the positions of the atoms.) First, determine approximately where this should be. (You might want to reexamine the ball-and-stick models.) Remember, the masses of the different atoms are different. To find out how much each atom weighs; consult a periodic table of the elements. The mass number for each type of atom appears at the bottom of the square in which the atomic symbol appears. By convention, the z-axis is the unique axis, if there is one. This axis is also called the molecular axis, or axis of highest symmetry. In a linear molecule, it corresponds to the line formed by the molecule; this is true for carbon dioxide. For benzene, the z-axis is the out-of-plane axis since it is the unique axis. If there doesn’t seem to be a unique axis, then place the heaviest atoms in the molecule along the z-axis (often, there is more than one way to do this). Once the z-axis is assigned, the in-plane axis usually is the y-axis and the out-of-plane axis is the x-axis. In addition, axes should be placed along the molecular bonds whenever possible.

Example 3.1

The y- and z-axes for water and carbon dioxide are shown in Figure 3.3. The x-axis is out-of-plane from the origin (just below the O atom and pointing straight out at you for water, but centered in the C atom and pointing directly away from you for carbon dioxide).

Draw in the three Cartesian coordinate axes in a picture of each molecule assigned to you in Table 3.2 as outlined in the steps below. (If you do not assign them correctly now, you will have a chance later to re-label them.)

  • Estimate, by eye, the center of mass for each molecule, keeping in mind the atomic masses for each type of atom.
  • Draw in the z-axis using the rules above.
  • Draw in the two remaining axes using the rules above.

Problem #2: Molecular Vibrations

The types of molecular vibrations a molecule has determine whether or not it absorbs infrared light. Hence, you need to determine the types of vibrations your molecules make. To ensure that all of them have been identified, we need to know how many are possible. Consider a molecule that is a collection of N atoms connected together in a specific way by chemical bonds. In order to describe the motions of the molecule, we need to consider the motions of each individual atom. This means that we need 3 degrees of freedom for every atom within the molecule for a total of 3N degrees of freedom. However, the atoms within the molecule have a specific geometric relationship to the other atoms in the molecule; this results in a redistribution of the number of independent degrees of freedom. The motion through space of the molecule uses three degrees of freedom, reducing the 3N degrees of freedom to 3N - 3. Since the molecule also can rotate (like a spinning baton or Frisbee), we require two degrees of freedom to describe the coordinates about which a linear molecule can spin and three degrees of freedom for a non-linear molecule (for a linear molecule there is no concerted rotation about the molecular axis (z-axis). This leaves 3N - 5 degrees of freedom for the linear molecule and 3N - 6 for the non-linear molecule still unaccounted for; each of the remaining degrees of freedom describes a distinct coordinated internal motion, or vibration, of the atoms within the molecule.

For example, when there are only two atoms in the molecule (e.g. O 2 , N 2 , or CO), there is only one vibrational motion: 3(2) - 5 = 1. In the case of benzene (C 6 H 6 ) there are 12 atoms and 3(12) - 6 = 30 vibrational motions possible! As it turns out, not all of these vibrations are capable of absorbing infrared radiation. For the simplest molecules, such as water, it is easy to draw pictures representing the vibrational motions.

Example 3.2

Water has 3(3)-6=3 vibrations and carbon dioxide has 3(3)-5=4 vibrations as shown in Figures 3.4 and 3.5.

Consider the water molecule as it undergoes the asymmetric stretch; as it reaches its most extreme position, it has one “arm” extended and the other “arm” contracted. The bend is a change largely in angle and not inter-atom distances.

The four vibrations for CO 2 consist of two stretches (one symmetric and one asymmetric) and two bends, that are degenerate (with the same energy) but involve perpendicular motions.

For each molecule, calculate the number of vibrational motions using the formula given above. Then, use a computer program to view the vibrational motions for water, carbon dioxide and the other molecules assigned. Record the motions of the atoms in the molecule using the symbols from the examples above, and record the frequency calculated for each vibration. Note that some of these motions are out-of-plane motions. Be sure to rotate the molecules on the computer screen so that you examine the motions from many different angles. (Many of the programs calculate the frequencies in cm -1 ; this is actually the frequency, in s -1 , divided by the speed of light (3.00 x 10 10 cm/s). In this exercise, make note of the frequencies in cm -1 given by the HyperChem program.) Again, you are to complete the following steps for the molecules assigned to you in Table 3.2 . This assignment will require a lot of space in your lab notebook due to all of the vibrations you will be drawing.

