WO2023058052A1

WO2023058052A1 - Photometric system and methodology for easy detection and quantification of components from binary mixtures

Info

Publication number: WO2023058052A1
Application number: PCT/IN2022/050872
Authority: WO
Inventors: Yoosaf KARUVATH; Raji Bhaskaran NAIR; Neethu EMMANUEL
Original assignee: Council Of Scientific & Industrial Research
Priority date: 2021-10-04
Filing date: 2022-09-30
Publication date: 2023-04-13

Abstract

The present invention discloses a device and a rapid nondestructive methodology for the detection and quantification of components from their binary mixtures (e.g. methanol-ethanol). The developed device acquires the inelastically scattered photons from the sample and predicts the concentration of a component in the mixture through data processing and analysis as sequenced in the algorithm. In particular, the device can automatically predict the relative percentage of the components in binary mixtures. The analysis methodology includes a unique mathematical model based on the characteristic peak intensity/area ratios of components present in the binary mixture. Algorithm for determining the component concentration in mixture has been developed to analyse and implement in a mathematical model for automatically predicting the composition.

Description

Photometric system and methodology for easy Detection and Quantification of components from binary mixtures

TECHNICAL FIELD OF THE INVENTION

The present invention relates to the field of chemical analysis and estimating the composition of mixtures.

The present invention relates to a spectrophotometric device and a method for predicting the composition of binary mixtures (mixtures containing at least two components, for example, methanol and ethanol).

In particular, the present invention relates to a measurement technique for acquiring Raman scattered photons by the analyte and an algorithm for data processing and analysis and determine the percentage of the components in a binary mixture.

More particularly, the present invention relates to signal processing methodology of the inelastically scattered photons and quantifying the components through the analysis of relative intensities/area of the characteristic Raman peaks of components in the mixture.

Particularly, the present invention relates to a specific mathematical model for enabling the relative quantification of components based on the property of inelastic (Raman) scattering.

Particularly, the present invention relates to the mathematical model, which is independent of the experiment parameters, especially the excitation laser intensity, integration time, etc.

BACKGROUND OF THE INVENTION

Estimation of the chemical composition of mixtures is of prime importance in situations like a chemical spill, quality control of raw materials and products in industries, contaminant testing in food products, disease diagnosis, etc. The analysis becomes more complicated when the chemical components in the mixture have close similarity in structure and physical properties. In such situations, suspected samples need to be sent to sophisticated laboratories wherein they are analysed with state of the art instruments like gas chromatography, liquid chromatography, nuclear magnetic resonance (NMR) spectroscopy, capillary electrophoresis, mass spectrometry, etc. Although these methods yield precise and accurate results, the limitations are the requirement for costly equipment, time-consuming procedures and trained professional for operations. Furthermore, bulkiness and high power requirements make them unsuitable for field testing, and emergencies like detection of contamination, adulteration of drug and food items and chemical spills -where fast and instantaneous results are demanded.

As an example, adulteration of food and beverages is one of the severe offences as it can lead to many health issues, including death. One such case is the blending of methanol in alcoholic beverages and is an illegal activity, though quite often malpracticed in underdeveloped and developing countries. Comparatively, ethanol is harmless, while methanol causes medical emergencies. Ingestion of even a few millilitres, cause damage to the central nervous system, vision, and even death due to its in vivo conversion to toxic metabolites. Not only for the prevention of such malpractices but also deciding the timely and effective treatment of affected patients, the knowledge of the type and extent of adulterant is highly required.

A handheld device, which is easy to operate and provides fast analytical results, will be needed in such a situation. Herein, we present an invention relates to a spectrophotometric device and a methodology for the identification and quantification of chemical components from its solid or liquid or gaseous mixture.

