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Organic Spectroscopy pp 7–51 Cite as

Ultraviolet (UV) and Visible Spectroscopy

• L. D. S. Yadav 2  

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Ultraviolet and visible spectroscopy deals with the recording of the absorption of radiations in the ultraviolet and visible regions of the electromagnetic spectrum. The ultaviolet region extends from 10 to 400 nm. It is subdivided into the near ultraviolet (quartz) region (200–400 nm) and the far or vacuum ultraviolet region (10–200 nm). The visible region extends from 400 to 800 nm.

• High Occupied Molecular Orbital
• Lower Unoccupied Molecular Orbital
• Carbonyl Compound
• Bathochromic Shift
• Visible Spectroscopy

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Yadav, L.D.S. (2005). Ultraviolet (UV) and Visible Spectroscopy. In: Organic Spectroscopy. Springer, Dordrecht. https://doi.org/10.1007/978-1-4020-2575-4_2

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• Published: 05 May 2022

Fluorescence and UV/visible spectroscopic investigation of orange and mango fruit juice quality in case of Adama Town

• Muktar Gebishu 1 ,
• Boka Fikadu 2 ,
• Bulcha Bekele 2 ,
• Leta Tesfaye Jule 2 , 3 ,
• Nagaprasad. N 4 &
• Krishnaraj Ramaswamy 3 , 5  

Scientific Reports volume  12 , Article number:  7345 ( 2022 ) Cite this article

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• Biochemistry
• Biogeochemistry
• Biological techniques
• Biotechnology
• Chemical biology
• Energy science and technology
• Engineering
• Materials science

Extracted Mango and Orange juices were investigated by using spectroscopic techniques such as UV/Visible and Fluorescence. Three portions of samples (fresh juice) were stored at 22 °C for eight days, stored in a water bath and heated at 40 °C, 60 °C, and 80 °C for ten minutes. The highest wavelengths (455 nm) were observed from the UV/Vis results for fresh Mango juices, while 270 nm and 460 nm were observed for stored Mango juices. Furthermore, wavelengths of 320 nm were observed in heat-treated mango juice (40 °C). No absorption peaks were observed at 60 °C and 80 °C due to temperature effects. Absorption peaks of fresh fruit were observed at 330 nm and 390 nm, while 260 nm and 320 nm reflect stored orange juices absorptions peaks. From heat-treated stored (40 °C and 60 °C) samples, 320 nm and 260 nm absorption peaks were observed, respectively. Wavelength observed (454 nm, 540 nm & 700 nm) peaks represent the fresh mango juice spectra, while 460 nm and 700 nm are for stored Mango juices. The peaks observed in the region of 400–500 nm and at 700 nm represent heat-treated mango juices at 40 °C. Heat stored Mango juices (60 °C & 80 °C) have peaks at 700 nm. Peaks observed at 700 nm, 500 nm, and 455 nm reflect fresh orange juice, while 460–500 nm and 700 nm represent the emission spectra of the samples. The stored orange juice peaks at 460–500 nm and at 700 nm, but heated-stored orange juice peaks only at 700 nm. The pH values for orange and mango juices were 3.52–3.73 and 4.02–4.72, respectively.

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Introduction

Fruit quality is essential for human beings due to its excellent tests like acidness, sweetness and bitterness. These tests make feeling in the mouth of a human being due to its structures. The quality of juices has a great role in balancing factors such as acidness, sweetness, and bitterness, which are the most important factors for peoples. Particularly the corrosive and sugar substance in natural products and their proportion are exceptionally vital components for the quality of assessment by buyers 1 , 2 , 3 , 4 . The causticity and sugar substance are ordinarily assessed by the acidometer, bricks division which depends on refract meter and titration individually. However, anticipated esteem demonstrated as it were the general causticity and sugar substance, and don’t have sufficient truthfulness. Hence, Characterization systems were applied to get the sum and corrosive sugar. These characterization equipment are Ultraviolet, Visible spectroscopy and fluorescence 5 , 6 , 7 , 8 , 9 , 10 .

Natural product quality, such as colour, test, sugar substance and etc., depends on longitudinal, climate, soil, and post-harvest administration variety 10 , 11 . Rack lives of items are decided in duration and have an obligation as producers, administrative offices and etc. Subordinate limits on physical condition, organoleptic qualities and microbiological safety. Numerous components yonder impact duration may be categorized under natural such as water action, pH, acidity, preservatives, biochemical, microbial composition and the outward variables such as time, temperature, weight, relative mugginess, ultrasonic light, bonding fabric, and dealing with strategies 12 , 13 , 14 , 15 , 16 . Shelf life of most home-made items is not at all like mechanical items, predisposed to uncertainty variable and standardized. Quality of natural products such that mango and orange juice were investigated by a spectroscopic technique such as UV/Visible and Fluorescence in Japan, Denmark, and China. Still now, testing natural product juices was not investigated in Ethiopia. Therefore, the researcher wants to investigate the excellence of orange and mango fruit juices using Fluorescence and Ultraviolet–visible spectroscopy. Besides, several literatures were done on Fluorescence and UV/vis Spectroscopic Investigation of Orange and Mango Fruit Juices in different areas, especially in developed countries in order to determine the quality of juices to save the human health. But, no research was done on the quality of juices in developing countries, like Ethiopia, in areas of high temperature and several consumers of juices. Juices were prepared and settled for a few moments. This research clearly reported what happens to juices when it is fresh, stored and treated with temperatures. Thus, the main objective of this study is to investigate the quality of mango and orange fruit juice by UV/Vis and Fluorescence spectroscopy.

Materials and methods

The explorations were carried out with the assistance of the taking after gadgets: containers, spatula, column tube, inquires, advanced electronic bar adjustments, cuvette used to take the samples for investigation, pipette used to sort, juice extractors, cone-shaped bottle, the machine connected fluorescence, and UV/vis spectrophotometer. Refrigerator and refined water were also utilized.From Literature it was reported that the pulp% for Mango Varieties (Local,Tommy,Keit,Kent,Dodo,Apple) were in the range of 65.45–78.14% 17 .since the local mango variety consists of large seed and small quality of fresh, in line to Apple Mango. Hence in this paper, Apple Mango and Orange natural product fruit juices with different colour and measures having a wide extent of colours were acquired in the city of Adama. Subsequently, the samples were protected under 6 °C temperature, and the juices were extracted with a domestic juice extractor (see Fig.  1 ). The natural products with comparative colour were considered as one test and extracted. Completely, natural products juice tests were arranged, and the canter was eliminated where the skin was reticent. Juices centrifuge was taken for 15 min by high rotation of 360 revolutions per minute at room temperature of 22 °C. Then, two parcels were seen by shifting the juices. One parcel was measured promptly (as fresh) and was put away at 22 °C stored for 10 days. The left parcels were warmed under the temperature of 40, 60 and 80 °C for 10 min. The juices were cooled quickly with an ice water bath and put away at 22 °C. Here, they are marked as heated stored is the reference of the measured spectra. Characterization equipment was used to test samples Fluorescence and UV/vis Spectroscopy of LAB4US UV Quartz 5 mm cuvette for spectrophotometer.The pH esteem of orange and mango juice were estimated by utilizing a pH meter where scale reading was permitted to normalize for a complete of a second. Then, a pH reading was taken. For readings, the terminals were purified with refined water and then taken to standard (pH 70 and pH 4.0) buffer solutions. Finally, data were collected from all characterization equipment and analyzed using Origin software.

Extracted ( a ) Mango and ( b ) Orange Juice.

