An Overview on the Applications of Typical Non-linear Algorithms Coupled With NIR Spectroscopy in Food Analysis


Near-infrared (NIR) spectroscopy as a low-cost technique with its non-destructive fast nature, precision, control, accuracy, repeatability, and reproducibility has been extensively employed in most industries for food quality measurements. Its coupling to different modeling techniques has been identified as a way of improving the accuracy and robustness of non-destructive measurement of foodstuffs. This review provides an overview of the application of non-linear algorithms in food quality and safety specific to NIR spectroscopy. The review also provides in-depth knowledge about the principle of NIR spectroscopy along with different non-linear models such as artificial neural network (ANN), AdaBoost, local algorithm (LA), support vector machine (SVM), and extreme learning machine (ELM). Moreover, non-linear algorithms coupled with NIR spectroscopy for ensuring food quality and their future perspective has been discussed.

This is a preview of subscription content, access via your institution.

Fig. 1
Fig. 2
Fig. 3
Fig. 4



Near-infrared spectroscopy


Artificial neural network


Backpropagation artificial neural network




Local algorithm


Support vector machine


Support vector regression


Extreme learning machine


Principal components


Least square support vector machine


Least square support vector regression


Laser emission diode


Principal component analysis


Soft independent modeling of class analogy


Processing element


Genetic algorithm


Genetic algorithm artificial neural network


Radial basis function


Radial basis function neural networks


Partial least square


Principal component regressions


Multiple linear regressions


Mallows augmented partial residual plot


Partial residual plot


Residual plot


Residual versus PC plot


Added variable plot/partial residual plot


Lack of fit


Mean average precision


Standard normal variate transformation


Multiplicative scatter correction smoothing


Wavelet transforms


Orthogonal signal correction


  1. 1.

    Albadra MAA, Tiuna S (2017) Extreme learning machine: a review. Int J Appl Eng Res 12:4610–4623

    Google Scholar 

  2. 2.

    Allen RG, Pereira L, Raes D, Smith M (1998) Crop evapotranspiration: guidelines for computing crop requirements, FAO irrigation and drainage paper no. 56 FAO, Rome, Italy:300

  3. 3.

    Altieri G, Genovese F, Admane N, Di Renzo GC (2016) On-line measure of donkey's milk properties by near infrared spectrometry. LWT Food Sci Technol 69:348–357

    CAS  Google Scholar 

  4. 4.

    Altieri G, Genovese F, Tauriello A, Di Renzo GC (2017) Models to improve the non-destructive analysis of persimmon fruit properties by VIS/NIR spectrometry. J Sci Food Agric

  5. 5.

    Ampazis N, Perantonis SJ (2002) Two highly efficient second-order algorithms for training feedforward networks. IEEE T Neu Net 13:1064–1074

    CAS  Google Scholar 

  6. 6.

    Anderson JA (1995) An introduction to neural networks. MIT press

  7. 7.

    Arslan M, Xiaobo Z, Xuetao H, Elrasheid Tahir H, Shi J, Khan MR, Zareef M (2018) Near infrared spectroscopy coupled with chemometric algorithms for predicting chemical components in black goji berries (Lycium ruthenicum Murr). J Near Infrared Spectrosc 26:275–286

    CAS  Google Scholar 

  8. 8.

    Arslan M, Xiaobo Z, Tahir HE, Zareef M, Xuetao H, Rakha A (2019) Total polyphenol quantitation using integrated NIR and MIR spectroscopy: a case study of Chinese dates (Ziziphus jujuba). Phytochem Anal

  9. 9.

    Bakhshipour A, Sanaeifar A, Payman SH, de la Guardia M (2017) Evaluation of data mining strategies for classification of black tea based on image-based features. Food Anal Methods:1–10

  10. 10.

    Barton FE, Shenk JS, Westerhaus MO, Funk DB (2000) The development of near infrared wheat quality models by locally weighted regressions. Journal of Near Infrared Spectroscopy 8(3):201–208

  11. 11.

    Berk KN, Booth DE (1995) Seeing a curve in multiple regression. Technometrics 37:385–398

    Google Scholar 

  12. 12.

    Berzaghi P, Shenk JS, Westerhaus MO (1999) LOCAL prediction with near infrared multi-product databases. J Near Infrared Spectrosc 8:1–9

    Google Scholar 

  13. 13.

    Bhavsar H, Panchal MH (2012) A review on support vector machine for data classification Int J Adv res comp. Eng Technol 1:185–189

    Google Scholar 

  14. 14.

