Use of near and mid infra-red spectroscopy for analysis of protein, fat, lactose and total solids in raw cow and camel milk

Integrating mid-infrared spectroscopy, machine learning, and graphical bias correction for fatty acid prediction in water buffalo milk

BACKGROUND

Buffalo milk, constituting 15% of global production, has higher fatty acids content than Holstein milk. Fourier-transform mid-infrared (FT-MIR) spectroscopy is widely used for dairy analysis, but its application to buffalo milk, with larger fat globules, remains understudied. The ultimate goal of this study is to develop machine learning models based on FT-MIR spectroscopy for predicting fatty acids in buffalo milk and to assess the accuracy of commercial milk analyzers. This research provides a convenient, fast, and environmentally friendly method for detecting the fatty acid composition in buffalo milk.

RESULTS

We employed six machine learning algorithms to establish a detection model for 34 fatty acids in buffalo milk. The predictive models demonstrated robust capabilities for high-content fatty acids [C14:0, C15:0, C16:0, C17:0, C18:0, C18:1, saturated fatty acid (SFA), monounsaturated fatty acid (MUFA)], with errors within a 15% range. Traditional FT6000 detection methods exhibited limitations in measuring SFAs and polyunsaturated fatty acids (PUFA). Implementing a mean difference correction of 0.21 for MUFAs and applying regression equations (SFA × 1.0639 + 0.0705; PUFA × 0.5472 + 0.0047) significantly improved measurement accuracy.

CONCLUSION

This study successfully developed a predictive model for fatty acids in Mediterranean buffalo milk based on FT-MIR spectroscopy. Additionally, a correction was applied to the existing measurement device, FT6000, enabling more accurate measurements of fatty acids in buffalo milk. The findings have practical implications for the food industry, offering a faster and more reliable approach to assess and monitor fatty acid composition in buffalo milk, potentially influencing product development and quality control processes. © 2024 Society of Chemical Industry.

Several Feature Extraction Methods Combined with Near-Infrared Spectroscopy for Identifying the Geographical Origins of Milk

Milk is a kind of dairy product with high nutritive value. Tracing the origin of milk can uphold the interests of consumers as well as the stability of the dairy market. In this study, a fuzzy direct linear discriminant analysis (FDLDA) is proposed to extract the near-infrared spectral information of milk by combining fuzzy set theory with direct linear discriminant analysis (DLDA). First, spectral data of the milk samples were collected by a portable NIR spectrometer. Then, the data were preprocessed by Savitzky-Golay (SG) and standard normal variables (SNV) to reduce noise, and the dimensionality of the spectral data was decreased by principal component analysis (PCA). Furthermore, linear discriminant analysis (LDA), DLDA, and FDLDA were employed to transform the spectral data into feature space. Finally, the k-nearest neighbor (KNN) classifier, extreme learning machine (ELM) and naïve Bayes classifier were used for classification. The results of the study showed that the classification accuracy of FDLDA was higher than DLDA when the KNN classifier was used. The highest recognition accuracy of FDLDA, DLDA, and LDA could reach 97.33%, 94.67%, and 94.67%. The classification accuracy of FDLDA was also higher than DLDA when using ELM and naïve Bayes classifiers, but the highest recognition accuracy was 88.24% and 92.00%, respectively. Therefore, the KNN classifier outperformed the ELM and naïve Bayes classifiers. This study demonstrated that combining FDLDA, DLDA, and LDA with NIR spectroscopy as an effective method for determining the origin of milk.

Properties of low-fat Cheddar cheese prepared from bovine-camel milk blends: Chemical composition, microstructure, rheology, and volatile compounds

Making cheese from camel milk (CM) presents various challenges due to its different physicochemical properties compared with bovine milk (BM). In this study, we investigated the chemical composition, proteolysis, meltability, oiling off, texture profile, color, microstructure, and rheological properties of low-fat Cheddar cheese (LFCC) prepared from BM-CM blends. LFCC was produced from BM or BM supplemented with 15% CM (CM15) and 30% CM (CM30), and analyzed after 14, 60, 120, and 180 d of ripening at 8°C. Except for salt content, no significant differences were observed among LFCC from BM, CM15, and CM30. The addition of CM increased the meltability and oiling off in the resulting cheese throughout storage. With respect to color properties, after melting, LFCC CM30 showed lower L* values than LFCC made from BM and CM15, and a* and b* values were higher than those of BM and CM15 samples. LFCC from CM30 also exhibited lower hardness compared with the other cheeses. Moreover, LFCC made from BM showed a rough granular surface, but cheese samples made from BM-CM blends exhibited a smooth surface. The rheological parameters, including storage modulus, loss modulus, and loss tangent, varied among cheese treatments. The determined acetoin and short-chain volatile acids (C2-C6) in LFCC were affected by the use of CM, because CM15 showed significantly higher amounts than BM and CM30, respectively. The detailed interactions between BM and CM in the cheese matrix should be further investigated.