Result and discussions

This studies bargains with the investigations, discourse and the overcome of work. The quality of orange and mango juices was examined utilizing spectroscopic techniques. Specifically, absorbance spectra of Mango juice were characterized by UV/Visible spectroscopy. The mango juices assimilation spectra were measured by isolating three portions of stored mango juice and fresh mango juice. The parcel of heated, stored mango juices at 40, 60, and 80 °C were the third category. The broad peak of fresh mango juice was seen at a wavelength of 455 nm, while 270 & 460 nm was the absorption peaks for stored mango juices. The absorption peak of 320 nm represents the heat stored in mango juice. No absorption peaks were observed in heat stored mango juice of 60 °C, and 80 °C of temperatures shown in Fig.  2 . This result depicts that the chemicals found in juices were destroyed at a higher annealing temperature. This reveals that annealing temperature can influence the rack life of fruit, also reported in the literature 18 , 19 , 20 . Additionally, coumarin was observed at absorption peas of 320 nm, whereas 270 nm represents polymethoxyavons, whiles 460 nm compares chlorophyll, also investigated in literature 14 , 15 , 21 , 22 .

UV/Vis absorption spectra of mango juices.

Figure  3 represents the UV/Vis absorption spectral of fresh, stored and heat-treated orange juices. The orange juices assimilation spectra were measured within the same methods as mango juices. The absorption peaks of fresh orange fruit were observed at 330 nm and 390 nm. Similarly, 260 nm and 326 nm represent the absorption spectra of stored orange juice. The peaks of absorption observed at a wavelength of 320 nm & 260 nm represent heat-treated or stored orange juices of 40 °C and 60 °C temperatures, respectively. There are no absorption peaks for the samples stored at a temperature of 80 °C. The absorption band found in the region of 260 to 280 nm represents vitamin C. Coumarin was found at an absorption peak of 330 nm. Coumarin could be a plant auxiliary metabolite which has capable of stopping or slowing a specific biological process. Moreover, known to be a plant controller development. The absorption peak observed at 390 nm compares vitamin A whereas 450 nm shows carotenoids 20 , 23 , 24 , 25 . Comparison stored, fresh, and heat-treated mango fruit juices with several literatures are depicted in Table 1 .

UV/vis absorption spectra of orange juice.

Figure  4 shows the emission spectra of fresh, stored and heat stored mango juices by fluorescence spectroscope. Here, the emission wavelength was adjusted in the range of 350 to 700 nm at one manometer increments, while the excitation wavelength was adjusted at 350 nm. The slit of emission was kept up under 5 nm, with the proofed speed of rotation 1200 manometer per minute, and the reaction was processed for 50 microseconds. Mango a sample was totally synthesized from stored, fresh Mango fruit juices and heat-treated stored mango fruit juices form. As depicted in Fig.  4 , sharp peaks 386, 389, 385, 355 and 387 nm were the emission peaks observed in fresh mango juice, heated stored mango juice at 40 °C, heated stored mango juice at 60 °C, heated stored mango juice at 80 °C and stored mango juice respectively. Other broad peaks 538, 546, 554, and 555 nm were also seen from fresh mango juice, heated stored mango juice at 40 °C, heated stored mango juice at 60 °C, and heated stored mango juice at 80 °C, respectively. However, the emission peaks observed at 386, 389, 385, 355 and 387 nm reflect carotenoids and depict that carotenoids seen in all samples had good agreement with the result reported in the literature 16 . In addition, polymethoxyavons were seen at broad peaks of 538 nm and 546 nm, and chlorophyll was seen at emission spectra of 554 and 555 nm 20 , 24 , 26 , 27 , 28 , 29 , 30 .

The fluorescence emission spectra of mango juices.

The emission and excitation spectra of synthesized orange fruit juices in the condition of fresh, stored and heat store were expressed in Fig.  5 , and the excitation spectra of orange fruit juices were settled at 350 nm. The emission peaks observed from fresh orange fruit juices were 190 and 239 nm, and similarly, 192 nm and 332 nm emission peaks were seen from stored orange fruit juices, which depicts overall chlorophyll. Emission peaks observed at 189 and 233 nm reflects heated stored orange fruit juices at 40 °C and express total phenol compounds. The peaks of spectral emission observed at 188 and 228 nm reflect heated stored orange juices at 60 °C and express overall carotenoids 31 . However, no emission peaks observed in the case of heated orange juices at 80 °C are due to an increment in thermal energy 19 , 21 , 22 , 23 , 32 , 33 , 34 . Comparison stored, fresh, and heat-treated orange fruit juices with several literatures are depicted in Table 2 .

Fluorescence spectral emission of Orange juices.

The quantum yield investigation of Mango juices was expressed in Fig.  6 , and the quantum yields of chlorophyll in fresh mango juices were compared with mango juices (stored). Measurements of chlorophyll observed in Mango juices (fresh and stored) are taken as constant. Quantum abdicates, or yields of chloroplast seen in stored and fresh mango juices were determined by utilizing Eq. ( 1 ) and its esteem was 0.26.

Slope of juices samples corresponding to samples of Fluorescence quantum yield chlorophyll observed in Mango Juice.

The esteem of quantum surrender of chloroplast observed in mango juice (fresh) was 0.1 based on Eq. ( 2 )

The index of refraction juice solvent is similar in quantum surrenders estimations is agreed with Eq. ( 1 ). The green pigments found in the chloroplast of mango juices (fresh) were considered as standard esteem of chlorophyll. They are essential, utilizing Eq. ( 2 ), the chlorophyll of quantum yield of mango juice (stored) was in the range of 0.254–0.26, which is similar to the calculated values from Eq. ( 1 ).

Quantum abdicates Fluorescence of Orange juice expressed in Fig.  7 . The Fluorescence quantum abdicates chlorophyll found in stored and fresh orange fruit juices were the estimation. Stored Orange juices are considered standard when the chlorophyll or quantum abdicates were measured. Quantum surrender found in chlorophyll which is seen in Orange fruit (stored), was estimated utilizing Eq. ( 1 ) and its esteem was 0.27. Quantum yields observed in Orange juices (fresh) were 0.38 by utilizing Eq. ( 2 ) above in which refractive indexes of solvents quantum yield measurements are the same as refractive indexes calculated by Eq. ( 1 ). In similar steps, fresh orange juice chlorophyll is taken as standard. They are additionally utilizing conditions written in Eq. ( 2 ), quantum abdicates of chlorophyll of orange juice (stored) was 0.27.

The proportionality of samples found in chlorophyll Fluorescence quantum esteem on Orange juice.

The Orange and Mango natural products juice Fluorescence duration (lifetime) was investigated, and the sample's lifetime was measured. The result obtained from measurement was 102.8 µs which is determined from Eq. ( 3 ).

The natural lifetime of stored mango and fresh juice is calculated utilizing Eq. ( 3 ) was 395.38 µs and 1028 µs, respectively. In addition, the time lifetimes of stored orange juice and fresh orange juice are 395.38 µs and 277.8 µs, respectively. The pH results of samples were seen in three forms, i.e. fresh mango and orange fruit, stored, and heat-treated mango and orange fruit juice at 40 °C; 60 °C; 80 °C temperatures are depicted in Fig.  8 . The graph depicts that the orange fruit juice has a pH value which is less than four, which indicates strong acid when compared to mango juice 17 , 19 , 20 , 21 , 22 , 23 , 24 , 25 , 26 , 27 , 28 , 29 , 30 , 31 , 33 , 34 , 35 . Moreover, fresh, stored, the heat stored at a temperature of 40, 60 and 80 °C Orange juice has pH values of 3.7, 3.71, 3.67, and 3.85, respectively. Similarly, fresh, stored and heated stored at 40, 60, and 80 °C mango juices have the pH values of 3.49, 3.71, 3.67, 3.85, 4.65, and 4.72, respectively. This results clearly validates the comparison of pH value and lifetimes of mango and Orange fruit juices with various literature was observed in Table 3 .

pH value of mango and orange juices.

