As milk adulteration and contamination continue to raise a serious threat to public health, there is a growing demand for rapid, reliable, and non-destructive techniques for milk quality assessment in the dairy industry. This comprehensive review provides recent advancements in non-invasive spectroscopic approaches, with a particular focus on near infrared (NIR) spectroscopy and visible-NIR hyperspectral imaging (Vis-NIR-HSI) for detecting both adulterants and contaminants in milk. Combined with chemometrics and machine learning techniques, these approaches can be used as effective qualitative and quantitative methods. The complete analytical workflow is examined in-depth with the significance of preprocessing strategies on spectral quality, the requirements of wavelength selection, and the roles of targeted and non-targeted chemometric models on classification and regression tasks. Finally, this review addresses existing limitations, including insufficient databases and the complexities of multi-adulterant detection, as well as highlights the emerging trends as future opportunities in multimodal data fusion, Internet of Things (IoT), and deep learning to support real-time monitoring for milk. The purpose of this review is to facilitate the practical, large-scale adoption of robust, non-destructive NIR and Vis-NIR-HSI tools for milk authentication.
AssenKAAbegazMA. Review on quality attributes of milk and commonly produced dairy products in Ethiopia. Heliyon2024; 10: e40089.
7.
MooreJCSpinkJLippM. Development and application of a database of food ingredient fraud and economically motivated adulteration from 1980 to 2010. J Food Sci2012; 77: R118–R126.
8.
DarweshOMMostafaAEl-SayedHS, et al.Recent technologies for detection of milk contaminants: characterization and mechanism of action: a review article. Discov Food2025; 5: 140.
9.
GetachewMSeidM. Laboratory manual for common milk quality testing. International Journal of Bioorganic Chemistry2025; 10(2): 53–58.
10.
KhomaneVBKambleDKLokhandeAT, et al.A comprehensive review on adulteration of milk. Int J Adv Biochem Res2024; 8(4S): 316–324.
11.
Duque-SotoCMExpósito-AlmellónXRueda-RoblesA, et al.Analysis of milk and dairy products by non-destructive techniques. In: Jiménez-CarveloAMArroyo-CerezoACuadros-RodríguezL (eds). Non-invasive and Non-destructive Methods for Food Integrity. Springer, 2024.
12.
AmigoJMCozzolinoD. Near-infrared techniques for fraud detection in dairy products: a review. J Food Sci2023; 88(8): 4007–4022.
13.
FazliSHarunMHJambariNN. Recent advances in the determination of milk adulterants and contaminants by mid-infrared spectroscopy. J Food Qual2023; 2023: e9738421.
14.
Barrera MorelliJMcGoverinCNieuwoudtM, et al.Chemometric techniques for the prediction of milk composition from MIR spectral data: a review. Food Chem2025; 469: 142465.
15.
YangZMaCGuJ, et al.A sensitive surface-enhanced Raman spectroscopy method for detecting tetracycline in milk. Appl Spectrosc2021; 75(5): 589–595.
16.
CattaneoTMPHolroydSE. The use of near infrared spectroscopy for determination of adulteration and contamination in milk and milk powder: updating knowledge. J Near Infrared Spectrosc2013; 21(5): 341–349.
17.
Applications of Near Infrared Spectrometry for the Analysis of Milk and Milk Products. SE Holroyd, A Niemoller, Bulletin of the international dairy Federation, 497/2019.
18.
AbbasOPissardAVincentB. 3 - near-infrared, mid-infrared, and Raman spectroscopy. In: PicoY (eds). Chemical analysis of food (2nd ed.), Academic Press, 2020, pp. 77–134.
19.
HolroydSE. The use of near infrared spectroscopy on milk and milk products. J Near Infrared Spectrosc2013; 21: 311–322.
20.
XuRAdilMZJabeenS, et al.Recent advancements in chemometrics based non-destructive analytical techniques for rapid detection of adulterants in milk and dairy products – a review. Food Control2025; 174: 111247.
21.
KovacsZLukacsMBazarG, et al.Recent results of near infrared spectroscopy on the way “from farm to fork” or even further. In: BecKHuckC (eds). Proceedings of the 21st International Conference on Near Infrared Spectroscopy. ICNIR 2023. Springer, 2023.
