Principles Techniques And Limitations Of Near Infrared Spectroscopy PdfBy PehuГ©n M. In and pdf 03.12.2020 at 23:25 10 min read
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Metrics details. Near-infrared spectroscopy NIRS has become an increasingly valuable tool to monitor tissue oxygenation T oxy in vivo.
- Principles, Techniques, and Limitations of Near Infrared Spectroscopy
- Multi-time-point analysis: A time course analysis with functional near-infrared spectroscopy
- Fourier-transform infrared spectroscopy
- Novel method for shark age estimation using near infrared spectroscopy
Principles, Techniques, and Limitations of Near Infrared Spectroscopy
This chapter provides a review on the state of art of the use of the visible near-infrared vis-NIR spectroscopy technique to determine mineral nutrients, organic compounds, and other physical and chemical characteristics in samples from agricultural systems—such as plant tissues, soils, fruits, cocomposted sewage sludge and wastes, cereals, and forage and silage. Currently, all this information is needed to be able to carry out the appropriate fertilization of crops, to handle agricultural soils, determine the organoleptic characteristics of fruit and vegetable products, discover the characteristics of the various substrates obtained in composting processes, and characterize byproducts from the industrial sector.
All this needs a large number of samples that must be analyzed; this is a time-consuming work, leading to high economic costs and, obviously, having a negative environmental impact owing to the production of noxious chemicals during the analyses.
Therefore, the development of a fast, environmentally friendly, and cheaper method of analysis like vis-NIR is highly desirable. Our intention here is to introduce the main fundamentals of infrared reflectance spectroscopy, and to show that procedures like calibration and validation of data from vis-NIR spectra must be performed, and describe the parameters most commonly measured in the agricultural sector.
Developments in Near-Infrared Spectroscopy. One of the challenges of the twenty-first century is to achieve a more productive agriculture, while improving the safety and quality of food. The food industry has to feed a population that is in continuous increase, bearing in mind that these systems have to respect the environment, should optimize natural resources in each area, and anticipate changes in temperature and rainfall that will occur in the future as a result of climate change.
Proper soil management and fertilization of crops will be crucial to increasing the capacity of agriculture, to the provision of products of high added value, and to the protection of crops against pests and diseases. To do this, in each of the steps ranging from the production of fruits and vegetables in the field to the development of industrial products, it is necessary to determine a great number of physical and chemical parameters in the soil, plants, fruits, compost, and byproducts from food processing industries.
Currently, the traditional techniques of analysis of such samples are being replaced by spectroscopic techniques—one of which is visible near-infrared spectroscopy vis-NIRS. This technique has a number of advantages over the traditional methods, as it i is a method of nondestructive analysis, ii does not pollute the environment, because it does not use chemical reagents, iii is cheap and fast, iv measures many parameters in a single analysis, and v can perform analyses in situ and online for a large number of samples per minute.
The aim of this chapter is to provide an updated review of the current state of vis-NIRS as a technique for the estimation of physical and chemical parameters in samples derived from agricultural systems, such as soils, plants, fruit, compost, and products derived from food processing industries.
The chapter starts by describing the basic principles of this technique and the different ways in which the equipment can be calibrated, detailing the statistical tools that are useful to establish that the calibration and the estimation of the desired parameters are valid.
We will describe the parameters that can be measured by vis-NIRS in samples, with the emphasis on soil, plants, fruit, compost, and byproducts from the industrial sector that processes the output of agricultural systems.
A basic explanation of the parameters measured in these samples will be given, together with a description of how they are measured and the mathematical tools used, focusing on the most novel issues. Spectroscopy in the near infrared or NIRS near-infrared reflectance spectroscopy is a tool that has been used widely for the rapid determination of organic components.
The only pretreatments of the sample required prior to analysis are drying, crushing, and mixing, in the case of solid matrices. Samples can also be scanned when fresh, as in the work of Huang et al. All this bestows on this technique several advantages over other, more sophisticated spectroscopic or analytical methods.