The said spectrophotometric device is working based on the principle of inelastic scattering of materials, specifically the Raman scattering. This phenomenon was invented by Sir CV Raman in 1928 and awarded the Nobel Prize in the year 1930. When light interacts with matter, it can scatter either elastically or inelastically. The elastic collision results in Rayleigh scattering and the scattered photons will have the same energy/frequency as that of incident radiation. In contrast, the inelastically scattered photons will have energy/frequency different from that of incident radiation and known as Raman scattering. The difference in energy corresponds to vibrational transitions of materials and hence serves as fingerprint information for their identification. Compared to other spectroscopies, techniques based on scattering has the advantages of minimum or no sample preparation requirement and non-destructiveness, enabling analysis of even complex samples, including living organisms. Moreover, the feasibility to fabricate handheld and battery operable devices elevates their potential for point of care applications. Herein, we disclose a method based on Raman spectroscopy as a tool for automated detection and quantification of components from solid, liquid or gaseous binary mixture. As Raman signal intensities depend on the scattering cross-section of analyte and their concentration, it can be used as a tool for quantification. A few methods based on chemometric tools are available for quantification of analytes using various spectroscopic techniques including Raman spectroscopy. The most commonly used chemometric tools are partial least squares regression (PLSR) and principal component regression (PCR). These multivariate analytical methods model the response variables (concentrations) using a large number of predictor variables (Raman spectra). These methods will extract new predictor variables from the dataset, known as components, as linear combinations of the original predictor variables, but each technique extracts those components differently. For example, EP Pat No. 0877923B1 (Daniel Charles Alsmeyer) involves analysing a sample of unknown constitution using Raman spectroscopy combined with multivariate analysis. This requires constructing a calibration from multivariate signal responses and also involves a normalization step to compensate any interferences and sample and reference data are acquired by two different apparatus.

Another invention disclosed in U.S. Pat. No. 4,620,284 (Robert P. Schnell) is a hydrocarbon analyser dedicated for PNA analysis based on Raman scattering and the data analysis are obtainable without exercise of human judgment or human interpretation facilitating on-line in the field or laboratory applications. The analysis is obtained by comparing a Raman spectrum of the unknown sample to Raman spectra of samples whose analysis is known and is stored in the digital form. In other words, this method involves finding a relation between the area of a specific spectral region of the reference sample with its concentration, a calibration process. Later to determine the concentrations of preselected substances in the said unknown sample by applying the relationship established between said reference spectra region areas and concentrations. However, especially in the case of Raman spectroscopy, wherein the data is highly influenced by experimental conditions such as laser intensity, detector integration time and other environmental and instrumental interfering factors necessitates calibration under each of these conditions.

U.S Pat. No. 4,068,953 (Harney et al.) presents methods and apparatus based on the Raman effect for measuring isotope ratios and isotopic abundances and is achieved by treating mathematically the detected scattered photons. More precisely, the apparatus measures the scattered photons in distinct frequency ranges for each isotope using different detectors simultaneously. This measured photon count is related with the density of the isotope in the sample and is determined using the invented mathematical relation. Thus, this invention is applicable only to the measurement of isotope ratios and also requires simultaneous detection of each distinct frequency range using multiple number of detectors.

U.S. Pat. No. 0228594A1 (Wendy Pryce-Lewis) described the use of Raman spectroscopy for quantitative measurements of concentration and solubility of a solute in a homogeneous liquid or a solid and is based on constructing a calibration curve. The present invention provides new methods for the measurement of concentration and solubility of an API in a multi-component system without the need of preparing a plurality of standard samples with a distribution of well- defined concentrations or the generation of a traditional calibration curve. However, the method requires a set of calibration data and applying convolutions to yield unknown sample spectra by analyzing a peak common to components in the mixture.

Another invention disclosed in U.S Pat. No. 0184502A1 (Albert Ballard Andrews and Andrew Speck) describes a Raman spectroscopy based apparatus and methodology for determining the composition and molar densities of alkanes in natural gases. This involves the use of a reference optics for nullifying any variations in signal intensities which are inherent to Raman spectroscopy.

U.S Pat. No. 5652653 (Daniel Charles Alsmeyer) explained a method for quantitatively monitoring selected constituents of chemical composition in situ by Raman spectrometer. Using the standard Raman spectrum of reference material and the chemical compositions, the convolution function of convolved spectra was determined. The convolution function was applied to adjust the convolved Raman spectra of chemical composition to produce the standard Raman spectrum of composition. The chemical composition of acquired Raman spectra was calculated by applying predetermined calibration functions.