Camel Milk: Antimicrobial Agents, Fermented Products, and Shelf Life

The camel milk (CM) industry has witnessed a notable expansion in recent years. This expansion is primarily driven by the rising demand for CM and its fermented products. The perceived health and nutritional benefits of these products are mainly responsible for their increasing popularity. The composition of CM can vary significantly due to various factors, including the breed of the camel, its age, the stage of lactation, region, and season. CM contains several beneficial substances, including antimicrobial agents, such as lactoferrin, lysozyme, immunoglobulin G, lactoperoxidase, and N-acetyl-D-glucosaminidase, which protect it from contamination by spoilage and pathogenic bacteria, and contribute to its longer shelf life compared to bovine milk (BM). Nevertheless, certain harmful bacteria, such as Listeria monocytogenes, Yersinia enterocolitica, and Escherichia coli, have been detected in CM, which is a significant public health concern. Therefore, it is crucial to understand and monitor the microbial profile of CM and follow good manufacturing practices to guarantee its safety and quality. This review article explores various aspects of CM, including the types of beneficial and harmful bacteria present in it, the composition of the milk, its antimicrobial properties, its shelf life, and the production of fermented CM products.

A novel simultaneous quantitative method for differential volatile components in herbs based on combined near-infrared and mid-infrared spectroscopy

A novel method based on GC-MS, near-infrared (NIR) and mid-infrared (MIR) spectroscopy combined with chemometrics was established to simultaneously analyze differential volatile components (DVCs) of herb samples. Herein, Florists Chrysanthemum was adopted as the representative sample. Through the introduction of Automatic data analysis workflow (AntDAS) and one-class partial least squares discriminant analysis (O-PLSDA) model, five kinds of terpenes and five kinds of alcohols were efficiently screened as DVCs. By using the selected NIR-MIR spectra sections combined with O-PLSDA, it could achieve the accurate identification of Florists Chrysanthemum from Chrysanthemum morifolium Ramat. What's more, since the selected spectra sections were closely related to the structural and content of DVCs, they could be further used for simultaneous quantitative analysis of DVCs combined with optimized variable-weighted least-squares support vector machine based on particle swarm optimization (PSO-VWLS-SVM). This method only adopted the same NIR-MIR sections for multiple component accurate quantification, highlighting its convenience.

Detection of protein, starch, oil, and moisture content of corn kernels using one-dimensional convolutional autoencoder and near-infrared spectroscopy

Background

Analysis of the nutritional values and chemical composition of grain products plays an essential role in determining the quality of the products. Near-infrared spectroscopy has attracted the attention of researchers in recent years due to its advantages in the analysis process. However, preprocessing and regression models in near-infrared spectroscopy are usually determined by trial and error. Combining newly popular deep learning algorithms with near-infrared spectroscopy has brought a new perspective to this area.

Methods

This article presents a new method that combines a one-dimensional convolutional autoencoder with near-infrared spectroscopy to analyze the protein, moisture, oil, and starch content of corn kernels. First, a one-dimensional convolutional autoencoder model was created for three different spectra in the corn dataset. Thirty-two latent variables were obtained for each spectrum, which is a low-dimensional spectrum representation. Multiple linear regression models were built for each target using the latent variables of obtained autoencoder models.

Results

R², RMSE, and RMSPE were used to show the performance of the proposed model. The created one-dimensional convolutional autoencoder model achieved a high reconstruction rate with a mean RMSPE value of 1.90% and 2.27% for calibration and prediction sets, respectively. This way, a spectrum with 700 features was converted to only 32 features. The created MLR models which use these features as input were compared to partial least squares regression and principal component regression combined with various preprocessing methods. Experimental results indicate that the proposed method has superior performance, especially in MP5 and MP6 datasets.