  • Determine the number of vibrational motions, 3N – 6 or 3N – 5, to make sure that you know how many vibrational motions to record.
  • Record the vibrational motions for your molecules, following the notation used in the examples above.
  • Record the frequency for each of these vibrations.

'16 Operation

  • Double-click “PC Spartan '16” on desktop. The Spartan computer is located in the TA area of 3475 and has a bright orange name tag.
  • Click the new page icon in the top left corner. Atoms with various bond choices will appear. If you prefer a more advanced setting, click the Expert tab.
  • Draw your molecule by clicking on the atom with the correct number of bonds needed. To join two atoms, repeat this process by touching the mouse arrow to the open bond.
  • Once you are done drawing your molecule, click the Glasses ("View") icon located just below the Geometry scroll-down. This will finalize your drawing.
  • Calculate: Equilibrium Geometry with Hartree-Fock 3-21G(*)
  • Compute: IR
  • Print: Vibrational Modes
  • Go to Setup and scroll down to Submit. Make a new folder with your group's letter in the Chem 115 folder. Label within your folder as you see fit.
  • After you save the file you will be prompted twice that Spartan has started and completed. Press OK both times.
  • Go to Display and scroll to Spectra. Here you will find the frequencies associated with the different vibrations of your molecule. Click on any of the checkboxes to view the animation for the vibration associated with that particular frequency.

Problem #3: Symmetry Elements and Symmetry Operations

Because the molecules we are examining are very small, the rules of quantum mechanics govern the processes in which we are interested in. According to quantum mechanics, not all light absorption processes are allowed; many are “forbidden” by symmetry. If we want to determine which molecular vibrations absorb infrared light, we need to examine the actions of the symmetries of the molecules on the coordinate axes and on the molecular vibrations. We begin by determining the symmetry elements that each molecule possesses.

A symmetry operation on a molecule is an action that moves the molecule into a position that is indistinguishable from the starting position. A symmetry element of a molecule is a geometric feature of the molecule about which a symmetry operation is performed. Symmetry elements include planes and axes; symmetry operations include reflections across planes and rotations about axes. In the case of water, 180° rotation about the z-axis is a symmetry operation, denoted C 2 ; while the z-axis itself is a symmetry element, a C 2 axis. The symbol C 3 indicates a three-fold axis of symmetry, a symmetry element; while C 3 indicates a 120°rotation about a C 3 axis, a symmetry operation. Rotation by 240° about a C 3 axis is denoted C 3 2 . Rotation by 360° about a C 3 axis is equivalent to doing nothing; that is, C 3 3 =Ê, where Ê is the identity operation.

The plane of symmetry, σ, is also referred to as a reflection plane or mirror plane. The symbol σ v is used to denote a “vertical” plane of symmetry that is parallel to an axis of highest symmetry (z-axis, or C n with largest n), while the symbol σ h is used to denote a “horizontal” plane of symmetry that is perpendicular to the axis of highest symmetry (taken as z-axis). The symbol σ d denotes a “dihedral” plane of symmetry that bisects an angle between atoms.

Example 3.3

Figures 3.6 and 3.7 illustrate several of the symmetry elements listed in Table 3.3.


Example 3.4

Figures 3.8 and 3.9 show the symmetry elements and operations of water and carbon dioxide, respectively.


In the case of water the symmetry elements are E, C 2 (shown), σ v (xz-plane, perpendicular to the plane of the molecule), and σ v’ (yz-plane, the plane of the molecule). Note that it does not matter whether σ v represents the xz- or yz-plane. The corresponding symmetry operations for water are Ê, C 2 , σ v , and σ v’ .

The symmetry elements not shown above are E, σ v (yz-plane, the plane of the paper), σ h (xy-plane, perpendicular to the C 2 axis), and i (center of inversion at the coordinate origin). The symmetry operations for carbon dioxide are Ê, infinitely many C 2 , C ∞ , infinitely many σv, S ∞ , σ h , and i . A subscript of ∞ means that rotation through any angle about that axis results in a valid symmetry operation. Note that σ h is identical to S 0 and is often omitted.

Reexamine the ball-and-stick models for molecules assigned to you in Table 3.2 . Determine all of the symmetry elements and corresponding symmetry operations for each molecule. (Hint: At least one molecule on the list contains the symmetry element σ h and one contains an S 3 symmetry element.)