22.
GaoGLiQLuoH, et al.Hyperspectral imaging coupled with chemometrics for non-destructive quantitative analyses of moisture content in simulated earthen sites. J Near Infrared Spectrosc2025; 0(0): 09670335251339783.
23.
ChaudhariAManeRKhotA, et al.Machine learning-based classification for milk quality assessment. In: 2025 3rd International Conference on Intelligent Data Communication Technologies and Internet of Things (IDCIoT), 2025, pp. 1441–1446. DOI: 10.1109/IDCIOT64235.2025.10914727.
24.
WindarsihARohmanAIrnawati, et al.The combination of vibrational spectroscopy and chemometrics for analysis of milk products adulteration. Int J Food Sci2021; 2021: 8853358.
25.
DeshpandeADeshpandeSDhandeS. NIR spectroscopy based milk classification and purity prediction. 2021 IEEE Pune Section International Conference (PuneCon), 2021, pp. 1–5. DOI: 10.1109/PuneCon52575.2021.9686473.
26.
MohamedHNagyPAgbabaJ, et al.Use of near and mid infra-red spectroscopy for analysis of protein, fat, lactose and total solids in raw cow and camel milk. Food Chem2021; 334: 127436.
27.
PageMJMcKenzieJEBossuytPM, et al.The PRISMA 2020 statement: an updated guideline for reporting systematic reviews. BMJ2021; 372: n71.
28.
ModiPBhanushaliJShahM, et al. A spectral dataset of both liquid and solid milk with impurities for some milk impurity detection. Mendeley Data. 2024; V1.
29.
Diaz-OlivaresJAvan NuenenAGoteMJ, et al.Near-infrared spectra dataset of milk composition in transmittance mode. Data Brief2023; 51: 109767.
30.
KhosroshahiMEPatelYWoll-MorisonV. Non-destructive assessment of milk quality using pulsed UV photoacoustic, fluorescence and near FTIR spectroscopy. Laser Phys Lett2022; 19(7): 075602.
31.
EsplandiúPLMarín-MéndezJ-JAlonso-SantamaríaM, et al.Fraud detection in the fishing sector using hyperspectral imaging. J Near Infrared Spectrosc2024; 32(3): 69–80.
32.
BarretoACruz-TiradoJPSicheR, et al.Determination of starch content in adulterated fresh cheese using hyperspectral imaging. Food Biosci2018; 21: 14–19.
33.
AsaduzzamanMKerschbaumerMBodnerM, et al.Short-wave near infrared spectroscopy for the quality control of milk. J Near Infrared Spectrosc2019; 28(1): 3–9.
34.
BećKBGrabskaJSieslerHW, et al.Handheld near-infrared spectrometers: where are we heading?NIR News2020; 31(3-4): 28–35.
35.
PuYPérez-MarínDO’SheaN, et al.Recent advances in portable and handheld NIR spectrometers and applications in milk, cheese and dairy powders. Foods2021; 10: 2377.
36.
RiuJGorlaGChakifD, et al.Rapid analysis of milk using low-cost pocket-size NIR spectrometers and multivariate analysis. Foods2020; 9: 1090.
37.
UusitaloSDiaz-OlivaresJSumenJ, et al.Evaluation of MEMS NIR spectrometers for on-farm analysis of raw milk composition. Foods2021; 10(11): 2686.
38.
PannuratNPhothisonothaiMSooknuanT, et al.Development of mobile application monitoring for Thai food and beverage based on portable near-infrared spectroscopy technology. In: Proc 2023 20th Int Conf Electr Eng/Electronics, Comput, Telecommun Inf Technol (ECTI-CON), 2023, pp. 1–4. DOI: 10.1109/ECTI-CON58255.2023.10153135.
39.
MunawarAAHizirCErikaC, et al.Fast and simultaneous prediction of inner quality parameters on intact mangos by near infrared spectroscopy: impact of spectra pre-processing on prediction accuracy. Future Foods2024; 10: 100463.
40.
BhujleHVVadavadagir BH. Preprocessing of hyperspectral imaging. Inter J Sci Engine Tech2024; 12(6): 2348–4098.
41.
