Submitted Successfully!
To reward your contribution, here is a gift for you: A free trial for our video production service.
Thank you for your contribution! You can also upload a video entry or images related to this topic.
Version Summary Created by Modification Content Size Created at Operation
1
Joel Benjamin Johnson
 -- 1845 2023-05-04 13:51:01 |
2 Reference format revised.
Lindsay Dong
 Meta information modification 1845 2023-05-06 07:43:58 |

Video Upload Options

Do you have a full video?
Send video materials Upload full video

Confirm

Are you sure to Delete?
Yes No
Cite
If you have any further questions, please contact Encyclopedia Editorial Office.
Johnson, J.B.; Walsh, K.B.; Naiker, M.; Ameer, K. Infrared Spectroscopy in Quantification of Food Bioactive Compounds. Encyclopedia. Available online: https://encyclopedia.pub/entry/43767 (accessed on 28 August 2024).
Johnson JB, Walsh KB, Naiker M, Ameer K. Infrared Spectroscopy in Quantification of Food Bioactive Compounds. Encyclopedia. Available at: https://encyclopedia.pub/entry/43767. Accessed August 28, 2024.
Johnson, Joel B., Kerry B. Walsh, Mani Naiker, Kashif Ameer. "Infrared Spectroscopy in Quantification of Food Bioactive Compounds" Encyclopedia, https://encyclopedia.pub/entry/43767 (accessed August 28, 2024).
Johnson, J.B., Walsh, K.B., Naiker, M., & Ameer, K. (2023, May 04). Infrared Spectroscopy in Quantification of Food Bioactive Compounds. In Encyclopedia. https://encyclopedia.pub/entry/43767
Johnson, Joel B., et al. "Infrared Spectroscopy in Quantification of Food Bioactive Compounds." Encyclopedia. Web. 04 May, 2023.
Infrared Spectroscopy in Quantification of Food Bioactive Compounds
Edit
This entry is adapted from the peer-reviewed paper 10.3390/molecules28073215

Infrared spectroscopy (wavelengths ranging from 750–25,000 nm) offers a rapid means of assessing the chemical composition of a wide range of sample types, both for qualitative and quantitative analyses. Its use in the food industry has increased significantly and it is now an accepted analytical technique for the routine analysis of certain analytes. Furthermore, it is commonly used for routine screening and quality control purposes in numerous industry settings, albeit not typically for the analysis of bioactive compounds. 

phenolics bioactive compounds infrared spectroscopy NIR spectroscopy

1. Infrared Spectroscopy

Infrared (IR) spectroscopy is a well-established tool in analytical chemistry, offering a non-invasive, non-destructive and rapid means of assessing the chemical composition of a wide range of sample types. For the purposes of analytical spectroscopy, the infrared spectrum can be divided into three main regions: near-infrared (NIR; 750–2500 nm), mid-infrared (MIR; 4000–400 cm−1) and far-infrared (400–10 cm−1; rarely used in the food analysis sector). Historically, NIRS has been and continues to be utilised more than MIRS in the food industry due to its lower cost, greater penetrative power (i.e., lower absorption by the sample) that allows for more representative sampling [1] and reduced sample preparation times [2]. Wavelengths in this NIR region are absorbed due to the overtone and combination bands of IR-active bonds, rather than their fundamental tones.
Compared to other analytical methods, the main advantages of IR spectroscopy are its speed, relatively low price of the instrument, and the fact that it is typically non-destructive and non-invasive, lowering or eliminating sample preparation time [3][4]. Furthermore, IR spectroscopy is highly sensitive, requires a small amount of sample and allows users to analyse samples from a wide variety of matrix types, including solids, powders, films, gels, liquids and gases [3], and does not produce any waste [5]. Conversely, the challenges involve interpreting spectra from complex mixtures and the need to create and maintain robust calibration models for quantitative analysis [3]. Briefly, a robust model refers to one which can be used year-after-year without losing accuracy over time, or when applied to different population groups (e.g., different varieties, different geographic locations).
In addition, IR spectroscopy—particularly NIRS—is best suited for the analysis of macroconstituents (usually those present at concentrations of ~0.5% or higher). Below this concentration range, it is difficult to separate out the signal of the analyte from the rest of the spectral peaks. In many cases reporting the detection of analytes at much lower concentrations, it is likely that NIRS is actually detecting a different analyte present at macro-levels—the concentration of which is correlated with the targeted analyte. This is known as a secondary, or surrogate, correlation [4]. In many cases, this correlation may be unavoidable due to both analytes absorbing in similar regions [6]. In other situations, it may be the only way through which IR spectroscopy can be used to estimate the microconstituent concentration. The use of such secondary correlations is acceptable in many cases—as long as the correlation holds true for all samples analysed. Some publications have reported that these correlations may change between different sample populations or harvest years [6], which may explain the poor performance of independent test sets found in some studies using IR spectroscopy for the analysis of microconstituents.

