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Calibration transfer between NIR spectrometers

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Calibration transfer between NIR spectrometers

  1. 1. COMPARATIVE STUDY BETWEEN NEAR- INFRARED (NIR) SPECTROMETERS IN THE MEASUREMENT OF SUCROSE CONCENTRATION ZCT 390/6: PURE PHYSICS PROJECT NUR FATIHA AKMA BINTI ISMAIL 109012 DR. AHMAD FAIRUZ BIN OMAR SCHOOL OF PYHSICS UNIVERSITI SAINS MALAYSIA 2012/2013
  2. 2. ACKNOWLEDGEMENT This final year project would be impossible without the guidance and help of several individuals. First of all, I would like to express my deep and sincere gratitude to my final year project supervisor, Dr. Ahmad Fairuz bin Omar, Lecturer of the School of Physics, University of Science Malaysia. He had shared a lot of his knowledge in order for my partner and me to finish our project. He also willing to sacrifice his busy schedule to give my partner and me the guidance to complete this thesis. His understanding, encouraging and personal guidances make me not hesitate to proceed this project until its completion. Besides that, my full appreciation goes to my project partner, Nurul Izati binti Azizan whose gave me a lot of courage to finish our project in duration of two semesters. I owe for her patient with my attitude and let her intelligence to be shared with me. She also willing to sacrifice her busy duty task in order to discuss about our project. On top of that, my heartfully thankful to the Engineering Lab of School of Physics assistants who had given us permission to use the lab even during the semester break. Without their permission and assistant, we absolutely cannot finish our project since the apparatus and materials in the engineering lab are very important in our project. Furthermore, I want to give my morally appreciation to my family, roommates and friends. Without their understanding, encouragement and motivation, it is impossible for me to finish this project. My special appreciation goes to my parents, Norzaini binti Ismail and Ismail bin Hassim for their understanding and loving support. On top of that, I also want to express my thanks to the School of Physics, University of Science Malaysia because all the financial support come from them. Finally, I would like to thanks to everybody who was involved in the process of the completion of this project. I also want to apologize because I could not mention one by one. Thanks a lot for the cooperation given whether on direct or indirect way. ii
  3. 3. TABLE OF CONTENTS ACKNOWLEDGEMENT ii TABLE OF CONTENTS iii LIST OF TABLES vi LIST OF FIGURES vii LIST OF ABBREVIATIONS viii LIST OF SYMBOL ix ABSTRAK x ABSTRACT xi CHAPTER 1 – INTRODUCTION 1.1 Spectroscopy 1 1.1.1 NIR Spectroscopy 2 1.1.2 Spectroscopic Properties of Aqueous Sucrose Solution 4 1.2 The importance of Calibration Transfer 5 1.3 The Importance of Measuring Sucrose in Fruit 6 1.4 Objectives 8 1.5 Proble Statement 8 1.6 Outline of Thesis 8 iii
  4. 4. CHAPTER 2 – LITERATURE REVIEW 2.1 Response Analysis between NIR Spectrometers 9 2.2 Application of NIR spectroscopy in Various Field 12 CHAPTER 3 – MATERIALS AND METHOD 3.1 Apparatus and Material Background 16 3.1.1 Jaz Spectrometer 16 3.1.2 QE65000 Spectrometer 19 3.1.3 NIR Quest Spectrometer 21 3.1.4 Refractometer 23 3.1.5 Sample - Sucrose 26 3.2 Experiment Setup 28 3.3 Methodology 31 iv
  5. 5. CHAPTER 4 – RESULTS AND DISCUSSION 4.1 Response Analysis between Jaz and QE65000 Spectrometer 34 4.2 Response Analysis of NIRQuest Spectrometer 40 4.3 Response Analysis between NIR Spectrometer 44 CHAPTER 5 – CONCLUSIONS AND RECOMMENDATIONS 46 REFERENCES 48 APPENDIX 50 v
  6. 6. LIST OF TABLES Table 2.1 Summary of the linear relationship between absorbance and sucrose concentrations. Table 3.1 Properties of Jaz Spectrometer. Table 3.2 The Technical Properties of QE65000 spectrometer. Table 3.4 The characteristics and properties of NIR spectrometers. Table 3.5 Samples Characteristics. Table 4.1 Results from MLR using Wavelengths from O-H and C-H absorbance bands for Jaz and QE65000 spectrometer. Table 4.2 Results from MLR using Wavelengths from O-H and C-H absorbance bands for NIRQuest spectrometer. vi
  7. 7. LIST OF FIGURES Figure 1.1 Electromagnetic Spectrum. Figure 1.2 An Example of NIR Absorption Spectrum of Paper. Figure 1.3 Scatter plots between actual and NIR-predicted values for sucrose in mangoes Figure 1.4 The example of starch test. Figure 2.1 Linear Relationship between Absorbance and Concentration of Aqueous Sucrose at wavelength 959 nm. Figure 2.2 Peak shift in NIR absorbance spectra from different water-sucrose concentration. Figure 2.3 Measured vs. predicted values of the soluble solids content (%Brix) of Jonagold apple based on NIR reflectance spectra. Figure 2.4 Correlation of banana sugar contents measured chemically as well as non destructively by means of NIR spectroscopy. Figure 3.1 The Jaz Spectrometer. Figure 3.2 QE65000 Spectrometer. Figure 3.3 NIRQuest512-2.2 Spectrometer. Figure 3.4 A modern digital handheld refractometer being cleaned under a faucet. Figure 3.5 Skeletal Formula for Sucrose. Figure 3.6 The Formation of Sucrose from Glucose and Fructose. Figure 3.7 Experiment Setup for NIR Measurement (Side view). Figure 3.8 Flow chart of experiment. Figure 4.1 Linear relationship between absorbance and concentration of aqueous sucrose at λ = 959 nm by using (a) Jaz spectrometer, and (b) QE65000 spectrometer. Figure 4.2 Coefficient of determination generated at different wavelength for aqueous sucrose concentration by using (a) Jaz spectrometer, and (b) QE65000 spectrometer. vii
  8. 8. Figure 4.3 Calculated VS actual concentrations of sucrose by using (a) Jaz spectrometer, and (b) QE65000 spectrometer. Figure 4.4 Coefficient of determination generated at different wavelengths for aqueous sucrose concentration by using NIRQuest spectrometer. Figure 4.5 Calculated VS actual concentrations of sucrose by using NIRQuestspectrometer. viii
  9. 9. LIST OF ABBREVIATIONS IR Infra-red RMSEC Root Mean Square of Calibration model CCD Charge-Coupled Device DPU Data Processing Unit FWHM Full-Width Half-Maximum A/D Analog-to-Digital FFT Fast Fourier Transform OLED Organic Light- Emitting Diode RI Refractive index ATC Air Traffic Control QA Quality Assurance RDS Refractometric Dried Substance OD Optical density MLR Multiple Linear Regression ix
  10. 10. LIST OF SYMBOLS λ Wavelength nm Nanometer °Brix Degree Brix %w/w Percentage by weight mm Millimeter Aλ Absorbance at wavelength λ Sλ Sample intensity at wavelength λ Dλ Dark intensity at wavelength λ Rλ Reference intensity at wavelength λ Aλ Absorbance at wavelength, Eλ Extinction coefficient of the absorbing species at wavelength lamda, C Concentration of the absorbing species, l Optical path length of the absorption. x
  11. 11. ABSTRAK Pemindahan penentukuran merupakan topik yang amat penting dan hangat diperkatakan bagi tujuan aplikasi sains dan praktikal dalam bidang spektroskopi. Tesis ini bertujuan menyiasat pemindahan penentukuran antara spektrometer-spektrometer inframerah-dekat yang berlainan. Antara jenis spektrometer yang digunakan di dalam penyiasatan ini adalah Jaz, QE65000, dan inframerah-dekat. Pengukuran kandungan gula merupakan salah satu kaedah yang penting untuk memastikan tanaman dituai pada masa yang tepat supaya buah-buah yang baik dan berkualiti dapat dihasilkan. Sampel yang digunakan di dalam penyiasatan ini adalah sukrosa, C12H22O11, yang dipelbagaikan kepekatannya kepada lima puluh data. Kepekatan sukrosa diukur menggunakan refraktometer di dalam unit darjah brix (°Brix). Darjah brix merupakan kandungan gula di dalam larutan berair. Satu darjah brix didefinisikan sebagai 1 gram sukrosa di dalam 100 gram larutan dan mewakili kekuatan larutan menerusi peratusan berat (%w/w). Jika larutan mengandungi pepejal yang dilarut selain daripada sukrosa tulen, maka °Brix hanya menganggarkan kandungan pepejal yang dilarut. Pengukuran spekstroskopi di dalam kerja ini dijalankan menggunakan julat panjang gelombang antara 650-1100 nm untuk spektrometer Jaz dan QE65000. Manakala, untuk spektrometer NIRQuest, 900-2200 nm. Bagi spektrometer Jaz dan QE65000, panjang gelombang pada nilai 959 nm dikenalpasti sebagai penghasil pekali penentuan, R2, yang paling tinggi antara penyerapan dan kepekatan sukrosa dan bagi spektrometer NIRQuest, panjang gelombang dikenalpasti pada pada nilai 1363 nm. Kombinasi antara panjang gelombang infra-merah dekat (NIR) (ƛ =730, 830, 915, dan 960 nm) untuk spektrometer Jaz, (ƛ =909 dan 960 nm) untuk spektrometer QE65000 , dan (ƛ =980, 1156, 1163, 1195, 1337, 1350, 1395, 1606, 1670, 1676, dan 1682 nm) untuk spektrometer NIRQuest antara lingkaran C-H dan O-H membolehkan kita mengkuantitikan kepekatan sukrosa. Selepas kombinasi panjang gelombang dilakukan, pekali penentuan meningkat untuk spektrometer Jaz (R2= 0.992; RMSEC = 0.907 °Brix), untuk spektrometer QE65000 (R2= 0.995; RMSEC = 0.760 °Brix), dan untuk spektrometer NIRQuest (R2 = 0.982; RMSEC = 1.613 °Brix). xi
  12. 12. ABSTRACT The calibration transfer is a very important and popular topic in the science and practical application of spectroscopy. In this thesis, we investigate the calibration transfer between different types of spectrometer. The types of spectrometer used in this project are Jaz spectrometer, QE65000 spectrometer, and Near-Infrared (NIR) spectrometer. The measurement of sugars is one of the important procedure in the determination of the right time to harvest the crops in order to obtain a good and high quality of fruits. The sample used is sucrose, C12H22O11 with the variation of concentration divided almost equally to fifty data. The sucrose concentration is measured by using refractometer and the result obtained in unit of degree brix (°Brix). Degree brix is a sugar content of an aqueous solution. One degree Brix is 1 gram of sucrose in 100 grams of solution and represents the strength of the solution as percentage by weight (% w/w). If the solution contains dissolved solids other than pure sucrose, then the °Brix only approximates the dissolved solid content. Spectroscopic measurement in this work was conducted on the range of wavelength between 6501100 nm for Jaz and QE65000 spectrometer and 900-2200 nm for NIRQuest spectrometer. For Jaz and QE65000 spectrometer, wavelength at 959 nm is identified as producing the highest coefficient of determination, R2 , between absorbance and aqueous sucrose concentration (°Brix). Whereas, for NIRQuest spectrometer, wavelength at 1363 nm is identified as producing the highest coefficient of determination, R2 , between absorbance and aqueous sucrose concentration (°Brix). Combination of NIR wavelengths (λ=730, 830, 915, and 960 nm), (λ=909 and 960 nm), and (λ=980, 1156, 1163, 1195, 1337, 1350, 1395, 1606, 1670, 1676, and 1682) within C-H and O-H bands can reliably quantify sucrose by using Jaz spectrometer (R2= 0.992; RMSEC = 0.907 °Brix), QE65000 spectrometer (R2= 0.995; RMSEC = 0.760 °Brix), and NIRQuest (R2 = 0.982; RMSEC = 1.613 °Brix) respectively. xii