The operating principle of the NIRS technique requires that the energy absorbed in the near-infrared region by a sample causes covalent bonds of C-H, O-H, and N-H, important components of organic substances, to vibrate in different forms [ 1 ].
Within the field of NIRS, two main types of fundamental vibrations are considered: stretching, which involves a change in the length of a bond, and bending, which involves a change in the angle between two bonds.
Overtones appear when a vibrational mode is excited at a frequency higher than that of the fundamental vibration. Absorbance signals in the near infrared for the major chemical groups present in organic matter.
There is a relationship, both quantitative and qualitative, between the chemical composition and the spectrum recorded in the near-infrared. Hence, samples having different organic compositions have different infrared spectra. But, interpretation of the spectra is tremendously complex, although the spectral characteristics of each compound are unique, as their amplitudes sometimes overlap.
Before the NIR spectrum of a sample can be used for the determination of a compound or specific element, a calibration for this compound or element must be developed. In an NIRS spectrum, the various constituents of the sample have some overlapping peaks; thus, the measurements made with NIRS must be calibrated with samples of known chemical composition in order to extract the desired information using NIRS [ 2 ]. Chemometrics includes all methods of multivariate calibration in the field of analytical chemistry.
Unlike univariate calibration, where a spectral peak height or area is correlated with the reference concentration, multivariate calibration uses the entire spectrum structure with a large amount of spectral information to correlate with the reference concentration.
The establishment of a model for the use of NIRS data in the analysis of samples consists of the following steps: 1 introduction of the spectral and concentration data; 2 preprocessing of the spectral data; 3 definition of the appropriate frequency range; 4 validation and optimization of the method; 5 definitive calibration; and 6 routine analysis. This process begins with the selection of the group of samples for calibration, which must be well defined statistically, and pretreatment of the samples to assess measurement errors.
The dispersion of incident radiation, also known as the scatter effect, produces a low selectivity quality of being able to tune in to one particular frequency while blocking out other unwanted frequencies of the NIR spectral information [ 3 ].
This is due to physical phenomena—such as the texture, size, and geometry of the particles that make up the sample [ 4 , 5 ]—and to changes in the refractive index of the material which interacts with the radiation, causing numerous unwanted variations in the NIR spectral data [ 6 — 9 ]. Depending on the complexity of the samples, between 20 and samples are necessary to develop a multivariate calibration method. The greater the number of samples, the more representative is the calibrations achieved.
The samples should have a normal distribution, cover the entire range of concentrations of the parameters that are to be estimated by NIRS, and should not have areas where uncertainty is high and errors can be significant.
Finally, for each sample a classical analysis of the desired components is carried out, to obtain the so-called reference values, and its NIR spectrum is obtained. For example, the problems of baseline displacement need to be eliminated.
The optimum method depends on the system to be analyzed. Once the calibration samples have been selected and then analyzed by the reference method and NIRS, a correlation between the spectral and analytical data is searched for [ 14 ].
For this purpose different statistical treatments are used, such as multiple linear regression MLR [ 15 ] , principal components regression PCR , and partial least squares regression PLSR as linear methods and use of artificial neural networks ANN as a nonlinear method.
PLSR is the one most commonly used [ 16 , 17 ]. Typically, an R 2 value of 0. Good values for R 2 are larger than 0. The use of dried samples prevents interference of water in the aforementioned frequency range. Thus, this region should not be included to establish a calibration. Values of A between 0. Besides, modern FT-spectrometers allow the use of absorbance values of up to 2. To choose the best calibration for the regression equation with linear models PLS algorithm, for instance , the instrumental software combines different methods of data pretreatment and frequency ranges.
Then, it provides as output the corresponding mean error of prediction and R 2 for a given number of factors. The quality of the calibration is evaluated by the validation, which consists of comparing the concentrations predicted by the calibration with the reference values of samples not used in this calibration [ 18 ]. There are two types of validation: internal or cross validation and external validation.