Conclusions

Mango and orange fruit juice quality was investigated using VU/Vis and fluorescence spectroscopy. The UV/vis and fluorescence spectra of the prepared samples clarify the physical behaviour of fresh, stored and heat-stored mango and orange juices. The absorption ability of the sample was seen from UV/vis spectroscopy, while fluorescence gives emission properties. The broad peaks observed at 455 nm, 270 nm, 460 nm, and 320 nm represent the fresh, stored heat of mango juice at 40 °C. No absorption peaks were observed for heat-stored samples at temperatures of 60 °C and 80 °C. Here, peaks observed at 32 nm, 270 nm, and 460 nm represent coumarin, polymethoxyavons, and chlorophyll, respectively. The spectral peaks observed at 330 nm and 390 nm, and 260 and 320 nm represent fresh and stored orange juices. The peaks of the spectrum observed at heat-stored orange juice at temperatures of 40 °C and 60 °C represent 320 nm and 260 nm, respectively. No absorption peak was seen at 80 °C of temperature. Emission spectra of mango juices (fresh) were observed at 700 nm, 540 nm, and 453 nm, while 460 nm and 700 nm represent stored mango juices. The peaks observed in the region of 420 nm to 500 nm and 700 nm represent heat-treated samples (stored) at 40 °C, whereas the samples stored at 60 °C and 80 °C temperatures have spectral peaks of 700 nm. Spectral emission was observed at 700 nm, 646 nm, and 454 nm, total carotenoids, polymethoxyavons, and chlorophyll, respectively; the most intense peaks represent chlorophyll and carotenoids in these samples. Fewer carotenoids were observed at 60 °C and 80 °C, and the amount of vitamins was reduced due to heat. As observed from the results, orange juices are more influenced by temperature than mango juices. The spectroscopic investigation of fluorescence quantum yields of samples were carried out and results of 0.10 and 0.37 correspond to mango and orange juices. Additionally, the lifetime of mango and orange juices was investigated. The pH values of the juices were measured by a pH meter and resulted in regions of 4.02–4.72 and 3.52–3.73 for mango and orange juices, respectively. Using fresh juices was more important than stored, heat stored juices, and the researcher may investigate for other natural juices stored in the fridge.

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Department of Applied Physics, School of Applied Natural Science, Adama Science and Technology University, Adama, Ethiopia

Muktar Gebishu

Department of Physics, College of Natural and Computational Science, Dambi Dollo University, Dambi Dolo, Ethiopia

Boka Fikadu, Bulcha Bekele & Leta Tesfaye Jule

Centre for Excellence-Indigenous Knowledge, Innovative Technology Transfer and Entrepreneurship, Dambi Dollo University, Dambi Dolo, Ethiopia

Leta Tesfaye Jule & Krishnaraj Ramaswamy

Department of Mechanical Engineering, ULTRA College of Engineering and Technology, Madurai, Tamil Nadu, 625 104, India

Nagaprasad. N

Department of Mechanical Engineering, College of Engineering, Dambi Dollo University, Dambi Dollo, Ethiopia

Krishnaraj Ramaswamy

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Conceptualization, M.G. and B.B., B.F.; Data curation, M.G. and B.B., B.F.; Formal analysis, B.B., N.N., L.T.J., and K.R.; Investigation, M.G., B.B., B.F.; Methodology, M.G.; B.F. and B.B.; Project administration, K.R, L.T.J., M.G. and N.N. resources, B.B, K.R., B.F., L.T.J., and N.N.; Software, B.B, B.F., M.G.; Supervision, K.R.; L.T.J. Validation, K.R., L.T.J. and N.N.; Visualization, M.G., B.B. and B.F.; Writing—original draft, M.G., B.F., B.B.; Data Visualization, Editing and Rewriting, B.B., M.G., K.R., L.T.J., and N.N.

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Gebishu, M., Fikadu, B., Bekele, B. et al. Fluorescence and UV/visible spectroscopic investigation of orange and mango fruit juice quality in case of Adama Town. Sci Rep 12 , 7345 (2022). https://doi.org/10.1038/s41598-022-11471-7

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UV-Vis Spectroscopy: A New Approach for Assessing the Color Index of Transformer Insulating Oil

Yang sing leong.

1 Institute of Power Engineering, College of Engineering, Universiti Tenaga Nasional, Kajang 43000, Selangor, Malaysia; ym.ude.netinu@gnoelsy (Y.S.L.); ym.ude.netinu@iniazdm (M.Z.J.); ym.ude.netinu@niddufias (S.M.N.); ym.ude.netinu@namiA (A.I.); ym.ude.netinu@zuriaf (F.A.)

Pin Jern Ker

M. z. jamaludin, saifuddin m. nomanbhay, aiman ismail, fairuz abdullah, hui mun looe.

2 Tenaga Nasional Berhad (TNB) Research Sdn. Bhd., Bandar Baru Bangi, Kajang 43000, Selangor, Malaysia; [email protected] (H.M.L.); [email protected] (C.K.L.)

Chin Kim Lo

Monitoring the condition of transformer oil is considered to be one of the preventive maintenance measures and it is very critical in ensuring the safety as well as optimal performance of the equipment. Various oil properties and contents in oil can be monitored such as acidity, furanic compounds and color. The current method is used to determine the color index ( CI ) of transformer oil produces an error of 0.5 in measurement, has high risk of human handling error, additional expense such as sampling and transportations, and limited samples can be measured per day due to safety and health reasons. Therefore, this work proposes the determination of CI of transformer oil using ultraviolet-to-visible (UV-Vis) spectroscopy. Results show a good correlation between the CI of transformer oil and the absorbance spectral responses of oils from 300 nm to 700 nm. Modeled equations were developed to relate the CI of the oil with the cutoff wavelength and absorbance, and with the area under the curve from 360 nm to 600 nm. These equations were verified with another set of oil samples. The equation that describes the relationship between cutoff wavelength, absorbance and CI of the oil shows higher accuracy with root mean square error ( RMSE ) of 0.1961.

1. Introduction

Power transformer is one of the main components in any transmission networks and they are responsible for transmitting electric power over long distances with minimal power loss. It is essential for power transformer to operate optimally in order to provide continuous and stable electricity supply in assuring homes and businesses run smoothly with minimum disruptions. Any failures in power transformers could lead to huge problems to both consumers and the power utilities. Therefore, it is important to carefully maintain, inspect, and monitor the power transformer over times.

The lifetime of power transformer is generally related to the degradation of insulating materials in power transformer. Although various sensors such as temperature sensor, pressure sensor, and humidity sensor are installed for online monitoring, the output of these sensors cannot provide clear indication on the conditions of the power transformer. One of the insulating materials is the transformer oil which acts as an electrical insulator and also a coolant for the power transformer. After years of services, the transformer oil is subjected to thermal and electrical stresses [ 1 , 2 , 3 ], hence causing oxidative stress on the oil. With the presence of oxygen and moisture [ 4 ], polar compounds and oil sludge are formed [ 5 ], and these oxidative by-products affect the quality of the transformer oil. The quality of the oil can be reflected on the properties of the oil and its content, such as acidity, dielectric breakdown voltage, dissolved gases, and furanic compounds [ 6 ]. Thus, many diagnostic methods were introduced and are capable of providing reliable assessment on the condition of power transformer in order to detect early faults and avoid potential failures [ 7 ].

Conventionally, the quality of the transformer oil is assessed based on its properties using IEC 60422 standard [ 6 ]. Various properties are tested with different techniques. For instance, dissolved gases are conventionally measured using gas chromatography (GC) [ 8 ], and interfacial tension of the oil is typically measured using the Ring method [ 9 ]. Generally, these techniques involve complex sample preparation, tedious measurement procedures, expensive instruments, and require trained experts to conduct the measurements. Thus, researchers are proposing alternative solution to assess the quality of the oil. Differential Scanning Calorimetry (DSC) method [ 10 ], dielectric dissipation factor measurements [ 11 ], infrared (IR) spectroscopy [ 11 ], capacitance measurement [ 12 ], and photoluminescence spectroscopy [ 13 ] have been proposed recently to evaluate the degradation of the transformer oil. Recently, as an attractive and promising analytical tool, the optical spectroscopy technique starts to emerge in assessing the degradation of transformer oil.