    Binetti G, del Coco L, Ragone R, Zelasco S, Perri E, Montemurro C, Valentini R, Naso D, Fanizzi FP, Schena FP (2017) Cultivar classification of Apulian olive oils: use of artificial neural networks for comparing NMR, NIR and merceological data. Food Chem 219:131–138

    CAS  PubMed  Google Scholar 

  15. 15.

    Blanco M, Pages J (2002) Classification and quantitation of finishing oils by near infrared spectroscopy. Anal Chim Acta 463:295–303

    CAS  Google Scholar 

  16. 16.

    Bona E, Marquetti I, Link JV, Makimori GYF, da Costa Arca V, Lemes A L G,... Poppi RJ (2017) Support vector machines in tandem with infrared spectroscopy for geographical classification of green arabica coffee. LWT Food Sci Technol 76:330–336

  17. 17.

    Burns DA, Ciurczak EW (2007) Handbook of near-infrared analysis. CRC press

  18. 18.

    Candolfi A, De Maesschalck R, Jouan-Rimbaud D, Hailey P, Massart D (1999) The influence of data pre-processing in the pattern recognition of excipients near-infrared spectra. J Pharm Biomed 21:115–132

    CAS  Google Scholar 

  19. 19.

    Cao J, Chen J, Li H (2014) An adaboost-backpropagation neural network for automated image sentiment classification. Sci World J:2014

  20. 20.

    Cen H, He Y (2007) Theory and application of near infrared reflectance spectroscopy in determination of food quality. Trends Food Sci Technol 18:72–83

    CAS  Google Scholar 

  21. 21.

    Cen H, Bao Y, He Y (2006) Pattern recognition of visible and near-infrared spectroscopy from bayberry juice by use of partial least squares and a backpropagation neural network. Appl Opt 45:7679–7683

    PubMed  Google Scholar 

  22. 22.

    Centner V, De Noord O, Massart D (1998) Detection of nonlinearity in multivariate calibration. Anal Chim Acta 376:153–168

    CAS  Google Scholar 

  23. 23.

    Che W, Sun L, Zhang Q, Zhang D, Ye D, Tan W, Wang L, Dai C (2017) Application of visible/near-infrared spectroscopy in the prediction of azodicarbonamide in wheat flour. J Food Sci 82:2516–2525

    CAS  PubMed  Google Scholar 

  24. 24.

    Xu Y, Kutsanedzie FY, Hassan M, Zhu J, Ahmad W, Li H, Chen Q (2020) Mesoporous Silica Supported Orderly-spaced Gold Nanoparticles SERS-based Sensor for Pesticides Detection in Food Food Chemistry:126300

  25. 25.

    Chen Q, Zhao J, Guo Z, Wang X (2010) Determination of caffeine content and main catechins contents in green tea (Camellia sinensis L.) using taste sensor technique and multivariate calibration. J Food Compos Anal 23:353–358

    CAS  Google Scholar 

  26. 26.

    Chen Q, Cai J, Wan X, Zhao J (2011) Application of linear/non-linear classification algorithms in discrimination of pork storage time using Fourier transform near infrared (FT-NIR) spectroscopy. LWT Food Sci Technol 44:2053–2058

    CAS  Google Scholar 

  27. 27.

    Chen L, Wang J, Ye Z, Zhao J, Xue X, Vander Heyden Y, Sun Q (2012a) Classification of Chinese honeys according to their floral origin by near infrared spectroscopy. Food Chem 135:338–342

    CAS  PubMed  Google Scholar 

  28. 28.

    Chen Q, Ding J, Cai J, Zhao J (2012b) Rapid measurement of total acid content (TAC) in vinegar using near infrared spectroscopy based on efficient variables selection algorithm and nonlinear regression tools. Food Chem 135:590–595

    CAS  PubMed  Google Scholar 

  29. 29.

    Chen Q, Guo Z, Zhao J, Ouyang Q (2012c) Comparisons of different regressions tools in measurement of antioxidant activity in green tea using near infrared spectroscopy. J Pharm Biomed Anal 60:92–97

    CAS  PubMed  Google Scholar 

  30. 30.

    Chen Q, Zhang D, Pan W, Ouyang Q, Li H, Urmila K, Zhao J (2015) Recent developments of green analytical techniques in analysis of tea's quality and nutrition. Trends Food Sci Technol 43:63–82

    CAS  Google Scholar 

  31. 31.