SimJMcGoverinCOeyI, et al.Optimisation of vibrational spectroscopy instruments and pre-processing for classification problems across various decision parameters. Farmaco2024; 3: 52–63.
42.
Jiao YLiZChenX, et al.Preprocessing methods for near-infrared spectrum calibration. J Chemometr2020; 34: e3306.
43.
IskandarCDGholibGPutriRAN, et al.Rapid detection of bacterial contaminants in cow’s milk using near infrared reflectance spectroscopy. Int J Des Nat Ecodyn2025; 20: 683–689.
44.
VestergaardRJVasavaHBAspinallD, et al.Evaluation of optimized preprocessing and modeling algorithms for prediction of soil properties using VIS-NIR spectroscopy. Sensors2021; 21: 6745.
45.
KangRWangXZhaoM, et al.A comparison of benchtop and micro NIR spectrometers for infant milk formula powder storage time discrimination and particle size prediction using chemometrics and denoising methods. J Food Eng2022; 329: 111087.
46.
AriantiNDSaputraESitorusA. An automatic generation of pre-processing strategy combined with machine learning multivariate analysis for NIR spectral data. J Agric Food Res2023; 13: 100625.
47.
RogerJ-MBiancolilloAMariniF. Sequential preprocessing through ORThogonalization (SPORT) and its application to near infrared spectroscopy. Chemometr Intell Lab Syst2020; 199: 103975.
48.
WangSLinMMengY, et al.Self-expansion full information optimization strategy: convenient and efficient method for near infrared spectrum auto-analysis. Spectrochim Acta Mol Biomol Spectrosc2023; 303: 123224.
49.
WangSZhangPChangJ, et al.A powerful tool for near-infrared spectroscopy: synergy adaptive moving window algorithm based on the immune support vector machine. Spectrochim Acta Mol Biomol Spectrosc2022; 282: 121631.
50.
LiuM-CXueH-RLiuJ-P. Study on nondestructive testing of milk varieties based on hyperspectral image technology. In: 2021 International Conference on Communications. Information System and Computer Engineering (CISCE), 2021, pp. 419–422. DOI: 10.1109/CISCE52179.2021.9445910.
51.
LiQXieJZhangJ, et al.A global model for the determination of prohibited addition in pesticide formulations by near infrared spectroscopy. Infrared Phys Technol2020; 105: 103191.
52.
LiXFuXLiH. A CARS-SPA-GA feature wavelength selection method based on hyperspectral imaging with potato leaf disease classification. Sensors2024; 24: 6566.
53.
LiuSTingLGuipuL, et al.Rapid detection of micronutrient components in infant formula milk powder using near-infrared spectroscopy. Front Nutr2023; 10: 2296–2861X.
54.
ZhaoXWangYLiuX, et al.Single- and multiple-adulterants determinations of goat milk powder by NIR spectroscopy combined with chemometric algorithms. Agric2022; 12: 434.
55.
AliAHamrazMKumamP, et al.A k-nearest neighbours based ensemble via optimal model selection for regression. IEEE Access2020; 8: 132095–132105.
56.
PuHYuJSunD-W, et al.Feature construction methods for processing and analysing spectral images and their applications in food quality inspection. Trends Food Sci Technol2023; 138: 726–737.
57.
GrassiSTarapoulouziMD’AlessandroA, et al.How chemometrics can fight milk adulteration. Foods2023; 12(1): 139.
58.
KarunathilakaSRYakesBJHeK, et al.Non-targeted NIR spectroscopy and SIMCA classification for commercial milk powder authentication: a study using eleven potential adulterants. Heliyon2018; 4(12): e00806.
59.
de Moraes-NetoVFCosta-SantosACPalloneJAL. Portable near-infrared spectrometer in tandem with chemometrics as an option for the authenticating commercial A2 bovine milk. Food Anal Methods2025; 18(6): 1009–1018.
60.
TeixeiraJLDPCaramêsETSBaptistaDP, et al.Vibrational spectroscopy and chemometrics tools for authenticity and improvement the safety control in goat milk. Food Control2020; 112: 107105.
61.
ChenHTanCLinZ, et al.Classification of different liquid milk by near-infrared spectroscopy and ensemble modeling. Spectrochim Acta Mol Biomol Spectrosc2021; 251: 119460.