Optical spectroscopy studies the interaction of light by matter [ 14 ]. It is a non-destructive test, which provides rapid result analysis, and it can be used for qualitative and quantitative analysis [ 15 ]. The technique also gives high sensitivity measurements and does not require complex sample preparation and calibration. For instance, Deepa et al. [ 16 ] uses multidimensional fluorescence techniques like synchronous fluorescence spectroscopy (SFS) and excitation emission matrix fluorescence (EEMF) to determine the unique characteristic of degraded transformer oil in the spectrum. Kamenchuk et al. [ 17 ] have also applied nuclear magnetic resonance (NMR) in determining the chemical composition of the degraded oils. To provide complementary interpretations and full product description, Godinho et al. [ 18 ] combined data from three different optical sensing techniques, which are the near-infrared (NIR), EEMF, and NMR spectroscopy, to evaluate the quality of the transformer oil. Bakar et al. [ 9 ] have also proposed to measure the interfacial tension of transformer oil using ultraviolet-to-visible (UV-Vis) spectroscopy which reflects the insulation aging activity in the power transformer. Hussain et al. [ 19 ] have also made an evaluation of the state of the transformer oil using the combination of UV-Vis, Fourier transformed infrared (FTIR), and NMR spectroscopy techniques. Results show that each sensing technique is able to provide different analysis in determining the degradation level of the transformer oil.

To assess the degradation level of transformer oil using optical sensing techniques, researchers either determines the special characteristic of degraded transformer oil in the spectrum [ 16 ], or the change in concentration of certain content in transformer oil, such as inhibitor content [ 17 ] and furanic compounds [ 18 ], or the change in properties of transformer oil such as interfacial tension [ 20 ]. The color of the transformer oil can also be used to assess the degradation level of the transformer oil [ 7 , 21 ]. Conventionally, using the American Society for Testing and Materials (ASTM) D 1500 standard (Standard test method for ASTM Color of Petroleum Product (ASTM Color Scale) ) [ 22 ], the color of the transformer oil is determined using a color comparator, and represented by a color index ( CI ) from a scale. The scale contains 16 ASTM CI , with increment in steps of 0.5, starting from 0.5 for the lightest color to 8.0 for the darkest color. However, this method has its disadvantages. Firstly, the step size of 0.5 CI is fairly large. If an exact match is not found for the sample, the darker of the two ASTM colors is reported instead. In many cases, the sample does not fall exactly on the 16 values used in the ASTM D 1500. Secondly, this method relies on the manual visual inspection by an operator and the results may also be affected by the quality of standard light source. Different operators working on an identical oil sample could report a different CI . Likewise, in color measurement of palm oil, an operator is also required to manually operate Lovibond® Tintometer [ 23 ] to determine the right color index of the palm oil. Subsequently, operators that carry out this measurement also need to go for eye checking every 3–6 months due to safety and health issues. According to occupational safety and health guideline, long exposure time with high intensity of visible light on the eye could pose health issues such as headache and eye-related problems. Finally, this issue leads to limited number of samples per day for color index analysis as the operator is required to rest their eyes after testing each sample.

Hence, a more accurate, scientific and automated method in observing the color of the oil is needed. Therefore, this paper proposes to use UV-Vis spectroscopy as an optical sensing technique to determine the color index of transformer oil. The application of UV-Vis spectroscopy in sensing the color of oil dated as early as 1999 where Chantrapornchai et al [ 24 ] studied on the spectral reflectance and color of oil-in-water emulsions. Tan et al. [ 23 ] have proposed a prototype colorimeter that utilizes UV-Vis spectroscopy to determine the color of palm oil and made comparison between their propose method and the conventional method. UV-Vis spectroscopy also has been widely used in evaluating olive oil since the components within such as chlorophyll and lutein are responsible for the color of olive oil [ 25 , 26 ]. Besides palm oil and olive oil, researchers have also applied the UV-Vis spectroscopy in determining the color in crude petroleum oil [ 27 ], hydraulic oil [ 28 ], automotive lubricating oil [ 29 ], essential oil [ 30 ], pumpkin seed oil [ 31 ], and edible oils (sunflower oil, soya oil, corn oil, canola oil, and olive oil) [ 32 ]. Moreover, Hadjadj et al. [ 21 ] have proposed the determination of the correlation between proposed parameters, which are turbidity and dissolved decay product (DDP) value, and the traditional parameters such as interfacial tension, acidity and color index of transformer oil using UV-Vis spectroscopy. Results show a good correlation between the parameters, but the color index is instead based on the 18 Gardner color scale in accordance with ASTM D 1544 [ 21 ]. We have also demonstrated the possibility of determining the CI of transformer oil based on ASTM D 1500 using UV-Vis spectroscopy [ 33 ]. In this work, the utilization of UV-Vis spectroscopy to determine the CI of transformer oil is fully established through a detailed study on the optical spectral response of oil samples with different CI . Furthermore, mathematical models that describe the relationship between the optical response of the oil sample and its respective CI were formulated and verified.

2. Materials and Methods

2.1. sampling and sample design.

For the purpose of this study, transformer oil samples were sampled from different operating transformers. The transformer oil used in these power transformers are generally naphthenic-based transformer oil without additives (uninhibited), produced by Hyrax, Petronas or Shell. The transformer oil product typically contains 55% naphthenic carbons, 38% paraffinic carbons and 7% aromatic carbons, and it fulfills the performance requirement set by the (International Electrotechnical Commission) IEC 60296 standard. The procedure of collecting and transporting these samples adhere strictly to the IEC 60475 standard [ 34 ]. This is to ensure that there was no contamination and mislabeled of the oil samples that would affect the analysis. By taking this into account, two 1 litre amber glass bottles were prepared for transformer oil sampling. One bottle of the sample was sent to an accredited lab for conventional analysis, while the other bottle was used for the purpose of this study. This was done to guarantee that there was minimal time gap between the conventional lab analysis and the experiment of this study. In accordance to the ASTM D 1500 standard, the conventional analysis involves a color comparator to measure the color index of oil samples and report them with one of the 16 ASTM color index. Throughout this paper, the color index measurement carried out at the accredited lab in accordance to the ASTM D 1500 standard will be referred to as conventional analysis or conventional measurement.

2.2. Optical Measurement Setup

The samples sent for the purpose of this study were measured for their absorbance spectrum to the range of 200 nm to 800 nm. The measurements were carried out using Agilent Cary 5000 Ultraviolet-to-Visible-to-Near-Infrared (UV-Vis-NIR) spectrophotometer (Agilent Technologies, Petaling Jaya, Malaysia). The Cary 5000 is a double beam spectrophotometer that can operate in the range of 200 nm to 3300 nm. The general operation of a double spectrophotometer can be found in [ 35 ]. Approximately 3 mL of oil sample and clean uninhibited transformer oil are transferred using a 5-mL disposable plastic pipette into quartz cuvette which has an outer cell dimension of 12 mm × 12 mm × 44 mm, with an optical path-length of 10 mm. The clean oil serves as the reference for the measurement. For each oil sample, the optical spectrum measurements were repeated 3 times to ensure its consistency and repeatability. During the spectrum scan, the light beam passes through both oil sample and reference sequentially and optical transmittance was recorded. The absorbance values were calculated by applying the Beer-Lambert Law [ 36 ] as in Equation (1).

where, Abs is the absorbance, S λ is the transmittance of light passing through the sample in sampling slot, R λ is the transmittance of light passing through the sample in reference slot, B λ is the baseline, ε λ is the absorbance coefficient of the absorbing sample at a certain wavelength, c is the concentration of the absorbing sample, and l is the path-length traversed by the light.

2.3. Initial Results and Modification

Figure 1 shows the absorbance spectral response together with the noise of oil samples with CI of 1.5, 3.0, 5.0 and 6.5.

Initial optical absorbance spectrums of four oil samples with different color indices ( CI ) based on ASTM D 1500.