There are a few things to keep in mind while trying to determine the symmetry elements for chemical compounds. The first is that molecules are three-dimensional objects. This means that we can tell the difference between the “front” and “back” or the “top” and “bottom” of planar molecules. For instance, the σ v’ reflection of the water molecule across the yz-plane is not the same as the identity operation Ê. Second, since atoms of the same kind (or color) are indistinguishable, you may want to number the atoms in the models in order to keep track of the results of the symmetry operations. Finally, when molecules have hexagonal rings with alternating double bonds, all of these bonds---both single and double---are equivalent (e.g. benzene and toluene). It is only the orientation of the atoms themselves that can be “seen” spectroscopically and hence needs to be considered here.

After you have determined the symmetries of the molecules, you can double-check your axis assignments. The z-axis should be the axis of highest C n symmetry. In H 2 O, there is only one C n axis, C 2 , so it is the z-axis. In CO 2 , there is a C 2 axis and a C ∞ axis, so the C ∞ axis is the z-axis. Check the other molecules to make sure that the z-axis you assigned is the one of highest symmetry. Remember that the axis of highest symmetry may not be unique.

Problem #4: Orders of Symmetry Operations

The order of a symmetry operation is the number of times the operation must be applied to obtain the identity operation, Ê. More specifically, the order of a symmetry operation  is n, if n is the smallest positive integer such that  n = Ê. For instance, for H 2 O, the non-identity symmetry operations each have order 2. Note that an inversion always has order 2. The symmetry operation S 4 has order 4 because it must be applied four times in succession to return the molecule to its original orientation when the outside atoms are labeled. (Try it for methane!) Therefore, S 4 generates 4 symmetry operations: S 4 , S 4 2 = C 2 , S 4 3 , and S 4 4 = Ê o f orders 4, 2, 4, and 1, respectively.

For each of the molecules assigned to you in Table 3.2 , find the order of each symmetry operation. Use the description of order to aid you.

Problem #5: Symmetry Groups

You may have noticed that the set of symmetry operations forms a group under composition of operations, called the symmetry group of the molecule. The symmetry elements of a molecule can be used to determine the group to which the molecule belongs. Using Figure 3.10 and the molecule's symmetry elements, the group can be identified.


Identify the symmetry group for each of the molecules assigned to you in Table 3.2 . The order of a symmetry group is the number of operations which comprise the group. What is the order of each group? Verify by determining the group for your molecules from HyperChem and by examining the character tables in Chapter 12 of McQuarrie and Simon (Chem 110B text) or Chemical Applications of Group Theory , by F. A. Cotton. There are also tutorials available on to help identify the group and practice finding groups of other molecules.

Problem #6: Action of Symmetries on Coordinate Axes

Your overall goal is to identify the molecular origins of the infrared peaks observed in a spectrum of contaminated air. Since a molecule’s infrared light absorption depends on how the Cartesian axes transform under the symmetry operations, the next step is to determine what happens to each of the Cartesian axes as the different symmetry operations are performed upon the molecule.

Example 3.5

In Figure 3.11 and Table 3.4, we illustrate this process for water. Note that under the identity operation, Ê, none of the axes are inverted or reversed. When the molecule is rotated about the C 2 axis, the orientation of the z-axis remains the same but the x- and y-axes are oriented in the opposite direction; each point (x,y,z) is moved to the point (-x,-y,z). In this case, the rotation is equivalent to multiplying the x and y values by -1. For any operation, -1 indicates that there is a reversal in the orientation of the axis relative to the original orientation, whereas +1 indicates that the orientation remains the same.

Construct similar tables for the FIRST THREE molecules assigned to you in Table 3.2 . First, check with your TA to make sure you have drawn the Cartesian coordinate axes in the standard way for each of these molecules.

Problem #7: Action of Symmetries on Vibrational Motions

The vibrational motions of the molecule that absorb infrared radiation are the ones that transform under the symmetry operations of the molecule in the same way as do the Cartesian coordinate axes of the molecule. Therefore, we need to determine how the molecular vibrations behave under each of the symmetry operations, so that we can compare them to the transformations of the Cartesian coordinate axes.

Example 3.6

Let us examine the vibrations of water again ( Figure 3.12 ).