    Chen Q, Hu W, Su J, Li H, Ouyang Q, Zhao J (2016) Nondestructively sensing of total viable count (TVC) in chicken using an artificial olfaction system based colorimetric sensor array. J Food Eng 168:259–266

    Google Scholar 

  32. 32.

    Chen J, Zhu S, Zhao G (2017) Rapid determination of total protein and wet gluten in commercial wheat flour using siSVR-NIR. Food Chem 221:1939–1946

    CAS  PubMed  Google Scholar 

  33. 33.

    Cheng J-H, Dai Q, Sun D-W, Zeng X-A, Liu D, Pu H-B (2013) Applications of non-destructive spectroscopic techniques for fish quality and safety evaluation and inspection. Trends Food Sci Technol 34:18–31

    CAS  Google Scholar 

  34. 34.

    Cortés V et al (2017) Prediction of the level of astringency in persimmon using visible and near-infrared spectroscopy. J Food Eng 204:27–37

    Google Scholar 

  35. 35.

    Das B et al (2018) Quantitative monitoring of sucrose, reducing sugar and total sugar dynamics for phenotyping of water-deficit stress tolerance in rice through spectroscopy and chemometrics. Spectrochim Acta A 192:41–51

    CAS  Google Scholar 

  36. 36.

    Deng W-Y, Zheng Q-H, Lian S, Chen L, Wang X (2010) Ordinal extreme learning machine. Neurocomputing 74:447–456

    Google Scholar 

  37. 37.

    Devos O, Downey G, Duponchel L (2014) Simultaneous data pre-processing and SVM classification model selection based on a parallel genetic algorithm applied to spectroscopic data of olive oils. Food Chem 148:124–130

    CAS  PubMed  Google Scholar 

  38. 38.

    Ding S, Xu X, Nie R (2014) Extreme learning machine and its applications. Neural Comput Applic 25:549–556

    Google Scholar 

  39. 39.

    Dong C, Zhu H, Wang J, Yuan H, Zhao J, Chen Q (2017) Prediction of black tea fermentation quality indices using NIRS and nonlinear tools. Food Sci Biotechnol 26:853–860

    CAS  PubMed  PubMed Central  Google Scholar 

  40. 40.

    Draper N, Smith H (1981) Applied regression analysis, 2nd edn. Wiley, New York, NY

    Google Scholar 

  41. 41.

    Fossaceca JM, Mazzuchi TA, Sarkani S (2015) MARK-ELM: application of a novel multiple kernel learning framework for improving the robustness of network intrusion detection. Expert Syst Appl 42:4062–4080

    Google Scholar 

  42. 42.

    Freund Y, Schapire RE (1995) A desicion-theoretic generalization of on-line learning and an application to boosting. In: European conference on computational learning theory. Springer, pp 23–37

  43. 43.

    Giese E, Winkelmann O, Rohn S, Fritsche J (2017) Determining quality parameters of fish oils by means of 1 H nuclear magnetic resonance, mid-infrared, and near-infrared spectroscopy in combination with multivariate statistics. Food Res Int:116–128

  44. 44.

    Grossi M, Di Lecce G, Arru M, Toschi TG, Riccò B (2015) An opto-electronic system for in-situ determination of peroxide value and total phenol content in olive oil. J Food Eng 146:1–7

    CAS  Google Scholar 

  45. 45.

    Guo Z, Chen L, Zhao C, Huang W, Chen Q (2011) Nondestructive estimation of total free amino acid in green tea by near infrared spectroscopy and artificial neural networks. In: International Conference on Computer and Computing Technologies in Agriculture. Springer, pp 43–53

  46. 46.

    Guo W, Gu J, Liu D, Shang L (2016a) Peach variety identification using near-infrared diffuse reflectance spectroscopy. Comput Electron Agric 123:297–303

    Google Scholar 

  47. 47.

    Guo Y, Ni Y, Kokot S (2016b) Evaluation of chemical components and properties of the jujube fruit using near infrared spectroscopy and chemometrics. Spectrochim Acta A 153:79–86

    CAS  Google Scholar 

  48. 48.

    Guo Z, Huang W, Peng Y, Chen Q, Ouyang Q, Zhao J (2016c) Color compensation and comparison of shortwave near infrared and long wave near infrared spectroscopy for determination of soluble solids content of ‘Fuji’apple. Postharvest Biol Technol 115:81–90

    CAS  Google Scholar 

  49. 49.

