1. Introduction
The dairy sector has seen profound technological changes and significant growth over recent decades [
1]. Global milk production is projected to grow at an annual rate of 1.7% and to reach 981 million metric tons by 2028 [
2]. The consumption of milk is rapidly increasing, particularly in highly populated and poor regions of the world, where milk is the main source of protein (as meat and other protein-rich foods are rarely available [
3]), which can include milk from various animals [
4]. This fact, along with the perishable nature of milk, transportation challenges and the lack of access to adequate refrigeration methods, has created an urgent need to enhance processing efficiency in the dairy industry [
5,
6,
7]. Various processing methods have been applied to extend the shelf life of milk, including the manufacturing of milk powders through evaporation and drying techniques. The most common method to produce powdered milk is spray drying. This method is based on the principle of convection, where water is removed from finely dispersed milk particles upon contact with circulating hot dry air, resulting in the almost instantaneous formation of dry product particles with an average diameter of 50 microns [
8,
9].
Powdered and liquid forms of milk serve similar nutritional purposes but have different characteristics, which make them suitable for various applications. Liquid milk has a shorter shelf life and requires refrigeration, making it more suitable for immediate consumption [
10,
11]. It is a versatile raw material used in many food formulations due to its creamy texture and flavor. It is commonly used in dairy-based beverages (milkshakes, flavored milks), frozen desserts (ice cream, frozen yogurt) and cereal bars. On the other hand, powdered milk has an extended shelf life and does not require refrigeration, making it ideal for long-term storage and various applications, including industrial use, in specialized diets and in emergency food reserves. It is preferred for applications where precise control of the concentration and moisture content is required due to its ability to be reconstituted [
8,
9]. Lately, powdered milk has become a major attraction for many industrial applications due to its physical and functional attributes [
5]. It is frequently incorporated as an ingredient in the formulation of numerous dairy and processed food products, including ice cream, cultured milk and yogurts, chocolate, confectionery, bakery products, soups and sauces [
12,
13,
14].
Camel and mare milk have gained a lot of attraction in recent years due to their nutritional benefits, their medical attributes and the ability of camels and mares to survive harsh climates. This makes them excellent alternatives to cow milk, particularly in areas where cows struggle to survive [
15]. Additionally, camel and mare milk are less prone to causing allergies, making them a perfect substitute for cow milk in certain food applications. Camel milk is homologous to human milk due to its distinctive chemical composition full of bioactive and functional compounds, such as essential amino acids [
16]. Hence, there is an increasing interest in investigating the suitability of camel milk as an alternative to cow’s milk-based hypoallergenic infant formulas [
17,
18]. Currently, no infant formulas based on camel milk are commercially available in the EU market, as further in-depth investigations are required, in addition to the fact that there is limited production of camel milk in the EU [
19]. Similarly, mare’s milk has been suggested as a possible alternative to bovine milk in pediatric dietetics [
20,
21,
22]. It has an ideal ratio of caseins and whey proteins, which makes it highly digestible compared to cow’s milk. Additionally, fermented mare’s milk, known as Kumis, is considered a very valuable nutritional drink due to its content of probiotics, such as
Lactobacillus acidophilus and
Bifidobacterium [
23].
The quality of milk powders and their applicability depend on their chemical, physical and functional attributes, all of which are interdependent [
24]. In addition to other physicochemical parameters, the amino acid and fatty acid composition are often used to assess the quality of milk [
25], including human breast milk [
26,
27]. Additionally, the conditions under which milk is reconstructed, such as temperature and concentration, significantly impact these parameters.
Processing conditions, particularly pH and temperature, have a great impact on the above-mentioned parameters. Generally, an increase in temperature leads to a decrease in viscosity [
2]. However, when exposed to higher temperatures, the viscosity increases because of protein denaturation. The extent to which an increase in the viscosity occurs was proven to be linked to the applied pH as well, with a significant increase when low pH is coupled to heat treatment and an opposite effect otherwise [
28,
29]. The insolubility index is another important quality indicator that is highly affected by the impact of pH and temperature conditions on the milk protein behavior. A high insolubility index as a result of milk protein denaturation is considered a serious defect and can lead to the rejection of the milk powder product [
24].