2. Sample Presentation

In order to gain an accurate assessment of the sample matrix using infrared spectroscopy techniques, it is essential that the portion of the sample that the instrument “sees” is representative of the whole sample. Furthermore, due to the wide range of sample types which can be analysed using IR spectroscopy (such as solids, liquids, films, gels and powders), there are a variety of sample presentation methods that have been adopted for IR spectroscopy.
Perhaps the simplest form of sample presentation is the full transmittance mode (180° light-sample-detector). This is also the only method for which the Beer–Lambert law holds true. In this presentation mode, the IR light enters one side of the sample and some wavelengths are absorbed by the sample, with the remaining light measured as it exits the other side of the sample. As long as the length of the light path is sufficiently low, transmittance mode ensures that the emitted light has an opportunity to interact with nearly all of the analytes present in the light path. Consequently, it is usually quite representative of the true matrix composition. However, it is only suitable for analysing relatively thin samples due to the high level of absorbance in aqueous-based matrices. As shown by Beer–Lambert’s law, increasing the light path length will proportionally increase the absorbance, making it more difficult to detect the signal of the resultant spectra. For example, a path length of only a few millimetres is often required when using transmittance NIR spectroscopy for the analysis of aqueous-based solutions. Due to path length limitations, the use of transmittance spectroscopy for the analysis of solid or powder substances can be more challenging compared to reflectance modes; however, analysis of whole fruits is possible using higher incidence light intensities and more sensitive detectors [7][8].
One variation of the full transmittance mode is partial transmission spectroscopy, also known as interactance spectroscopy. This refers to the mode where the infrared light is partially transmitted through the sample matrix, before being detected by another sensor at the matrix surface, but located adjacent to the source. These instruments utilise a physical barrier between the light source and detector to prevent the detector from receiving any IR light reflected from the sample surface. The benefits of this method are a reduced path length compared to full transmittance mode, and increased interaction between the IR light and the sample matrix compared to reflectance geometry.
Reflectance mode is one of the most commonly used presentation modes in IR spectroscopy applications, particularly for NIRS. In this mode, the infrared light enters one side of the sample and interacts with the sample matrix as it penetrates into the sample. The majority of non-absorbed light is then reflected back to the surface of the sample, where it is detected by the instrument sensor. Some non-absorbance scattering of the IR light can also occur, which can bias the resultant spectra. One of the main advantages of reflectance mode is its one-dimensionality (i.e., the instrument only needs access to the sample surface in one location, as opposed to transmittance spectroscopy where both sides of the sample must be accessible), allowing it to be used in a much broader range of applications compared to transmittance spectroscopy. However, it is reliant on the assumption that the composition of the surface material is representative of the entire sample matrix [4].
Within the food sector, reflectance NIR spectroscopy is widely reported in publications for the analysis of horticultural produce [9][10] and in the grains industry [11][12]. There are no commercial instruments designed to use this geometry mode for the analysis of whole fruits, as fruit skin composition (e.g., thickness, starch/fibre content, chlorophyll content) can change in populations from year to year, depending on other variables such as rainfall, amount of sunlight, application of fertiliser, etc. In turn, this variability in skin composition would interfere with the NIR spectra and reduce the robustness of the model, which is designed to only measure the internal composition of the fruit. However, reflectance NIR spectroscopy is commonly used for the analysis of ground grain products in industry/commercial settings, as the surfaces of these samples are generally quite representative of the entire sample.