    Gutiérrez S, Tardaguila J, Fernández-Novales J, Diago MP (2015) Support vector machine and artificial neural network models for the classification of grapevine varieties using a portable NIR spectrophotometer. PLoS One 10:e0143197

    PubMed  PubMed Central  Google Scholar 

  50. 50.

    Han Z, Cai S, Zhang X, Qian Q, Huang Y, Dai F, Zhang G (2017) Development of predictive models for total phenolics and free p-coumaric acid contents in barley grain by near-infrared spectroscopy. Food Chem 227:342–348

    CAS  PubMed  Google Scholar 

  51. 51.

    He Y, Feng S, Deng X, Li X (2006) Study on lossless discrimination of varieties of yogurt using the visible/NIR-spectroscopy. Food Res Int 39:645–650

    Google Scholar 

  52. 52.

    Hildrum KI (1992) Near infrared spectroscopy: bridging the gap between data analysis and NIR applications. Ellis Horwood Ltd

  53. 53.

    Holroyd SE (2013) The use of near infrared spectroscopy on milk and milk products. J Near Infrared Spectrosc 21:311–322

    CAS  Google Scholar 

  54. 54.

    Huang Y (2009) Advances in artificial neural networks–methodological development and application. Algorithms 2:973–1007

    Google Scholar 

  55. 55.

    Huang G-B, Chen L (2008) Enhanced random search based incremental extreme learning machine. Neurocomputing 71:3460–3468

    Google Scholar 

  56. 56.

    Huang G-B, Zhu Q-Y, Siew C-K (2006) Extreme learning machine: theory and applications. Neurocomputing 70:489–501

    Google Scholar 

  57. 57.

    Huang G-B, Zhou H, Ding X, Zhang R (2012) Extreme learning machine for regression and multiclass classification. IEEE Trans Syst Man Cybern B (Cybernetics) 42:513–529

    Google Scholar 

  58. 58.

    Huang L, Zhao J, Chen Q, Zhang Y (2014) Nondestructive measurement of total volatile basic nitrogen (TVB-N) in pork meat by integrating near infrared spectroscopy, computer vision and electronic nose techniques. Food Chem 145:228–236

    CAS  PubMed  Google Scholar 

  59. 59.

    Iñón FA, Garrigues S, de la Guardia M (2006) Combination of mid-and near-infrared spectroscopy for the determination of the quality properties of beers. Anal Chim Acta 571:167–174

    PubMed  Google Scholar 

  60. 60.

    Jiang H, Chen Q (2015) Chemometric models for the quantitative descriptive sensory properties of green tea (Camellia sinensis L.) using Fourier transform near infrared (FT-NIR) spectroscopy. Food Anal Methods 8:954–962

    Google Scholar 

  61. 61.

    Jiang H, Zhu W (2013) Determination of pear internal quality attributes by Fourier transform near infrared (FT-NIR) spectroscopy and multivariate analysis. Food Anal Methods 6:569–577

    Google Scholar 

  62. 62.

    Jiang H, Yoon S-C, Zhuang H, Wang W, Yang Y (2017) Evaluation of factors in development of Vis/NIR spectroscopy models for discriminating PSE, DFD and normal broiler breast meat. Br Poult Sci 58:673–680

    CAS  PubMed  Google Scholar 

  63. 63.

    Shi J, Zou X, Huang X, Zhao J, Li Y, Hao L, Zhang J (2013) Rapid detecting total acid content and classifying different types of vinegar based on near infrared spectroscopy and least-squares support vector machine. Food Chem 138:192–199

  64. 64.

    Kong W, Zhang C, Liu F, Gong A, He Y (2013) Irradiation dose detection of irradiated milk powder using visible and near-infrared spectroscopy and chemometrics. J Dairy Sci 96:4921–4927

    CAS  PubMed  Google Scholar 

  65. 65.

    Kutsanedzie FY, Agyekum AA, Annavaram V, Chen Q (2020) Signal-enhanced SERS-sensors of CAR-PLS and GA-PLS coupled AgNPs for Ochratoxin A and Aflatoxin B1 detection Food Chem :126231

  66. 66.

    Kutsanedzie FY, Chen Q, Hassan MM, Yang M, Sun H, Rahman MH (2018) Near infrared system coupled chemometric algorithms for enumeration of total fungi count in cocoa beans neat solution. Food Chem 240:231–238

    CAS  PubMed  Google Scholar 

  67. 67.