Implications of placentation type on species-specific colostrum properties in mammals

Maternal care is essential to optimally support survival of the offspring. During evolution of mammalian species, different phenotypes have evolved in relation to gestation length, number, size, and maturation stage of the offspring at parturition, as well as colostrum and milk composition. The aim of the present review is to describe relationships between placental function and colostrum and milk composition in different mammalian species. Species covered in this article include humans, rabbits, rodents (rat and mouse), carnivores (cats and dogs), and a variety of ungulate species (cattle, sheep, goats, pigs, and horses). Species-specific aspects are elucidated with a special focus on the transfer of passive immunity. In this regard, the structure and thus the capability of the placenta to transport immunoglobulins from maternal to fetal circulation in utero dictates the necessity of the passive transfer of immunity via colostrum. Consequently, species with exclusive postpartal transfer of immunity such as in all ungulate species have greater immunoglobulin G concentrations in colostrum than species with a prepartal transfer in utero, where especially immunoglobulin A with its local immune function in the gastrointestinal tract is present in colostrum (e.g., rabbit and human). In terms of the nutritional purpose, suckling frequency is an important factor determining the gross composition of colostrum as well as in the mature milk of these species. Milk of nidicolous animals with long intervals in-between suckling events contains more fat than milk of nidifugous animals with constant access to their mother. However, the importance of colostrum and milk consumption for newborn animals and human babies goes beyond nutrition and the transfer of immunity. Numerous bioactive components such as growth factors, hormones, and oligosaccharides are enriched in colostrum and transition milk, which support the development of the intestinal tract and local immune system.

Separation and purification of antioxidant peptides from purple speckled kidney bean by macroporous adsorption resin and analysis of amino acid composition

The protein hydrolysate of purple speckled kidney bean (PSKB) was used as the raw material in this study, and the antioxidant peptide of the PSKB protein hydrolysate was purified using macroporous resin. The XAD-7HP macroporous resin was selected as the best purification material, and the static adsorption-desorption of the purified PSKB antioxidant peptide was optimized. The optimum static adsorption and desorption conditions were as follows: the adsorption capacity reached 11.93 ± 0.11 mg/ml at pH 7 for 24 h, and the desorption capacity was 5.24 ± 0.04 mg/ml with 60% ethanol for 30 min. Under this condition, the amount of antioxidant peptide obtained by adsorption-desorption was the highest. The optimum process conditions were as follows: the appropriate flow rate was 1 ml/min, and the optimal injection volume was 40 ml. The adsorption amount at this time can reach 12.19 ± 0.15 mg/ml. The components with an elution time of 10-30 min were separated using the reversed-phase high-performance liquid chromatography (RP-HPLC) technique to obtain three main components, namely, RP₁, RP₂, and RP₃. The DPPH free radical scavenging ability reached 56.26 ± 0.56, 66.42 ± 0.56, and 78.57 ± 0.56%, respectively, which were 36.65, 46.34 ± 0.56, and 54.39 ± 0.56% higher than those before purification. The amino acid sequences of the three components were identified as Phe-Leu-Val-Asp-Arg-Ile, Phe-Leu-Val-Ala-Pro-Asp-Asp, and Lys-Asp-Arg-Val-Ile-Ser-Glu-Leu.

Yak (Bos grunniens) milk improves bone mass and microarchitecture in mice with osteoporosis

The effect of milk on bone health is controversial. In this study, the effects of yak milk in mice with retinoic acid-induced osteoporosis (OP) were evaluated. Yak milk was provided to OP mice as a nutrition supplement for 6 wk. The results showed that yak milk significantly reduced bone turnover markers (tartrate acid phosphatase and alkaline phosphatase). The yak milk treatment was also associated with remarkably increased bone mineral density, bone volume, trabecular thickness, and trabecular number, as well as improved biomechanical properties (maximum load and stress) of the tibia. Furthermore, yak milk mitigated the deterioration of the network and thickness of trabecular bone in treated OP mice compared with the OP model group. The results indicated that yak milk could improve bone mass and microarchitecture through the inhibition of bone resorption in OP mice.

Bioactive natural products in donkey and camel milk: a perspective review

Mammalian milk has numerous components that exhibit chemical and functional activities. They support human homeostasis. Immunoglobulins, peptides with antibacterial and antimicrobial activities, carbohydrates, lipids, and minor molecules have positive effects on health. Beyond the nutritional values of milk, milk-borne biologically active compounds such as proteins and other minor constituents exhibit essential physiological and biochemical functions. Human milk guarantees a healthy development and improves immunity. It is hypoallergenic. Sometimes, it is necessary to substitute this food with other milk for different reasons. Cow, sheep, goat, camel and donkey milk are natural alternatives. We evaluated the different compounds within donkey and camel milk analysing their biomolecular characteristics and potential benefits for human health. Camel and donkey milk bioactive products could be good candidates for controlling several diseases and excellent substitutes in the case of milk protein allergies in infants. However, more research should be conducted to further evaluate their nutraceutical potential.