Initial results show that noises can be observed at the peak absorbance of the oil samples with CI of 3.0 or higher, and the spectral response saturates at Abs > 5. Referring to Equation (1), if the ratio of S λ to R λ is extremely low, Abs will be extremely high, resulting in noises due to the fluctuation of S λ . In order to increase the ratio of S λ to R λ , S λ has to be increased or R λ has to be decreased. S λ cannot be increased further as the brightness of the light source in the spectrometer affects both sample and reference sides. Therefore, R λ needs to be decreased to remove the noises. To reduce R λ , a neutral density (ND) filter (model: FNDU-20C02-0.1), which provides significant optical attenuation (Transmittance = 1%–3%) in the UV-Vis region, was applied at the reference slot of the spectrometer. The noises can also be reduced by decreasing l , which means a shorter path-length cuvette is used. However this method reduces the interaction of light with the oil samples, thus reducing the sensitivity of the measurement. Due to the shortcomings of a shorter path-length, the ND filter was chosen to reduce the measurement noise. The outcome is shown in Figure 2 , where the noises at the peaks (top) of absorbance spectral response of the same four oil samples in Figure 1 , are eliminated and clear smooth optical absorbance spectrum can be observed.

Optical absorbance spectrums of four oil samples with different CI based on ASTM D 1500 after applying ND filter.

3. Final Results and Data Analysis

3.1. absorbance spectra of transformer oil samples.

The oil samples were re-measured with the application of the ND filter. Figure 3 shows the absorbance spectral response of several oil samples with increasing CI .

Optical absorbance spectrum of transformer oils with increasing CI based on ASTM D 1500.

Figure 3 shows clearly that there is a direct relationship between the absorbance spectral response of the oil samples and their CI . The optical bandwidth of the spectrum at a certain Abs will be referred as the cutoff wavelength ( CW ) throughout the paper. For instance, the dotted line across the graph and marker x represents the cutoff wavelength ( CW ) at Abs = 1.0, for oil sample with CI of 2.0, which is 437 nm. It can be observed that the lowest CI of 1.0 shows the lowest peak absorbance and the shortest CW at Abs = 1.0, while the highest CI of 7.5 shows the highest peak absorbance and the longest CW at Abs = 1.0. It is worth noting that the sample with CI of 0.0 represents the new uninhibited transformer oil and it is used as a reference only.

3.2. Mathematical Modeling

Further analysis on Figure 3 shows that different variables can be used to correlate with CI . If a different Abs is chosen, the CW value will change accordingly. Figure 4 a shows the plot of CI against CW for Abs = 0.5, 1.0 and 1.5 respectively. Referring to [ 21 ], Hadjadj et al. calculates the integration of the area under the graph ( Area ) from 360 nm to 600 nm to determine the DDP value in accordance to the ASTM D 6802 [ 37 ], and then relating it with the 18 Gardner color index in accordance with ASTM D 1544 [ 21 ]. Similarly, the same method can also be applied to correlate Area with 16 ASTM CI based on ASTM D 1500. Figure 4 b shows the plot of CI against Area.

( a ) CW verses CI of transformer oil samples at Abs = 0.5; 1.0 and 1.5, ( b ) Area verses CI of transformer oil samples.

Based on Figure 4 a, a linear relationship can be observed between the CI and CW at Abs = 0.5, 1.0 and 1.5. Likewise, a linear relationship can also be observed between CI and Area in Figure 4 b. In order to measure the strength of the correlation between the variables, Pearson product-moment correlation coefficient ( r ) of the data was calculated. The r value determines the strength and direction of the linear relationship between two variables [ 38 ]. Generally, r > 0 indicates a positive relationship while r < 0 indicates a negative relationship. Table 1 shows a guideline in determining the strength of relationship for absolute value of r [ 39 ].

Guideline for interpretation of strength of relationship for absolute values of correlation.

The calculated r values between CI and CW in Figure 4 a are 0.9867, 0.9799 and 0.9772 for Abs = 0.5, 1.0 and 1.5 respectively. Based on Figure 4 b, the calculated r between CI and Area is 0.9835. All four correlation coefficients are very close to 1 and it shows a very strong positive linear correlation. Thus, linear regression models can be formed based on the data in Figure 4 a,b. However, it is worth noting that different linear models could be formed if different Abs is chosen for data in Figure 4 a. Therefore a model that describes the relationship between CI , CW and Abs was formulated.

A 3-dimentional plot was generated to study the relationship between CI , CW and Abs . Three regression methods, which were Linear, Paraboloid and Gaussian regressions, were applied to the collected data to determine the method that provides the best fit. To evaluate the performance of the regression methods, the coefficient of determination ( R 2 ), adjusted R 2 , and standard error of estimate ( S ) were calculated. R 2 provides a descriptive measure of how well the regression line makes the prediction [ 38 ] while the adjusted R 2 is a modified version of R 2 that has been adjusted for the number of predictors in the model [ 40 ]. The range of both R 2 and adjusted R 2 are between 0 and 1, where 0 indicates that the measured data is far from the regression line while 1 indicates that all measured data is on the regression line. S measures the average distance that the measured data fall from the regression line. A smaller S value generally indicates that the data are closer to the regression line. Table 2 shows the analysis results of the three regression methods used.

Regression analysis results for linear, paraboloid and Gaussian.

Based on Table 2 , Gaussian regression shows the highest R 2 and adjusted R 2 , and the lowest S value compared to linear and paraboloid regressions. Therefore, Gaussian regression was chosen for the mathematical model. Figure 5 shows a 3-dimentional plot of CW vs. CI vs. Abs of the transformer oil with the Gaussian regression plane.

Graph of CW vs. CI of transformer oil samples vs. Abs (Red Circles with black borders) with Gaussian regression (White plane).

According to the results of the Gaussian regression analysis, the mathematical model that describes the relationship between the CW , CI and Abs of transformer oil is as Equation (2).

Furthermore, linear regression was applied to the collected data in Figure 4 b to describe the relationship between CI and Area , and it is plotted in Figure 6 .

Area vs. CI of transformer oil samples with linear regression line (dotted line).

According to the results of the linear regression analysis, the regression is able to produce a R 2 value of 0.9412, adjusted R 2 value of 0.9412, and S value of 55.8578. The mathematical model that describes the relationship between the Area and CI of transformer oil is as Equation (3).

3.3. Verification of Mathematical Modeling

To validate the mathematical models of Equations (2) and (3), a second set of transformer oil samples were sampled. The oil samples were then sent for conventional lab analysis to determine the color index in accordance to ASTM D 1500 standard. The optical absorbance of the oil samples was also measured using Cary 5000.

For the verification of Equation (2), the CW of each absorbance spectral response of the oil samples at Abs values of 0.25, 0.5, 0.75, 1.0, 1.25, 1.5, 1.75 and 2.0 were collected, and the estimated CI were calculated using Equation (2). The difference between the measured CI and the calculated CI were then calculated and analyzed to determine the average difference and maximum absolute difference at different Abs values. Table 3 shows an example of the calculated CI using Equation (2), CI measured using ASTM D 1500 and their differences for each sample at Abs = 0.75.

Verification data for 42 oil samples (S1–S42) at Abs = 0.75 using Equation (2).

1 Color index based on measurement in accordance of ASTM D 1500, 2 Color index calculated based on Equation (2), 3 Difference in CI = Calculated CI – Measured CI .

Based on Table 3 , the results show that the calculated CI is close to the measured CI value for each sample. The standard deviation and standard error for CI = 0.5, 1.0, 1.5, 2.0, 2.5 and 3.0 were calculated. The maximum absolute difference between the calculated and measured CI is 0.4 and the average absolute difference is 0.1632. The maximum absolute difference between the calculated and measured CI , and the average absolute difference at different Abs values were calculated and plotted in Figure 7 .

Plot of average absolute difference and maximum absolute difference at different Abs value.

Based on Figure 7 , Abs of 0.75 produced the least average absolute difference (0.1632), and the Abs of 0.75, and 1.0 produced the lowest maximum absolute difference (0.4).

For the verification of Equation (3), the area under the graph from 360 nm to 600 nm for the second set of oil samples were recorded and the estimated CI were calculated using Equation (3). Likewise, the difference between the measured CI and the calculated CI were then calculated and analyzed to determine the average difference and maximum absolute difference. Table 4 shows the calculated CI using Equation (3), CI measured using ASTM D 1500 and their differences for each sample.

Verification data for 42 oil samples (S1–S42) using Equation (3).

1 Color index based on measurement in accordance of ASTM D 1500, 2 Integration of area under the graph from 360 nm to 600 nm, 3 Color index calculated based on Equation (3), 4 Difference in CI = Calculated CI – Measured CI .

