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The global dairy market has been increasingly diversified with more dairy product offerings of milk products from different animal species. Meanwhile, milk powders remain the main exported dairy product format due to their ease of transportation. In this work, we studied the structural changes, protein hydrolysis and nutrient delivery during dynamic gastric digestion and small intestinal digestion of cow, goat and sheep milk reconstituted from commercial whole milk powders. The results show that the reconstituted milks digest similarly to processed fresh milk. The digestion behaviors of the three reconstituted ruminant milks are broadly similar (gastric coagulation, kinetics of gastric emptying of protein and fat and the high digestibility in the small intestine) with some differences, which are likely contributed by the processing history of the milk powders. The delivery of individual amino acids to the small intestine differed between the early and late stages of gastric digestion, which were primarily affected by the abundance of amino acids in caseins and whey proteins but also by the difference between milk types associated with their gastric coagulation behaviors. This work showed that powdered milk is similar to fresh processed milk in digestion behavior, and the inherent differences between ruminant milks can be modified by processing treatments.

Lactoferrin (LF), an iron-binding glycoprotein with immunological properties and a high nutritional value, has emerged as a prominent research focus in the field of food nutrition. Lactoferrin is widely distributed in raw milk and milk that has undergone low-temperature heat treatment during pasteurization, making its rapid and accurate detection crucial for ensuring the quality control of dairy products. An enzyme-linked immunosorbent assay-based analytical protocol has often been referred to for the detection of LF in real samples. Signal amplification was accomplished using the streptavidin-biotin system. Here, an automated magnetic beads-based sandwich chemiluminescence enzyme immunoassay (MBs-sCLEIA) system was developed for the quantification of lactoferrin in pasteurized milk. The MBs-sCLEIA system consists of an automated chemiluminescence-based analyzer and a lactoferrin MBs-sCLEIA assay kit. Notably, our proposed method eliminates the need for pretreatment procedures and enables the direct addition of milk samples, allowing for the automatic quantitative detection of lactoferrin within a rapid 17 min timeframe for up to eight samples simultaneously. The MBs-sCLEIA was linear over the range of 7.24-800 ng/mL and displayed a limit of detection (LOD) of 2.85 ng/mL. As its good recovery and CV values indicate, the method exhibited high precision and accuracy. Furthermore, it was verified that it was selective towards five additional common milk proteins. A good correlation was observed between the results from the MBs-sCLEIA and heparin affinity column-HPLC (r 2 = 0.99042), which proves to be a useful and practicable way of conducting an accurate analysis of lactoferrin in dairy products.

In order to explore the aromatic differences between Xinjiang cow milk powder and specialty milk powder (donkey, camel, and horse milk powder), Gas Chromatography-Ion Mobility Spectrometry (GC-IMS) analysis was employed to investigate the volatile compounds in these four types of milk powders. A total of 61 volatile substances were detected, with ketones, aldehydes, and alcohols being the primary flavor components in the milk powders. While the aromatic components of the different milk powders showed similarities in terms of types, there were significant differences in their concentrations, exhibiting distinct characteristics for each type. The Partial Least Squares Discriminant Analysis (PLS-DA) showed that there were 15, 14, and 23 volatile compounds that could be used for discrimination of cow milk powder against specialty milk powders, respectively. And it was validated by Receiver Operating Characteristic (ROC) analysis, and finally, 8, 6, and 19 volatile compounds were identified as valid differential marker substances. To facilitate visual discrimination between the different milk powders, we established GC-IMS fingerprint spectra based on the final discriminant markers. These studies provide theoretical guidance for the application of volatile compounds to discriminate adulteration of milk powder marketed in Xinjiang.