A few works are also reported using Raman spectroscopy for detecting the presence and percentage of adulterant, example methanol in alcoholic beverages. These are mainly based on utilizing the intensity of a characteristic peak of methanol from the mixture. Reference may be made to Zeren, C.; A^ikgoz, G.; kahraman, s., Using Raman Spectroscopy for Determination Methanol Quantity in Illegal Alcoholic Beverages. 2016; Vol. 38 and ‘Online detection of distilled spirit quality based on laser Raman spectroscopy’, Li-mei Song, Institute of Brewing & Distilling

In some instances, estimations are based on the area of the characteristic peak of methanol/ethanol. Reference may be made to Boyaci, I. H.; Genis, H. E.; Guven, B.; Tamer, U.; Alper, N., A novel method for quantification of ethanol and methanol in distilled alcoholic beverages using Raman spectroscopy. Journal of Raman Spectroscopy 2012, 43 (8), 1171-1176.

Another published work which has close similarity with the current invention is D. I. Ellis, H. Muhamadali, Y. Xu, R. Eccles, I. Goodall and R. Goodacre, Rapid through-container detection of fake spirits and methanol quantification with handheld Raman spectroscopy, Analyst, 2019, 144, 324]. Here D. I. Ellis and team utilised handheld Raman spectroscopy for through-container differentiation of spirit drinks, detecting multiple adulterants in the form of denaturants and flavorings at extremely low concentrations, as well as quantifying levels of methanol using PCA analysis.

Thus, the already available prior art utilizes onsite calibration, complicated device configuration involving multiple number of detectors, reference optics etc or advanced statistical processes such as PCA, PLSR etc. Thus there exists an unmet need for developing simpler, cost- effective and rapid analysis devices which provide results independent of experimental conditions such as especially the excitation laser intensity, integration time, etc. So, to make the results independent of the experiment parameters, we have developed a new method where the intensity ratio of components in the mixture is being measured. The technique involves hardware development of a Raman spectrometer, software for analysing the Raman spectrum and finally predicting the concentration of component from the Raman spectrum. This technique does not require trained professional for operations, and provide instantaneous results.

OBJECTIVES OF THE INVENTION

The main objective of the present invention thereof relates to the development of a photometric system (100) for the detection and quantification of constituents from a binary mixture of any components.

Yet another objective of the present invention relates to deriving mathematical relation of the intensity of the characteristically scattered photons by the components from such mixtures to enable their composition prediction with a lower detection limit of 1%.

Another objective of the present invention relates to incorporating the above mathematical model into a device for automated concentration prediction of a component from its binary mixture with any other part. Yet another objective of the present invention is to provide an easy, rapid, nondestructive, and measurement parameter independent method for identifying the concentration of components from binary mixtures.

Yet another objective of the present invention is to provide an algorithm for data acquisition, processing and analysis to enable automated determination of the relative percentage of components from bicomponent mixtures.

SUMMARY OF THE INVENTION

In view of the foregoing, the present invention provides systems and methods for signal acquisition and analysis of any sample containing at least two components. It estimates their composition without the need for human intervention or technical expertise.

In an embodiment of the invention, the system includes a signal acquisition unit (101), signal processing and analysis unit (102). The signal acquisition unit (101) comprises an illumination source (110), specific optics for directing light into the sample (often called probe optics) (120) and a detector (130). The scattered photons are collected by specifically designed optics and are characterized by the detector, preprocessed and analysed. As a result, that Raman spectrum of any mixture is essentially the algebraic sum of the each component’s (e.g. methanol and ethanol) spectral fingerprint, a suitable mathematical relation can be derived to predict concentrations from the observed intensity values.

In another embodiment of the invention, the present invention extracts the Raman fingerprint of the component to be quantified from the acquired spectrum using a set of preprocessing methods and predicts its concentration in the mixture using a mathematical model developed herein.

In an embodiment of the invention, the concentration of one of the components of the claimed mathematical equation is related to the peak intensity (or area) ratios of the two.

Y i represents the intensity (or area) of the identified peak of component 1 in the mixture

Y2 represents the intensity (or area) of the identified peak of component 2 in the extracted spectrum X is the concentration of component 2 in the binary mixture

Ri, R2 are two constants

Other aspects of the present invention are described below.

BRIEF DESCRIPTION OF THE DRAWINGS

Fig.l shows the schematic layout of the entire measurement system.

Fig.2 shows an example of the optical layout of the A) device and B) detector.

Fig.3 Raman spectra of A) pure ethanol and methanol and B) their bicomponent mixtures of varying compositions. (Excitation wavelength: 638 nm, Exposure time: 10 s, No. of averages: 5, Optical power: 100 mW).