3. Data Processing

The final stage in the use of infrared spectroscopy for analytical purposes is the processing of the spectral data. In many cases, the signal of the desired analyte may be obscured by other matrix components present in much higher concentrations, such as water or carbohydrate-based structures. The use of modern mathematical data analysis techniques—termed chemometrics—can aid in uncovering minor analyte signals and developing optimum models for the quantification of the analytes. However, it is important to note that no amount of data analysis or chemometrics can “uncover” an analyte if the signal from the analyte is either not present or too low to be detected by the instrument.

3.1. Spectral Pre-Processing

Typically, IR spectra are subjected to pre-processing before they can be used for quantitative analytical purposes. The aim of this procedure is to remove spectroscopic artefacts from the measurement process, such as random noise, scatter or baseline drift [13][14]. The effects of these artefacts are particularly detrimental when attempting to analyse complex mixtures or analytes present in very low concentrations [15].
A variety of spectral pre-processing methods are available. These include smoothing, multiplicative scatter-correction (MSC), standard normal variate (SNV), normalisation by range (NBR) and the calculation of derivatives [16].
Standard normal variate (SNV) is a normalisation-based pre-processing method. In this pre-processing method, the mean value of each spectrum is calculated and this constant value is subtracted across the entire spectrum, before the spectrum is divided by the standard deviation of the entire spectrum.
Calculating the derivative of spectra is another common approach to account for baseline shift or amplitude differences in the spectra. First and second derivatives are the most commonly used. Although higher order derivatives, such as the third derivative, have been successfully used in some applications [17][18][19], there is an accompanying decrease in the signal-to-noise ratio as the derivative order increases [17].
Finally, it is important to note that pre-processing methods are often combined. For example, typical pre-processing of spectra for use in analytical spectroscopy could involve calculating the SNV of the spectra, before subsequently calculating the first derivative of the SNV-processed spectra.
The choice of optimum spectral pre-processing methods is poorly defined and strongly dependent upon both the matrix type and analyte of interest. Furthermore, the need for and choice of pre-processing method may also vary with the sample size of the population [15]. In the absence of definitive guidelines, trial and error is often the best approach when developing new applications for infrared spectroscopy.

3.2. Data Analysis Methods

For quantitative applications of IR spectroscopy, regression modelling is among the most commonly used data analysis methods. One of the first chemometric methods applied in quantitative IR spectroscopy applications was multiple linear regression (MLR), which attempts to predict the analyte concentration from the spectral absorbance at several different wavelengths. However, it cannot be used for the analysis of entire spectra, due to the high multicollinearity of the datapoints comprising the spectra.
Partial least squares (PLS) regression is a derivative of MLR suited to datasets with high levels of multicollinearity, such as infrared spectra [20]. Through a variety of algorithms, the key contributing variables are identified and weighted such that the wavelengths most closely correlated with the analyte concentration have the greatest contribution to the PLS model [20]. PLS regression is widely used for the development of IR spectroscopy models across the food science sector [21][22][23].
In recent years, there has also been an emerging interest in machine learning techniques, such as artificial neural networks (ANNs), support vector machine (SVM) and deep learning [24][25][26][27]. These non-linear techniques look for patterns within the data in order to optimise model weighting and extract the desired information from the data. As more datapoints are added to the dataset, the model can update over time in order to provide more accurate prediction results.