    Li J, Huang W, Zhao C, Zhang B (2013) A comparative study for the quantitative determination of soluble solids content, pH and firmness of pears by Vis/NIR spectroscopy. J Food Eng 116:324–332

    CAS  Google Scholar 

  68. 68.

    Li L, Xie S, Zhu F, Ning J, Chen Q, Zhang Z (2017) Colorimetric sensor array-based artificial olfactory system for sensing Chinese green tea’s quality: a method of fabrication. Int J Food Prop:1762–1773

  69. 69.

    Lin H, Chen Q, Zhao J, Zhou P (2009) Determination of free amino acid content in Radix Pseudostellariae using near infrared (NIR) spectroscopy and different multivariate calibrations. J Pharm Biomed Anal 50:803–808

    CAS  PubMed  Google Scholar 

  70. 70.

    Lin H, Zhao J, Sun L, Chen Q, Zhou F (2011) Freshness measurement of eggs using near infrared (NIR) spectroscopy and multivariate data analysis. Innov Food Sci Emerg Technol 12:182–186

    Google Scholar 

  71. 71.

    Lin C, Chen X, Jian L, Shi C, Jin X, Zhang G (2014) Determination of grain protein content by near-infrared spectrometry and multivariate calibration in barley. Food Chem 162:10–15

    CAS  PubMed  Google Scholar 

  72. 72.

    Liu F, He Y (2008) Classification of brands of instant noodles using Vis/NIR spectroscopy and chemometrics. Food Res Int 41:562–567

    CAS  Google Scholar 

  73. 73.

    Liu X, Xu L (2018) The universal consistency of extreme learning machine. Neurocomputing

  74. 74.

    Liu Y, Zhou Y (2013) Quantification of the Soluble Solids Content of Intact Apples by Vis–NIR Transmittance Spectroscopy and the LSSVM Method Spectroscopy 28:1–7

  75. 75.

    Liu W, Liu Q, Ruan F, Liang Z, Qiu H (2007) Springback prediction for sheet metal forming based on GA-ANN technology. J Mater Process Technol 187:227–231

    Google Scholar 

  76. 76.

    Liu Y, Sun X, Ouyang A (2010) Nondestructive measurement of soluble solid content of navel orange fruit by visible–NIR spectrometric technique with PLSR and PCA-BPNN. LWT Food Sci Technol 43:602–607

    CAS  Google Scholar 

  77. 77.

    Liu Y-D, Zhou Y-R, Peng Y-Y (2013) Detection of egg quality by near infrared diffuse reflectance spectroscopy. Guangxue Jingmi Gongcheng (Opt Precis Eng) 21:40–45

    Google Scholar 

  78. 78.

    Liu Y, Peng Y, Wang W, Zhang L (2014) Classification of pork comprehensive quality based on partial least squares projection and Vis/NIR spectroscopy. Trans Chin Soc Agric Eng 30:306–313

    Google Scholar 

  79. 79.

    Liu C, Yang SX, Deng L (2015) Determination of internal qualities of Newhall navel oranges based on NIR spectroscopy using machine learning. J Food Eng 161:16–23

    CAS  Google Scholar 

  80. 80.

    Luna AS, da Silva AP, Pinho JS, Ferré J, Boqué R (2013) Rapid characterization of transgenic and non-transgenic soybean oils by chemometric methods using NIR spectroscopy. Spectrochim Acta A 100:115–119

    CAS  Google Scholar 

  81. 81.

    Luypaert J, Heuerding S, Vander Heyden Y, Massart D (2004) The effect of preprocessing methods in reducing interfering variability from near-infrared measurements of creams. J Pharm Biomed 36:495–503

    CAS  Google Scholar 

  82. 82.

    Malegori C, Marques EJN, de Freitas ST, Pimentel MF, Pasquini C, Casiraghi E (2017) Comparing the analytical performances of micro-NIR and FT-NIR spectrometers in the evaluation of acerola fruit quality, using PLS and SVM regression algorithms. Talanta 165:112–116

    CAS  PubMed  Google Scholar 

  83. 83.

    Mallows CL (1986) Augmented partial residuals. Technometrics, 28:313–319

  84. 84.

    Massart DL, Dijkstra A, Kaufman L (1978) Evaluation and optimization of laboratory methods and analytical procedures. 105-1

  85. 85.

    Massart DL (1988) Chemometrics: a textbook Data handling in science and technology 2:53

  86. 86.