62.
SamadATazeSKürsad UçarM. Enhancing milk quality detection with machine learning: a comparative analysis of KNN and distance-weighted KNN algorithms. Int J Innov Sci Res Technol2024; 9: 2021–2029. DOI: 10.38124/ijisrt/IJISRT24MAR2123.
63.
MohapatraRKSethuramanPJayalakshmiD, et al.Analysing model performance: KNN and Random Forest-Enhanced KNN in milk quality prediction. In: 2025 6th International Conference on Intelligent Communication Technologies and Virtual Mobile Networks (ICICV), 2025, pp. 832–836. DOI: 10.1109/ICICV64824.2025.11085865.
64.
WangYHuangYShenW, et al.Feasibility of hyperspectral imaging as rapid detection tool for potassium sorbate in milk. Spectrosc Lett2021; 54(4): 1–10.
65.
Soto-BarajasMCGonzález-MartínMISalvador-EstebanJ, et al.Prediction of the type of milk and degree of ripening in cheeses by means of artificial neural networks with data concerning fatty acids and near infrared spectroscopy. Talanta2013; 116: 50–55.
66.
ElamshityMGAlhamdanAM. Non-destructive evaluation of the physiochemical properties of milk drink flavored with date syrup utilizing VIS-NIR spectroscopy and ANN analysis. Foods2024; 13: 524.
67.
BehkamiSZainSMGholamiM, et al.Classification of cow milk using artificial neural network developed from the spectral data of single- and three-detector spectrophotometers. Food Chem2019; 294: 309–315.
68.
Allende-PrietoCFernándezLAgúnS, et al.Near infrared spectroscopy for bacterial detection in the dairy industry. IEEE Sens J2023; 23(21): 26107–26113.
69.
MusaMAYangS. Detection and quantification of cow milk adulteration using portable near-infrared spectroscopy combined with chemometrics. Afr J Agric Res2021; 17(2): 198–207.
70.
MusaMA. Evaluation of portable NIR assay for detection of milk adulteration with water. Adv Dairy Res2022; 10(1): 1000599.
71.
NazriFMIsaNMMuhamadM, et al.Non-invasive detection of urea adulteration in milk using optical sensing techniques. Springer, 2025. DOI: 10.1007/978-3-031-78918-2_38.
72.
SoulatJAnduezaDGrauletB, et al.Comparison of the potential abilities of three spectroscopy methods: Near-infrared, mid-infrared, and molecular fluorescence, to predict carotenoid, vitamin and fatty acid contents in cow milk. Foods2020; 9: 592.
73.
LiangQXiaYFCheJK, et al.Detection of water adulteration levels in milk using near-infrared spectroscopy combined with chemometrics. J Dairy Sci2025; 108(7): 6852–6866.
74.
WilliamsPDardennePFlinnP. Tutorial: items to be included in a report on a near infrared spectroscopy project. J Near Infrared Spectrosc2017; 25(2): 85–90.
75.
MishraPPassosDMariniF, et al.Deep learning for near-infrared spectral data modelling: hypes and benefits. TrAC Trends Anal Chem2022; 157: 116804.
76.
OthmanSMavaniNRHussainMA, et al.Artificial intelligence-based techniques for adulteration and defect detections in food and agricultural industry: a review. J Agric Food Res2023; 12: 100590.
77.
TsenkovaRMuncanJ. Near-infrared spectroscopy for milk quality analysis: the state of the art. In: Aquaphotomics for Bio-diagnostics in Dairy. Springer, 2022.
78.
DurgunM. Real-time milk quality control using multi-spectral sensing and edge computing: advancing On-Site detection of milk components with XGBoost. Appl Sci2024; 14: 10916.
79.
MahendrakumarSGovindanKMuthuvinayagamM, et al.Cloud technology-enabled IoT milk quality assessment and billing system for dairy cooperatives. In: 2025 International Conference on Multi-Agent Systems for Collaborative Intelligence (ICMSCI), 2025, pp. 496–501. DOI: 10.1109/ICMSCI62561.2025.10894500.
80.