Identification of milk quality and adulteration by surface-enhanced infrared absorption spectroscopy coupled to artificial neural networks using citrate-capped silver nanoislands

Milk is one of the most important multicomponent superfoods owing to its rich macronutrient composition. It requires quality control at all the production stages from the farm to the finished products. A localized surface plasmon resonance optical sensor based on a citrate-capped silver nanoparticle (Cit-AgNP)–coated glass substrate was developed. The fabrication of such sensors involved a single-step synthesis of Cit-AgNPs followed by surface modification of glass slides to be coated with the nanoparticles. The scanning electron microscope micrographs demonstrated that the nanoparticles formed monolayer islands on glass slides. The developed surface-enhanced infrared absorption spectroscopy (SEIRA) sensor was coupled to artificial neural networking (ANN) for the qualitative differentiation between cow, camel, goat, buffalo, and infants’ formula powdered milk types. Moreover, it can be used for the quantitative determination of the main milk components such as fat, casein, urea, and lactose in each milk type. The qualitative results showed that the obtained FTIR spectra of cow and buffalo milk have high similarity, whereas camel milk resembled infant formula powdered milk. The most difference in FTIR characteristics was evidenced in the case of goat milk. The developed sensor adds several advantages over the traditional techniques of milk analysis using MilkoScan™ such as less generated waste, elimination of pre-treatment steps, minimal sample volume, low operation time, and on-site analysis.

The Texture of Camel Milk Cheese: Effects of Milk Composition, Coagulants, and Processing Conditions

Numerous people in African, Middle Asian, Middle Eastern, and Gulf Cooperation Council (GCC) countries highly value camel milk (CM) as it plays a vital role in their diet. The protein composition of CM as well as the structure of its casein micelles differs significantly from bovine milk (BM). Cheeses made from CM have a weak curd and soft texture compared to those made from BM. This review article presents and discusses the effect of milk protein composition, processing conditions (pasteurization and high-pressure treatment), and coagulants (camel chymosin, organic acids, plant proteases) on the quality of CM cheeses. CM cheese's weak texture is due to compositional characteristics of the milk, including low κ-casein-to-β-casein ratio (≈0.05 in CM vs. ≈0.33 in BM), large micelle size, different whey protein components, and higher proteolytic activity than BM. CM cheese texture can be improved by preheating the milk at low temperatures or by high pressure. Supplementing CM with calcium has shown inconsistent results on cheese texture, which may be due to interactions with other processing conditions. Despite their structure, CM cheeses are generally well liked in sensory studies.

Low-fat akawi cheese made from bovine-camel milk blends: Rheological properties and microstructural characteristics

Camel milk (CM) can be used as an ingredient to produce various dairy products but it forms weak rennet-induced and acid-induced gels compared with bovine milk (BM). Therefore, in this study, we aimed to investigate the effect of blending bovine milk with camel milk on the physicochemical, rheological (amplitude sweep and frequency sweep), and microstructural properties of low-fat akawi (LFA) cheese. The cheeses were made of BM only or BM blended with 15% (CM15%) or 30% (CM30%) camel milk and stored at 4°C for 28 d. The viscoelastic properties as a function of temperature were assessed. The LFA cheeses made from blended milks had higher moisture, total Ca, and soluble Ca contents, and had higher pH 4.6-water-soluble nitrogen compared with those made from BM. Analysis by scanning electron microscopy demonstrated that the microstructures formed in BM cheese were rough with granular surfaces, whereas those in blended milk cheeses had smooth surfaces. Hardness was lower for LFA cheeses made from blended milk than for those made from BM only. The LFA cheeses demonstrated viscoelastic behavior in a linear viscoelastic range from 0.1 to 1.0% strain. The storage modulus (G') was lower in LFA cheese made from BM over a range of frequencies. Adding CM reduced the resistance of LFA cheeses to flow as temperature increased. Blended cheeses exhibited lower complex viscosity values than BM cheeses during temperature increases. Thus, the addition of camel milk improved the rheological properties of LFA cheese.

