Yakın Kızılötesi Spektroskopisinde Kullanılan Veri Analizi Tekniklerinin Bir Derlemesi

Öz

Nesnelerin yapılarının ve onları oluşturan bileşenlerin analizi onlarca yıldır yapılsa da bu analizi hızlı ve örneğe zarar vermeden yapmak günümüzün araştırma konularından biridir. Yakın kızılötesi spektroskopisi, temassız ölçüm, hızlı analiz ve yüksek doğruluk özellikleri nedeniyle birçok alanda kullanılmaktadır. Yakın kızılötesi spektroskopisi, bu bölgede etkileşen kimyasal bağlar nedeniyle özellikle tarım ve gıda sektöründe ürünlerin sınıflandırılmasında veya kalite analizinde sıkça kullanılmaktadır. Yakın kızılötesi spektroskopisinde başarılı bir sonuç elde etmenin en kritik kısmı, doğru yöntemi kullanarak spektral verileri ön işleme ve analiz etmektir. Bu çalışmada, gıda üretimi ve tarımda yakın kızılötesi spektroskopisi kullanan son çalışmaların bir derlemesini gerçekleştirdik. Literatürde bu alanda çok sayıda çalışma olduğu için çalışma son beş yıldaki çalışmaları kapsayacak şekilde sınırlandırılmıştır. Derlemenin ana sorusu, bu çalışmalarda kullanılan ön işleme ve veri analizi yöntemleri ve bu yöntemlerin temel özellikleridir. İncelenen çalışmalarda en sık kullanılan ön işleme yöntemi standart normal dağılım, en sık kullanılan analiz yöntemi ise kısmi en küçük kareler regresyon olarak bulunmuştur. Ayrıca, kullanılan yazılım araçları ve spektrum aralığı da çalışma kapsamında incelenmiştir.

Anahtar Kelimeler

• Yakın Kızılötesi Spektroskopi
• Kemometri
• Ön İşleme Yöntemleri
• Regresyon
• Sınıflandırma

A Review of Data Analysis Techniques Used in Near-Infrared Spectroscopy

Abstract

Although the analysis of the structure of objects and the components that makeup them has been done for decades, it is one of today's research topics to do this analysis quickly and without damaging the sample. Near-infrared spectroscopy is used in many areas due to its non-contact measurement, fast analysis, and high accuracy features. Near-infrared spectroscopy is used in the classification or quality analysis of products, especially in the agriculture and food sector, due to the chemical bonds interacting in this region. The most critical part of achieving a successful result in near-infrared spectroscopy is pre-processing and analyzing the spectral data using the correct method. In this review, we perform a survey of recent studies that use near-infrared spectroscopy in food production and agriculture. Since there are many studies in this field in the literature, the survey is limited to cover works in the last five years. The review's main question is the pre-processing and data analysis methods used in these studies and the main features of these methods. Among the examined studies, the most frequently used pre-processing method was standard normal variate, and the most frequently used analysis method was partial least squares regression. In addition, the software tools and the spectrum range were also examined within the scope of the study.

Keywords

• Near-infrared Spectroscopy
• Chemometric
• Pre-processing Methods
• Regression
• Classification