HassounAJagtapSGarcia-GarciaG, et al.Food quality 4.0: from traditional approaches to digitalized automated analysis. J Food Eng2023; 337: 111216.
81.
AnandanKSubbarajPKJeyakumarV, et al.Milk pit: IoT-Integrated milk analyzer with cloud-connected Mobile application. In: Proc 2025 3rd Int Conf on Intelligent Data Communication Technologies and Internet of Things (IDCIoT), 2025, pp. 680–686. DOI: 10.1109/IDCIOT64235.2025.10914881.
82.
ZhangWFuKXueH, et al.Predictive modeling of milk adulteration with urea content using the gray wolf optimization algorithm and long and short-term memory network model. Spectrosc Lett2024; 57(4): 201–212.
83.
CheXTianZBiZ, et al. Spectral technology for water quality detection: a review. Appl Spectrosc Rev. 2025; 1–26. doi:10.1080/05704928.2025.2529903.
84.
NguyenTHPNguyen VanHThanhXNT, et al.Advances in machine learning framework for near-infrared spectroscopy: a taxonomic review on food quality assessment. Informatica2025; 49: 121–148.
85.
Joolaei AhranjaniPDehghanKEsfandiariZ, et al. A systematic review of spectroscopic techniques for detecting milk adulteration. Crit Rev Anal Chem. 2025; 1–32.
86.
BecKBGrabskaJHuckCW. Interpretability in near-infrared (NIR) spectroscopy: current pathways to the long-standing challenge. TrAC Trends Anal Chem. 2025; 189: 118254.
87.
MohammadiSGowenALuoJ, et al.Prediction of milk composition using multivariate chemometric modelling of infrared, Raman, and fluorescence spectroscopic data: a review. Food Control2024; 165: 110658.
88.
VinothkannaADarOILiuZ, et al.Advanced detection tools in food fraud: a systematic review for holistic and rational detection method based on research and patents. Food Chem2024; 446: 138893.
89.
NíFÁMO’DonnellCPLuoJ, et al.A review on MIR, NIR, fluorescence and Raman spectroscopy combined with chemometric modeling to predict the functional properties of raw bovine milk. ACS Food Sci Technol2024; 4(10): 2258–2271.
90.
HossainMEKabirMAZhengL, et al.Near-infrared spectroscopy for analysing livestock diet quality: a systematic review. Heliyon2024; 10(22): e40016.
91.
TianWLiYGuzmanC, et al.Quantification of food bioactives by NIR spectroscopy: current insights, long-lasting challenges, and future trends. J Food Compos Anal2023; 124: 105708.
92.
YakubuHGKovacsZTothT, et al.The recent advances of near-infrared spectroscopy in dairy production—a review. Crit Rev Food Sci Nutr2020; 62(3): 1–22.
93.
XuesongHPuCJingyanL, et al.Commentary on the review articles of spectroscopy technology combined with chemometrics in the last three years. Appl Spectrosc Rev2023; 59(4): 423–482.
94.
GuerraADe MarchiMNieroG, et al.Application of a short-wave pocket-sized near-infrared spectrophotometer to predict milk quality traits. J Dairy Sci2024; 107(6): 3413–3419.
95.
GoiACostaADe MarchiM. The ability of a handheld near-infrared spectrometer to do a rapid quality assessment of bovine colostrum, including the immunoglobulin G concentration. J Dairy Sci2024; 107(7): 4344–4356.
96.
BuoioEColomboVIghinaE, et al.Rapid classification of milk using a cost-effective near infrared spectroscopy device and variable cluster–support vector machine (VC-SVM) hybrid models. Foods2024; 13: 3279.
97.
RisolutiRGullifaGMateraziS. Assessing the quality of milk using a multicomponent analytical platform MicroNIR/Chemometric. Front Chem2020; 8: 614718.
98.
Diaz-OlivaresJAAdriaensIStevensE, et al.Online milk composition analysis with an on-farm near-infrared sensor. Comput Electron Agric2020; 178: 105734.
99.
IwekaPKawamuraSMitaniT, et al.Online near-infrared spectroscopy for the measurement of cow milk quality in an automatic milking system. Eng Proc2023; 56: 145.
100.