References

  1. Almeida, M.; Torrance, K.; Datta, A. Measurement of optical properties of foods in near-and mid-infrared radiation. Int. J. Food Prop. 2006, 9, 651–664.
  2. Burks, C.S.; Dowell, F.E.; Xie, F. Measuring fig quality using near-infrared spectroscopy. J. Stored Prod. Res. 2000, 36, 289–296.
  3. Bureau, S.; Cozzolino, D.; Clark, C.J. Contributions of Fourier-transform mid infrared (FT-MIR) spectroscopy to the study of fruit and vegetables: A review. Postharvest Biol. Technol. 2019, 148, 1–14.
  4. Walsh, K.B.; Blasco, J.; Zude-Sasse, M.; Sun, X. Visible-NIR ‘point’ spectroscopy in postharvest fruit and vegetable assessment: The science behind three decades of commercial use. Postharvest Biol. Technol. 2020, 168, 111246.
  5. Johnson, J.B.; Naiker, M. Seeing red: A review of the use of near-infrared spectroscopy (NIRS) in entomology. Appl. Spectrosc. Rev. 2019, 55, 810–839.
  6. Velasco, L.; Schierholt, A.; Becker, H.C. Performance of near-infrared reflectance spectroscopy (NIRS) in routine analysis of C18 unsaturated fatty acids in intact rapeseed. Lipid Fett 1998, 100, 44–48.
  7. Clark, C.J.; McGlone, V.A.; Jordan, R.B. Detection of Brownheart in ‘Braeburn’ apple by transmission NIR spectroscopy. Postharvest Biol. Technol. 2003, 28, 87–96.
  8. Fraser, D.G.; Jordan, R.B.; Künnemeyer, R.; McGlone, V.A. Light distribution inside mandarin fruit during internal quality assessment by NIR spectroscopy. Postharvest Biol. Technol. 2003, 27, 185–196.
  9. Ncama, K.; Tesfay, S.Z.; Fawole, O.A.; Opara, U.L.; Magwaza, L.S. Non-destructive prediction of ‘Marsh’ grapefruit susceptibility to postharvest rind pitting disorder using reflectance Vis/NIR spectroscopy. Sci. Hortic. 2018, 231, 265–271.
  10. Kumar, S.; Singh, R.; Dhanani, T. Rapid Estimation of Bioactive Phytochemicals in Vegetables and Fruits Using Near Infrared Reflectance Spectroscopy. In Fruit and Vegetable Phytochemicals, 2nd ed.; Yahia, E.M., Ed.; John Wiley & Sons: Hoboken, NJ, USA, 2017; pp. 781–802.
  11. Yang, Y.; Xu-zhen, C.; Gui-xing, R. Application of Near-Infrared Reflectance Spectroscopy to the Evaluation of D-chiro-lnositol, Vitexin, and Isovitexin Contents in Mung Bean. Agric. Sci. China 2011, 10, 1986–1991.
  12. Caporaso, N.; Whitworth, M.B.; Fisk, I.D. Near-Infrared spectroscopy and hyperspectral imaging for non-destructive quality assessment of cereal grains. Appl. Spectrosc. Rev. 2018, 53, 667–687.
  13. Gautam, R.; Vanga, S.; Ariese, F.; Umapathy, S. Review of multidimensional data processing approaches for Raman and infrared spectroscopy. EPJ Tech. Instrum. 2015, 2, 8.
  14. Rinnan, Å. Pre-processing in vibrational spectroscopy—When, why and how. Anal. Methods 2014, 6, 7124–7129.
  15. Schoot, M.; Kapper, C.; van Kollenburg, G.H.; Postma, G.J.; van Kessel, G.; Buydens, L.M.C.; Jansen, J.J. Investigating the need for preprocessing of near-infrared spectroscopic data as a function of sample size. Chemom. Intell. Lab. Syst. 2020, 204, 104105.
  16. Dotto, A.C.; Dalmolin, R.S.D.; ten Caten, A.; Grunwald, S. A systematic study on the application of scatter-corrective and spectral-derivative preprocessing for multivariate prediction of soil organic carbon by Vis-NIR spectra. Geoderma 2018, 314, 262–274.