  13. 13. CHAPTER 1 INTRODUCTION 1.1 Spectroscopy Spectroscopy is a scientific discipline studying interactions of light with matter. Light can be of different wavelengths, which is represented by the electromagnetic spectrum. The IR region is roughly divided into three intervals: near, mid and far-IR. The near-IR (NIR) region covers the wavelength range 780 - 2500 nm. Figure 1.1 shows the electromagnetic spectrum. Absorption of light in the IR region causes molecules to vibrate and rotate. Absorption of light in the matter is usually not uniform and depends on molecular structure. At certain intervals the absorption is more intense, which is represented in the form of absorption bands. In NIR, the absorption bands are related to the combination vibrations and overtones of C-H, O-H and N-H chemical moieties in the material. Plots of absorbance vs. wavelength are called absorption spectra. Almost each absorption spectrum is unique and spectra of slightly different materials are only slightly different[1]. Figure 1.1 Electromagnetic Spectrum[1]. 1
  14. 14. Spectroscopic studies were central to the development of quantum mechanics and included Max Planck's explanation of blackbody radiation, Albert Einstein's explanation of the photoelectric effect and Niels Bohr's explanation of atomic structure and spectra. Spectroscopy is used in physical and analytical chemistry because atoms and molecules have unique spectra. As a result, these spectra can be used to detect, identify and quantify information about the atoms and molecules. Spectroscopy is also used in astronomy and remote sensing on earth. Most research telescopes have spectrographs. The measured spectra are used to determine the chemical composition and physical properties of astronomical objects such as their temperature and velocity. 1.1.1 NIR Spectroscopy NIR Spectroscopy is a spectroscopic method that uses the near-infrared region of the electromagnetic spectrum (from about 800 nm to 2500 nm). Typical applications pharmaceutical, medical diagnostics (including blood sugar and pulse oximetry), food and agrochemical quality control, and combustion research, as well as research in functional neuroimaging, sports medicine and science, elite sport training, etraining, ergonomics, rehabilitation, neonatal research, brain computer interface, neurology (neurovascular coupling)[2]. NIR spectra have only a few significant peaks, but they are exceptionally information-rich due to the number of overlapping absorption bands. Thus, interpretation of NIR spectra is usually combined with mathematical and statistical methods (i.e. chemometric methods) in order to extract the necessary information. Near-infrared spectroscopy is based on molecular overtone and combination vibrations. Such transitions are forbidden by the selection rules of quantum mechanics. As a result, the molar absorptivity in the near IR region is typically quite small. One advantage is that NIR can typically penetrate much farther into a sample than mid infrared radiation. Near-infrared spectroscopy is, therefore, not a particularly sensitive technique, but it can be very useful in probing bulk material with little or no sample preparation. 2
  15. 15. Figure 1.2 An Example of NIR Absorption Spectrum of Paper [1] The molecular overtone and combination bands seen in the near IR are typically very broad, leading to complex spectra; it can be difficult to assign specific features to specific chemical components. Multivariate (multiple variables) calibration techniques (e.g., principal components analysis, partial least squares, or artificial neural networks) are often employed to extract the desired chemical information. Careful development of a set of calibration samples and application of multivariate calibration techniques is essential for near-infrared analytical methods. Near Infrared (NIR) spectroscopy is used for fast, reliable, and non-destructive measurements that simultaneously control manufacturing processes and product quality, assuring that final product specifications and quality are met. The functionality of the product can be determined in both a quantitative and qualitative manner, and measurements can be carried out on solid or liquid samples. Combining a high sampling rate with a flexible optical delivery method, Polytec-designed spectrometers utilize fiber-optic probes to fill a wide range of applications in process control. NIR Spectroscopy – Simple and Flexible Optical spectroscopy covers wavelengths from 200 nm to 25 µm (see Figure 1) and is divided into three important spectral ranges: Ultraviolet/visible: 200 – 760 nm, Near Infrared : 760 – 2500 nm, Mid-Infrared : 2500 nm – 25 µm. Photons in the Ultraviolet/Visible spectral range have enough energy to excite or ionize materials by raising the energy level of bound electrons. 3
  16. 16. NIR radiation has less energy/photon but does excite molecular vibrations. Vibrational spectroscopy in the NIR range is used for process monitoring and quality control. The NIR wavelengths enable a very flexible measurement setup and a high measuring rate. Another advantage of using the NIR spectral range is the low coefficient of absorbance, allowing relatively deep penetration of the radiation in the sample. Therefore, samples can be measured without substantial preparation and information on both surface and volume parameters can be acquired. Finally, these measurements are non-destructive and samples are not altered and can be reused. The advantages of NIR spectroscopy make integration into automated production processes much easier, and the availability of fiber-coupled measuring heads allows for flexible measurement set-ups[3]. 1.1.2 Spectroscopic Properties of Aqueous Sucrose Solution In this thesis, the focus was on spectroscopic measurement of sucrose. Sucrose is a disaccharide sugar that can be made from the combinations of glucose and fructose and has a molecular number of C12H22O11. The determination of the amount of sugar in fruits has been conducted a long time ago. One of the noticeable research was done by Stephen R. Delwiche, Weena Mekwatanakarn and Chien Y. Wang. They investigate the potential of NIR spectroscopy to predict soluble solids content (SSC) and individual and combined concentrations of sucrose, glucose, and fructose nondestructively in mango. From their research, the result for sucrose was presented in the figure 1.2. Based on their research, they found that sucrose produced better PLS performance compared to glucose and fructose for probable reasons of its greater abundance and higher correlation to SSC. From the standpoint of NIR analysis, the relative abundance of sucrose is fortunate, considering that sucrose is the best indicator of sweetness in mango fruit. During fruit development, sucrose was found to accumulate the greatest amount of the carbon released from the breakdown of starch. Therefore, sucrose concentration, and perhaps that of one or more of the reducing sugars, could be used, along with SSC, as an indicator of ripeness. However, determination of these constituents requires destructive methodology and, in the case of the sugars, expensive and labor-intensive equipment. Near-infrared (NIR) spectroscopy, which is a nondestructive method for fruit quality evaluation, has become a very popular technique and has been used to evaluate the internal quality of many fruit[6]. 4
  17. 17. Figure 1.3 1.2 Scatter plots between actual and NIR-predicted values for sucrose in mangoes[6]. The Importance of Calibration Transfer Calibration transfer means that we transfer the calibration of one instrument to the another instrument. Meaning that, both of the instruments shared the same calibration. Not only on different instruments, calibration transfer may also applied to the experiment that using different sample of materials. In this kind of experiment, the instrument used need to be fixed. Calibration transfer in multivariate calibration is one of the most important and key issues in near-infrared spectral analysis technology. The model was transferred by means of finding the transformation relation between two instruments of the same type, so that the model established on one instrument could be used on the other to predict the spectral response[4]. calibration transfer is a series of approaches or techniques used to attempt to apply a single spectral database, and the calibration model developed using that database, to two or more instruments. Calibration transfer involves several steps. The basic spectra are initially measured on at least one instrument (parent, primary, or master instrument) and combined with the corresponding reference chemical information (actual values) for the development of calibration models. These models are maintained on the original instrument over time, are used to make the initial calibration, 5
  18. 18. and are transferred to other instruments (child, secondary, or transfer instruments). This process enables analysis using the child instruments with minimal intervention and recalibration. We note that the issue of calibration transfer disappears if the instruments are precisely alike. If instruments are the "same" then one sample placed on any of the instruments will predict or report precisely the "same" result. Because instruments are not alike, and in fact change over time, the use of calibration transfer techniques is often applied to produce the best attempt at calibration model or data transfer[5]. 1.3 The Importance of Measuring Sugar Content in Fruit The measurement of sugar in fruit is the important aspect to test the maturity of fruit and obtain the right time to harvest it. In the case of most stone fruits, when the fruit has colored well and is beginning to soften, it is ripe for picking. Some of the newerpeach and nectarine varieties have been developed with high red color and firmer texture, making it more difficult to tell when they are ready to pick. Taste is still a good indicator of ripeness. Sample one, and if its level of sweetness is good even though the texture is a bit crunchy, it is probably ready. Fruits that you want to transport or save for display should be picked firm but mature. Fruit can be placed in a box lined with newspaper or other padding, with the stem end down. Avoid packing peaches and nectarines more than two layers deep or the bottom layer of fruit may be damaged. In a few days the fruit will soften and be ready to eat. Sugar levels are a commonly used measurement in a wide range of crops. In the citrus industry this is a measure of the total soluble solids in the juice. These soluble solids are primarily sugars; sucrose, fructose, and glucose. As the flesh of fruit forms it deposits nutrients as starch that, as the fruit ripens, transform to sugars. The percentage sugar, measured in degrees Brix ( oBrix), indicates the sweetness of the fruit by measuring the number of soluble solids in the juice. Sugar measurement is one of the step taken in maturity testing of fruit. Other than that, the result from the sugar measurement will contribute to the calculation of sugar-acid ratio with the formula concentration of sugar in o Brix divided by citric acid concentration. Calculation of sugar-acid concentration is also one of the step needed in order to test the maturity of fruit. 6