    McGlone VA, Jordan RB, Martinsen PJ (2002) Vis/NIR estimation at harvest of pre-and post-storage quality indices for ‘Royal Gala’apple. Postharvest Biol Technol 25:135–144

    CAS  Google Scholar 

  87. 87.

    Neruda R, Vidnerovà P (2009) Learning errors by radial basis function neural networks and regularization networks. International Journal of Grid and Distributed Computing 1(2):49–58

  88. 88.

    Neruda R, Vidnerová P (2009) Learning errors by radial basis function neural networks and regularization networks networks. 5:6

  89. 89.

    Nicolai BM, Beullens K, Bobelyn E, Peirs A, Saeys W, Theron KI, Lammertyn J (2007) Nondestructive measurement of fruit and vegetable quality by means of NIR spectroscopy: a review. Postharvest Biol Technol 46:99–118

    Google Scholar 

  90. 90.

    Ouyang Q, Zhao J, Chen Q, Lin H, Sun Z (2012) Rapid measurement of antioxidant activity in dark soy sauce by NIR spectroscopy combined with spectral intervals selection and nonlinear regression tools. Anal Methods 4:940–946

    CAS  Google Scholar 

  91. 91.

    Ouyang Q, Chen Q, Zhao J, Lin H (2013) Determination of amino acid nitrogen in soy sauce using near infrared spectroscopy combined with characteristic variables selection and extreme learning machine. Food Bioprocess Technol 6:2486–2493

    CAS  Google Scholar 

  92. 92.

    Ouyang Q, Chen Q, Zhao J (2016) Intelligent sensing sensory quality of Chinese rice wine using near infrared spectroscopy and nonlinear tools. Spectrochim Acta A 154:42–46

    CAS  Google Scholar 

  93. 93.

    Ouyang Q, Liu Y, Chen Q, Zhang Z, Zhao J, Guo Z, Gu H (2017) Intelligent evaluation of color sensory quality of black tea by visible-near infrared spectroscopy technology: a comparison of spectra and color data information. Spectrochim Acta A 180:91–96

    CAS  Google Scholar 

  94. 94.

    Ouyang Q et al (2019) Rapid sensing of total theaflavins content in black tea using a portable electronic tongue system coupled to efficient variables selection algorithms. J Food Compos Anal 75:43–48

    CAS  Google Scholar 

  95. 95.

    Pan W, Zhao J, Chen Q, Zhang D (2015) Simultaneous and rapid measurement of main compositions in black tea infusion using a developed spectroscopy system combined with multivariate calibration. Food Anal Methods 8:749–757

    Google Scholar 

  96. 96.

    Pierna JF, Baeten V, Dardenne P (2006) Screening of compound feeds using NIR hyperspectral data. Chemometr Intell lab Syst 84:114–118

    Google Scholar 

  97. 97.

    Pontes M, Santos S, Araujo M, Almeida L, Lima R, Gaiao E, Souto U (2006) Classification of distilled alcoholic beverages and verification of adulteration by near infrared spectrometry. Food Res Int 39:182–189

    CAS  Google Scholar 

  98. 98.

    Qiu S, Gao L, Wang J (2015) Classification and regression of ELM, LVQ and SVM for E-nose data of strawberry juice. J Food Eng 144:77–85

    CAS  Google Scholar 

  99. 99.

    Richards TJ, Patterson PM, Van Ispelen P (1998) Modeling fresh tomato marketing margins: econometrics and neural networks. J Agric Resour Econ 27:186–199

    Google Scholar 

  100. 100.

    Roberts CA, Workman J, Reeves JB (Eds.) (2004) Near-infrared spectroscopy in agriculture. Am Soc Agro Mad 44

  101. 101.

    Ropodi AI, Panagou EZ, Nychas G-JE (2018) Rapid detection of frozen-then-thawed minced beef using multispectral imaging and Fourier transform infrared spectroscopy. Meat Sci 135:142–147

    CAS  PubMed  Google Scholar 

  102. 102.

    Schmutzler M, Beganovic A, Böhler G, Huck CW (2015) Methods for detection of pork adulteration in veal product based on FT-NIR spectroscopy for laboratory, industrial and on-site analysis. Food Control 57:258–267

    CAS  Google Scholar 

  103. 103.

    Shang L, Gu J, Guo W (2013) Non-destructively detecting sugar content of nectarines based on dielectric properties and ANN. Trans Chin Soc Agri Eng 29:257–264

  104. 104.