In internal validation, a sample, or group of samples, is taken from the set of samples. With the calibration obtained using the remaining samples, the concentrations in the previously separated samples are predicted. The samples are interchanged until all have been used once for the validation. In external validation, all samples are used for calibration and prediction is performed for additional samples [ 19 ].
Since optimal frequency windows and pretreatments of signals cannot be anticipated, they are generally determined empirically by trial and error. These values are calculated for a growing number of factors. In many cases, there are several combinations of frequency window and pretreatment of spectral data of comparable quality for the prediction of analytical results.
In these cases the combination that has fewer factors is recommended, as it generally will be more stable Table 2. The optimum method is number 2 mean error of prediction 0. However, it is possible to manually set a lower rank in order to get a better result. The higher the value in the t -test, the more important it is. If it is higher than 10, it is considered essential to take part in the calibration equation. SEC is the standard error of the calibration, and H is the spectral error.
Chemical outliers can be recognized after applying a t -test since they present significative differences between the composition value provided by the reference method and the regression model. To detect spectral outliers, the Mahalanobis distance is particularly useful. For MLR models it is calculated as follows:. The statistics used in the evaluation, selection, and validation of the calibration equations are as follows:.
This establishes a correlation between the analytical data obtained in the laboratory and those predicted by the calibration equations for each of the components analyzed. As mentioned above, an R 2 value of 0. Thus, an R 2 value of 0. This is the error associated with the differences between the analyses performed in the laboratory using the reference methods and the results of the analysis by NIRS technology, for each of the parameters determined in the samples used in the calibration.
This value of this statistical parameter should be as low as possible. It is calculated using the formula:. It is preferable to compare this type of error with the error that can occur with traditional methods of analysis and decide whether the error is acceptable for routine use.
The prediction error P is the accumulation of the errors of the reference concentrations R , of the NIRS data, and of the calibration. Following calibration the cross validation error is obtained. This error is the one that should be taken into account most closely when evaluating the calibration. To calculate it, considering the number of samples in the set and the differences between the estimated values and those obtained by standard methods of analysis, the following formula is used:.
RPD is of the same significance as R 2 explained variance. The R 2 also allows a qualitative evaluation of the error of prediction during the validation process. This is the difference between the mean value predicted by FT-NIRS and the mean value of the reference predictive model and the residual prediction deviation RPD, [ 20 , 25 , 26 ] : M is the number of samples used in the calibration, xi is the result obtained by NIRS, and yi is the result obtained by the reference method for sample i :.
In the presence of laborious and troublesome datasets, it is possible to ask for high-performance external NIR calibration services such as those provided by private companies to optimize and validate the method. Here the optimum chemometric model is used to analyze quickly unknown samples. Fruit and vegetable crops, in order to achieve good vegetative growth and maximum production with good quality fruit, require a good nutritional status, maintaining a proper balance of nitrogen, potassium, phosphorus, and trace elements such as manganese, boron, copper, and magnesium.
Currently, the available knowledge of reflectance spectroscopy in the near-infrared NIR part of the spectrum can be used to determine the nutritional status of crops quickly and cheaply.
The mineral composition of an organic matrix can be estimated by NIRS, from the spectra in the range — nm, due to the association between the minerals and the organic functional groups or the organic matrix itself [ 28 ]. There are no infrared absorption bonds in the mineral species of macro- and micronutrients, but NIRS determines bonds within organic compounds that are negatively related to inorganic materials.
If mineral matter is bound to organic compounds, the distortion of the spectrum is detectable at certain wavelengths, suggesting that NIRS can quantify inorganic materials using their ratio to the organic matter [ 29 ].
Numerous studies show that the NIRS technique, together with multivariate analysis and partial least squares regression PLSR , provides a powerful tool for the interpretation and analysis of spectra.
For example, NIRS technology has been used successfully to predict the nutritional status of leaves of apple [ 30 ], alfalfa [ 31 ], sugar cane [ 32 ], root crops [ 33 ], yerba mate [ 34 ], and citrus [ 35 , 36 ]. However, good calibrations for the estimation of P, B, Cu, and Mn were not obtained.