Miniaturized cost-effective broadband spectrometer employing a deconvolution reconstruction algorithm for resolution enhancement

We demonstrate a miniaturized broadband spectrometer employing a reconstruction algorithm for resolution enhancement. We use an opto-digital co-design approach, by firstly designing an optical system with certain residual aberrations and then correcting these aberrations with a digital algorithm. The proposed optical design provides an optical resolution less than 1.7 nm in the VIS-channel (400-790 nm) and less than 3.4 nm in the NIR-channel (760-1520 nm). Tolerance analysis results show that the components are within a commercial class, ensuring a cost-efficient design. We build the prototype with a size of 37x30x26 mm³ and demonstrate that by applying a restoration algorithm, the optical resolution can be further improved to less than 1.3 nm (VIS-channel) and less than 2.3 nm (NIR-channel).

Camel Milk Targeting Insulin Receptor—Toward Understanding the Antidiabetic Effects of Camel Milk

Camel milk (CM) is known for its beneficial virtues in the human diet and health. This includes its antidiabetic properties demonstrated in many in vitro and in vivo studies. Nevertheless, the scientific rationale behind the molecular and cellular basis of such beneficial effects and the exact antidiabetic agent(s)/mechanism(s) are still elusive. In this review, we focused on the recent advances supporting the targeting of insulin receptor (IR) by CM components. Indeed, our recent work reported that CM proteins and derived peptides pharmacologically target IR in vitro leading to its activation and potentiation of insulin-mediated responses. The review describes the experimental approaches used to investigate the effects of CM on IR in vitro based on the fractionation of CM whey proteins to purify functional proteins and their hydrolysis by gastric proteases to generate bioactive peptides. In addition, we illustrated our cellular and molecular model consisting of studying the functional activity of CM fractions on IR and its downstream signaling pathways in the hepatocarcinoma (HepG2) and the human embryonic kidney (HEK293) cells using the bioluminescence resonance energy transfer (BRET), phosphorylation, and glucose uptake assays. Overall, our work demonstrated for the first time that CM lactoferrin and CM-derived bioactive peptides positively modulate IR and its related signaling pathways in HepG2 and HEK293 cells. As a conclusion, the pharmacological targeting of IR by CM sheds more light on the antidiabetic properties of CM by providing its molecular basis that may constitute a solid rationale for the development of new generation of antidiabetic tools from CM-derived proteins and peptides and the utilization of CM in the management of diabetes. The sequencing and the synthesis of the potent bioactive CM peptides may open promising perspectives for their application as antidiabetic agents.

Camel and bovine milk lactoferrins activate insulin receptor and its related AKT and ERK1/2 pathways

Lactoferrin (LF) is a milk protein that may be an interesting candidate for the antidiabetic properties of milk due to its well-documented bioactivity and implication in diabetes. Here, we investigated the functional action of LF purified from camel and bovine milk (cLF, bLF) on insulin receptors (IR) and their pharmacology and signaling in hepatocarcinoma (HepG2) and human embryonic kidney (HEK293) cells. For this, we examined IR activation by bioluminescence resonance energy transfer (BRET) technology and the phosphorylation of its key downstream signaling kinases by western blot. The purified cLF and bLF induced phosphorylation of IR, AKT, and ERK1/2 in HepG2 and HEK293 cells. The BRET assays in HEK293 cells confirm the pharmacological action of cLF and bLF on IR, with a possible allosteric mode of action. This reveals for the first time the bioactivity of LF toward IR function, indicating it as a potential bioactive protein behind the antidiabetic properties of camel milk.

Measurement of lactose concentration in milk by using engineered bacteria producing lycopene

Lycopene is an orange-red carotenoid, which confers a visual phenotype to assess genetic transformation of microorganisms. In this study, the lycopene synthesis pathway was constructed in engineered Escherichia coli BL21 (DE3) by transforming plasmid pET-15b-crtBEI, wherein crtB, crtE, and crtI could be expressed under the control of T7 promoter and LacO operator gene, and lycopene could be accumulated in the engineered bacteria upon induction of lactose. A good linear relationship was observed between the lycopene content in engineered bacterial culture and lactose concentration in the range of 4-52 g/L; using this relation, the lactose concentration in milk could be determined. This method could be used to overcome several limitations of the high-performance liquid chromatography (HPLC) method for lactose detection, such as cumbersome sample preparation and expensive detection equipment. Moreover, this method required only a clean bench, shaker, and spectrophotometer for lactose analysis. Additionally, no significant difference was observed between this method and HPLC in terms of lactose measurement in milk, indicating that this method is reasonable and simple.