  19. 19. The maturity testing of fruit will provide the information to the farmer the right time to harvest the crop. The ripening of fruit is a complex procedure. Release of ethylene gas triggers whole families of enzymes, including amylases, kinases, hydrolases and pectinases to work their magic and neutralize acids, form anthrocyanins that give colors to fruit, and soften hard, inedible fruits into toothsome, delicious ones. One critical element of the ripening involves the conversion of starches to sugars. So, in order to complete the ripening process, the farmer needs to know the sugar level in the fruit. But, certain fruit have different sugar level depends on their cluster. Usually the fruit from high of the cluster contained more sugar than the fruit from bottom of the cluster. The fruit from clusters exposed to the sun contain more sugar than the fruit from cluster growing in heavy shade. So, because of the large variations in sugar content, large size samples must be collected to produce accurate results[6]. When a sample fruit is cut horizontally through the core and sprayed with a mild iodine solution, the iodine turns the cells containing starch dark, but does not color those cells containing sugar. Figure 1.3 shows the starch test, which indicates visibly the stage of ripeness that a fruit has reached. It is one of the easiest and most useful indicators available for the home orchardist. When only the area of the core is clear of starch, and the rest is dark, the fruit is usually unripe and immature. Fruit that we want to store should be picked when one-half to three-quarters of the sample cross section area is clear of starch. Usually at that point it has developed enough sugar to taste good (mature), and still retains sufficient starch to continue developing in storage (pre-climacteric). If most of the cross section of the fruit is clear of starch, it is too ripe for long storage and should be consumed at once or stored short-term only. Figure 1.4 The example of starch test[7] 7
  20. 20. 1.5 Problem Statement The NIR spectrometers are widely used in the spectroscopy field. Calibration transfer in multivariate calibration is one of the most important and key issues in near-infrared spectral analysis technology. The model was transferred by means of finding the transformation relation between two instruments of the same type, so that the model established on one instrument could be used on the other to predict the spectral response. The calibration transfer between NIR spectrometers should be practiced in spectroscopy field to improve the efficiency in energy, time and work. But, in order to complete the calibration transfer process, the response analysis on each spectrometer needs to be observed first. So, the comparative study between NIR spectrometers need to be done in order to give the information about the properties of each spectrometer. 1.4 Objectives 1. To compare the response analysis between lower and same range of NIR wavelength (700 – 1100 nm) by using QE65000 spectrometer with the previous research conducted by Ahmad Fairuz Omar, Hanafi Atan, and Mohd Zubir Mat Jafri, [8] by using Jaz spectrometer. 2. To identify response analysis of higher range of wavelength ( 900 – 2500 nm) by using NIRQuest spectrometer. 3. To compare response analysis between lower and higher range of NIR wavelength spectrometer. 1.6 Outline of Thesis This thesis consists of five chapters. Chapter 1 presents the objective of study, problem satatement regarding the study, theoretical background about spectroscopy field, the definition and the importance of calibration transfer and the uses of measuring glucose in food industry. Chapter 2 comprises a review of relevant literature based on the past research. Chapter 3 clarify the experiment details on the the background of the apparatus and material used, sample preparation, apparatus set up and the procedure of the experiment conducted. Next, the results and data obtained from the experiment conducted are discussed in chapter 4 to get the equation of calibration transfer between NIR spectrometers in the sugar measurement. Finally, chapter 5 summarizes the main findings in this study and concludes the thesis. 8
  21. 21. CHAPTER 2 LITERATURE REVIEW 2.1 Response Analysis of NIR Spectrometer on Aqueous Sucrose Solution There are some research that has been done by using the Jaz spectrometer and sucrose. For example the research titled Peak Response Identification through Near-Infrared Spectroscopy Analysis on Aqueous Sucrose, Glucose, and Fructose Solution that has been done by Ahmad Fairuz Omar, Hanafi Atan, and Mohd Zubir Mat Jafri. They used 50 set of sugar concentration for calibration and validation. For their research, let us highlight the result on sucrose solution. Their result shows that four wavelengths that produced the highest efficiency algorithm for sucrose are 830, 909, 960, and 965 nm. The calibration algorithm obtained are shown below: SC = 122 + 1375λ730 - 942λ830 + 855λ915 - 736λ960 (1) (R2= 0.992; RMSEC = 0.907 °Brix); Where : SC is the sucrose concentration in °Brix The linear relationship between absorbance and aqueous sucrose concentration has also been obtained in their research as shown in the Figure 2.1. The relationship is determined at wavelength 959 nm. The absorbance decrease as the concentration of the sucrose increase. So, we can conclude that the absorbance is inversely proportional to the concentration of sucrose. In their research paper, they also mentioned that from their investigation towards glucose, fructose, and sucrose, they found that the absorbance loose its linearity once it has moved further than wavelength 960 nm. 9
  22. 22. Figure 2.1 Linear Relationship between Absorbance and Concentration of Aqueous Sucrose at wavelength 959 nm[8]. Before this research, Amad Fairuz Omar alone has published the other research article titled Quantifying water-sucrose solutions through NIR spectral absorbance linearisation and gradient shift. In this research article, we can get the information about the sucrose properties with variation of wavelength. Two analyses have been performed in this research. The first analysis is in the measurement of sucrose concentration through spectral absorbance linearisation. The value of R2 between absorbance and sucrose concentration was observed to be lower for higher concentration of sucrose, indicating that there is an improvement in spectral linearity. This observation was then quantified to attain the value of spectral linearisation that is presented by each spectrum R2 against the sucrose concentration. The second analysis is the quantification of sucrose concentration through the changes of NIR spectral gradient. It was observed that from 50 absorbance spectra, for samples with sucrose concentration between 0-35oBrix, lower NIR spectral gradient is produced for higher sucrose concentration. This observation was then quantified by generating R 2 between the spectral gradient and sucrose concentration. 10
  23. 23. In order to express his result from this research, he took the research by Giangiacomo as the reference. Giangiacomo stated that for the NIR evaluation of sucrose concentration using the range of wavelength between 1,100 nm and 2,400 nm, the increase in sugar concentration will alter the water band to become more symmetric and shifts the absorption peak toward longer wavelengths. But, surprisingly, the experimental result presented in his paper which using the range of wavelength between 900 nm and 1,100 nm does produce similar response as shown in Figure 2.2. Figure 2.2 Peak shift in NIR absorbance spectra from different water-sucrose concentration[9]. The summary of the linear relationship between absorbance and sucrose concentration are tabulated in Table 1. From the table, he obtained that for single wavelength analysis, 960 nm, which is one of the absorbance peaks for water has produced the best correlation and highest accuracy of prediction in quantifying aqueous sucrose concentration. The accuracy of measurement starts to decay as the wavelengths moves further from 960 nm. 11
  24. 24. Table 1 Summary of the linear relationship between absorbance and sucrose concentrations[9]. Wavelength (nm) 940 945 950 955 960 965 2.2 R2 RMSEC 0.883 0.920 0.962 0.976 0.978 0.974 3.402 2.823 1.941 1.546 1.490 1.591 Wavelength (nm) 970 975 980 985 990 995 R2 RMSEC 0.974 0.963 0.956 0.939 0.922 0.894 1.610 1.927 2.100 2.468 2.784 3.240 Application of NIR Spectroscopy in Various Field There is no research that has been done using aqueous sucrose solution together with QE65000 spectrometer or NIRQuest spectrometer. So, in order to do the comparison between other spectrometer, we can only analyse the properties of spectrometer itself. One of the research that used QE65000 spectrometer has been done by Joel M. Kralj, Adam D. Douglass, Daniel R. Hochbaum, Dougal Maclaurin, and Adam E. Cohen with the title Optical recording of action potentials in mammalian neurons using a microbial rhodopsin. Through their research, we would like to highlight the properties of the QE65000 spectrometer only. There is no relation with the materials they used. The result from the spectroscopy side is the Arch and Arch(D95N) protein both had emission maxima at 687 nm. We cannot do the prediction of our result yet because of the sample they used is protein whereas we are using sucrose[10]. Besides that, there is also a research article that has been done by Peter Stchur, Danielle Cleveland, Jack Zhou, and Robert G. Michel titled A Review of Recent Applications of NearInfrared Spectroscopy, and of The Characteristics of a Novel PbS CCD Array- Based Near-Infrared Spectrometer. In their article, they also mentioned about the application of NIR spectrometer in the agricultural field. A universal method for visualizing the sugar content in the flesh of melons has been developed by Tsuta who used a CCD detector with band-pass filters. Each filter created a spatial image of the melon sample for a specific spectral region. This method had been previously employed for measurement of sugar distribution in green-flesh melon and kiwifruit. It was demonstrated that the chlorophyll absorbance near 676 nm shows a strong inverse correlation with the sugar content. However, it cannot be applied to a red-flesh melon due to the lack of chlorophyll. 12