Besides its physical and functional properties, powdered milk is valued for its nutritional value and chemical characteristics, mainly titratable acidity, fat, protein content and amino acid profile. The fat content of milk powders can dictate their storage ability and their applicability as well [
30]. High fat levels in milk powders can result in them becoming rancid easily; hence, they can reduce their shelf life [
13]. Moreover, fat content is an important quality attribute, especially in the manufacturing of chocolate, where milk powder is used as the main ingredient for the preparation of milk crumbs [
31]. Titratable acidity is another important parameter that must be monitored, as it is an indicator of the microbiological quality of milk powders. Additionally, the determination of the amino acid profile is crucial, particularly for milk powders destined for the processing of high-value food products such as infant formulas and fortified blended foods for vulnerable groups [
32].
The development of rapid monitoring methods has become an urgent task to explore the applicability of quality control of various food products including milk powders and milk reconstructed from milk powder in food formulations, as well as export potential [
33,
34,
35]. Recently, there has been a growing emphasis on the potential of near-infrared spectroscopy (NIRS) for the quality assessment of dairy products. This technique is non-destructive and very environmentally friendly compared to other analytical methods such as gas chromatography, high-performance liquid chromatography, mass spectroscopy and electrophoresis. Additionally, no reagents are required, and no hazardous waste is produced. The NIRS technique has been applied increasingly for food quality evaluation in recent years, and several of these applications are currently in use as routine analyses and in online monitoring systems [
36]. NIR is a highly accurate, versatile and multi-analytical technique based on the absorption of near-infrared light by the sample at different wavelengths (800–2500 nm), recording molecular vibration of all molecules containing C-H, N-H or O-H groups [
37]. Numerous studies have investigated the applicability of NIRS for quality assessment and classification of different milk powders. Wu et al. [
38] investigated the feasibility of the short-wave NIRS technique for the analysis of fat, protein and carbohydrates in milk powder. Additionally, Wang et al. [
7] studied the potential of visible–NIRS for the prediction of functional quality attributes of milk powders from different brands, types and sources. In similar studies, Inácio et al. [
39] and Chen et al. [
40] studied the applicability of NIRS for the classification of milk powders according to their brand and the prediction of their protein content.
While research has been conducted on using NIRS to assess the quality of milk powder, there has been less focus on how NIR can be applied to reconstructed milk, particularly in detecting defects that may occur due to improper reconstruction conditions. Additionally, research on camel and mare milk is considerably scarcer, despite their significant importance in the diets in certain regions and ethnic communities.
Various kinds of milk powders, each possessing unique chemical compositions, can influence food functionality and their ability to better suit specific groups with different ethnic backgrounds. In the present work, we aim to explore the feasibility of NIRS coupled with chemometrics as a novel technique for the rapid characterization of milk powders from different animals (cows, mares and camels). Another objective is to distinguish between different types of milk and various reconstruction conditions using NIR spectra, to evaluate the technique’s effectiveness as a quality control tool.
3. Materials and Methods
3.1. Analyzed Milk Powders
Three types of commercial milk powder purchased from the market were used in this study. Skimmed cow milk powder from Budapest, Hungary, by a brand called Tutti, and whole camel and mare milk powders from brands called Saumal and Sydyk obtained in Almaty, Kazakhstan, were investigated.
3.2. Preparation of Reconstructed Milk Samples
Considering the limited number of milk powder samples available in the market, particularly in the case of mare and camel milk, we attempted to incorporate different variations during the reconstruction of the milk powder to allow characteristics such as concentration and temperature to have more representativity in our models.
Milk powders from cow, camel or mare milks were mixed with Milli-Q water to create samples at three different concentrations (C1 = 5%, C2 = 10%, C3 = 12.5%). These mixtures were then dissolved at three different temperatures (T1 = 25 °C, T2 = 40 °C or T3 = 65 °C), with three replicates performed for each combination of concentration and temperature.
All prepared samples were mixed by using a vortex mixer for 6 min to ensure the homogeneity of the samples. This preparation resulted in a total of 81 samples (3 milk powders × 3 concentrations × 3 temperatures × 3 repeats). After cooling, the prepared milk samples were immediately refrigerated and stored at 4 °C until further analysis.