Based on Table 4 , the results show that the calculated CI is close to the measured CI value for each sample. The standard deviation and standard error were also calculated for CI = 0.5, 1.0, 1.5, 2.0, 2.5 and 3.0. The maximum absolute difference between the calculated and measured CI is 1.8 and the average absolute difference is 0.4647.

For further verification, the Root Mean Square Error ( RMSE ) for results in Table 3 and Table 4 are calculated and compared. Generally, RMSE is the standard deviation of the difference between the actual value, y and the estimated value, ŷ as shown in Equation (4) [ 41 ]. RMSE is able to measure how much the actual data varies around the regression line.

where n is the total number of samples.

In this analysis, y represents the measured CI value, and ŷ represents the calculated CI value. RMSE values were calculated for both verification data in Table 3 and Table 4 . Table 5 summarizes the maximum absolute differences, average absolute differences and RMSE values for both Equations (2) and (3).

Maximum absolute difference, average absolute difference, and RMSE value for verification data.

4. Discussion

As described in Section 2.2 , the oil samples were measured for at least 3 times to ensure that the spectrophotometer can produce consistent and repeatable results. Based on the obtained optical absorbance spectrum, the standard deviation among the calculated CI for each oil sample was computed. For all the samples that were measured for their repeatability, it was found that the standard deviation was in the range of 0 to 0.013.

In addition, for a particular CI , five different oil samples were measured and the standard deviation and standard error were calculated, as shown in Table 3 and Table 4 . Due to the lack of oil samples with CI > 3.0 from operating power transformers, only one or two samples with CI > 3.0 were measured. The difficulty in getting oil samples with CI > 3.0 was because oil samples with CI > 3.0 indicate a critical level in other properties such as acidity and interfacial tension, thus the oil will be sent for oil reclamation to restore the quality of the transformer oil. Nevertheless, for CI ≤ 3.0, the standard deviation and standard error were calculated. Based on Table 3 , the standard deviation ranges from 0.0733 to 0.2238, with the maximum percentage of standard deviation from the average value of 13%. It is important to note that not all the oil samples for this experiment were chosen such that their CI were determined with direct matching to the color disk. This means that the color of the oil samples could be intermediate between two standard ASTM colors. Moreover, considering that the conventional method could also cause an error of 0.5 due to its large step size, the measured CI also has an error of 0.5. Even with a large error, the regression is able to produce a maximum absolute difference of 0.4, and an average absolute difference of 0.1632. However, the modeled equation can be further improved by inputting more training samples with wider range of CI .

Results from Table 5 suggested that the determination of CI using Equation (2) should be done using the CW at Abs of 0.75 in order to obtain the lowest maximum absolute difference of 0.4, the least average absolute difference of 0.1632, and the smallest RMSE value of 0.1961. On the other hand, the results show that Equation (3) produces CI with a maximum absolute difference of 1.8, an average absolute difference of 0.4647 and RMSE value of 0.6274.

It is clear that using optical spectroscopy to determine the CI of the transformer oil will provide significant advantages over the conventional method. It is able to provide a smaller error due to higher resolution while the conventional method produces an error of 0.5. Moreover, the proposed technique does not require a human observer to measure the CI , and thus human error is eliminated.

In addition, there is also a possibility that this technique can be applied and developed into a small portable handheld measuring device for on-site measurement or online monitoring. Since the interested range of wavelength is within the visible light region, a miniature UV-Vis spectrometer with a visible light source can be easily obtained at a reasonably low price. With a sample holder, a simple measuring prototype can be built. A microcontroller can also be incorporated for data processing and users interface. Figure 8 shows a simple block diagram of the measuring prototype. This prototype can be readily used by a maintenance operator for on-site CI measurement by extracting 3 mL of oil sample, inserting it in a cuvette, and placing the cuvette in the prototype. Besides transformer oil, the prototype can also be used for quick color measurement of other oils such as engine oil, biodiesel oil, and olive oil.

Simple block diagram of potential portable measuring prototype.

5. Conclusions

This work was carried out to explore the optical properties of transformer oil using optical detection. It focuses on the determination of CI of the transformer oil using optical spectroscopy. The existing method of determining CI of the transformer oil was first researched and it was found that the technique has accuracy issues, and relies on manual observations. The existing technique has the disadvantages of causing an error of 0.5 during measurement due to its large measurement step size, and the requirement of a human observer. Therefore, this paper proposes a new optical technique to quantify the CI of transformer oil.

Results have shown that there is a strong, positive linear correlation between the CW of the spectral response at a certain Abs and the CI of the transformer oil, and also between area under the curve from 360 nm to 600 nm and the CI of the transformer oil. Two mathematical models were formed and verified using a second set of oil samples. It was found that the equation that describes the relationship between CW of the spectral response at a certain Abs and the CI of the transformer oil shows smaller maximum absolute difference of 0.4, average absolute difference of 0.1632, and RMSE value of 0.1961 at Abs = 0.75.

This proposed new technique of determining the color index of transformer oil has several advantages over the current method as it has the possibility to be performed on-site, and has a higher accuracy by minimizing the risk of human error and having a higher resolution. It also has the potential to be applied to other products such as engine oil, biodiesel fuel and olive oil.

Acknowledgments

The authors gratefully acknowledge TNB Research Sdn. Bhd. and TNBR QATS Sdn. Bhd. Transformer Oil Lab for the access to the laboratory equipment and facilities, and supplying transformer oil samples.

Author Contributions

This research work was mainly conducted by Y.S.L. and he composed the manuscript. P.J.K. and M.Z.J. contributed their effort in designing the experimental setup, result analysis and manuscript revising. S.M.N., A.I. and F.A. have contributed in results analysis. H.M.L. and C.K.L. provided access to the laboratory equipment, facilities and oil samples from power transformers.

This research was funded by the UNITEN-TNBR research grant U-SN-CR-17-07 and the UNITEN BOLD Grant 10289176/B/9/2017/41.

Conflicts of Interest

The authors declare no conflict of interest.

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UV-Vis Spectroscopy: Principle, Strengths and Limitations and Applications

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Ultraviolet-visible (UV-Vis) spectroscopy is a widely used technique in many areas of science ranging from bacterial culturing , drug identification and nucleic acid purity checks and quantitation, to quality control in the beverage industry and chemical research. This article will describe how UV-Vis spectroscopy works, how to analyze the output data, the technique's strengths and limitations and some of its applications.

UV-Vis spectroscopy is an analytical technique that measures the amount of discrete wavelengths of UV or visible light that are absorbed by or transmitted through a sample in comparison to a reference or blank sample. This property is influenced by the sample composition, potentially providing information on what is in the sample and at what concentration. Since this spectroscopy technique relies on the use of light, let’s first consider the properties of light. Light has a certain amount of energy which is inversely proportional to its wavelength. Thus, shorter wavelengths of light carry more energy and longer wavelengths carry less energy. A specific amount of energy is needed to promote electrons in a substance to a higher energy state which we can detect as absorption. Electrons in different bonding environments in a substance require a different specific amount of energy to promote the electrons to a higher energy state. This is why the absorption of light occurs for different wavelengths in different substances. Humans are able to see a spectrum of visible light, from approximately 380 nm, which we see as violet, to 780 nm, which we see as red. 1 UV light has wavelengths shorter than that of visible light to approximately 100 nm. Therefore, light can be described by its wavelength, which can be useful in UV-Vis spectroscopy to analyze or identify different substances by locating the specific wavelengths corresponding to maximum absorbance (see the Applications of UV-Vis spectroscopy section).

How does a UV-Vis spectrophotometer work?

Whilst there are many variations on the UV-Vis spectrophotometer, to gain a better understanding of how an UV‑Vis spectrophotometer works, let us consider the main components, depicted in Figure 1.

Light source

As a light-based technique, a steady source able to emit light across a wide range of wavelengths is essential. A single xenon lamp is commonly used as a high intensity light source for both UV and visible ranges. Xenon lamps are, however, associated with higher costs and are less stable in comparison to tungsten and halogen lamps.