  25. 25. The authors extracted a 25-mm-diameter cylindrical sample from the ‘‘equator’’ of a melon, and a spectrum was obtained using a fiber optic probe. The wavelengths of 902 and 874 nm were used to correlate sugar content, while the wavelengths 846 and 930nm were used to calculate the second derivative absorbances. These two latter wavelengths were chosen because they gave the highest correlation with sugar concentration using the least number of band-pass filters[11]. Other than that, Kerry B. Walsh, John A. Guthrie, and Justin W. Burney in their article titled Application of commercially available, low-cost, miniaturised NIR spectrometers to the assessment of the sugar content of intact fruit also discussed about the uses of NIR spectrometers in measuring sugar. They developed the calibration using reflectance spectra of filter paper soaked in range of concentrations (0–20% w/v) of sucrose, using a modified partial least squares procedure. The results obtained are coefficient of correlation of 0.90 and 0.62, and standard error of cross-validation of 1.9 and 5.4%, respectively[12]. Next research about the NIR spectroscopy related with fruit was done by Bart M. Nicolai, Katrien Beullens, Els Bobelyn, Ann Peirs, Wouter Saeys, Karen I. Theron, and Jeroen Lammertyn. Their research title is Nondestructive measurement of fruit and vegetable quality by means of NIR spectroscopy: A review. The result obtained in their research as shown in Figure 2.3. In their research, different spectrophotometer designs and measurement principles are compared, and novel techniques, such as time and spatially resolved spectroscopy for the estimation of light absorption and scattering properties of vegetable tissue, as well as NIR multi- and hyperspectral imaging techniques are reviewed. On top of that, there is also a research that has been done by Manuela Zude titled Nondestructive prediction of banana fruit quality using VIS/NIR spectroscopy. Since we are studying the NIR spectroscopy, we will only highlight their result in NIR spectroscopy. Figure 2.4 shows the result obtained in his research. The correlation coefficients of skin sugar contents and pulp sugar contents were R2 = 0.89 for glucose, R2 = 0.41 for sucrose and R2 = 0.96 for fructose. He concluded that internal fruit sugar contents were predicted with high accuracy using the near-infrared region of the spectrum wavelength range. So, by using NIR spectroscopy, we can predict the maturity of the fruit. 13
  26. 26. Figure 2.3 Measured vs. predicted values of the soluble solids content (%Brix) of Jonagold apple based on NIR reflectance spectra[13]. Figure 2.4 Correlation of banana sugar contents measured chemically as well as non destructively by means of NIR spectroscopy[14]. 14
  27. 27. The last research is done by F.J. Rambla, S. Garrigues, and M. de la Guardia with the title PLS-NIR determination of total sugar, glucose, fructose and sucrose in aqueous solutions of fruit juices. The method is based on the partial least-squares (PLS) treatment of first derivative near infrared (NIR) spectroscopic data obtained between 1200 and 2450 nm, using 1 mm pathlength cell and a multicomponent calibration set, including seven binary mixtures and 10 ternary mixtures of glucose, fructose and sucrose. The highlighted result is the NIR spectrum of pure water has four intense absorbance bands at 970, 1190, 1450 and 1950 nm which reduce the wavelength range at which absorbance measurements can be made in water solutions[15]. 15
  28. 28. CHAPTER 3 MATERIALS AND METHOD 3.1 Apparatus and Materials Background 3.1.1 Jaz Spectrometer Jaz is a community of stackable, modular and autonomous instruments that combine to create the ultimate in smart sensing for lab, field and anywhere. Jaz is unfettered by the limits of traditional optical sensing instrumentation. Its unique features and expandable platform make it uniquely suited for field applications, remote sensing, process flow, quality assurance and more. Jaz spectrometer is designed to incorporate a number of autonomous modules that share common networking and electronics. Because of its modular design, high-performance spectrometer, Ethernet connectivity, battery operation and PC-free performance, Jaz is nimble in a virtually endless array of applications. It can be customized to include light sources, multiple channels and more. Figure 3.1 The Jaz Spectrometer. 16
  29. 29. A basic Jaz includes the spectrometer module and onboard DPU. All other modules are optional, so we can mix and match for the configuration that best handles our application. Jaz has a home in the lab, the field, the process line and anywhere we need reliable, accurate optical sensing. Operatingsoftware and development packages are available separately. Choose from multiple gratings for each Jaz spectrometer channel. The choice of grating groove density helps to determine optical resolution, spectral range and blaze wavelength. Jaz provides a particularly compelling option for bioreflectance applications in the field, where portability, flexibility and ease of use are critical. Jaz is a modular spectrometer-based system that integrates into a single stack those components that otherwise would have to be handled separately: the spectrometer, microprocessor with low-power display (in place of a PC), light source, battery pack and even Ethernet capability for remote measurements. Reflection probes and other sampling optics connect easily to the Jaz, keeping the overall system footprint compact and manageable[16]. Table 3.1 Properties of Jaz Spectrometer[16]. Spectrometer Physical: 109.2 mm x 63.5 mm x 57 mm LWH; 352 g (JAZ-COMBO only) Detector: Sony ILX511B linear silicon CCD array (200-1100 nm) Wavelength range: Grating dependent (extended-range grating available for 200-1025 nm coverage) Optical resolution: ~0.3-10.0 nm FWHM Signal-to-noise ratio: 250:1 (at full signal) A/D resolution: signal) A/D resolution: 16 bit Dark noise: 50 RMS counts Dynamic range: 8.5 x 107 (system); 1300:1 for a single acquisition Integration time: 870 μs to 65 seconds (20 s typical maximum) Stray light: <0.05% at 600 nm; <0.10% at 435 nm Sensitivity: 75 photons/count at 400 nm; 41 photons/count at 600 nm Fiber optic connector: SMA 905 to 0.22 numerical aperture optical fiber Electronics connector: 19-pin MHDMI connector; use ADP-MHDMI-RS232 adapter to interface to RS-232 17
  30. 30. Power options: Wall transformer (+5VDC); Power over Ethernet (Class III PoE provides 12 Watts); USB; integrated battery module (JAZ-B); Solar charger and external batteries Inputs/Outputs: Yes, 4 onboard digital user-programmable GPIOs OEM integration supported: Yes Channels supported: Up to 8 spectrometers Communications and Software Computer interface: Onboard Blackfin® microprocessor Operating systems: Windows XP, Vista (32/64 bit), Windows 7 (32/64 bit); OS X and Linux when using the USB interface on PCs Ethernet Module (optional) IEEE 802.3-compliant 10/100; includes 2 GB SD card Industrial Communications Module (optional): Interfaces (RS-232, RS-485); 4 analog I/O, 8 digital I/O Trigger modes: Normal (free-running), Software, Synchronization and External Hardware Strobe functions: Continuous, Single, Lamp Enable Operating software: Basic Jaz software (included) operates from DPU interface; SpectraSuite (separate purchase) acquires data from USB or Ethernet connection; Overture software also available Applications software: Irradiance measurement and other options available; application is loaded to an SD card and operates from DPU interface Scripting program and API option for writing your own applications Development software: Battery Options JAZ-B Module (optional integrated battery): Rechargeable battery accessories: SD card storage: Light Source Options JAZ-UV-VIS (optional module): JAZ-PX (optional module): Rechargeable Lithium-Ion; lifetime depends on number of modules (~8 hours for JAZ-COMBO only) Lithium-Polymer solar battery, ~12 hours lifetime w/JAZCOMBO; Lithium-Ion external battery, 21 hours lifetime w/JAZCOMBO JAZ-B module includes (2) 2-GB SD cards Deuterium-Tungsten Halogen (210-1100 nm); lifetime is ~1,500 hours (recommended for UV absorbance) Pulsed Xenon (190-1100 nm); lifetime is 4 x 108 flashes to 50% of initial intensity 18
  31. 31. JAZ-VIS-NIR (optional module): LEDs (optional module w/replaceable bulbs): Tungsten Halogen (360-1100 nm); lifetime is 500-10,000 hours depending on power setting 365 nm, 405 nm, 470 nm, 590 nm, 640 nm and White wavelength options Compliance CE mark: RoHs: Yes (all modules) Yes (all modules) 3.1.2 QE65000 Spectrometer The QE65000 Spectrometer is a unique combination of detector and optical bench technologies that provides high spectral response and high optical resolution in one spectrometer package. The Hamamatsu FFT-CCD matrix detector used in the QE65000 provides 90% quantum efficiency. It is a "2D" area detector that can bin a vertical row of pixels, which offers significant improvement in the signal-to-noise ratio and signal processing speed of the detector compared with a linear CCD, where signals are digitally added by an external circuit. In the QE65000, the 2D area detector can better take advantage of the height of the slit and the additional light, greatly improving system sensitivity. Because the detector in the QE65000 is back-thinned, it has great native response in the UV and does not require the additional coatings that typically apply to other detectors for UV applications. The QE65000 Spectrometer is a great option for low-light level applications such as fluorescence, Raman spectroscopy, DNA sequencing, astronomy and thin-film reflectivity. The thermoelectric-cooled (down to -15 °C) detector features low noise and low dark signal, which enables low-light-level detection and long integration times from 8 milliseconds to 15 minutes. Figure 1.3 shows the QE65000 spectrometer[17]. 19
  32. 32. Figure 3.2 QE65000 Spectrometer. Table 3.2 The Technical Properties of QE65000 spectrometer[17]. PHYSICAL Dimensions: 182 x 110 x 47 mm Weight: 1.18 kg (without power supply) DETECTOR Detector: Hamamatsu S7031-1006 back-thinned FFT-CCD Detector range: 200-1100 nm Pixels: 1024 x 58; 24.6 mm square size Pixel well depth: 300,000 electrons/well -1.5 mill. electrons/column Sensitivity: 400 nm: 22 electrons/count, 250 nm: 26 photons/count OPTICAL BENCH Design: f/4, Symmetrical crossed Czerny-Turner Focal length: 101.6 mm input, 101.6 mm output Entrance aperture: 100 um wide slit SMA 905 to 0.22 numerical aperture single-strand fiber SPECTROSCOPIC Wavelength range: 200-950 nm Optical resolution: 0.75 nm FWHM Signal-to-noise ratio: 1000:1 (at full signal) Dark noise: 2.5 RMS counts Dynamic range: 25000:1 a single acquisition Integration time: 8 milliseconds to 15 minutes ELECTRONICS Power consumption: 500 mA @ 5 VDC no TE cool; 3A@5 VDC with TE cool Data transfer speed: Full spectrum to memory every 4 ms with USB 2.0 port, 8 ms with USB 1.1 port Inputs/Outputs: 10 onboard digital user-programmable GPIOs TEMPERATURE & THERMOELECTRIC COOLING Temperature limits: 0 °C to 50 °C for spectrometer, no condensation Lowest set point: 40 °C below ambient, to -15 °C Stability: ±0.1 °C of set temperature in <2 minutes 20