TangorraFMLopezAIghinaE, et al.Handheld NIR spectroscopy combined with a hybrid LDA-SVM model for fast classification of retail milk. Foods2024; 13: 3577.
101.
Zinia ZaukuuJ-LAouadiBLukácsM, et al.Detecting low concentrations of nitrogen-based adulterants in whey protein powder using benchtop and handheld NIR spectrometers and the feasibility of scanning through plastic bag. Molecules2020; 25(11): 2522.
102.
WangYZhangKShiS, et al.Portable protein and fat detector in milk based on multi-spectral sensor and machine learning. Appl Sci2023; 13: 12320.
103.
GrassiSStraniLAlampreseC, et al.A FT-NIR process analytical technology approach for milk renneting control. Foods2022; 11: 33.
104.
EhsaniSDastgerdyEMYazdanpanahH, et al.Ensemble classification and regression techniques combined with portable near infrared spectroscopy for facile and rapid detection of water adulteration in bovine raw milk. J Chemom2023; 37(1): e3395.
105.
SitorusALapcharoensukR. Development of automatic tuning for combined preprocessing and hyperparameters of machine learning and its application to NIR spectral data of coconut milk adulteration. Food Chem2024; 457: 140108.
106.
da Silva PereiraECruz-TiradoJPLourenço CrippaB, et al.Portable near infrared (NIR) spectrometer coupled with machine learning to classify milk with subclinical mastitis. Food Control2024; 163: 110527.
107.
ZhangWYangKYuN, et al.Daily milk yield prediction of dairy cows based on the GA-LSTM algorithm. In: 2020 15th IEEE International Conference on Signal Processing (ICSP). IEEE, 2020. DOI: 10.1109/ICSP48669.2020.9320926.
108.
HosseiniEGhasemiJBDaraeiB, et al.Near-infrared spectroscopy and machine learning-based classification and calibration methods in detection and measurement of anionic surfactant in milk. J Food Compos Anal2021; 104: 104170.
109.
XiaZYangJWangJ, et al.Optimizing rice near-infrared models using fractional order savitzky–golay derivation (FOSGD) combined with Competitive Adaptive Reweighted Sampling (CARS). Appl Spectrosc2020; 74(4): 417–426.
110.
NavarroNSAlbanellEDe MarchiM, et al.An attempt to identify milk protein fraction genotypes using unsupervised and supervised near-infrared spectroscopy methods. Ital J Anim Sci2024; 23(1): 313–319.
111.
YuanLMYangXFuX, et al.Consensual regression of lasso-sparse PLS models for near-infrared spectra of food. Agriculture2022; 12: 1804.
112.
WuXWangYHeC, et al.Several feature extraction methods combined with near-infrared spectroscopy for identifying the geographical origins of milk. Foods2024; 13(11): 1783.
113.
AqeelMSohaibAIqbalM, et al.Milk adulteration identification using hyperspectral imaging and machine learning. J Dairy Sci2025; 108(2): 1301–1314.
114.
ShreeSKVijayarajanVBamaBS, et al.Milk quality inspection using hyperspectral imaging. In: 2023 International Conference on Signal Processing, Computation, Electronics, Power and Telecommunication (IConSCEPT), 2023, pp. 1–6.
115.
SharifiFNaderi-BoldajiMGhasemi-VarnamkhastiM, et al.Feasibility study of detecting some milk adulterations using a LED-based Vis-SWNIR photoacoustic spectroscopy system. Food Chem2023; 424: 136411.
116.
MazivilaSJPáscoaRNMJCastroRC, et al.Detection of melamine and sucrose as adulterants in milk powder using near-infrared spectroscopy with DD-SIMCA as one-class classifier and MCR-ALS as a means to provide pure profiles of milk and of both adulterants with forensic evidence: a short communication. Talanta2020; 216: 120937.
117.
KambojUKaushalNMishraS, et al.Application of selective near infrared spectroscopy for qualitative and quantitative prediction of water adulteration in milk. Mater Today Proc2020; 24: 2449–2456.
118.
MaboodFAliLBoqueR, et al.Robust Fourier transformed infrared spectroscopy coupled with multivariate methods for detection and quantification of urea adulteration in fresh milk samples. Food Sci Nutr2019; 8(10): 5249–5258.