Kaynakça

1. Grunert KG. Food quality and safety: Consumer perception and demand. European Review of Agricultural Economics 2005:32, 369-391, doi: https://doi.org/10.1093/eurrag/jbi011.
2. Rajput H, Rehal J, Goswami D Mandge HM. Methods for food analysis and quality control. In State-of-the-art technologies in food science; ed.; Eds; 2017; 396.
3. Porep JU, Kammerer DR Carle R. On-line application of near infrared (nir) spectroscopy in food production. Trends in Food Science & Technology 2015:46, 211-230, doi: https://doi.org/10.1016/j.tifs.2015.10.002.
4. Johnson JB Naiker M. Seeing red: A review of the use of near-infrared spectroscopy (nirs) in entomology. Applied Spectroscopy Reviews 2019:55, 810-829, doi: https://doi.org/10.1080/05704928.2019.1685532.
5. Salzer R. Practical guide to interpretive near-infrared spectroscopy. By jerry workman, jr. And lois weyer; 2008.
6. Dix LML, van Bel F, Baerts W Lemmers PMA. Comparing near-infrared spectroscopy devices and their sensors for monitoring regional cerebral oxygen saturation in the neonate. Pediatric Research 2013:74, 557-563, doi: https://doi.org/10.1038/pr.2013.133.
7. Woolley JT. Reflectance and transmittance of light by leaves. Plant Physiology 1971:47, 656-662, doi: https://doi.org/10.1104/pp.47.5.656.
8. Siesler HW, Ozaki, Y.,Kawata, S., Heise, H.M. Near‐infrared spectroscopy: Principles, instruments, applications; WILEY‐VCH Verlag GmbH: 2001.