  33. 33. 3.1.3 NIRQuest Spectrometer NIRQuest spectrometers are compact units capable of analysing the spectrum from 900 to 2500 nm. It delivers a high performance optical bench with lownoise electronics and more customization for a wider variety of applications including medical diagnostics, pharmaceutical analysis, environmental monitoring and process control. If we use NIR spectroscopy for research, process or diagnostics, NIRQuest is a less costly, less complex alternative to FT-IR and comparable technologies. Figure 3.3 shows the real picture of NIRQuest spectrometer. Figure 3.3 NIRQuest512-2.2 Spectrometer. One of the application of the NIRQuest spectrometer is can classify and separate 100% of the incoming feed. This unique system allows the classification and separation of 100% of the incoming feed. Its fast operation saves time and , helps eliminate worker intervention – making the process fully automatic. The system analyses the whole sample from an optical sensor to obtain a high representative result. By working in this manner, the truck classifiication and destiny decisions reach optimum safety levels. 21
  34. 34. Besides that, this NIRQuest spectrometer can also be used to determine moisture, protein and fat content. Besides the analysis to classify incoming materials, moisture, protein and fat content of soybeans, wheat and corn can be determined. Argentina has a total grain reception average of 450 trucks per day. So, being able to classify any incoming truck in real time gives a company valuable information. The system provides critical data such as automatic quality averages per truck and automatic ID by sampling quickly and accurately. The NIRQuest-512 Spectrometer's diffractive grating-based optical bench and 16-bit USB A/D converter are conveniently mounted in the same housing. This integrated design makes the NIRQuest512 a 182 mm x 110 mm x 47 mm small-footprint system and eliminates the need for additional spectrometer-to-A/D converter cabling. A +5 VDC wall transformer (included) is required to operate the system's high-performance InGaAs array detector. The NIRQuest-512 standard grating (NIR3) provides a wavelength range of 850-1700 nm. Five other gratings are available. The usable range is 900-1700 nm[18]. Table 4.3 The characteristics and properties of NIR spectrometers[16,17,18]. Characteristics Application Jaz -Modular, stackable and autonomous components -Czemy-Turner optical bench -On-board microprocessor and OLED display -Replaceable slits and gratings -Ethemet and memory module -Battery and external memory module QE65000 -200-1100 nm spectral range-grating dependent -Resolution 0.14-7.7 nm (FWHM) -Peak quantum efficiency 90% -Back thinned 2DCCD detector -Thermoelectric cooling -6 slit options -14 grating option -Fluorescence -Biotechnology -Raman spectroscopy -DNA sequencing -Remote sensing -Dosimetry -Spectroscopy -Medical -Biomedical imaging analysis -Fluorescence -Luminescence detection 22 NIRQuest -900-2050 nm spectral range -Less than 1 nm optical resolution FWHM -15000:1 signal to noise -On board thermoelectric cooling -16 bit USB A/D converter -Crossed czemyTurner optical bench -Various trigger modesgrating options -Luminescence detection -Spectroscopy of emission and absorption lines spectroscopy
  35. 35. 3.1.4 Refractometers In this experiment, refractometer is used to determine the calibration value for sucrose concentration. Refractometers work according to the principle that when a ray of light passes from one medium to another, the speed of the light changes according to the density of the transmitting medium. At the interface between two media, the ray changes direction as its speed suddenly changes. This effect is known as refraction and is a familiar concept. The refractive index (RI) of a substance is a measure of the speed of light in a substance relative to that in a vacuum (very close to the speed in air). The RI is a physical property that depends upon temperature and the wavelength of the light. For a particular substance the RI is a unique number when measured using a monochromatic light source (single wavelength) at a fixed temperature. Handheld refractometers and bench refractometers are devices that measure the RI of a substance, usually a liquid, but sometimes a solid. Laboratory bench refractometers utilize monochromatic light, usually that of sodium at 589.3 nm. They also have a means for controlling temperature or at least measuring it precisely in order to 'compensate' for any variance. A bench refractometer can typically measure the refractive index to within 0.0001 or better. Thus, the refractive index of water when measured with sodium light (589.3 nm) at 20 °C is 1.33299. Scientists may wish to measure the RI when studying the physical properties of different liquids and solids. However, bench and handheld refractometers are usually used for more pragmatic purposes, usually to measure the concentration of a dissolved substance. The simplest and most popular use of a bench or handheld refractometer is in measuring the concentration of sugar in water. As the concentration of sugar increases the RI increases. A bench or handheld refractometer can therefore be used to measure concentration of sugar provided the relationship between RI and concentration (and temperature) is known. The Brix scale is the most widely used scale and is based on the relationship between pure sucrose in water concentration (weight %) and RI. The Brix scale is more popular than RI itself. Brix is used for testing 'liquid food' products. Even when the food does not just contain sucrose in water, but other dissolved ingredients, the Brix scale is used as a measure of 'nutritional value'. Thus soft drinks, juices, sauces, preserves etc. are assigned 'a Brix value' as part of the Quality Assurance for the product. Indeed, in the juice and soft drink industries, the Brix value is arguably the most important parameter in quality control. 23
  36. 36. For this experiment, the refractometer used is handheld refractometer. The use of a handheld refractometer facilitates convenient and rapid measurement of concentration in a number of liquid and semi-solid samples. Handheld refractometers are low-cost, simple devices that are popular in a multitude of applications. Handheld refractometers are popular because they are easy and convenient to use and cost a fraction of a typical bench instrument. Unlike bench refractometers handheld refractometers are limited in terms of accuracy and applicability because they utilize natural (white) light, there is no way to control temperature and light must be transmitted by the sample. Using white light means that the handheld refractometer's borderline cannot be as sharp as that obtained in a laboratory instrument. White light is made up of wavelengths from about 350 to 800 nm (the visible spectrum). Light of each wavelength travels at a different speed. This manifests itself as blurring and coloring of the borderline. This is called dispersion (splitting into different wavelengths). Some substances have high dispersive power and therefore will distort a 'white light' borderline to a greater extent. This effect gets worse as the RI increases, such that some liquids may not be measurable to an acceptable accuracy using a handheld refractometer. The accuracy and precision is also limited by the size and optical arrangement. Typically, a handheld refractometer can read on an RI scale to about 0.001 units, compared to a resolution of up to 0.00001 on a bench Abbe or automatic refractometers. These values equate to about °0.2 Brix for a handheld refractometer versus a resolution to 0.01 Brix on a bench refractometer. Accuracy on the bench refractometer will vary according to sample type, but can be about °0.02 for a sugar solution or soft drink, i.e. about 100 times more accurate than a handheld refractometer. Most handheld refractometers are calibrated initially at 20 °C in the factory. When the ambient temperature changes, the handheld refractometer temperature also changes and the calibration is no longer valid. Certain handheld refractometers such as the Eclipse have a 'zero' adjuster. This means the handheld refractometer can be re-set using a reliable standard such as water. This is in fact a better way to combat changes in temperature with a handheld refractometer than relying upon ATC. Sudden changes in temperature or applying very hot or very cold samples (e.g. out of the refrigerator) can incur quite large errors with handheld refractometers. 24
  37. 37. The way to deal with this is to have good experimental technique/protocol to ensure thermal equilibration. Because handheld refractometers work with transmitted light, highly colored or opaque samples may not give very sharp borderlines. The position of the borderline then becomes a subjective judgment of the user, which reduces the reproducibility of the handheld refractometer. Automatic bench refractometers surmount this problem by measuring the reflected light from a sample surface, rather than transmitted light. Thus, for example in the tomato paste industry, automatic bench refractometers are considerably easier to use and much more reliable than handheld refractometers where borderlines are often not discernible. In spite of these limitations handheld refractometers have proven to be quite useful in practice and many users are happy with the lower accuracy of handheld refractometers, accepting that they provide a quick and convenient way to check their product, rather than a highly precise QA method. Handheld refractometer food applications include the measurement of sugar in grape must and other fresh fruit, processed food, sugar solutions and beverages. Starch, honey and salt solutions may also be measured with handheld refractometers. Handheld refractometers are also commonly used to control blend ratios of various industrial liquids. Chemical blends such as glycols, battery acid, heat exchange fluids, automotive antifreeze, aviation wing de-icing chemicals and jet fuel ice inhibitors can be controlled easily with a handheld refractometer whilst measurement of industrial fluids such as coolants, quenchants and hydraulic oils are also commonplace[19]. Figure 3.4 A modern digital handheld refractometer being cleaned under a faucet[19]. 25
  38. 38. 3.1.5 Sample - Sucrose Sucrose is the organic compound commonly known as table sugar and sometimes called saccharose. A white, odorless, crystalline powder with a sweet taste, it is best known for its nutritional role. The molecule is a disaccharide composed of the monosaccharides glucose and fructose with the molecular formula C12H22O11. Figure 3.5 shows the formula for sucrose. Figure 3.5 Skeletal Formula for Sucrose[20]. Sucrose is made from glucose and fructose units. Sucrose or table sugar is obtained from sugar cane or sugar beets. The glucose and fructose units are joined by an acetal oxygen bridge in the alpha orientation. The structure is easy to recognize because it contains the six member ring of glucose and the five member ring of fructose. The alpha acetal is is really part of a double acetal, since the two monosaccharides are joined at the hemiacetal of glucose and the hemiketal of the fructose. There are no hemiacetals remaining in the sucrose and therefore sucrose is a non-reducing sugar. Figure 3.6 shows the formation of sucrose from glucose and fructose. Scientists and the sugar industry use degrees Brix (symbol °Bx), introduced by Adolf Brix, as units of measurement of the mass ratio of dissolved substance to water in a liquid. A 25 °Bx sucrose solution has 25 grams of sucrose per 100 grams of liquid; or, to put it another way, 25 grams of sucrose sugar and 75 grams of water exist in the 100 grams of solution. 26