    Shenk JS, Westerhaus MO, Berzaghi P (1998) Investigation of a LOCAL calibration procedure for near infrared instruments. J Near Infrared Spectrosc 5:223–232

    Google Scholar 

  105. 105.

    StatSoft I (2006) Electronic statistics textbook Tulsa, OK: Electronic Statistics Textbook

  106. 106.

    Tahir HE, Xiaobo Z, Tinting S, Jiyong S, Mariod AA (2016) Near-infrared (NIR) spectroscopy for rapid measurement of antioxidant properties and discrimination of Sudanese honeys from different botanical origin. Food Anal Methods 9:2631–2641

    Google Scholar 

  107. 107.

    Tan W, Sun L, Zhang D, Ye D, Che W Classification of wheat grains in different quality categories by near infrared spectroscopy and support vector machine. In: Cloud Computing and Internet of Things (CCIOT), 2016 2nd International Conference on, 2016. IEEE, pp 124–128

  108. 108.

    Tang K, Wu X-h, Sun J, Qiu S-w (2013) Application of adaboost-based supervised locality preserving projection algorithm in classif ication of pork NIR spectra. Food Sci Technol 5:079

    CAS  Google Scholar 

  109. 109.

    Teye E, Huang X (2015) Novel prediction of total fat content in cocoa beans by FT-NIR spectroscopy based on effective spectral selection multivariate regression. Food Anal Methods 8:945–953

    Google Scholar 

  110. 110.

    Teye E, Huang X, Dai H, Chen Q (2013) Rapid differentiation of Ghana cocoa beans by FT-NIR spectroscopy coupled with multivariate classification. Spectrochim Acta A 114:183–189

    CAS  Google Scholar 

  111. 111.

    Teye E, Huang X-y, Lei W, Dai H (2014) Feasibility study on the use of Fourier transform near-infrared spectroscopy together with chemometrics to discriminate and quantify adulteration in cocoa beans. Food Res Int 55:288–293

    CAS  Google Scholar 

  112. 112.

    Teye E, Huang X, Sam-Amoah LK, Takrama J, Boison D, Botchway F, Kumi F (2015) Estimating cocoa bean parameters by FT-NIRS and chemometrics analysis. Food Chem 176:403–410

    CAS  PubMed  Google Scholar 

  113. 113.

    Teye E, Uhomoibhi J, Wang H (2016) Nondestructive authentication of cocoa bean cultivars by FT-NIR spectroscopy and multivariate techniques. Focus Sci 2:1–10

  114. 114.

    Thissen U, Pepers M, Üstün B, Melssen W, Buydens L (2004) Comparing support vector machines to PLS for spectral regression applications. Chemom Intell Lab Syst 73:169–179

    CAS  Google Scholar 

  115. 115.

    Tingting S, Xiaobo Z, Jiyong S, Zhihua L, Xiaowei H, Yiwei X, Wu C (2016) Determination geographical origin and flavonoids content of goji berry using near-infrared spectroscopy and chemometrics. Food Anal Methods 9:68–79

    Google Scholar 

  116. 116.

    Udelhoven T, Schütt B (2000) Capability of feed-forward neural networks for a chemical evaluation of sediments with diffuse reflectance spectroscopy. Chemom Intell Lab Syst 51:9–22

    CAS  Google Scholar 

  117. 117.

    Vandeginste BG, Massart DL, De_Jong S, Buydens L, Lewi P, Smeyers-Verbeke J (1998) Handbook of chemometrics and qualimetrics. Elsevier

  118. 118.

    Wei X-K, Li Y-H, Feng Y (2006) Comparative study of extreme learning machine and support vector machine. Adv Neural Netw-ISNN 2006:1089–1095

    Google Scholar 

  119. 119.

    Williams PC, Sobering D (1996) How do we do it: a brief summary of the methods we use in developing near infrared calibrations Near infrared spectroscopy: The future waves: 185–188

  120. 120.

    Workman J (1996) A Closer Look at NIR Measurements. NIR News, 7(2):8–9.

  121. 121.

    Wu D, He Y, Feng S, Sun D-W (2008) Study on infrared spectroscopy technique for fast measurement of protein content in milk powder based on LS-SVM. J Food Eng 84:124–131

    CAS  Google Scholar 

  122. 122.