Fig.4 illustrates the variation of intensity at (A) 880 cm^-1 and (B) 2815 cm^-1 as a function of methanol concentration at different laser power and excitation wavelength.

Fig. 5 illustrates the actual and predicted ratio of intensity at 880 cm^-1 and 2815 cm^-1 as a function of methanol concentration.

Fig.6. Raman spectra of toluene-cyclohexane bicomponent mixtures of varying compositions (Excitation wavelength: 638 nm, Exposure time: 10 s, No. of averages: 5, Optical power: 50 mW). Fig.7. illustrates the variation of intensity at (A) 2845 cm^-1 and (B) 1596 cm^-1 as a function of cyclohexane concentration with excitation laser 638 nm and laser power 50mW.

Fig.8. Raman spectra of tryptophan-thiourea bicomponent mixtures with varying compositions. (Excitation wavelength: 785 nm, Exposure time: 5 s, No. of averages: 1, Optical power: 35 mW). Fig.9 Plots showing the intensity variation at (A) 734 cm^-1 and (B) 1424 cm^-1 as a function of thiourea concentration.

Fig.10 Plot showing the predicted versus the actual concentration of A), methanol in ethanolmethanol mixture with various powers and two different instruments. B). cyclohexane in Toluenecyclohexane mixture and C). thiourea in tryptophan- thiourea mixture.

Fig.ll represents the algorithm for estimating the concentration of a component from a binary mixture. DETAILED DESCRIPTION OF THE INVENTION

Overview

As stated above, there is currently a vital need in the art for simple, reliable, fast, nondestructive and affordable systems and methods for easy estimation of components from mixtures. No present system exists for measuring the concentration of components from their binary mixture by utilizing Raman spectroscopy, which is independent of measurement parameters. The advantages of spectroscopy based on scattering include non-destructiveness, no or minimum sample pretreatment/preparation, fast response, easy acquisition, etc.

In view of the various advantages associated with the present techniques as a material quantification tool, this invention provides a method of detecting the relative concentration of components from their binary mixtures and is independent of the experimental conditions. It is understood that the techniques described herein may be applied to any application relating to the estimation of concentration from any bicomponent mixtures of solid, liquid, or gaseous such as methanol-ethanol, toluene-cyclohexane, tryptophan-thiourea or any such complex mixtures including food, beverages, medicines, etc.

The present invention includes mainly three parts, a signal (scattered photon) acquisition unit (101), signal processing unit and an analysis unit (102) for the estimation of constituents in binary mixtures (e.g. methanol and ethanol) and a mathematical model for their quantification, represented by Figure 1. The signal acquisition unit (101) comprises of illumination light source (110), the optics for sample excitation and signal collection and the detection unit. The scattered photons are collected by the optics, detected and analysed using the detector illustrated by Figure 2 A The detector comprises of a slit (131), a collimator (132), a dispersive element (133), a focusing element (134), a photoresponsive element (135) and allied electronics as shown in Figure 2 B. The collected signal is preprocessed and analysed. Because of the characteristic scattered signal information of constituents in the binary mixture, their percentage composition can be estimated. The present invention extracts the Raman spectral features/characteristics of the component to be estimated from the acquired spectrum using preprocessing techniques, and predicts its concentration from the solid, liquid or gaseous binary mixture using a mathematical equation.

Raman spectrum acquisition unit Raman signal acquisition unit (101) comprises an illumination light source (110), focusing and collection optics (120) and a detector (130). The light source generally provides monochromatic radiation (e.g. laser) of a specific wavelength with a narrow linewidth. This light beam interacts with the sample/analyte and results in processes like absorption, transmission, scattering, refraction, fluorescence, etc. Of these, the inelastically scattered light will have different frequency/energy compared to that of the incident beam and corresponds to the unique molecular vibrations of the analyte. The illumination light is fed to the sample through a set of specifically designed optics. This optics essentially comprises a beam splitter and focusing element. The inelastically scattered photons are collected back by the same optics and are separated from elastically scattered ones using a set of filters and then fed into a detector through either free-space optics or an optical fibre. The detector is essentially a dispersive spectrometer consisting of an entrance slit (131), a collimator (132), a dispersive element (133), a focusing element (134), and a photoresponsive element (135). The photons emerging out of the slit is first collimated using a concave mirror or a convex lens and dispersed according to their frequency/wavelength using either a prism or grating. These dispersed radiations are then focused onto a photoresponsive element having an array of units called pixels (like CCD or CMOS, diode array, etc.), and each wavelength is registered on a specific pixel or a set of pixels. The whole setup was initially tested on an optical tabletop configuration and then translated into a small compact unit. The measured signal can be either used as such or can be converted into wavelength, Raman shift (0-4000 cm^-1) through proper calibrations. The acquired Raman spectrum of the sample is further analysed using the detection algorithm described below.