  39. 39. Figure 3.6 The Formation of Sucrose from Glucose and Fructose[21]. The Brix degrees are measured using an infrared sensor. This measurement does not equate to Brix degrees from a density or refractive index measurement, because it will specifically measure dissolved sugar concentration instead of all dissolved solids. When using a refractometer, one should report the result as "refractometric dried substance" (RDS). One might speak of a liquid as having 20 °Bx RDS. This refers to a measure of percent by weight of total dried solids and, although not technically the same as Brix degrees determined through an infrared method, renders an accurate measurement of sucrose content, since sucrose in fact forms the majority of dried solids. The advent of in-line infrared Brix measurement sensors has made measuring the amount of dissolved sugar in products economical using a direct measurement[20]. 27
  40. 40. 3.2 Experiment Setup The overall experimental setup was conducted using spectroscopic instrumentations from Ocean Optics. The setup is illustrated in Figure 3.7. The chemical (sucrose) was diluted using pure water (reverse osmosis) and was calibrated using PAI-I refractometer from Atago, Co.(Tokyo, Japan) with range of measurement from 0 to 93 "Brix, resolution of 0.1 °Brix, and accuracy of +0.2 "Brix. "Brix is used (and will be used through the entire research) as the measurement unit to standardize the unit of measurement for the entire research since it is scientifically used in representing sugar concentration and SSC in fruits and is a conventional analytical technique for quality monitoring in the sugar industry. In the experiment, the response was due to mixture between water with sucrose for different type of spectrometer. This is done in order to retrieve direct relationship between optical parameters with the chemical composition. The characteristics of sucrose sample used in this work are listed in Table 3.3. 28
  41. 41. Figure 3.7 Experiment Setup for NIR Measurement (Side view). 29
  42. 42. Table 3.5 Spectrometer Sample Characteristics. Range of Sucrose Mean (°Brix) N (Calibration) Jaz 0.9-35.0 17.1 50 QE65000 0.1-39.1 8.8 50 NIRQuest512-2.2 0.2-38.8 18.8 50 The value of absorbance was measured using 2 Channel Jaz Spectrometer (Channel 0: 200€50 nm; Channel 1: 550-1100nm), which uses Sony IIX5118 linear silicon CCD array detector with sensitMty of up to 75 photons/count at 400nm and 47 photons/ count at 500nm. Other custom setup prior to the experiment includes integration time: J ms, spectra averaged: 5, and boxcar smoothing: 1. Light source used was tungsten halogen lamp with spectral emission between3S0 and 2000nm and color temperature of 2960K. In the original representation of spectrum graph, the y-axis of the graph is represented in the unit of counts for its intensity. Counts are the raw output data produced by the analogue to digital converter of the spectrometer. SpectraSuite software allows this measurement to be converted into absorbance in the unit of OD (optical density). 30
  43. 43. 3.3 Methodology First, the apparatus is set up and calibrated exactly as shown in Figure 3.7. The spectrometer used is QE65000 spectrometer. Then, sucrose was diluted using pure water (reverse osmosis) until the 39.1(°Brix) concentration aqueous sucrose is reached. It is very important to use pure water in order to get the most accurate result. The reference spectrum was collected through an empty quartz cuvette. Then, the concentration of aqueous sucrose in the quartz cuvette is varied until 50 data of calibration are collected. But, we need to be very careful when pour the aqueous sucrose into the quartz cuvette because outside of it cannot keep in touch with anything especially water and sucrose. This is because of we are using the light that passes through it. In order to allow the light to pass the quartz cuvette without obstacle, we need to make sure that quartz cuvette always clean without dirty. Then, the result shown on the pc screen is saved for the purpose of analysis. All of the above procedure are repeated for NIRQuest spectrometer. SpectraSuite uses an equation to determine the concentration of a species in solution. The software uses this equation to evaluate each pixel on the detector and produce the absorbance spectrum is shown in equation (2). Aλ = - log10 {(Sλ – Dλ) / (Rλ – Dλ)} (2) where Aλ:Absorbance at wavelength λ, Sλ: Sample intensity at wavelength λ, Dλ: Dark intensity at wavelength λ, Rλ: Reference intensity at wavelength λ. The concentration of a species in a solution directly affects the absorbance of the solution. This relationship, known as Beer's Law, is expressed in equation (3): Aλ = Ɛ λcl (3) 31
  44. 44. where Aλ:Absorbance at wavelength, Eλ:Extinction coefficient of the absorbing species at wavelength lamda, c: Concentration of the absorbing species, l : the optical path length of the absorption. The performance of the calibration models and the accuracy of prediction results were evaluated using coefficient of determination, R2, and root mean square of error, RMSE (in (°Brix), which were calculated using Minitab (version 14) software through equation (4): R2 = { [nΣ xy – ( Σ x) ( Σ y )] 2 } / { n Σ x2 – ( Σ x ) Г n Σ y2 - ( Σ y ) 2 ] (4) Where n is the number of data points, and RMSE = (5) Where Y is the predicted y values. 32
  45. 45. 3.3.4 Flow Chart All of the procedure taken in 3.3 are summarised in the Figure 3.8. Apparatus setup Sample preparation Data analysis Repeat experiment with other spectrometer Collect reference spectrum Reduce concentration of sucrose until 50 data Save data obtained Figure 3.8 Flow chart of experiment. 33
  46. 46. CHAPTER 4 RESULTS AND DISCUSSIONS 4.1 Response Analysis between Jaz and QE65000 spectrometer The result of Jaz spectrometer on aqueous sucrose is taken from the research article by Ahmad Fairuz Omar, Hanafi Atan, and Mohd Zubir Mat Jafri as a reference[8]. Their result of the research is already stated in the literature review. Experiment was conducted for calibration process only. Significant results were managed to be located at wavelength between approximately 940 and 985 nm. Therefore, detail analysis was performed within this range of wavelength for Jaz and QE65000 spectrometer. Figure 4.1 shows the resultant linear regression generated between absorbance and sucrose concentration by using Jaz and QE65000 spectrometer and their data are tabulated in Appendix A. Wavelengths of 959 nm and 950 nm managed to generate the highest coefficient of determination and lowest RMSE for sucrose by using Jaz spectrometer (R2 = 0.9794; RMSE = 1.43) and QE65000 spectrometer (R2 = 0.956 ; RMSE = 2.15) respectively. From Figure 4.1, we can summarize that absorption of a specific range of NIR wavelength decreases linearly with the increases of sucrose concentration. Higher °Brix means higher concentration of percentage of sugar content per amount of water. Therefore, we expected that the lower absorbance of NIR in this case would caused by the less percentage of water in the sample compared to the increment of sugar concentration. The pattern behaviour of linear relationship between absorbance and the sucrose concentration against wavelengths for Jaz and QE65000 spectrometer is shown in Figure 4.2. Their data are tabulated in the Appendix B. It can be seen that the correlation between absorbance and sucrose concentration starts to loose its linearity once it has moved further than 960 nm. This pattern of response is very useful for future development of a specialized optical instrument for the measurement of SSC in fruits where an application of a single-pixel detector with specific wavelength responsivity is required. The most common photo detectors available in the market are those with peak sensitivity approximately between 850 and 960 nm. Therefore, from this result, the development of a high-sensitivity single-pixel measurement system for quantifying water-sucrose concentration is possible. 34
  47. 47. (a) 0.12 y = -0.001394x + 0.1193; R-Sq = 0.9794 Absorbance 0.11 0.10 0.09 0.08 0.07 0 10 20 30 40 Sucrose Concentration (Brix) (b) 0.165 y = - 0.000714x + 0.1645 0.160 Absorbance 0.155 0.150 0.145 0.140 0.135 0.130 0 10 20 30 40 Sugars concentration (Brix) Figure 4.1 Linear relationship between absorbance and concentration of aqueous sucrose at λ = 959 nm by using (a) Jaz spectrometer[8], and (b) QE65000 spectrometer. 35
  48. 48. (a) (b) Figure 4.2 Coefficient of determination generated at different wavelength for aqueous sucrose concentration by using (a) Jaz spectrometer[8], and (b) QE65000 spectrometer. 36
  49. 49. Further analysis through the application of Multiple Linear Regression (MLR) was conducted to the aqueous sucrose absorbance data using the combination of O-H (960 nm) and C-H (909-915 nm) absorbance bands wavelengths. This combination has successfully improved the correlation for sucrose measurement. The results of these analyses are tabulated in Table 4.1. Table 4.1 Results from MLR using Wavelengths from O-H and C-H absorbance bands for Jaz and QE65000 spectrometer. Wavelengths (nm) Spectrometer Jaz spectrometer QE65000 R2 RMSE R2 RMSE 909, 960 0.988 1.085 0.995 0.760 910, 960 0.987 1.152 0.994 0.797 910, 965 0.985 1.247 0.992 0.896 912, 960 0.988 1.081 0.993 0.907 915, 960 0.989 1.067 0.993 0.861 730, 915, 960 0.990 1.008 0.993 0.863 830, 915, 960 0.990 1.008 0.993 0.916 730, 909, 960 0.752 0.995 830, 909, 960 0.769 0.995 730, 912, 960 0.916 0.992 830, 912, 960 0.916 0.992 0.865 3.80 830, 909, 960, 965 0.776 0.995 730, 912, 960, 965 0.909 0.993 730, 830, 915, 960 0.992 0.907 37
  50. 50. The highest efficiency algorithm has been identified by using different wavelengths: 730, 830, 915, and 960 nm for Jaz spectrometer, and 909 and 960 nm for QE65000 spectrometer. The calibration algorithm, R2, and RMSE for calibration (RMSEC) are shown in equation (1) and (2). Equation (1) was taken from literature review[8]. For Jaz spectrometer: SC = 122 + 1375λ730 - 942λ830 + 855λ915 - 736λ960 (1) (R2= 0.992; RMSEC = 0.907 °Brix); For QE65000 spectrometer: Sucrose concentration (°Brix) = 135 + 777λ909 - 824λ960 (6) (R2= 0.995; RMSEC = 0.760 °Brix) Where SC is the sucrose concentration in °Brix. The linearity of the calculated models is illustrated by Figure 4.3 and their data are tabulated in Appendix C(1) and C(2) for Jaz and QE65000 spectrometer respectively. 38
  51. 51. (a) Calculated Concentration (Brix) 35 30 25 20 15 10 5 0 0 5 10 15 20 25 30 35 Actual Concentration (Brix) Calculated Concentration (Brix) (b) 40 35 30 25 20 15 10 5 0 0 5 10 15 20 25 30 35 40 Actual Concentration (Brix) Figure 4.3 Calculated VS actual concentrations of sucrose by using (a) Jaz spectrometer[8], and (b) QE65000 spectrometer. 39