Application of Optical Quality Control Technologies in the Dairy Industry: An Overview

Sustainable development of the agricultural industry, in particular, the production of milk and feed for farm animals, requires accurate, fast, and non-invasive diagnostic tools. Currently, there is a rapid development of a number of analytical methods and approaches that meet these requirements. Infrared spectrometry in the near and mid-IR range is especially widespread. Progress has been made not only in the physical methods of carrying out measurements, but significant advances have also been achieved in the development of mathematical processing of the received signals. This review is devoted to the comparison of modern methods and devices used to control the quality of milk and feed for farm animals.

Infrared spectroscopy combined with random forest to determine tylosin residues in powdered milk

The contamination of milk by antibiotic residues is a worldwide health and food safety problem. There is a need to develop new methods for the rapid determination of antibiotic residues in milk. A method has been developed for determining tylosin residues directly in powdered milk using Fourier Transformed Infrared spectroscopy (FTIR). Tylosin is a broad-spectrum macrolide antibiotic. The spectra obtained were submitted to chemometric analysis to obtain a prediction model for tylosin concentration in powdered milk. Using the Boruta algorithm, the absorption bands related to the milk contamination by the antibiotic were identified. Random forest was shown to be adequate for the prediction of tylosin residues in milk at low concentrations (≤ 100 μg L^-1) and the prediction model generated showed high correlation and determination coefficients (greater than 0.95). The proposed methodology proved to be efficient for the investigation of antibiotic residues in powdered milk.

Effects of Pasteurization and High-Pressure Processing of Camel and Bovine Cheese Quality, and Proteolysis Contribution to Camel Cheese Softness

The effects of high-pressure processing (HPP) compared to thermal treatments on the quality of camel vs. bovine cheeses were studied. The study showed that camel milk has a lower microbial load compared to bovine milk, which is maintained during 7 days' storage of the processed milk. The effect of three HPP treatments (350, 450, and 550 MPa for 5 min at 4°C) and two pasteurization treatments (65°C for 30 min and 75°C for 30 s) on the quality of soft unripened camel and bovine milk cheeses were accessed. The cheeses were evaluated for pH, yield, proximate composition, textural and rheological properties, microstructure, and protein profile by SDS-PAGE electrophoresis. The effects of the treatments on cheese's hardness were different between the camel and bovine cheeses; while heat treatment at 65°C for 30 min gave the hardest bovine milk cheese (1,253 ± 20), HPP treatment at 350 MPa for 5 min gave the highest value for camel milk cheese (519 ± 5) (p < 0.05). The hardness of the cheeses was associated with low yield and moisture content. SDS-PAGE electrophoresis revealed that extensive proteolysis might have contributed to the softness of camel cheeses compared to bovine and suggested the involvement of some residual enzyme activities.

Milk as a Complex Multiphase Polydisperse System: Approaches for the Quantitative and Qualitative Analysis

Milk is a product that requires quality control at all stages of production: from the dairy farm, processing at the dairy plant to finished products. Milk is a complex multiphase polydisperse system, whose components not only determine the quality and price of raw milk, but also reflect the physiological state of the herd. Today’s production volumes and rates require simple, fast, cost-effective, and accurate analytical methods, and most manufacturers want to move away from methods that use reagents that increase analysis time and move to rapid analysis methods. The review presents methods for the rapid determination of the main components of milk, examines their advantages and disadvantages. Optical spectroscopy is a fast, non-destructive, precise, and reliable tool for determination of the main constituents and common adulterants in milk. While mid-infrared spectroscopy is a well-established off-line laboratory technique for the routine quality control of milk, near-infrared technologies provide relatively low-cost and robust solutions suitable for on-site and in-line applications on milking farms and dairy production facilities. Other techniques, discussed in this review, including Raman spectroscopy, atomic spectroscopy, molecular fluorescence spectroscopy, are also used for milk analysis but much less extensively. Acoustic methods are also suitable for non-destructive on-line analysis of milk. Acoustic characterization can provide information on fat content, particle size distribution of fat and proteins, changes in the biophysical properties of milk over time, the content of specific proteins and pollutants. The basic principles of ultrasonic techniques, including transmission, pulse-echo, interferometer, and microbalance approaches, are briefly described and milk parameters measured with their help, including frequency ranges and measurement accuracy, are given.