9. Handbook of near-infrared analysis; Burns DA Ciurczak EW. Boca Raton: CRC Press, 2007.
10. Petisco C, García-Criado B, Vázquez-de-Aldana BR, de Haro A García-Ciudad A. Measurement of quality parameters in intact seeds of brassica species using visible and near-infrared spectroscopy. Industrial Crops and Products 2010:32, 139-146, doi: https://doi.org/10.1016/j.indcrop.2010.04.003.
11. De Girolamo A, Arroyo MC, Cervellieri S, Cortese M, Pascale M, Logrieco AF Lippolis V. Detection of durum wheat pasta adulteration with common wheat by infrared spectroscopy and chemometrics: A case study. LWT 2020:127, 109368, doi: https://doi.org/10.1016/j.lwt.2020.109368.
12. Mabood F,Boqué R,Alkindi AY,Al-Harrasi A,Al Amri IS,Boukra S,Jabeen F,Hussain J,Abbas G,Naureen Z et al. Fast detection and quantification of pork meat in other meats by reflectance ft-nir spectroscopy and multivariate analysis. Meat Science 2020:163, 108084, doi: https://doi.org/10.1016/j.meatsci.2020.108084.
13. Pereira EVdS, Fernandes DDdS, de Araújo MCU, Diniz PHGD Maciel MIS. Simultaneous determination of goat milk adulteration with cow milk and their fat and protein contents using nir spectroscopy and pls algorithms. LWT 2020:127, 109427, doi: https://doi.org/10.1016/j.lwt.2020.109427.
14. Du Q, Zhu M, Shi T, Luo X, Gan B, Tang L Chen Y. Adulteration detection of corn oil, rapeseed oil and sunflower oil in camellia oil by in situ diffuse reflectance near-infrared spectroscopy and chemometrics. Food Control 2021:121, 107577, doi: https://doi.org/10.1016/j.foodcont.2020.107577.
15. Rodionova OY, Fernández Pierna JA, Baeten V Pomerantsev AL. Chemometric non-targeted analysis for detection of soybean meal adulteration by near infrared spectroscopy. Food Control 2021:119, 107459, doi: https://doi.org/10.1016/j.foodcont.2020.107459.
16. Roggo Y, Chalus P, Maurer L, Lema-Martinez C, Edmond A Jent N. A review of near infrared spectroscopy and chemometrics in pharmaceutical technologies. Journal of Pharmaceutical and Biomedical Analysis 2007:44, 683-700, doi: https://doi.org/10.1016/j.jpba.2007.03.023.
17. Pinheiro PP, Santos JCFD França MBDM. Development, testing, and validation of a prototype for qualification of substances based on near-infrared spectroscopy. IEEE Access 2019:7, 25650-25659, doi: https://doi.org/10.1109/ACCESS.2019.2900800.
18. Zhu G Tian C. Determining sugar content and firmness of ‘fuji’ apples by using portable near-infrared spectrometer and diffuse transmittance spectroscopy. Journal of Food Process Engineering 2018:41, e12810, doi: https://doi.org/10.1111/jfpe.12810.
19. Sampaio PS, Soares A, Castanho A, Almeida AS, Oliveira J Brites C. Optimization of rice amylose determination by nir-spectroscopy using pls chemometrics algorithms. Food Chemistry 2018:242, 196-204, doi: https://doi.org/10.1016/j.foodchem.2017.09.058.
20. Rinnan Å, Berg Fvd Engelsen SB. Review of the most common pre-processing techniques for near-infrared spectra. TrAC Trends in Analytical Chemistry 2009:28, 1201-1222, doi: https://doi.org/10.1016/j.trac.2009.07.007.
21. Lu B, Morgan SP, Crowe JA Stockford IM. Comparison of methods for reducing the effects of scattering in spectrophotometry. Applied Spectroscopy 2006:60, 1157-1166, doi: https://doi.org/10.1366/000370206778664725.
22. Maleki MR, Mouazen AM, Ramon H De Baerdemaeker J. Multiplicative scatter correction during on-line measurement with near infrared spectroscopy. Biosystems Engineering 2007:96, 427-433, doi: https://doi.org/10.1016/j.biosystemseng.2006.11.014.
23. Chen JY, Iyo C, Terada F Kawano S. Effect of multiplicative scatter correction on wavelength selection for near infrared calibration to determine fat content in raw milk. Journal of Near Infrared Spectroscopy 2002:10, 301-307, doi: https://doi.org/10.1255/jnirs.346.
24. Rebellato AP, Caramês ETdS, Moraes PPd Pallone JAL. Minerals assessment and sodium control in hamburger by fast and green method and chemometric tools. LWT 2020:128, 109438, doi: https://doi.org/10.1016/j.lwt.2020.109438.