  52. 52. 4.2 Response Analysis of NIRQuest Spectrometer Significant result is managed to be located at wavelength between approximately 980 and 1700 nm. Therefore, detail analysis was performed within this range of wavelength for NIRQuest spectrometer. The relationship between absorbance and sucrose concentration at wavelength 1363 nm is tabulated in Appendix A.Wavelength of 1363 nm managed to generate the highest coefficient of determination and lowest RMSE for sucrose by using NIRQuest spectrometer (R2 = 0.813; RMSE = 4.64). From the data, we can summarize that absorption of a specific range of NIR wavelength decreases linearly with the increases of sucrose concentration. Higher °Brix means higher concentration of percentage of sugar content per amount of water. Therefore, we expected that the lower absorbance of NIR in this case would caused by the less percentage of water in the sample compared to the increment of sugar concentration. The pattern behaviour of linear relationship between absorbance and the sucrose concentration against wavelengths for NIRQuest spectrometer is shown in Figure 4.4 and its data is tabulated in the Appendix B. It can be seen that the correlation between absorbance and sucrose concentration starts to loose its linearity once it has moved further than 1400 nm. This pattern of response is very useful for future development of a specialized optical instrument for the measurement of SSC in fruits where an application of a single-pixel detector with specific wavelength responsivity is required. 40
  53. 53. Figure 4.4 Coefficient of determination generated at different wavelengths for aqueous sucrose concentration by using NIRQuest spectrometer. 41
  54. 54. Further analysis through the application of Multiple Linear Regression (MLR) was conducted to the aqueous sucrose absorbance data using the combination of absorbance bands wavelengths. This combination has successfully improved the correlation for sucrose measurement. The results of these analyses are tabulated in Table 4.2. Table 4.2 Results from MLR using Wavelengths from O-H and C-H absorbance bands for NIRQuest spectrometer Wavelengths NIR Quest R2 RMSE 980, 1682 0.829 4.482 1156, 1682 0.780 5.090 1163, 1670 0.799 4.865 1195, 1606 0.845 4.266 1337, 1350, 1395 0.847 4.294 980, 1350, 1682 0.841 4.370 1156, 1395, 1676 0.821 4.643 1162, 1337, 1670 0.804 4.852 980, 1195, 1395, 1676 0.827 4.609 1156, 1350, 1606, 1682 0.830 4.575 1162, 1337, 1395, 1669 0.834 4.516 980, 1156, 1337, 1676, 1682 0.889 3.732 1163, 1195, 1350, 1606, 1670 0.871 4.018 1156, 1163, 1337, 1395, 1606 0.933 2.898 980, 1195, 1350, 1670, 1676 0.857 4.241 980, 1163, 1337, 1395, 1682 0.863 4.156 980, 1163, 1337, 1395, 1670, 1682 0.882 3.889 1156, 1195, 1337, 1350, 1606, 1676 0.976 1.760 980, 1156, 1163, 1195, 1337, 1350, 1395, 1606, 1670, 1676 0.981 1.637 980, 1156, 1163, 1195, 1337, 1350, 1395, 1606, 1670, 1676, 1682 0.982 1.613 (nm) 42
  55. 55. The highest efficiency algorithm has been identified by using different wavelengths: 980, 1156, 1163, 1195, 1337, 1350, 1395, 1606, 1670, 1676, and 1682 nm. The following are the calibration algorithm, R2, and RMSE for calibration (RMSEC): SC = 171 + 79λ980 + 2909λ1156 - 1550λ1163 - 422λ1195 (7) + 445λ1337 - 1783λ1350 + 310λ1395 – 41.8λ1606 + 298λ1670 - 298λ1676 + 195λ1682 (R2 = 0.982; RMSEC = 1.613 °Brix) Where SC is the sucrose concentration in °Brix. The linearity of the calculated model is illustrated by Figure 4.6 and its data is tabulated in the Appendix C(3). Calculated Concentration (Brix) 40 30 20 10 0 0 10 20 30 40 Actual Concentration (Brix) Figure 4.5 Calculated VS actual concentrations of sucrose by using NIRQuestspectrometer. 43
  56. 56. 4.3 Response Analysis between NIR Spectrometers Now, we are comparing all of the NIR spectrometers that we used in the experiment. They are divided into small and large range of NIR that is 650-1100 nm and 900-2200 nm respectively. By comparing the results presented in this paper and those conducted by Ahmad Fairuz Omar, Hanafi Atan and Mohd Zubir Mat Jafri, it is observed that the results obtained by their Jaz spectrometer for sucrose is quite alike with QE65000 spectrometer. All of the behaviour are the same except the numerical value because they are actually from the same range of wavelength. This will develop a calibration transfer between Jaz and QE65000 spectrometer. But, to do calibration transfer, a validation data need to be considered in order to get the most accurate result. Then, by comparing higher NIR wavelength and lower NIR wavelength spectrometer, it is observed that the results obtained by conducting measurement at higher wavelength (900-2200 nm) has also managed to produce a very useful measurement of individual sucrose concentration. It is obviously seen that the range of absorbance in Figure 4.5 managed to produce higher responsivity. This is because the stronger absorbance for water and other organic composition is located at the range NIR wavelength. The combination of wavelengths using C-H and O-H bands through the application of MLR has significantly improved the calibration model through the increase in efficiency calibration algorithm. The presence of water, which also absorbs strongly in the near infra-red has limited the use of NIRs for assessment of quality of fresh produce. The NIRS technology can be applied to the sorting of fruit at commercial packline speeds. Indeed, it is in commercial use in Japan, primarily for melons and citrus fruit. The Japanese firms are currently marketing NIR based sorting and grading systems for use with citrus, pome fruits and stone fruits in Japan. It is perhaps not surprising that the technology has been applied quickly in Japan, where fruit are with huge price (single melons selling for routinely at $30). However, with modification and reduction of price, the technology is applicable to markets with less reward for premium quality. 44
  57. 57. There are some of the applications of NIR spectroscopy that have been investigated. In the quality attribution, NIR spectroscopy can be applied to fruits such as Macadamia kernel, citrus, pineapples, mangoes, strawberries, melons, and stone fruit. In the form of moisture determination, it can be applied to coal. To sweet corn, NIR spectroscopy will be applied for insect damage and insect detection. In the future, we should expect to see the application of NIR technology to assessing a range of food products for various aspects of quality and safety. 45
  58. 58. CHAPTER 5 CONCLUSIONS AND RECOMMENDATIONS This study managed to quantify the concentration of sucrose solution through the application of NIR spectroscopy between lower range of NIR wavelength and higher range of NIR wavelength. For a single-wavelength application, the peak absorbance was identified occur at wavelength 959 nm for both Jaz and QE65000 spectrometer, and 1363 nm for NIRQuest spectrometer. This result is mainly due to absorbance of water content in the solutions. From the application of MLR, by including the absorbance band for carbohydrate, we found that the value of R2 significantly improved with lower RMSE. The best results were generated by selections of wavelengths with combination of NIR wavelengths (λ=730, 830, 915, and 960 nm), (λ=909 and 960 nm), and (λ=980, 1156, 1163, 1195, 1337, 1350, 1395, 1606, 1670, 1676, and 1682) within C-H and O-H bands can reliably quantify sucrose by using Jaz spectrometer (R2= 0.992; RMSEC = 0.907 °Brix), QE65000 spectrometer (R2= 0.995; RMSEC = 0.760 °Brix), and NIRQuest (R2 = 0.982; RMSEC = 1.613 °Brix) respectively. These wavelengths exert an important combination in development of calibration algorithm for individual spectrometer measurement. By comparing the three spectrometers, all of them show the same properties. There are the absorption of a specific range of NIR wavelength decreases linearly with the increases of sucrose concentration and further analysis through MLR has successfully improved the correlation for sucrose measurement. Table 5.1 summarizes the comparative study between NIR spectrometers. For future experiment, the validation data need to be recorded since it will confirm the prediction analysis. Besides that, for more challenging experiment, the application of NIR spectroscopy to determine fruit quality need to be practiced. So, we will be able to improve the fruit quality. 46
  59. 59. Type of NIR Single Wavelength Combination of Observation from Spectrometer that Generates the Wavelengths that Graphs and Data Highest Coefficient of Generate the Highest Determination Coefficient of Determination λ=730, 830, 915, and 959 nm *The absorption of a 960 nm Jaz Spectrometer specific range of NIR 2 R = 0.9794 wavelength decreases R2= 0.992 RMSE = 1.43 RMSE = 0.907 °Brix linearly with the increases of sucrose concentration. QE65000 Spectrometer λ=909 and 960 nm 959 nm * Further analysis R2 = 0.956 RMSE = 2.15 NIRQuest R2= 0.995 RMSE = 0.760 1363 nm λ=980, 1156, 1163, the correlation for 1195, 1337, 1350, sucrose measurement. Spectrometer R2 = 0.813 1395, 1606, 1670, RMSE = 4.64 1676, and 1682 nm R2 = 0.982 RMSE = 1.613 47 through MLR has successfully improved
  60. 60. REFERENCES 1. SurveNIR: Near-Infrared (NIR) Spectroscopy. http://www.science4heritage.org (accessed 02.05.2013). 2. Wikipedia the Free Encyclopedia: Near-Infrared (NIR) Spectroscopy. http://en.wikipedia.org (accessed 02.05.2013. 3. Process Control using NIR Spectroscopy. Polytec GmbH Analytics Application Note Spectroscopy November 2007, 1-4. 4. Li QB; Zhang GJ; Xu KX; Wang Y., Application of DS algorithm to the calibration transfer in near-infrared spectroscopy. US National Library of Medicine National Institutes of Health 2007. 5. Jerome Workman; Jr.; Howard Mark, Calibration Transfer, Part II: The Instrumentation Aspects. Spectroscopy 2013, 28(S5), 12-25. 6. Lum Eisenman, Harvest Decision. Harvest 1999. 7. Gary Moulton; Jacky King, Tree Fruit Harvest and Storage Tips. Washington State University Extension. 8. Ahmad Fairuz Omar; Hanafi Atan; Mohd Zubir Mat Jafri, Peak Response Identification through Near-Infrared Spectroscopy Analysis on Aqueous Sucrose, Glucose, and Fructose Solution. An International Journal for Rapid Communication 2012, 45:3, 190-201. 9. Ahmad Fairuz Omar, Quantifying water-sucrose solutions through NIR spectral absorbance linearisation and gradient shift. Journal of Optics 2012. 10. Kalj JM; Douglass AD; Hochbaum DR; Maclaurin D; Cohen AE, Optical recording of action potentials in mammalian neurons using a microbial rhodopsin. US National Library of Medicine National Institutes of Health, 2011, 9(1):90-5. 11. Peter Stchur; Danielle Cleveland; Jack Zhou; Robert G. Michel, Review OF Recent Applications of Near-Infrared Spectroscopy, and of the Characteristics of a Novel Pbs CCD Array-Based Near-Infrared Spectrometer. Applied Spectroscopic Reviews, 2013, 37-41. 12. Kerry B. Walsh; John A. Guthrie; Justin W. Burney, Application of Commercially Available, Low-Cost, Miniaturised NIR Spectrometers to the Assessment of the Sugar Content of Intact Fruit. Australian Journal of Plant Physiology, 2000, 27(12), 1175-1186. 48