3.3. Characterization of the Milk Powder Samples
The following main quality parameters of the milk powder samples were determined in triplicate.
3.3.1. Water Activity
The water activity (aw) of the milk powders was measured at 25 °C using a Novasina LabMaster-aw neo device (Novasina AG, Switzerland) with internal temperature control (0–60 °C). The sample cup was filled with 1.5 ± 0.5 g of milk powder and placed in the measurement chamber, and the cover was tightly closed. Once stability had been reached, the water activity of the sample was read at 25 °C.
3.3.2. Loose Bulk Density
The density of milk powders was measured as loose bulk density. Loose density was measured by weighing a 100 mL calibrated cylinder filled with dried milk that was carefully leveled, without compacting, until reaching an established level, and expressed with the following expression:
3.3.3. Insolubility Index
The insolubility index of the milk powders was analyzed as described by Pugliese et al. [
11]: A total of 10 g of dried milk powder was properly mixed with 100 mL of water at 25 °C for approximately 5 min. The obtained suspension rested for 5–15 min, then it was stirred with a spatula. A volume of 50 mL was centrifuged for 5 min at 5×
g. After the removal of the supernatant, the tube was filled up again with water. The tube was centrifuged in the same manner. The volume of the sediment was then noted down. Additionally, the sediment was dried in an oven at 70 °C until it reached a constant weight. The insolubility index is expressed both as the volume of wet residue (mL), according to the IDF method, and as the weight of the sediment after drying (mg).
3.3.4. Amino Acid Profile
Totals of 80–120 mg of milk powder samples were hydrolyzed in a closed hydrolyzing vessel (KUTESZ, Budapest, Hungary) at 110 °C for 24 h in a blocked thermostat with 10 mL 6 M HCl under a nitrogen atmosphere (FALC Instruments, Treviglio, Italy). Following neutralization (10 mL of 4 M NaOH), samples were filtered twice, first through a standard paper filter and then through a 0.22 µm membrane filter (Nalgene, Rochester, NY, USA). An Automatic Amino Acid Analyzer AAA400 (Ingos Ltd., Prague, Czech Republic) equipped with an Ionex Ostion LCP5020 cation-exchange column (220 × 37 mm) was used for the analysis. After post-column derivatization with a ninhydrin reagent, colorimetric detections were achieved at 570 nm and 440 nm.
The assay was carried out in a strongly acidic medium, with a series of eluents of gradually weakening acidity, with step gradient elution (buffer 1: 0.18 M Li citrate, pH 2.80; buffer 2: 0.20 M Li citrate, pH 3.05; buffer 3: 0.36 M Li citrate, pH 3.35; buffer 4: 0.33 M Li citrate, pH 4.05; buffer 5: 1.20 M Li citrate, pH 4.65). Chromatograms were evaluated using the CHROMuLAN082 program, by comparison with standard amino acid mixtures.
3.4. Characterization of Reconstructed Milk Samples
The following main quality parameters of the reconstructed milk powder samples were determined in triplicate.
3.4.1. Dry Matter Content
Dry matter content is expressed as a percentage by mass of matter remaining after completion of the specified drying process. The measurement was determined by drying around 2 g of milk samples in a classic drying oven at 105 °C for constant mass.
3.4.2. pH and Conductivity
The pH and the electrical conductivity of the samples were measured with a dual pH/conductivity meter (Mettler Toledo SevenMulti, Columbus, OH, USA). Before the measurement, the instrument was calibrated at a given temperature, currently established at 25 °C.
3.4.3. Acidity According to Soxhlet–Henkel (Titratable Acidity)
Titratable acidity was recorded as the Soxhlet–Henkel degree (SH°). The measurement was performed by titration with 0.1 N NaOH solution with a phenolphthalein indicator. The following formula was used to obtain the acidity of the milk in terms of Soxhlet–Henkel (°SH) acidity:
3.4.4. Viscosity
The flow curves of the reconstituted milk samples were measured at a temperature of 25 ± 0.2 °C with an MCR302 modular compact rheometer (Anton Paar, Austria) in coaxial cylindrical geometry (CC27) using Rheo Compass software (version 3.63). In the first stage of the measurement, the shear rate increased from 0.1 s−1 to 1000 s−1 with a linear scale, and 30 points were recorded with decreasing acquisition according to the logarithmic scale between 10 s and 2 s. In the second stage, the viscosity values were recorded every three seconds and resulted in 30 points at a shear rate of 1000 s−1. The apparent viscosity at a 750 s−1 shear rate in the increasing stage and the average value of the dynamic viscosity at a constant maximum shear rate were determined.