25. Zhang S, Ma H, Pan H, Shao Q, Liu X Wu Y. Quantitative real-time release testing of rhubarb based on near-infrared spectroscopy and method validation. Vibrational Spectroscopy 2019:104, 102964, doi: https://doi.org/10.1016/j.vibspec.2019.102964.
26. López-Maestresalas A, Insausti K, Jarén C, Pérez-Roncal C, Urrutia O, Beriain MJ Arazuri S. Detection of minced lamb and beef fraud using nir spectroscopy. Food Control 2019:98, 465-473, doi: https://doi.org/10.1016/j.foodcont.2018.12.003.
27. Martens H, Nielsen JP Engelsen SB. Light scattering and light absorbance separated by extended multiplicative signal correction. Application to near-infrared transmission analysis of powder mixtures. Analytical Chemistry 2003:75, 394-404, doi: https://doi.org/10.1021/ac020194w.
28. Quelal-Vásconez MA, Lerma-García MJ, Pérez-Esteve É, Arnau-Bonachera A, Barat JM Talens P. Fast detection of cocoa shell in cocoa powders by near infrared spectroscopy and multivariate analysis. Food Control 2019:99, 68-72, doi: https://doi.org/10.1016/j.foodcont.2018.12.028.
29. Peiris KHS, Bean SR Jagadish SVK. Extended multiplicative signal correction to improve prediction accuracy of protein content in weathered sorghum grain samples. Cereal Chemistry n/a, doi: https://doi.org/10.1002/cche.10329.
30. Monago-Maraña O, Eskildsen CE, Galeano-Díaz T, Muñoz de la Peña A Wold JP. Untargeted classification for paprika powder authentication using visible – near infrared spectroscopy (vis-nirs). Food Control 2021:121, 107564, doi: https://doi.org/10.1016/j.foodcont.2020.107564.
31. Barnes RJ, Dhanoa MS Lister SJ. Correction to the description of standard normal variate (snv) and de-trend (dt) transformations in practical spectroscopy with applications in food and beverage analysis—2nd edition. NIR news 1994:5, 6-6, doi: https://doi.org/10.1255/nirn.248.
32. Zeaiter M Rutledge D. Preprocessing methods. In Comprehensive chemometrics; 1st ed.; Brown, S. D., Tauler, R. ,Walczak, B., Eds; Elsevier, 2009; 121-231.
33. Udompetaikul V, Phetpan K Sirisomboon P. Development of the partial least-squares model to determine the soluble solids content of sugarcane billets on an elevator conveyor. Measurement 2021:167, 107898, doi: https://doi.org/10.1016/j.measurement.2020.107898.
34. Genisheva Z, Quintelas C, Mesquita DP, Ferreira EC, Oliveira JM Amaral AL. New pls analysis approach to wine volatile compounds characterization by near infrared spectroscopy (nir). Food Chemistry 2018:246, 172-178, doi: https://doi.org/10.1016/j.foodchem.2017.11.015.
35. Firmani P, De Luca S, Bucci R, Marini F Biancolillo A. Near infrared (nir) spectroscopy-based classification for the authentication of darjeeling black tea. Food Control 2019:100, 292-299, doi: https://doi.org/10.1016/j.foodcont.2019.02.006.
36. Savitzky A Golay MJE. Smoothing and differentiation of data by simplified least squares procedures. Analytical Chemistry 1964:36, 1627-1639, doi: https://doi.org/10.1021/ac60214a047.
37. Luo J, Ying K Bai J. Savitzky–golay smoothing and differentiation filter for even number data. Signal Processing 2005:85, 1429-1434, doi: https://doi.org/10.1016/j.sigpro.2005.02.002.
38. Krepper G, Romeo F, Fernandes DDdS, Diniz PHGD, de Araújo MCU, Di Nezio MS, Pistonesi MF Centurión ME. Determination of fat content in chicken hamburgers using nir spectroscopy and the successive projections algorithm for interval selection in pls regression (ispa-pls). Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy 2018:189, 300-306, doi: https://doi.org/10.1016/j.saa.2017.08.046.
39. Puertas G Vázquez M. Cholesterol determination in egg yolk by uv-vis-nir spectroscopy. Food Control 2019:100, 262-268, doi: https://doi.org/10.1016/j.foodcont.2019.01.031.
40. Lu B, Wang X, Liu N, He K, Wu K, Li H Tang X. Feasibility of nir spectroscopy detection of moisture content in coco-peat substrate based on the optimization characteristic variables. Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy 2020:239, 118455, doi: https://doi.org/10.1016/j.saa.2020.118455.
41. Mishra P, Woltering E, Brouwer B Hogeveen-van Echtelt E. Improving moisture and soluble solids content prediction in pear fruit using near-infrared spectroscopy with variable selection and model updating approach. Postharvest Biology and Technology 2021:171, 111348, doi: https://doi.org/10.1016/j.postharvbio.2020.111348.