Concentration prediction algorithm

The collected signal is first subjected to a set of preprocessing like background and noise removal, and spectral smoothening and then analysed using the algorithm. The present invention extracts the characteristic signal of the component to be measured from the acquired sample signal using a set of preprocessing techniques and issued for predicting its concentration from the mixture using a mathematical equation, represented by Figure 11.

At first, the characteristic spectra of the different components are collected with any experimental conditions such as irradiation intensity, integration time, etc., processed to remove background, noise etc., and stored to the device for future utilisations. This data can be used as a reference when required as many times. This eliminates the need for acquiring the pure spectrum of the components each time while doing analysis. These data are primarily used to (i) identify at least one peak, which has no interference from that of the other component, (ii) in the next step, the intensities of these peaks are used to normalise the stored reference data to that of the mixture (iii) extract the characteristic spectrum of the component to be measured from the mixture. The Raman spectrum of one component in the mixture is extracted by subtracting the normalised preloaded Raman spectrum of the reference component from the acquired and preprocessed Raman spectrum of the mixture. Thus, the extracted Raman spectrum of the component from the mixture will be free of interference from any other component present in the mixture. In general, the observed intensity (or area) of any Raman peak of the materials is dependent on (i) its concentration (ii) laser power and (iii) integration/exposure time. In the case of a bicomponent mixture, at a particular laser intensity and integration times, the variation of the intensity (Yi) of the identified peaks of component 1 with respect to concentration of component 2 (X) can be expressed as

Similarly, the variation of the intensity (Y 2) of the identified peaks of component 2 with respect to its concentration (X) can be expressed as

mi and m2 are the slopes, which represent the rate of change peak intensities Y 1 and Y2 with respect to the concentration of component 2. C1 and C2 are the intercepts, the peak intensities at 0 % and 100 % of component 2 in the mixture.

Thus, the ratio of the intensities at any concentration X can be expressed as

The equation was verified by conducting the experiments with known concentration mixtures. Then the validity of the equation was further testified with mixtures of the various constitution and under different experimental conditions like excitation wavelength, optical power, exposure time etc. It was found that these equations are independent of the laser power or the acquisition parameters like integration time and average.

Alternatively, equation (3) can be rearranged as

R1, R2 are constants.

R1 = C1/m2

R2 = m1/m2.

The constants Ri and R2 remain the same for all laser powers and integration time.

Unlike the existing techniques, this is a universal equation applicable for measuring the concentration of components in a binary mixture using any laser wavelength and acquisition parameters.

For validation of the present invention, three examples of binary mixtures were studied. Mixture 1 contains ethanol and methanol [Figure 3 A], and here the concentration of methanol was predicted using the above described mathematical relation and confirmed the developed mathematical relation is valid in any experimental conditions. Mixture 2 [Figure 6] is also a liquid binary mixture, contains cyclohexane and toluene, where the concentration of cyclohexane in the mixture was predicted using the same algorithm and confirmed the mathematical relation is applicable for any bicomponent mixtures. This application can be used in chemical industries for the purity checking of chemical solvents. Mixture 3 [Figure 8] is a solid binary mixture which contains thiourea and tryptophan, and the concentration of thiourea was predicted and confirmed the equation is valid for solid mixtures as well.