Furthermore, the concentrations of nutrients could be estimated with a single calibration model, regardless of the variety of citrus analyzed. These data show that the NIR spectral response depends on the species studied, so for each species it is necessary to make the appropriate calibrations—but these are valid for different cultivars of the same species. Soil is a natural resource that is vital in agriculture for the production of food, fiber, and energy; but it serves also as a platform for human activities, constitutes an element of the landscape, is an archive of cultural heritage, and plays a central role as a habitat and gene pool.
It stores, filters, and transforms many substances, including water, nutrients, and carbon C.
Multi-time-point analysis: A time course analysis with functional near-infrared spectroscopy
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infrared (NIR) spectroscopy (NIRS). This review summarizes the most recent literature about the principles, techniques, advantages, limitations, and applications.
Fourier-transform infrared spectroscopy
Marcelo V. Fernanda S. Costa c. The aim of this study was to quantitatively determine the olanzapine in a pharmaceutical formulation for assessing the potentiality of near infrared spectroscopy NIR combined with partial least squares PLS regression.
In the data analysis of functional near-infrared spectroscopy fNIRS , linear model frameworks, in particular mass univariate analysis, are often used when researchers consider examining the difference between conditions at each sampled time point. However, some statistical issues, such as assumptions of linearity, autocorrelation and multiple comparison problems, influence statistical inferences when mass univariate analysis is used on fNIRS time course data. In order to address these issues, the present study proposes a novel perspective, multi-time-point analysis MTPA , to discriminate signal differences between conditions by combining temporal information from multiple time points in fNIRS.
Novel method for shark age estimation using near infrared spectroscopy
This chapter provides a review on the state of art of the use of the visible near-infrared vis-NIR spectroscopy technique to determine mineral nutrients, organic compounds, and other physical and chemical characteristics in samples from agricultural systems—such as plant tissues, soils, fruits, cocomposted sewage sludge and wastes, cereals, and forage and silage. Currently, all this information is needed to be able to carry out the appropriate fertilization of crops, to handle agricultural soils, determine the organoleptic characteristics of fruit and vegetable products, discover the characteristics of the various substrates obtained in composting processes, and characterize byproducts from the industrial sector. All this needs a large number of samples that must be analyzed; this is a time-consuming work, leading to high economic costs and, obviously, having a negative environmental impact owing to the production of noxious chemicals during the analyses. Therefore, the development of a fast, environmentally friendly, and cheaper method of analysis like vis-NIR is highly desirable.
Near Infrared Spectroscopy: fundamentals, practical aspects and analytical applications. It is addressed to the reader who does not have a profound knowledge of vibrational spectroscopy but wants to be introduced to the analytical potentialities of this fascinating technique and, at same time, be conscious of its limitations. Essential theory background, an outline of modern instrument design, practical aspects, and applications in a number of different fields are presented. This work does not intend to supply an intensive bibliography but refers to the most recent, significant and representative material found in the technical literature. Keywords: near-infrared spectroscopy, chemometrics, instrumentation, analytical applications. Introduction and Historical Overview. Near Infrared Spectroscopy NIR is a type of vibrational spectroscopy that employs photon energy hn in the energy range of 2.
NIRS has good reproducibility and agreement with gold standard techniques and can be used The major limitations of NIRS, suggested methods of circumventing them and assumptions Article Download PDFView Record in ScopusGoogle Scholar Principles, techniques, and limitations of near infrared spectroscopy.
Fourier-transform infrared spectroscopy FTIR  is a technique used to obtain an infrared spectrum of absorption or emission of a solid, liquid or gas. An FTIR spectrometer simultaneously collects high-resolution spectral data over a wide spectral range. This confers a significant advantage over a dispersive spectrometer, which measures intensity over a narrow range of wavelengths at a time. The term Fourier-transform infrared spectroscopy originates from the fact that a Fourier transform a mathematical process is required to convert the raw data into the actual spectrum.
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