If we ask how each of the vibrations of water behaves under each of the symmetry operations, we can add more entries to Table 3.5. When we examine what happens to the vibrating molecules as the symmetry operations are performed, we are interested only in whether or not the geometrical orientation of the molecule has changed. For instance, consider the water molecule as it undergoes the asymmetric stretch. Imagine the molecule (or stop the computer program) when it reaches its most extreme position, with one “arm” extended and the other “arm” contracted. Now perform the C 2 operation (rotation by 180°) on this “distorted” molecule. Its orientation after the C 2 operation is different from its orientation before. Note that the configuration has been reversed. The fact that its new position is distinguishable from its original position is represented in Table 3 by -1.

Now let’s examine the water molecule as it undergoes the bend or symmetric stretch. If we perform the C 2 operation (rotation by 180 ° ) on this vibrating molecule, its geometric orientation is unchanged. Its new position is indistinguishable from its original position. The fact that it appears unchanged is represented in Table 3.5 by +1.

Construct tables as in example 3.6 for the FIRST THREE molecules assigned to you in Table 3.2 . Examine the motions of the atoms for each different vibration. If you were to imagine the molecule (or stop the computer program) when it reaches its most extreme position, consider how that “version” of the molecular shape would behave under each of the different symmetry operations. That the molecule is indistinguishable after the symmetry operation is indicated by a 1, while a distinguishable molecule is represented by a -1. Fill in a line in your table for each vibrational motion. Note this procedure works for most, but not all simple molecules. For example, this procedure will not work for your third molecule.

Problem #8: Comparing Symmetry Operations on Axes and Vibrations

When a symmetry operation and an axis transform in the same way, the frequency associated with that symmetry operation will absorb light. By comparing the tables you generated in

Example 3.7

In the case of water, as illustrated in Table 3.5, the bending vibration transforms under the symmetry operations in the same way as does the z-axis. This is also true for the symmetric stretching motion. On the other hand, the asymmetric stretch transforms in the same way as does the y-axis. In this case, all three of the vibrational motions of water would absorb infrared light since each one of them transforms under the symmetry operations as does one of the Cartesian coordinate axes. All of the vibrations also have frequencies that are in the appropriate frequency range. We would expect the infrared spectrum of water to have three peaks corresponding to frequencies of 1840, 3587, and 3652 cm -1 .

For the FIRST THREE molecules assigned to you in Table 3.2 , use the tables you constructed in Problem #7 to determine how the vibrational motions transform under the symmetry operations. List the frequency calculated of the ones that transform as do the x-, y-, or z-axes. (You listed the frequencies you need for this problem in Problem #2.) Given that infrared spectrometers operate in the range of about 600 cm -1 to 4000 cm -1 (wavenumbers), which vibrational frequencies should you observe in the infrared spectra for your molecules? Make a rough sketch of the infrared spectrum you would expect to see for each of your molecules, labeling peaks with their frequencies. Assume that if peaks are separated by less than 25 cm -1 , they will not be resolved and will appear as a single peak.

Problem #9: Obtaining Infrared Spectra

You have now reached the experimental portion of this exercise. Before you can measure your contaminated air sample. You will need to measure some common air pollutants. The IR spectra that you measure for these molecules will help you decide which compound is in your air sample.

Breathing into the sample compartment of the infrared spectrometer can generate the spectrum of water and carbon dioxide. You will then take spectra of methyl iodide, ethylene, acetylene, trans-DCE, and a sample of contaminated air , which have already been prepared in infrared cells. Do not be overly concerned if the windows on the cells appear to be hazy; they will work fine. Do you observe the predicted absorptions? If not, try multiplying all of your vibrational frequencies by 0.89 (a factor theoretical chemists recommend to compensate for over calculations). Do they come closer to what you observe now?

Operating the Bruker FTIR Spectrometer (room 3475)

  • Start the OPUS 6.0 software on the computer next to the instrument.
  • Scans: 16 for sample and 16 for background
  • Resolution: 2.0
  • Signal Gain: 1
  • IR Data Type: Transmittance
  • Name your sample in the Filename: field
  • Close the sample compartment and wait 2-3 minutes for the sample compartment to be purged of air.
  • In the Measure Menu , go to the Basic tab . Click the “Background Single Channel” button, the instrument will start scanning the background. The progress status will be shown at the bottom of the screen.
  • Open the chamber, and load your sample in. Close the chamber.
  • To acquire your sample scan. Give your sample a description under the Basic tab . Run the sample spectrum by clicking on the “Sample Single Channel” button. The progress of the scan can be seen at the bottom of the screen.