Case study 1: ethanol-methanol mixture

Pure methanol and pure ethanol were used for preparing the samples. Twenty mixtures were created by varying volume percentage of methanol in ethanol from 0 to 100. The fingerprint Raman spectrum of methanol and ethanol are provided in Figure 3 A. The identified peaks are 1034 cm^-1, 1447 cm^-1, 2815 cm^-1 and 2940 cm^-1 for methanol and 880 cm^-1, 1046 cm^-1, 1095 cm^-1 , 1262 cm^-1, 1447 cm^-1, 2710 cm^-1, 2860 cm^-1, 2922 cm^-1 and 2969 cm^-1 for ethanol. Of these, the peaks at 1034 cm^-1 and 2815cm^-1 of methanol have less interference from that of ethanol, and any of these can be selected for implementing the present algorithm. In a similar way, the peaks of ethanol at 880 cm^-1, 1095 cm^-1, 1262 cm^-1, 2710 cm^-1 are having minimum interference of methanol and are selectable. As an example, Figure 3B represents the background subtracted composition dependent spectral variations with the two selected region of interests marked, C-H symmetric stretching of CH3 group of methanol (around 2815 cm^-1) and C-C stretching vibration of ethanol (around 880 cm^-1). It could be noted that the Raman peak intensity at 880 cm^-1 is linearly decreased with an increase in the volume percentage of methanol in the mixture Figure 4A and can be best expressed by equation 1. The linear fit of this plot yielded the values of mi and Ci as -62.665 and 6234, respectively. However, the peak at 2815 cm^-1 suffers interference from ethanol peaks, and the actual intensity information is extracted after subtracting normalised ethanol spectrum. Raman peak intensity at 2815 cm^-1 linearly increases with increase in the volume percentage of methanol in the mixture (Figure 4B and can be best expressed by equation 2. The linear fit of this plot yielded the values of m2 and C2 as 86.1 and 0, respectively. From these, the values of Ri and R2 were estimated to be 72.40 and 0.723, respectively. Figure 5 represents the dependence of the observed intensity ratio values with the composition and has a good agreement with the predicted values. In order to validate the methodology presented, we used the obtained values to calculate the concentration of mixtures with known component concentration. These two linear relations [equation 1 and equation 2] can be related by taking the ratio of Raman peak intensities at 880 cm' ¹ and 2815 cm^-1 and a mathematical relation is generated [equation 4] for quantification of volume concentration of methanol in ethanol -methanol mixture. On comparison, the ratio of experimentally acquired values of Raman peak intensities at 880 cm^-1 and 2815 cm^-1 with predicted values using mathematical relation [equation 4] , it was found to coincide, represented by Figure 10A.

Case study 2: toluene-cyclohexane mixture

Pure toluene and pure cyclohexane were used for preparing the samples. Twenty mixtures were created in the concentration range of 0-100 volume percentage of cyclohexane in toluene. The fingerprint Raman spectrum of cyclohexane (labelled as 100%) and toluene (labelled as 0%) are provided in Figure 6. The identified peaks are 787 cm -,¹1017 cm -,¹1258 cm ,^-11426 cm , - 2¹840 cm^-1 and 2912 cm^-1 for cyclohexane and 785 cm -,¹1017 cm ,^-11206 cm ,^-11374 cm , - 1¹596 cm^-1 and 2915 cm^-1 for toluene. Of these, the peaks at 1258 cm -,¹ 1426 cm^-1 and 2840 cm^-1 of cyclohexane have less interference from that of toluene and any of these can be selected for implementing the present algorithm. In a similar way the peaks of toluene at 1206 cm , - 1¹374 cm' ¹ and 1596 cm^-1 are having minimum interference of cyclohexane and are selectable. As an example, Figure 6 represents the background subtracted composition dependent spectral variations with peaks marked at the two selected regions of interest, CH2 stretching of cyclohexane (around 2845 cm^-1) and C=C stretching of toluene (around 1596 cm -)¹. It could be noted that the Raman peak intensity at 1596 cm^-1 is linearly decreases with an increase in volume percentage of cyclohexane in the mixture Figure 7B and the linear fit of this plot yielded the values of mi and Ci as -11.6743 and 1152 respectively. Being the selected Raman peak at 2845 cm^-1 is free from interference, the background subtracted data can be taken directly and eliminate the need for further subtraction of normalised standard Raman spectrum of toluene from that of mixture. Raman peak intensity at 2845 cm^-1 is linearly increases with increase in volume percentage of cyclohexane in the mixture Figure 7A and the linear fit of this plot yielded the values of m2 and C2 as 52.75 and 0 respectively. From these, the values of Ri and R2 were estimated to be 21.84 and 0.221 respectively. Figure 10B represents the dependence of the observed intensity ratio values with the composition and has a good agreement with the predicted values.