42. Femenias A, Gatius F, Ramos AJ, Sanchis V Marín S. Near-infrared hyperspectral imaging for deoxynivalenol and ergosterol estimation in wheat samples. Food Chemistry 2021:341, 128206, doi: https://doi.org/10.1016/j.foodchem.2020.128206.
43. Wold S, Antti H, Lindgren F Öhman J. Orthogonal signal correction of near-infrared spectra. Chemometrics and Intelligent Laboratory Systems 1998:44, 175-185, doi: https://doi.org/10.1016/S0169-7439(98)00109-9.
44. Ning Y, Zhang H, Zhang Q Zhang X. Rapid identification and quantitative pit mud by near infrared spectroscopy with chemometrics. Vibrational Spectroscopy 2020:110, 103116, doi: https://doi.org/10.1016/j.vibspec.2020.103116.
45. Yuan L-M, Mao F, Chen X, Li L Huang G. Non-invasive measurements of ‘yunhe’ pears by vis-nirs technology coupled with deviation fusion modeling approach. Postharvest Biology and Technology 2020:160, 111067, doi: https://doi.org/10.1016/j.postharvbio.2019.111067.
46. Samadi, Wajizah S Munawar AA. Near infrared spectroscopy (nirs) data analysis for a rapid and simultaneous prediction of feed nutritive parameters. Data in Brief 2020:29, 105211, doi: https://doi.org/10.1016/j.dib.2020.105211.
47. Bahrami ME, Honarvar M, Ansari K Jamshidi B. Measurement of quality parameters of sugar beet juices using near-infrared spectroscopy and chemometrics. Journal of Food Engineering 2020:271, 109775, doi: https://doi.org/10.1016/j.jfoodeng.2019.109775.
48. https://eigenvector.com/resources/data-sets/
49. Wold S. Chemometrics; what do we mean with it, and what do we want from it? Chemometrics and Intelligent Laboratory Systems 1995:30, 109-115, doi: https://doi.org/10.1016/0169-7439(95)00042-9.
50. Mark H Workman J. Chapter 4 - matrix algebra and multiple linear regression: Part 1. In Chemometrics in spectroscopy (second edition); ed.; Mark, H. ,Workman, J., Eds; Academic Press, 2018; 27-35.
51. Riffenburgh RH Gillen DL. 16 - multiple linear and curvilinear regression and multifactor analysis of variance. In Statistics in medicine (fourth edition); ed.; Riffenburgh, R. H. ,Gillen, D. L., Eds; Academic Press, 2020; 391-435.
52. Fritz M Berger PD. Chapter 10 - can you relate in multiple ways? Multiple linear regression and stepwise regression. In Improving the user experience through practical data analytics; ed.; Fritz, M. ,Berger, P. D., Eds; Morgan Kaufmann, 2015; 239-269.
53. Wang Y-J, Li T-H, Li L-Q, Ning J-M Zhang Z-Z. Evaluating taste-related attributes of black tea by micro-nirs. Journal of Food Engineering 2021:290, 110181, doi: https://doi.org/10.1016/j.jfoodeng.2020.110181.
54. Huang Y, Dong W, Sanaeifar A, Wang X, Luo W, Zhan B, Liu X, Li R, Zhang H Li X. Development of simple identification models for four main catechins and caffeine in fresh green tea leaf based on visible and near-infrared spectroscopy. Computers and Electronics in Agriculture 2020:173, 105388, doi: https://doi.org/10.1016/j.compag.2020.105388.
55. Berhow MA, Singh M, Bowman MJ, Price NPJ, Vaughn SF Liu SX. Quantitative nir determination of isoflavone and saponin content of ground soybeans. Food Chemistry 2020:317, 126373, doi: https://doi.org/10.1016/j.foodchem.2020.126373.
56. Shen F, Wu Q, Shao X Zhang Q. Non-destructive and rapid evaluation of aflatoxins in brown rice by using near-infrared and mid-infrared spectroscopic techniques. Journal of Food Science and Technology 2018:55, 1175-1184, doi: https://doi.org/10.1007/s13197-018-3033-1.
57. Abdi H Williams LJ. Partial least squares methods: Partial least squares correlation and partial least square regression. In Computational toxicology: Volume ii; ed.; Reisfeld, B. ,Mayeno, A. N., Eds; Humana Press, 2013; 549-579.
58. Guebel DV Torres NV. Partial least-squares regression (plsr). In Encyclopedia of systems biology; ed.; Dubitzky, W., Wolkenhauer, O., Cho, K.-H. ,Yokota, H., Eds; Springer New York, 2013; 1646-1648.
59. Huang Y, Lu R Chen K. Prediction of firmness parameters of tomatoes by portable visible and near-infrared spectroscopy. Journal of Food Engineering 2018:222, 185-198, doi: https://doi.org/10.1016/j.jfoodeng.2017.11.030.