The surface appearance of milk powders is a crucial quality property since the roughness of the milk powder determines its functional properties, and especially the purchaser perception of the milk powder. Unfortunately, powder produced from similar spray dryers, or even the same dryer but in different seasons, produces powder with a wide variety of surface roughness. To date, professional panelists are used to quantify this subtle visual metric, which is time-consuming and subjective. Consequently, developing a fast, robust, and repeatable surface appearance classification method is essential. This study proposes a three-dimensional digital photogrammetry technique for quantifying the surface roughness of milk powders. A contour slice analysis and frequency analysis of the deviations were performed on the three-dimensional models to classify the surface roughness of milk powder samples. The result shows that the contours for smooth-surface samples are more circular than those for rough-surface samples, and the smooth-surface samples had a low standard deviation; thus, milk powder samples with the smoother surface have lower Q (the energy of the signal) values. Lastly, the performance of the nonlinear support vector machine (SVM) model demonstrated that the technique proposed in this study is a practicable alternative technique for classifying the surface roughness of milk powders.

Due to the risk of heavy metal pollution in infant milk powder, it is significant to establish effective detection methods. Here, a screen-printed electrode (SPE) was modified with nanoporous carbon (NPC) to detect Pb(II) and Cd(II) in infant milk powder using an electrochemical method. Using NPC as a functional nanolayer facilitated the electrochemical detection of Pb(II) and Cd(II) due to its efficient mass transport and large adsorption capacity. Linear responses were obtained for Pb (II) and Cd(II) in the range from 1 to 60 µg L -1 and 5 to 70 µg L -1 , respectively. The limit of detection was 0.1 µg L -1 for Pb(II) and 1.67 µg L -1 for Cd(II). The reproducibility, stability, and anti-interference performance of the prepared sensor were tested as well. The heavy metal ion detection performance in the extracted infant milk powder shows that the developed SPE/NPC possesses the ability to detect Pb(II) and Cd(II) in milk powder.

Pre-spray-drying processing may affect stability after reconstitution of emulsion-based powders, such as infant formulas. This study aimed to evaluate the effects of pasteurization temperature and total solids (TS) of the feed on the stability of the emulsions obtained from the reconstituted powders. Four infant formula powders (50%-75 °C, 50%-100 °C, 60%-75 °C, and 60%-100 °C) were produced at pilot scale, from emulsions with 50 or 60% TS pasteurized at 75 or 100 °C for 18 s. Both the emulsion feeds and the emulsions from the reconstituted powders (12.5% TS) were analyzed. The results showed that feeds with 60% TS were flocculated, as indicated by the large particle size and viscosity and the pseudoplastic behavior. Light microscopy revealed that, during spray drying, the flocs were disrupted in 60%-100 °C, while the 60%-75 °C emulsion remained flocculated, reducing its stability post-reconstitution. Although all four emulsions were mainly stabilized by caseins, the presence of β-lactoglobulin was also detected at the oil-water interface, in native state in the formulas preheated at 75 °C and aggregated in the formulas preheated at 100 °C. In conclusion, both the degree of whey protein denaturation (resulting from pasteurization) and the TS of the concentrates during infant formula production affected the emulsion stability of the reconstituted powders.

A collaborative study was undertaken in which five international laboratories participated to determine amino acid fingerprints in 39 authentic nonfat dry milk (NFDM)/skim milk powder (SMP) samples. A rapid method of amino acid analysis involving microwave-assisted hydrolysis followed by ultra-high performance liquid chromatography-ultraviolet detection (UHPLC-UV) was used for quantitation of amino acids and to calculate their distribution. The performance of this rapid method of analysis was evaluated and was used to determine the amino acid fingerprint of authentic milk powders. The distribution of different amino acids and their predictable upper and lower tolerance limits in authentic NFDM/SMP samples were established as a reference. Amino acid fingerprints of NFDM/SMP were compared with selected proteins and nitrogen rich compounds (proteins from pea, soy, rice, wheat, whey, and fish gelatin) which can be potential economically motivated adulterants (EMA). The amino acid fingerprints of NFDM/SMP were found to be affected by spiking with pea, soy, rice, whey, fish gelatin and arginine among the investigated adulterants but not by wheat protein and melamine. The study results establish an amino acid fingerprint of authentic NFDM/SMP and demonstrate the utility of this method as a tool in verifying the authenticity of milk powders and detecting their adulteration.