  17. Rodriguez-Otero, J.L.; Hermida, M.; Cepeda, A. Determination of Fat, Protein, and Total Solids in Cheese by Near-Infrared Reflectance Spectroscopy. J. AOAC Int. 1995, 78, 802–806.
  18. Orman, B.A.; Schumann, R.A. Comparison of near-infrared spectroscopy calibration methods for the prediction of protein, oil, and starch in maize grain. J. Agric. Food Chem. 1991, 39, 883–886.
  19. Terhoeven-Urselmans, T.; Schmidt, H.; Joergensen, R.G.; Ludwig, B. Usefulness of near-infrared spectroscopy to determine biological and chemical soil properties: Importance of sample pre-treatment. Soil Biol. Biochem. 2008, 40, 1178–1188.
  20. Mehmood, T.; Ahmed, B. The diversity in the applications of partial least squares: An overview. J. Chemom. 2016, 30, 4–17.
  21. Cobaleda-Velasco, M.; Almaraz-Abarca, N.; Alanis-Bañuelos, R.E.; Uribe-Soto, J.N.; González-Valdez, L.S.; Muñoz-Hernández, G.; Zaca-Morán, O.; Rojas-López, M. Rapid determination of phenolics, flavonoids, and antioxidant properties of Physalis ixocarpa Brot. ex Hornem. and Physalis angulata L. by infrared spectroscopy and partial least squares. Anal. Lett. 2018, 51, 523–536.
  22. de Oliveira, G.A.; Bureau, S.; Renard, C.M.-G.C.; Pereira-Netto, A.B.; de Castilhos, F. Comparison of NIRS approach for prediction of internal quality traits in three fruit species. Food Chem. 2014, 143, 223–230.
  23. Hu, Y.; Pan, Z.J.; Liao, W.; Li, J.; Gruget, P.; Kitts, D.D.; Lu, X. Determination of antioxidant capacity and phenolic content of chocolate by attenuated total reflectance-Fourier transformed-infrared spectroscopy. Food Chem. 2016, 202, 254–261.
  24. Gabriëls, S.H.E.J.; Mishra, P.; Mensink, M.G.J.; Spoelstra, P.; Woltering, E.J. Non-destructive measurement of internal browning in mangoes using visible and near-infrared spectroscopy supported by artificial neural network analysis. Postharvest Biol. Technol. 2020, 166, 111206.
  25. Sharabiani, V.R.; Nazarloo, A.S.; Taghinezhad, E. Prediction of Protein Content of Winter Wheat by Canopy of Near Infrared Spectroscopy (NIRS), Using Partial Least Squares Regression (PLSR) and Artificial Neural Network (ANN) Models. Yüzüncü Yıl Üniversitesi Tarım Bilim. Derg. 2019, 29, 43–51.
  26. Le, B.T. Application of deep learning and near infrared spectroscopy in cereal analysis. Vib. Spectrosc. 2020, 106, 103009.
  27. Rajalakshmi, G.; Gopal, A. Performance evaluation of preprocessing techniques for near-infrared spectroscopy signals. Microprocess. Microsyst. 2020, 79, 103372.
More
© Text is available under the terms and conditions of the Creative Commons Attribution (CC BY) license; additional terms may apply. By using this site, you agree to the Terms and Conditions and Privacy Policy.
Upload a video for this entry
Information
Subjects: Spectroscopy
Contributors MDPI registered users' name will be linked to their SciProfiles pages. To register with us, please refer to https://encyclopedia.pub/register :
Joel B. Johnson
,
Kerry B. Walsh
,
Mani Naiker
,
Kashif Ameer
View Times: 350
Revisions: 2 times (View History)
Update Date: 06 May 2023
Table of Contents
    1000/1000
    Hot Most Recent
    ScholarVision Creations
    Feedback