  61. 61. 13. Bart M. Nicolai; Katrien Beullens; Els Bobelyn; Ann Peirs; Wouter Saeys; Karen l. Theron; Jeroen Lammertyn, Nondestructive Measurement of Fruit and Vegetable Quality by Means of NIR Spectroscopy: A Review. Postharvest Biology and Technology, 2007, 46(2), 99-118. 14. Manuela Zude, Non-Destructive Prediction of Banana Fruit Quality using VIS/NIR Spectroscopy. Fruits, 2003, 58, 153-142. 15. F.J. Rambla; S.Garrigues; M. De la Guardia, PLS-NIR determination of total sugar, glucose, fructose and sucrose in aqueous solutions of fruit juices. Analytica Chimica Acta, 1997, 344(1-2), 41-53. 16. Jaz Modular Spectroscopy. Jaz, 61-78. 17. Spectrometer QE-65000. The Laboratory of Analytical Opt0chemotronics, 2010. 18. NIRQuest NIR Spectrometers Data Sheet. Ocean Optics, 1-50. 19. Global Water: Handheld Refractometer Overview. http://www.globalw.com (accessed 27.05.2013) 20. Wikipedia the Free Encyclopedia: Sucrose. http://en.wikipedia.org (accessed 27.05.2013) 21. Charles E. Ophardt, Sucrose. Virtual Chembook Elmhurst College, 2003. 49
  62. 62. APPENDIX A. Relationship between Absorbance and Concentration of Aqueous Sucrose for different NIR Spectrometers A (NIRQuest) C (NIRQuest) 0.191 38.8 0.217 37.8 0.203 36.7 0.206 35.1 0.207 34.1 0.209 33.1 0.209 32.1 0.208 31.0 0.217 30.0 0.210 29.5 0.215 28.1 0.212 27.2 0.220 26.4 0.215 25.4 0.214 24.5 0.222 23.6 0.219 22.5 0.225 21.7 0.221 20.5 0.317 23.5 0.321 23.0 0.314 22.5 0.322 21.5 0.318 20.9 0.331 20.1 0.322 19.4 0.331 18.7 0.326 17.7 0.324 16.4 0.668 15.4 0.324 14.6 0.326 13.8 0.327 12.2 0.340 11.5 0.324 10.6 0.328 9.8 0.326 9.0 0.332 8.4 0.329 7.8 C (QE65000) 39.1 37.8 38.0 36.8 35.7 34.5 34.9 32.1 33.2 30.3 31.0 28.8 29.2 26.4 27.3 25.6 23.9 24.2 24.9 22.9 21.8 21.0 20.3 19.4 18.4 17.7 16.8 16.1 15.2 14.6 13.5 13.0 12.1 11.6 10.6 10.1 9.3 8.2 7.5 A (QE65000) 0.136 0.143 0.148 0.154 0.151 0.152 0.152 0.154 0.156 0.159 0.161 0.158 0.158 0.161 0.163 0.163 0.165 0.164 0.164 0.164 0.164 0.165 0.165 0.165 0.167 0.166 0.167 0.165 0.166 0.167 0.167 0.167 0.168 0.167 0.168 0.168 0.168 0.167 0.167 50 C (JAZ) 35.0 35.0 30.0 34.2 28.2 30.6 28.7 22.1 24.4 26.4 30.5 25.7 21.8 19.4 15.9 10.9 15.2 15.0 12.7 10.8 10.1 8.3 8.1 13.9 7.1 6.1 10.9 9.0 7.5 6.8 5.7 4.7 3.5 2.5 1.4 0.9 9.6 21.4 14.9 A (JAZ) 0.069 0.071 0.081 0.071 0.080 0.081 0.078 0.090 0.086 0.079 0.076 0.082 0.089 0.093 0.097 0.106 0.098 0.101 0.102 0.105 0.104 0.109 0.109 0.102 0.109 0.110 0.102 0.105 0.106 0.112 0.114 0.115 0.113 0.115 0.115 0.113 0.107 0.089 0.100
  63. 63. 0.333 0.329 0.327 0.328 0.333 0.333 0.329 0.334 0.331 0.324 0.324 6.7 6.0 5.4 4.7 3.6 3.1 2.0 1.2 0.2 13.1 17.1 6.7 6.0 5.0 4.1 3.8 2.8 2.3 1.4 1.0 0.6 0.1 0.167 0.168 0.167 0.167 0.167 0.166 0.167 0.167 0.167 0.168 0.169 12.9 27.4 35.4 27.3 23.8 21.1 17.7 15.4 19.4 16.1 11.7 0.101 0.081 0.068 0.078 0.084 0.089 0.094 0.099 0.091 0.102 0.104 B. Relationship between Wavelength and Coefficient of Determination for Different NIR Spectrometers Wavelength (JAZ) 919.99 930.01 939.99 949.91 960.11 970.26 980.03 990.06 1000.04 R2 (JAZ) 0.353 0.664 0.883 0.962 0.978 0.970 0.956 0.922 0.864 R2 (QE65000) 0.874887 0.720742 0.015044 0.900768 0.951000 0.900147 0.610916 0.018176 0.475360 0.956000 Wavelength (QE65000) 920.19 930.42 939.90 950.09 960.27 969.70 979.85 989.97 1000.08 959.13 51 Wavelength (NIRQuest) 1050 1150 1350 1250 1450 1363 R2 (NIRQuest) 0.442 0.696 0.784 0.442 0.451 0.125
  64. 64. C. The Relationship between Actual Concentration and Calculated Concentration for different NIR Spectrometers 1. JAZ Sectrometer: C 35.0 35.0 30.0 34.2 28.2 30.6 28.7 22.1 24.4 26.4 30.5 25.7 21.8 19.4 15.9 10.9 15.2 15.0 12.7 10.8 10.1 8.3 8.1 13.9 7.1 6.1 10.9 9.0 7.5 6.8 5.7 4.7 3.5 2.5 1.4 0.9 9.6 21.4 14.9 730.08 -0.039 -0.038 -0.037 -0.039 -0.038 -0.037 -0.039 -0.038 -0.037 -0.040 -0.038 -0.038 -0.038 -0.038 -0.038 -0.036 -0.037 -0.037 -0.037 -0.037 -0.037 -0.037 -0.036 -0.037 -0.037 -0.037 -0.038 -0.037 -0.037 -0.036 -0.036 -0.036 -0.037 -0.037 -0.037 -0.037 -0.036 -0.038 -0.036 830.12 -0.059 -0.058 -0.056 -0.059 -0.057 -0.055 -0.060 -0.057 -0.057 -0.061 -0.057 -0.058 -0.056 -0.056 -0.056 -0.052 -0.056 -0.054 -0.054 -0.054 -0.055 -0.054 -0.052 -0.054 -0.055 -0.055 -0.056 -0.055 -0.055 -0.052 -0.052 -0.053 -0.055 -0.054 -0.054 -0.054 -0.053 -0.056 -0.053 52 960.11 0.073 0.075 0.086 0.076 0.085 0.085 0.083 0.096 0.092 0.083 0.081 0.087 0.094 0.096 0.101 0.111 0.103 0.105 0.106 0.108 0.109 0.112 0.113 0.107 0.114 0.114 0.105 0.109 0.111 0.115 0.119 0.118 0.118 0.119 0.120 0.118 0.112 0.092 0.105 914.96 -0.041 -0.038 -0.037 -0.039 -0.039 -0.035 -0.041 -0.038 -0.038 -0.043 -0.038 -0.039 -0.038 -0.038 -0.038 -0.034 -0.038 -0.035 -0.036 -0.037 -0.038 -0.036 -0.035 -0.036 -0.037 -0.038 -0.039 -0.038 -0.037 -0.034 -0.034 -0.035 -0.037 -0.036 -0.036 -0.038 -0.035 -0.038 -0.034
  65. 65. 12.9 27.4 35.4 27.3 23.8 21.1 17.7 15.4 19.4 16.1 11.7 -0.037 -0.038 -0.038 -0.039 -0.038 -0.038 -0.038 -0.037 -0.038 -0.035 -0.037 -0.055 -0.056 -0.056 -0.057 -0.057 -0.056 -0.056 -0.054 -0.056 -0.051 -0.054 0.105 0.085 0.073 0.082 0.088 0.094 0.098 0.102 0.095 0.106 0.108 2. QE65000 Spectrometer: C 39.1 37.8 38.0 36.8 35.7 34.5 34.9 32.1 33.2 30.3 31.0 28.8 29.2 26.4 27.3 25.6 23.9 24.2 24.9 22.9 21.8 21.0 20.3 19.4 18.4 17.7 16.8 16.1 15.2 909.21 0.022 0.025 0.028 0.026 0.019 0.015 0.013 0.013 0.013 0.014 0.015 0.011 0.010 0.011 0.012 0.010 0.012 0.010 0.010 0.009 0.009 0.007 0.007 0.007 0.009 0.008 0.008 0.006 0.007 53 960.27 0.136 0.143 0.148 0.154 0.151 0.152 0.152 0.154 0.156 0.159 0.161 0.158 0.158 0.161 0.163 0.163 0.165 0.164 0.164 0.164 0.164 0.165 0.165 0.165 0.167 0.166 0.167 0.165 0.166 -0.037 -0.037 -0.036 -0.039 -0.039 -0.038 -0.038 -0.036 -0.038 -0.032 -0.036
  66. 66. 14.6 13.5 13.0 12.1 11.6 10.6 10.1 9.3 8.2 7.5 6.7 6.0 5.0 4.1 3.8 2.8 2.3 1.4 1.0 0.6 0.1 0.006 0.006 0.006 0.006 0.006 0.007 0.006 0.006 0.005 0.005 0.005 0.006 0.005 0.005 0.005 0.004 0.005 0.005 0.005 0.004 0.003 54 0.167 0.167 0.167 0.168 0.167 0.168 0.168 0.168 0.167 0.167 0.167 0.168 0.167 0.167 0.167 0.166 0.167 0.167 0.167 0.168 0.169
  67. 67. 3. NIRQuest Spectrometer: C 38.8 37.8 36.7 35.1 34.1 33.1 32.1 31.0 30.0 29.5 28.1 27.2 26.4 25.4 24.5 23.6 22.5 21.7 20.5 980.11 0.0757160 0.0859160 0.0812083 0.0812083 0.0831699 0.0831699 0.0835622 0.0835622 0.0816006 0.0851314 0.0831699 0.0851314 0.0827776 0.0867007 0.0843468 0.0847391 0.0874853 0.0870930 0.0882699 1156.15 0.157709 0.167909 0.163594 0.165555 0.166340 0.167909 0.168694 0.168694 0.172617 0.170263 0.173401 0.173009 0.176148 0.174578 0.174971 0.178501 0.178109 0.181248 0.180463 1162.64 0.172617 0.182424 0.178109 0.180463 0.180855 0.182817 0.183209 0.183209 0.187132 0.185171 0.187917 0.187917 0.190663 0.189094 0.189094 0.193409 0.192625 0.195371 0.194586 1195.06 0.209886 0.219694 0.214986 0.214986 0.216948 0.216556 0.217732 0.218125 0.217340 0.220871 0.218125 0.220479 0.219302 0.221656 0.220479 0.220086 0.223225 0.222440 0.225186 1337.13 0.315810 0.327579 0.324049 0.327972 0.329541 0.331895 0.333464 0.334641 0.339349 0.337780 0.341703 0.341310 0.344841 0.344449 0.345626 0.349941 0.350726 0.354257 0.355434 C 38.8 37.8 36.7 35.1 34.1 33.1 32.1 31.0 30.0 29.5 28.1 27.2 26.4 25.4 24.5 23.6 22.5 21.7 20.5 1350 0.367595 0.378972 0.375834 0.379757 0.380934 0.383680 0.385641 0.386426 0.390741 0.389172 0.393488 0.393095 0.397018 0.396626 0.397411 0.401726 0.402118 0.405649 0.406826 1394.99 0.467634 0.479011 0.474304 0.474304 0.475873 0.475873 0.475873 0.477050 0.475873 0.478619 0.477050 0.479796 0.477442 0.479404 0.478227 0.477834 0.480581 0.479011 0.481365 1606.04 0.87407 0.90428 0.90428 0.90428 0.90781 0.90820 0.90271 0.89761 0.90938 0.90467 0.91134 0.92350 0.90741 0.90467 0.91134 0.91016 0.91722 0.92585 0.91997 1669.68 0.839937 0.863476 0.862299 0.869753 0.871714 0.863084 0.860337 0.864260 0.861122 0.862691 0.872891 0.864653 0.867791 0.865437 0.868576 0.870930 0.878776 0.869753 0.891330 1676.04 0.837191 0.865437 0.865437 0.869753 0.872107 0.863868 0.859553 0.864260 0.861122 0.865045 0.872499 0.863868 0.863476 0.865437 0.868184 0.872499 0.878776 0.873284 0.890938 55
  68. 68. C 23.5 23.0 22.5 21.5 20.9 20.1 19.4 18.7 17.7 16.4 15.4 14.6 13.8 12.2 11.5 10.6 9.8 9.0 8.4 7.8 6.7 980.11 0.0890545 0.0874853 0.0874853 0.0851314 0.0886622 0.0898392 0.0867007 0.0863084 0.0890545 0.0874853 0.0929776 0.0933699 0.0929776 0.0925853 0.0898392 0.0906238 0.0910161 0.0906238 0.0894468 0.0969007 0.0906238 1156.15 0.214202 0.216163 0.214202 0.217340 0.216556 0.221263 0.218909 0.222440 0.220871 0.221263 0.251863 0.222048 0.223225 0.224794 0.229894 0.224402 0.226363 0.225971 0.227933 0.227148 0.229894 1162.64 0.227540 0.229894 0.227540 0.230679 0.229502 0.234209 0.231856 0.235386 0.234209 0.234209 0.251079 0.234994 0.236171 0.237740 0.242448 0.237348 0.239310 0.238525 0.240486 0.240094 0.242448 1195.06 0.224009 0.218517 0.221656 0.215771 0.218517 0.220871 0.218517 0.217340 0.219302 0.218517 0.222440 0.222832 0.223225 0.224009 0.220479 0.221263 0.222048 0.222440 0.220479 0.228325 0.222440 1337.13 0.384072 0.386426 0.384072 0.388388 0.387603 0.392703 0.390741 0.394272 0.393488 0.394665 0.511181 0.397018 0.398588 0.400549 0.405257 0.400549 0.402511 0.402511 0.404865 0.404472 0.407219 C 23.5 23.0 22.5 21.5 20.9 20.1 19.4 18.7 17.7 16.4 15.4 14.6 13.8 12.2 11.5 10.6 9.8 9.0 8.4 7.8 6.7 1350 0.437034 0.439388 0.437426 0.441742 0.440957 0.446842 0.444096 0.447627 0.446842 0.448411 0.506081 0.450373 0.451942 0.452727 0.458219 0.453511 0.454688 0.454688 0.457434 0.457434 0.459396 1394.99 0.481758 0.482150 0.484504 0.480188 0.482542 0.484111 0.481365 0.480581 0.482150 0.480973 0.486858 0.486073 0.484896 0.485288 0.482542 0.482934 0.484896 0.485288 0.482934 0.490781 0.483719 1606.04 0.94311 0.95842 0.96116 0.95449 0.95724 0.95724 0.95724 0.95332 0.96587 0.96155 1.00000 0.98117 0.97215 0.95606 0.95842 0.97450 0.97528 0.97175 0.98705 0.99490 0.98784 1669.68 0.856022 0.853668 0.847783 0.854453 0.847783 0.849353 0.845037 0.839545 0.841899 0.835230 0.786191 0.840330 0.839153 0.830914 0.827776 0.827776 0.826599 0.820714 0.822283 0.825814 0.825814 1676.04 0.850922 0.851707 0.848960 0.852883 0.844645 0.847783 0.842291 0.839153 0.839153 0.830522 0.784621 0.837976 0.836799 0.827383 0.829345 0.827383 0.823460 0.819537 0.819929 0.825422 0.822283 56
  69. 69. C 6.0 5.4 4.7 3.6 3.1 2.0 1.2 0.2 13.1 17.1 980.11 0.0945469 0.0914084 0.0902315 0.0918007 0.0914084 0.0910161 0.0914084 0.0965084 0.0937623 0.0933699 1156.15 0.228717 0.227540 0.228717 0.230286 0.231071 0.229894 0.231463 0.231856 0.222832 0.220479 1162.64 0.241663 0.240486 0.241271 0.243625 0.244017 0.243233 0.244410 0.244802 0.236171 0.234209 1195.06 0.226756 0.222440 0.222048 0.222832 0.223617 0.222832 0.223225 0.227148 0.225186 0.226756 1337.13 0.406434 0.406042 0.406434 0.411142 0.410749 0.409572 0.412319 0.411926 0.398588 0.393880 C 6.0 5.4 4.7 3.6 3.1 2.0 1.2 0.2 13.1 17.1 1350 0.458219 0.458219 0.458611 0.462142 0.462927 0.461750 0.463711 0.463319 0.451157 0.447234 1394.99 0.486858 0.484896 0.483719 0.484504 0.485681 0.483327 0.484896 0.488819 0.485681 0.486465 1606.04 0.98117 0.99765 0.98588 0.97842 0.98627 0.98431 0.97803 0.98588 0.97058 0.97372 1669.68 0.819145 0.816399 0.810514 0.823068 0.815222 0.808160 0.815222 0.804629 0.830522 0.836014 1676.04 0.819145 0.811691 0.806199 0.822283 0.810122 0.808160 0.811299 0.801098 0.826599 0.832091 57

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