For instruments employing two lamps, a tungsten or halogen lamp is commonly used for visible light, 2 whilst a deuterium lamp is the common source of UV light. 2 As two different light sources are needed to scan both the UV and visible wavelengths, the light source in the instrument must switch during measurement. In practice, this switchover typically occurs during the scan between 300 and 350 nm where the light emission is similar from both light sources and the transition can be made more smoothly.

Wavelength selection

In the next step, certain wavelengths of light suited to the sample type and analyte for detection must be selected for sample examination from the broad wavelengths emitted by the light source. Available methods for this include:  

• Monochromators - A monochromator separates light into a narrow band of wavelengths. It is most often based on diffraction gratings that can be rotated to choose incoming and reflected angles to select the desired wavelength of light. 1, 2 The diffraction grating's groove frequency is often measured as the number of grooves per mm. A higher groove frequency provides a better optical resolution but a narrower usable wavelength range. A lower groove frequency provides a larger usable wavelength range but a worse optical resolution. 300 to 2000 grooves per mm is usable for UV-Vis spectroscopy purposes but a minimum of 1200 grooves per mm is typical. The quality of the spectroscopic measurements is sensitive to physical imperfections in the diffraction grating and in the optical setup. As a consequence, ruled diffraction gratings tend to have more defects than blazed holographic diffraction gratings. 3 Blazed holographic diffraction gratings tend to provide significantly better quality measurements. 3  
• Absorption filters -  Absorption filters are commonly made of colored glass or plastic designed to absorb particular wavelengths of light. 2  
• Interference filters -   Also called dichroic filters, these commonly used filters are made of many layers of dielectric material where interference occurs between the thin layers of materials. These filters can be used to eliminate undesirable wavelengths by destructive interference, thus acting as a wavelength selector. 1, 2  
• Cutoff filters  - Cutoff filters allow light either below (shortpass) or above (longpass) a certain wavelength to pass through. These are commonly implemented using interference filters.  
• Bandpass filters  -Bandpass filters allow a range of wavelengths to pass through that can be implemented by combining shortpass and longpass filters together.

Monochromators are most commonly used for this process due to their versatility. However, filters are often used together with monochromators to narrow the wavelengths of light selected further for more precise measurements and to improve the signal-to-noise ratio.

Sample analysis

Whichever wavelength selector is used in the spectrophotometer, the light then passes through a sample. For all analyses, measuring a reference sample, often referred to as the "blank sample", such as a cuvette filled with a similar solvent used to prepare the sample, is imperative. If an aqueous buffered solution containing the sample is used for measurements, then the aqueous buffered solution without the substance of interest is used as the reference. When examining bacterial cultures, the sterile culture media would be used as the reference. The reference sample signal is then later used automatically by the instrument to help obtain the true absorbance values of the analytes.

It is important to be aware of the materials and conditions used in UV‑Vis spectroscopy experiments. For example, the majority of plastic cuvettes are inappropriate for UV absorption studies because plastic generally absorbs UV light. Glass can act as a filter, often absorbing the majority of UVC (100‑280 nm) 2 and UVB (280‑315 nm) 2 but allowing some UVA (315‑400 nm) 2 to pass through. Therefore, quartz sample holders are required for UV examination because quartz is transparent to the majority of UV light. Air may also be thought of as a filter because wavelengths of light shorter than about 200 nm are absorbed by molecular oxygen in the air. A special and more expensive setup is required for measurements with wavelengths shorter than 200 nm, usually involving an optical system filled with pure argon gas. Cuvette-free systems are also available that enable the analysis of very small sample volumes, for example in DNA or RNA analyses.

After the light has passed through the sample, a detector is used to convert the light into a readable electronic signal. Generally, detectors are based on photoelectric coatings or semiconductors.

A photoelectric coating ejects negatively charged electrons when exposed to light. When electrons are ejected, an electric current proportional to the light intensity is generated. A photomultiplier tube (PMT) 4 is one of the more common detectors used in UV‑Vis spectroscopy. 2 , 5 A PMT is based on the photoelectric effect to initially eject electrons upon exposure to light, followed by sequential multiplication of the ejected electrons to generate a larger electric current. 4 PMT detectors are especially useful for detecting very low levels of light.

When semiconductors are exposed to light, an electric current proportional to the light intensity can pass through. More specifically, photodiodes 6 and charge‑coupled devices (CCDs) 7 are two of the most common detectors based on semiconductor technology. 2 , 5

After the electric current is generated from whichever detector was used, the signal is then recognized and output to a computer or screen. Figures 2 and 3 show some simplified example schematic diagrams of UV-Vis spectrophotometer arrangements.

UV-Vis spectroscopy analysis, absorption spectrum and absorbance units

UV-Vis spectroscopy information may be presented as a graph of absorbance, optical density or transmittance as a function of wavelength. However, the information is more often presented as a graph of absorbance on the vertical y axis and wavelength on the horizontal x axis. This graph is typically referred to as an absorption spectrum; an example is shown in Figure 4.

Based on the UV‑Vis spectrophotometer instrumentation reviewed in the previous section of this article, the intensity of light can be reasonably expected to be quantitatively related to the amount of light absorbed by the sample.

The absorbance ( A ) is equal to the logarithm of a fraction involving the intensity of light before passing through the sample ( I o ) divided by the intensity of light after passing through the sample ( I ). The fraction I divided by I o is also called transmittance ( T ), which expresses how much light has passed through a sample. However, Beer–Lambert's law is often applied to obtain the concentration of the sample ( c ) after measuring the absorbance ( A ) when the molar absorptivity ( ε ) and the path length ( L ) are known. Typically, ε is expressed with units of L mol ‑1 cm ‑1 , L has units of cm, and c is expressed with units of mol L ‑1 . As a consequence, A has no units.

Sometimes AU is used to indicate arbitrary units or absorbance units but this has been strongly discouraged.

Beer–Lambert's law is especially useful for obtaining the concentration of a substance if a linear relationship exists using a measured set of standard solutions containing the same substance. Equation 1 shows the mathematical relationships between absorbance, Beer–Lambert's law, the light intensities measured in the instrument, and transmittance. 5 , 9

The term optical density (OD) is sometimes incorrectly used interchangeably with absorbance. OD and absorbance both measure the amount of light intensity lost in an optical component, but OD takes into consideration loss from light scattering whereas absorbance does not. If very little light scattering is present in a measurement, then OD may be approximated directly using absorbance and Beer–Lambert's law may be used.

Knowing the experimental conditions during measurements is important. Cuvettes designed for a 1 cm path length are standard and are most common. Sometimes, very little sample is available for examination and shorter path lengths as small as 1 mm are necessary. Where quantitation is required, absorbance values should be kept below 1, within the dynamic range of the instrument. This is because an absorbance of 1 implies that the sample absorbed 90% of the incoming light, or equivalently stated as 10% of the incoming light was transmitted through the sample. With such little light reaching the detector, some UV‑Vis spectrophotometers are not sensitive enough to quantify small amounts of light reliably. Two simple possible solutions to this problem are to either dilute the sample or decrease the path length. As mentioned above, recording a baseline spectrum using a “blank” reference solution is essential. If the instrument was absolutely perfect in every way, the baseline would have zero absorbance for every wavelength examined. In a real situation, however, the baseline spectrum will usually have some very small positive and negative absorbance values. For best practice, these small absorbance values are often automatically subtracted from the sample absorbance values for each wavelength of light by the software to obtain the true absorbance values. 1

Depending on the purpose of the analysis, the construction of a calibration curve may be desirable. Building a calibration curve requires some data analysis and extra work but it is very useful to determine the concentration of a particular substance accurately in a sample based on absorbance measurements. There are however, numerous circumstances in which a calibration curve is not necessary including OD measurements for bacterial culturing, taking absorbance ratios at specific wavelengths for assessing the purity of nucleic acids or identifying certain pharmaceuticals.