3.4.5. Fat Content
The determination of total fat content (
v/
v%) was carried out from the resolved samples using the Gerber (acido-butyrometric) method. The standards ISO 2446 [
57] and IDF 105 [
58] were used for this analysis. The method was carried out as described by Trout and Lucas [
59] and Esen and Güzeler [
60].
3.4.6. Color Properties
The color properties were measured using a ColorLite sph 850 spectrophotometer (ColorLite GmbH., Katlenburg-Lindau, Germany) over wavelengths ranging from 400 to 700 nm, with a D65 light source and a 2° observer angle. The results were expressed as CIE (Commission Internationale de la Éclargie) L*, a*, b* color parameters.
3.4.7. NIRS Analysis
The NIR spectra of the reconstructed milk samples were collected in transflectance mode with an XDS Rapid Content Analyzer (Metrohm, Denmark) in the wavelength range of 400–2500 nm. The samples were scanned in random order and about 1.5 mL of the reconstructed milk sample was poured in a transflection vessel and covered with a gold reflector plate, resulting in 0.5 mm layer thickness. Three consecutive scans were performed on each sample at a spectral resolution of 0.5 nm. Spectral acquisition was carried out at room temperature (25 °C).
3.5. Data Analysis
Descriptive statistics and two-way analysis of variance (ANOVA) were performed on the generated data using the software IBM SPSS27 (Armonk, NY, USA, 2020) as a statistical evaluation tool. Two-way ANOVA tests were conducted to assess the differences between groups of samples belonging to different types of milk powder, concentrations and temperatures used to reconstruct the milk. The normality of residuals of all dependent variables was checked by the Kolmogorov–Smirnov (KS) test. In addition, the homogeneity of variances was checked for all dependent variables according to Levine’s test. In the case of significant two-way ANOVA results (p < 0.05), a post hoc test was run using Tukey’s (in the case of homogeneity of variance) or the Games–Howell test (in the case of non-homogeneity of variance) to evaluate differences between groups.
Chemometric analysis of the NIRS data was performed with R-project software (version 4.3.1) [
61] using the package aquap2 [
62] with principal component analysis (PCA), linear discriminant analysis (LDA), partial least square regression (PLSR) and support vector regression (SVR) methods.
The chemometric analysis in this study was performed in the range of 1100–1850 nm since the most relevant wavelengths for building the classification and regression models were mainly concentrated in the long spectral wavelength region. However, absorbance higher than 2 is beyond the linear response region of the detector [
63]. Therefore, wavelengths beyond 1850 nm were not considered in the chemometric analysis.
A preliminary examination of the spectral datasets for unusual or outlying observations was performed by principal component analysis-based linear discriminant analysis (PCA-LDA). PCA-LDA was used to build classification models for the discrimination of the type of reconstructed milk, the preparation temperature and the applied concentration. In addition, three classification models were developed for each temperature level used to reconstruct the milk to discriminate between the type of milk and the applied concentration. For each type of milk, three classification models were developed for each applied concentration to discriminate between the temperature levels used to reconstruct the milk. The PLSR technique was used to build regression models for the quality attributes of the reconstructed milk samples.
The validation of the PCA-LDA models was performed by using the leave-one-repeat-out cross-validation method, whereby data representing one repeat were excluded from the training set and used as a validation set whilst the other two-thirds of the data (two repeats) were used for model building. This procedure was repeated three times, using a different repeat (1/3 of data) in the validation set each time. PLSR models were validated by using leave-one-replicate-out (three consecutive scans together) cross-validation. To prevent model overfitting when dealing with a limited number of samples, an appropriate data division method for creating training and test sets was also employed (test-set prediction) by using two-thirds of the data for training and one-third for predicting. In this study, we attempted to fairly divide the data by considering various factors such as concentration and temperature of reconstruction, replicates and consecutive scans. We aimed to ensure the fairness by adhering to these principles: (1) all consecutive scans of the same sample were placed in either training or test sets, (2) maintaining an equal ratio between training and test sets for samples within each temperature and concentration category. In the case of SVR, the same validation procedure was applied for comparative reasons. Dimension reduction was achieved via PCA by setting the threshold to 95% of the total variance; selected PCs were applied as input variables for SVR modelling [
52,
64]. Hyperparameter selection was achieved by tuning the error weight (C: 0.1–10) and maximal error value (ε: 0.01–0.5) parameters and by testing with different kernel functions (linear, polynomial and radial). The cost function was used to simultaneously minimize the coefficients and prediction errors to obtain the best performing model for each parameter [
55].