Case study 3: tryp tophan-thiourea mixture

Pure thiourea and pure tryptophan were used for preparing the samples. Ten mixtures were created in the concentration range 0-100 weight percentage of thiourea in tryptophan. The fingerprint Raman spectrum of thiourea (labelled as 100%) and tryptophan (labelled as 0%) are provided in Figure 8. The identified peaks are 734 cm -,¹ 1091cm - a¹nd 1380 cm^-1 for thiourea and 755 cm ,^-1 875 cm^-1, 1009 cm -,¹1350 cm ,^-11424 cm^-1 and 1556 cm^-1 for tryptophan. Of these, the peaks at 734 cm^-1and 1380 cm^-1 of thiourea have less interference from that of tryptophan. In a similar way the Raman peaks of tryptophan at 875 cm^-1, 1350 cm -,¹ 1424 cm -,¹ and 1556 cm^-1 are having minimum interference of thiourea and are selectable. As an example, Figure 8 represents the background subtracted composition dependent spectral variations with peaks marked at the two selected regions of interest, CS stretching of thiourea (around 734 cm^-1) and CH deformation of tryptophan (around 1424 cm^-1). The Raman peak intensity at 1424 cm^-1 is linearly decreases with an increase in weight percentage of thiourea in mixture Figure 9B and the linear fit of this plot yielded the values of mi and Ci as -52.6249 and 5614 respectively. Since the characteristic peaks of thiourea and tryptophan are clearly distinguished from each other, it is not necessary for the subtraction of the Raman spectrum of thiourea from that of the mixture. Raman peak intensity at 734 cm^-1 is linearly increases with an increase in weight percentage of thiourea in mixture Figure 9A and the linear fit of this plot yielded the values of m2 and C2 as 250 and 0 respectively. From these, the values of Ri and R2 were estimated to be 22.45 and 0.210 respectively. Figure 10C represents the dependence of the observed intensity ratio values with the composition and has a good agreement with the predicted values. The present invention has been described in the context of measuring component concentration from the binary mixture, in connection with the Raman spectroscopic instrumentation. The methods of the present invention can be used for any application related to monitoring of small concentration of lower limit (0.1M) of component from the binary mixture, wherein the small changes in component concentration of lower limit (0.1M) lead to a corresponding change in Raman peak intensity ratios. By utilizing the variation in peak intensities, a mathematical equation was derived for predicting the component concentration in the mixture using Raman spectroscopy. The major advantage of this derived mathematical equation was that it is independent of the measurement conditions like laser power, integration time, etc.

Claims

CLAIMS We Claim:

1.) A photometric system (100) for detection and quantification of constituents from a binary mixture of any components comprising: a) a signal acquisition unit (101), b) a signal processing and analysis unit (102)

2.) The photometric system as claimed in claim 1, wherein the signal acquisition unit comprises of illumination light source (110), focusing and collection optics (120) and a detector (130).

3.) The photometric system as claimed in claims 1 or 2, wherein detector comprises a slit (131), a collimator (132), a dispersive element (133), a focusing element (134) and a photoresponsive element (135).

4.) The photometric system as claimed in claim 1-3, wherein the quantification of components are based on inelastic (Raman) scattering.

5) A method for detection and quantification of constituents from a binary mixture by photometric system of claim 1 wherein the steps comprises; a) illuminating light source of signal acquisition unit, using optics to obtain a collected scattered photons, b) detecting and analyzing the collected scattered photons as obtained in step (a) using analysis unit to obtain a collected signal, c) preprocessing and analyzing of collected signal as obtained in step (b) using to extract the characteristics scattered signal, d) estimating the percentage composition of the constituents in binary mixture using characteristics scattered signal as obtained in step (c) by mathematical algorithm. 6) The method as claimed in claim 5, wherein the small, lower limit (0. 1 M), concentration of component can be monitored in the binary mixtures.

7) The method as claimed in claim 5, wherein the small, lower limit (0.1 M), changes in component concentration lead to a corresponding change in Raman peak intensity ratios.

8) The method as claimed in claim 5, wherein the concentration in the binary mixture predicted by a mathematical model. 9) The method as claimed in claim 8, wherein the mathematical algorithm to analyze the

Raman spectrum of the sample is

Y1 represent the intercity (or area) of Identified peak of componeEit 1 in the mixture

Y2 represent the intensity (or area) identified peak of component 2 in the extracted spectrum

X is the concentration of component 2 in the binary mixture

R1, R2 are two constants