In UV-Vis spectroscopy, the wavelength corresponding to the maximum absorbance of the target substance is chosen for analysis. This choice ensures maximum sensitivity because the largest response is obtained for a certain analyte concentration. 1 An example of a UV Vis absorption spectrum of Food Green 3 and a corresponding calibration curve using standard solutions are provided in Figure 5. Note that two maximum absorbance peaks are present in the Food Green 3 dye, a smaller maximum absorbance peak at 435 nm and a more intense maximum absorbance peak at 619 nm. To gain maximum sensitivity when calculating an unknown concentration of Food Green 3, the maximum absorbance peak at 619 nm was used for analysis. Standard solutions across a range of known concentrations were prepared by diluting a stock solution, taking absorbance measurements and then plotting these on a graph of absorbance versus concentration to build a numerical relation between concentration and absorbance. A calibration curve was created using a least squares linear regression equation. The closer the data points are to a straight line, the better the fit. The y intercept in the straight line equation was set to zero to indicate no absorbance when no dye was present. The equation shown in Figure 5 is used to calculate the concentration of Food Green 3 (variable x) in an unknown sample based on the measured absorbance (variable y). 

For data analysis, the graph of absorbance versus concentration can indicate how sensitive the system is when building a calibration curve. When a linear least squares regression equation is used, the slope from the line of best fit indicates sensitivity. If the slope is steeper, the sensitivity is higher. Sensitivity is the ability to differentiate between the small differences in the sample concentration. From Beer–Lambert's Law, the sensitivity can be partially indicated by the molar absorptivity ε . Knowing the ε values beforehand, if available, can help to determine the concentrations of the samples required, particularly where samples are limited or expensive.

For reliability and best practice, UV‑Vis spectroscopy experiments and readings should be repeated. When repeating the examination of a sample, in general, a minimum of three replicate trials is common, but many more replicates are required in certain fields of work. A calculated quantity, such as the concentration of an unknown sample, is usually reported as an average with a standard deviation. Reproducible results are essential to ensure precise, high quality measurements. Standard deviation, relative standard deviation, or the coefficient of variation help to determine how precise the system and measurements are. A low deviation or variation indicates a higher level of precision and reliability.

Strengths and limitations of UV-Vis spectroscopy

No single technique is perfect and UV‑Vis spectroscopy is no exception. The technique does, however, have a few main strengths listed below that make it popular.

• The technique is non‑destructive , allowing the sample to be reused or proceed to further processing or analyses.
• Measurements can be made quickly , allowing easy integration into experimental protocols.
• Instruments are easy to use , requiring little user training prior to use.
• Data analysis generally requires minimal processing , again meaning little user training is required.
• The instrument is generally inexpensive to acquire and operate, making it accessible for many laboratories.

Although the strengths of this technique seem overwhelming, there are also certain weaknesses:

• Stray light  - In a real instrument, wavelength selectors are not perfect and a small amount of light from a wide wavelength range may still be transmitted from the light source, 1 possibly causing serious measurement errors. 9 Stray light may also come from the environment or a loosely fitted compartment in the instrument. 1    
• Light scattering  -  Light scattering is often caused by suspended solids in liquid samples, which may cause serious measurement errors. The presence of bubbles in the cuvette or sample will scatter light, resulting in irreproducible results.  
• Interference from multiple absorbing species  - A sample may, for example, have multiple types of the green pigment chlorophyll. The different chlorophylls will have overlapping spectra when examined together in the same sample. For a proper quantitative analysis, each chemical species should be separated from the sample and examined individually.  
• Geometrical considerations  - Misaligned positioning of any one of the instrument's components, especially the cuvette holding the sample, may yield irreproducible and inaccurate results. Therefore, it is important that every component in the instrument is aligned in the same orientation and is placed in the same position for every measurement. Some basic user training is therefore generally recommended to avoid misuse.

Applications of UV-Vis spectroscopy

UV‑Vis has found itself applied to many uses and situations including but not limited to:

DNA and RNA analysis

Quickly verifying the purity and concentration of RNA and DNA is one particularly widespread application. A summary of the wavelengths used in their analysis and what they indicate are given in Table 1. When preparing DNA or RNA samples, for example for downstream applications such as sequencing, it is often important to verify that there is no contamination of one with the other, or with protein or chemicals carried over from the isolation process.

The 260 nm/280 nm absorbance (260/280) ratio is useful for revealing possible contamination in nucleic acid samples, summarized in Table 2. Pure DNA typically has a 260/280 ratio of 1.8, while the ratio for pure RNA is usually 2.0. Pure DNA has a lower 260/280 ratio than RNA because thymine, which is replaced by uracil in RNA, has a lower 260/280 ratio than uracil. Samples contaminated with proteins will lower the 260/280 ratio due to higher absorbance at 280 nm.

Table 1: Summary of useful UV absorbance when determining 260/280 and 260/230 absorbance ratios.

  Table 2: Summary of expected UV absorbance ratios for DNA and RNA analysis.

The 260 nm/230 nm absorbance (260/230) ratio is also useful for checking the purity of DNA and RNA samples and may reveal protein or chemical contamination. Proteins can absorb light at 230 nm, thus lowering the 260/230 ratio and indicating protein contamination in DNA and RNA samples. 10 Guanidinium thiocyanate and guanidinium isothiocyanate, two of the common compounds used in purifying nucleic acids, strongly absorb at 230 nm which will lower the 260/230 absorbance ratio too.

Pharmaceutical analysis

One of the most common uses of UV-Vis spectroscopy is in the pharmaceuticals industry. 12 , 13 , 14 , 15 , 16 , 17 In particular, processing UV-Vis spectra using mathematical derivatives allows overlapping absorbance peaks in the original spectra to be resolved to identify individual pharmaceutical compounds. 12 , 17 For example, benzocaine, a local anesthetic, and chlortetracycline, an antibiotic, can be identified simultaneously in commercial veterinary powder formulations by applying the first mathematical derivative to the absorbance spectra. 17 Simultaneous quantification of both substances was possible on a microgram per milliliter concentration range by building a calibration function for each compound. 17

UV/visible Spectroscopy in the Development of Biopharmaceuticals

Bacterial culture.

UV-Vis spectroscopy is often used in bacterial culturing . OD measurements are routinely and quickly taken using a wavelength of 600 nm to estimate the cell concentration and to track growth. 18 600 nm is commonly used and preferred due to the optical properties of bacterial culture media in which they are grown and to avoid damaging the cells in cases where they are required for continued experimentation.

Beverage analysis

Other applications.

This technique may also be used in many other industries. For example, measuring a color index is useful for monitoring transformer oil as a preventative measure to ensure electric power is being delivered safely. 21 Measuring the absorbance of hemoglobin to determine hemoglobin concentrations may be used in cancer research. 22 In wastewater treatments, UV-Vis spectroscopy can be used in kinetic and monitoring studies to ensure certain dyes or dye by‑products have been removed properly by comparing their spectra over time. 23  It also finds great utility in food authenticity analysis and air quality monitoring .

UV‑Vis spectroscopy is also qualitatively useful in some more specialized research. Tracking changes in the wavelength corresponding to the peak absorbance is useful in examining specific structural protein changes 24 , 25 , 26 and in determining battery composition. 27 Shifts in peak absorbance wavelengths can also be useful in more modern applications such as characterization of very small nanoparticles. 28 , 29 The applications of this technique are varied and seemingly endless.

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19.    Tadesse Wondimkun Z. The Determination of Caffeine Level of Wolaita Zone, Ethiopia Coffee Using UV-visible Spectrophotometer. Am J Appl Chem . 2016;4(2):59. doi: 10.11648/j.ajac.20160402.14

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21.    Leong Y, Ker P, Jamaludin M, et al. UV-Vis Spectroscopy: A New Approach for Assessing the Color Index of Transformer Insulating Oil. Sensors . 2018;18(7):2175. doi: 10.3390/s18072175

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What is UV-Vis spectroscopy? UV-Vis spectroscopy is an analytical technique that measures the amount of discrete wavelengths of UV or visible light that are absorbed by or transmitted through a sample in comparison to a reference or blank sample. This property is influenced by the sample composition, potentially providing information on what is in the sample and at what concentration.