Spectral pretreatments were applied to the raw spectra before developing any models to reduce the noise (smoothing techniques) and increase the signal from the chemical information (derivation, normalization, differentiation, etc.). The applied pretreatments included multiplicative scatter correction (MSC), the standard normal variate (SNV), the Savitzky–Golay second derivative with a 21-point window gap (Sgolay 2-21-0), de-trending (deTr) and a combination of these as well.
4. Conclusions
Milk powders offer many functional, nutritional and economic advantages for various industrial applications such as food formulations, among others. Having a deep understanding and investigating the general properties of milk powders are primordial tasks for the above-mentioned applications. Although it is traditionally analyzed in a powder state, our research aims to study the feasibility of NIRS combined with chemometrics for rapid determination of general quality attributes of reconstructed milk from various sources (cow, camel and mare). Differences between samples according to the type of milk powder, concentration (5%, 10% and 12.5%) and temperature (25 °C, 40 °C and 65 °C) were investigated using ANOVA tests and linear discriminant analysis. ANOVA tests revealed that the type of milk powder used for the reconstruction of the milk samples contributed the most to the significant differences found between the samples. This was confirmed in the PCA-LDA models as well, where milk samples were 100% correctly classified according to their type.
Relevant wavelengths and their corresponding functional groups provided key information about the compositional differences between the milk samples based on the type. Therefore, they are considered reliable for accurate model construction. The classification models, which are based on type, concentration and temperature, demonstrated high accuracies in both recognition and prediction. Additionally, the quantitative models could accurately predict the pH, viscosity, dry matter, fat content, conductivity and individual amino acid content of the reconstructed milk. However, the SVR models showed higher accuracies compared to the PLSR models.
Our research shows that NIRS is a reliable and versatile analytical method for both qualitative and quantitative analysis of reconstructed milk from various animal sources, including cow, camel and mare milk. Through qualitative analysis, NIRS allows for the identification of milk types and the detection of adulteration or contamination. By analyzing the spectral fingerprints of the milk powders, NIRS can differentiate between cow, camel and mare milk, as well as identify any deviations from standard profiles that may indicate quality defects or the presence of unwanted additives. Quantitatively, it offers an accurate determination of key nutritional and compositional parameters. The technique’s ability to generate accurate quantitative and qualitative data without the need for extensive sample preparation or chemical reagents positions it as a promising method for routine quality checks and product standardization in industrial settings. Additionally, NIRS has high potential in various applications in research and development. For instance, it can be used to monitor the effects of different processing conditions on milk powder composition, optimize formulations for specific nutritional requirements and support the development of new dairy products. The technique’s rapidity and user-friendliness also enable high-throughput screening, making it suitable for large-scale studies or production settings.
While our current models provide reliable results, the representativity and predictive accuracy can be significantly enhanced by broadening the dataset. Incorporating milk powders from a wider array of processing conditions, such as variations in drying techniques, thermal treatments and storage conditions, will help capture the full spectrum of variability in milk powder characteristics. Additionally, including samples from different brands and geographic locations will ensure that the models are robust and generalizable across different market segments and regions. To further improve the reliability and applicability of NIRS in milk powder analysis, future research should focus on expanding the calibration and validation datasets, exploring additional advanced nonlinear chemometric techniques for model development and integrating NIRS with other complementary analytical methods. Such research will contribute to the establishment of NIRS as a standard tool in the dairy industry, capable of meeting the growing demands for quality, safety and innovation in dairy products. To conclude, NIRS has high potential in the endeavor of the development of food production using modern digitalization tools.