Operating the FTIR Spectrometer (room 3480)

  • Start the OPUS 7.2 software on the computer next to the instrument.

advance measure 2.png

  • Signal Gain: auto

2022-Agilent's modular Cary 630 FTIR will be used with stainless steel gas cells, ask your TA, Paul or his staff.

Collect a spectrum of water and carbon dioxide:

  • Launch MicrolabPC on the laptop connected to the Cary 630.  user:Admin/pswd: 3000hanover. Yes, make changes. Enter pswd again. 
  • Ensure the transmission module is pictured and all indicators are GREEN. 
  • Click start to check the crystal and   prepare for background scanning.  Click next to acquire the background.
  • Prepare Sample: on the next screen enter sample ID or comments and without clicking next;  open the compartment of the transmission module and exhale into it.
  • You will observe the the live signal.  Clicking  next  will collect the data and display the FTIR spectum.
  • Pick peaks for frequency labeling by clicking on the white area outside and to the right side of the spectral window and inside the grey area of MicroLab.
  • Tap and drag to label the peaks desired. Clicking Data Handling and Print Report will provide a .pdf for hardcopy or to export as electronic data. 

Collect spectra of the four reference samples and your unknown:

  • Follow the above procedure for Cary 630.  Open the sample compartment and fan the lid to refresh the background. 
  • Some methods store the background for 20 minutes, others take the background for each sample.
  • Insert cells inside the cell holder by aligning the cylinder ridges with module's alignment grooves and ensure the ID tag is not caught in between.  Gently lower the cell.
  • Make sure you name the file before clicking next.
  • A second unknown sample may be available for Bonus Points, ask your TA or Paul.

To pick peaks: (This procedure may not work depending on the instrument you are using.)

Click on the peak picking icon, or select Peak Picking from the Evaluate menu.  Cary 630 peak picking is above.

  • The peak picking screen will show up. Choose the Interactive mode . Sliding the Threshold square up or down so that all the peaks are below it.
  • Each peak above the threshold will be labeled with its frequency and the peak list will be written to a report file that you must save by selecting Save Report from the File pulldown menu.
  • You can annotate the plot by selecting the annotator options from the Tools pulldown menu.
  • The file path and name are the default title for your plot. You can change this by selecting Title under the Display pulldown menu.
  • Once you have picked your peaks and annotated your plot, save the window as before by selecting Save Sample from the File pulldown menu.
  • Select Plot from the File pulldown menu. In the Plot menu select the window you wish to print. Check that the size of the plot is correct and then click on the “plot”. This will take 1-2 minutes. Be patient. You can then select another window to print or click on the “done” button.

Problem #10: Identifying the Pollutant

As a reminder: The sample was taken in a location 20 m away from an open farm field where strawberries were growing. In the field on one side, there were cows grazing; on the other side, there was a natural gas pumping station. Across the street, there was a gas station with an auto repair shop specializing in air conditioner repair and a dry cleaner. This information narrows the list of suspected chemicals to six. Test your air sample and determine the culprit. The spectrometer automatically purges the cell cavity with N 2 , which is transparent in infrared. This removes water and carbon dioxide, which exhibit strong IR absorptions, so you should be able to see the pollutant's spectrum relatively easily.

Examine the spectrum from your sample of the unknown, contaminated air. Note that it is a very different spectrum from that of carbon dioxide and water, which is shown in Figure 3.1. Which of the chemicals that you studied is responsible for this spectrum? To which potential polluter described above would you attribute this pollution?

Post-Lab Questions

1. Include all of the information recorded during the assignments.

2. Include all of the FTIR spectra

3. Identify the pollutant and predict the source of the pollutant.

4. Explain the process used to identify the pollutant.

Outside Links

  • http://symmetry.otterbein.edu/index.html
  • Point group symmetry character tables
  • McQuarrie, D. A.; Simon, J. D. Physical Chemistry: A Molecular Approach ; University Science Books: Sausalito, CA, 1997.
  • Cotton, F. A. Chemical Applications of Group Theory ; Wiley: New York, NY, 1990.
  • Drago, R. S. Physical Methods in Chemistry ; W. B. Saunders: Philadelphia, PA, 1977.
  • Skoog, D. A.; Holler, F. J.; Crouch, S. R. Principles of Instrumental Analysis, Seventh Edition; Cengage Learning: Boston, MA, 2016.

Contributors and Attributions

  • Dr. Christopher Brazier, Dr. Steven Morics, Dr. Teresa Longin, Dr. Dara Gilbert, Dr. David Goodin, and Brooke McMahon


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