    Wu Z, Xu E, Long J, Wang F, Xu X, Jin Z, Jiao A (2015) Rapid measurement of antioxidant activity and γ-aminobutyric acid content of Chinese rice wine by Fourier-transform near infrared spectroscopy. Food Anal Methods 8:2541–2553

    Google Scholar 

  123. 123.

    Wu Y, Li L, Liu L, Liu Y (2017) Nondestructive measurement of internal quality attributes of apple fruit by using NIR spectroscopy. Multimed Tools Appl:1–17

  124. 124.

    Xu Y, Dai Y, Dong ZY, Zhang R, Meng K (2013) Extreme learning machine-based predictor for real-time frequency stability assessment of electric power systems. Neural Comput Applic 22:501–508

    Google Scholar 

  125. 125.

    Xu Y, Kutsanedzie FY, Sun H, Wang M, Chen Q, Guo Z, Wu J (2017) Rapid Pseudomonas species identification from chicken by integrating colorimetric sensors with near-infrared spectroscopy. Food Anal Methods:1–10

  126. 126.

    Yetilmezsoy K, Demirel S (2008) Artificial neural network (ANN) approach for modeling of Pb (II) adsorption from aqueous solution by Antep pistachio (Pistacia Vera L.) shells. J Hazard Mater 153:1288–1300

    CAS  PubMed  Google Scholar 

  127. 127.

    Yu J, Zhan J, Huang W (2017) Identification of wine according to grape variety using near-infrared spectroscopy based on radial basis function neural networks and least-squares support vector machines. Food Anal Methods:1–6

  128. 128.

    Zareef M, Chen Q, Ouyang Q, Kutsanedzie F, Hassan MM, Annavaram V, Wang A (2018) Prediction of amino acids, caffeine, theaflavins and water extract in black tea by FT-NIR spectroscopy coupled chemometrics algorithms. Anal Methods 10:3023–3031

    CAS  Google Scholar 

  129. 129.

    Zhang L-G, Zhang X, Ni L-J, Xue Z-B, Gu X, Huang S-X (2014) Rapid identification of adulterated cow milk by non-linear pattern recognition methods based on near infrared spectroscopy. Food Chem 145:342–348

    CAS  PubMed  Google Scholar 

  130. 130.

    Zhao J, Chen Q, Huang X, Fang C (2006) Qualitative identification of tea categories by near infrared spectroscopy and support vector machine. J Pharm Biomed Anal 41:1198–1204

    CAS  PubMed  Google Scholar 

  131. 131.

    Zhaoyong Z, Yu L, Dong S, Haihui Z, Dongjian H, Yang C (2016) Detection of moldy core in apples and its symptom types using transmittance spectroscopy. Int J Agric Biol Eng 9:148–155

  132. 132.

    Zhong X, Ling J (2009) Adaboost detector based on multiple thresholds for weak classifier. Comp Eng Appl 45:160–162

    Google Scholar 

  133. 133.

    Zhu Q-Y, Qin AK, Suganthan PN, Huang G-B (2005) Evol Extreme Learn Mach. Pattern Recogn 38:1759–1763

    Google Scholar 

  134. 134.

    Zhu F, Zhang D, He Y, Liu F, Sun D-W (2013) Application of visible and near infrared hyperspectral imaging to differentiate between fresh and frozen–thawed fish fillets. Food Bioprocess Technol 6:2931–2937

    CAS  Google Scholar 

  135. 135.

    Zhuang X, Wang L, Chen Q, Wu X, Fang J (2017) Identification of green tea origins by near-infrared (NIR) spectroscopy and different regression tools science. Chin Technol Sci 60:84–90

    Google Scholar 

Download references


We would like to acknowledge our deep appreciation to all the members of the non-destructive research team of Jiangsu University for their diverse assistance in the course of this research.


This work has been financially supported by National Key Research and Development Program of China (2017YFC1600801), Natural Science Foundation of Jiangsu Province (BK20190100), the Key R&D Project of Jiangsu Province (BE2017357), and the Project of Faculty of Agricultural Equipment of Jiangsu University.

Author information



Corresponding author

Correspondence to Quansheng Chen.

Additional information

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Zareef, M., Chen, Q., Hassan, M.M. et al. An Overview on the Applications of Typical Non-linear Algorithms Coupled With NIR Spectroscopy in Food Analysis. Food Eng Rev 12, 173–190 (2020).

Download citation


  • Non-linear algorithm
  • NIR spectroscopy
  • Non-linear applications
  • BP-ANN
  • AdaBoost