60. Deng Y, Wang Y, Zhong G Yu X. Simultaneous quantitative analysis of protein, carbohydrate and fat in nutritionally complete formulas of medical foods by near-infrared spectroscopy. Infrared Physics & Technology 2018:93, 124-129, doi: https://doi.org/10.1016/j.infrared.2018.07.027.
61. Mabood F,Jabeen F,Ahmed M,Hussain J,Al Mashaykhi SAA,Al Rubaiey ZMA,Farooq S,Boqué R,Ali L,Hussain Z et al. Development of new nir-spectroscopy method combined with multivariate analysis for detection of adulteration in camel milk with goat milk. Food Chemistry 2017:221, 746-750, doi: https://doi.org/10.1016/j.foodchem.2016.11.109.
62. Das B, Manohara KK, Mahajan GR Sahoo RN. Spectroscopy based novel spectral indices, pca- and plsr-coupled machine learning models for salinity stress phenotyping of rice. Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy 2020:229, 117983, doi: https://doi.org/10.1016/j.saa.2019.117983.
63. Maraphum K, Saengprachatanarug K, Wongpichet S, Phuphaphud A Posom J. In-field measurement of starch content of cassava tubers using handheld vis-near infrared spectroscopy implemented for breeding programmes. Computers and Electronics in Agriculture 2020:175, 105607, doi: https://doi.org/10.1016/j.compag.2020.105607.
64. Genis HE, Durna S Boyaci IH. Determination of green pea and spinach adulteration in pistachio nuts using nir spectroscopy. LWT 2021:136, 110008, doi: https://doi.org/10.1016/j.lwt.2020.110008.
65. Yang B, Zhu Z, Gao M, Yan X, Zhu X Guo W. A portable detector on main compositions of raw and homogenized milk. Computers and Electronics in Agriculture 2020:177, 105668, doi: https://doi.org/10.1016/j.compag.2020.105668.
66. Yi J, Sun Y, Zhu Z, Liu N Lu J. Near-infrared reflectance spectroscopy for the prediction of chemical composition in walnut kernel. International Journal of Food Properties 2017:20, 1633-1642, doi: https://doi.org/10.1080/10942912.2016.1217006.
67. Næs T Martens H. Principal component regression in nir analysis: Viewpoints, background details and selection of components. Journal of Chemometrics 1988:2, 155-167, doi: https://doi.org/10.1002/cem.1180020207.
68. Mandel J. Use of the singular value decomposition in regression analysis. The American Statistician 1982:36, 15-24, doi: https://doi.org/10.2307/2684086.
69. Smola AJ Schölkopf B. A tutorial on support vector regression. Statistics and Computing 2004:14, 199-222, doi: https://doi.org/10.1023/B:STCO.0000035301.49549.88.
70. Dankowska A Kowalewski W. Tea types classification with data fusion of uv–vis, synchronous fluorescence and nir spectroscopies and chemometric analysis. Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy 2019:211, 195-202, doi: https://doi.org/10.1016/j.saa.2018.11.063.
71. Drucker H, Burges CJC, Kaufman L, Smola AJ Vapnik V. Support vector regression machines. Advances in Neural Information Processing Systems 1997:9, 155-161, doi:
72. Suykens JAK Vandewalle J. Least squares support vector machine classifiers. Neural Processing Letters 1999:9, 293-300, doi: https://doi.org/10.1023/A:1018628609742.
73. Yang R, Dong G, Sun X, Yang Y, Liu H, Du Y, Jin H Zhang W. Discrimination of sesame oil adulterated with corn oil using information fusion of synchronous and asynchronous two-dimensional near-mid infrared spectroscopy. European Journal of Lipid Science and Technology 2017:119, 1600459, doi: https://doi.org/10.1002/ejlt.201600459.
74. Hastie T, Tibshirani R Friedman J. The elements of statistical learning; Springer: California, 2009.
75. Moscetti R, Berhe DH, Agrimi M, Haff RP, Liang P, Ferri S, Monarca D Massantini R. Pine nut species recognition using nir spectroscopy and image analysis. Journal of Food Engineering 2021:292, 110357, doi: https://doi.org/10.1016/j.jfoodeng.2020.110357.
76. Ritthiruangdej P, Ritthiron R, Shinzawa H Ozaki Y. Non-destructive and rapid analysis of chemical compositions in thai steamed pork sausages by near-infrared spectroscopy. Food Chemistry 2011:129, 684-692, doi: https://doi.org/10.1016/j.foodchem.2011.04.110.