Milk powders produced from similar spray dryers have different visual appearances, while the surface appearance of the powder is a key quality attribute because the smoothness of the milk powder also affects flowability and handling properties. Traditionally quantifying this nuanced visual metric was undertaken using sensory panelists, which is both subjective and time consuming. Therefore, it is advantageous to develop an on-line quick and robust appearance assessment tool. The aim of this work is to develop a classification model which can classify the milk powder samples into different surface smoothness groups. This work proposes a strategy for quantifying the relative roughness of commercial milk powder from 3D images. Photogrammetry equipment together with the software RealityCapture were used to build 3D models of milk powder samples, and a surface normal analysis which compares the area of the triangle formed by the 3 adjacent surface normals or compares the angle between the adjacent surface normals was used to quantify the surface smoothness of the milk powder samples. It was found that the area of the triangle of the smooth-surface milk powder cone is smaller than the area of the triangle of the rough-surface milk powder cone, and the angle between the adjacent surface normals of the rough-surface milk powder cone is larger than the angle between the adjacent surface normals of the smooth-surface milk powder cone, which proved that the proposed area metrics and angle metrics can be used as tools to quantify the smoothness of milk powder samples. Finally, the result of the support vector machine (SVM) classifier proved that image processing can be used as a preliminary tool for classifying milk powder into different surface texture groups.

With the rising trend of consumers being offered by start-up companies portable devices and applications for checking quality of purchased products, it appears of paramount importance to assess the reliability of miniaturized sensors embedded in such devices. Here, eight sensors were assessed for food fraud applications in skimmed milk powder. The performance was evaluated with dry- and wet-blended powders mimicking adulterated materials by addition of either ammonium sulfate, semicarbazide, or cornstarch in the range 0.5-10% of profit. The quality of the spectra was assessed for an adequate identification of the outliers prior to a deep assessment of performance for both non-targeted (soft independent modelling of class analogy, SIMCA) and targeted analyses (partial least square regression with orthogonal signal correction, OPLS). Here, we show that the sensors have generally difficulties in detecting adulterants at ca. 5% supplementation, and often fail in achieving adequate specificity and detection capability. This is a concern as they may mislead future users, particularly consumers, if they are intended to be developed for handheld devices available publicly in smartphone-based applications.

Flour quality is influenced by the nature of the gluten and its various components. Gluten free flour made of pulses is known to enhance the nutritional quality of wheat flour. However, its addition can compromise the rheological and sensorial attributes of the bread. We used mixture design to optimize nutritional and technological qualities of a wheat-chickpea flour blend by adding milk powder as a natural organoleptic improver. A total of thirteen flour blends were prepared by incorporating 10 to 30% chickpea flour and 10 to 20% milk powder to wheat flour. Our results showed that the optimal flour blend consisted of 60% wheat, 24% chickpea, and 16% milk powder. Farinographic parameters of the optimal dough blend remained on par with those of the control dough (100% wheat flour), thereby preserving its bread-making quality. Sensory analysis of breads made from the optimal flour blend revealed no significant difference ( p ≤ 0.05) from wheat flour for crumb and chewiness. Appreciation was brought to the appearance, crust, aroma, and taste in the optimized bread. This study suggests that chickpea flour can be suitably incorporated into bread wheat flour up to a percentage of 24% with 16% milk powder to produce bread with optimal nutritional quality while improving its sensory attributes and consumer acceptability.

This study addressed the potential of 3D printing as a processing technology for delivering personalized healthy eating solutions to consumers. Extrusion-based 3D printing was studied as a tool to produce protein- and dietary fibre-rich snack products from whole milk powder and wholegrain rye flour. Aqueous pastes were prepared from the raw materials at various ratios, grid-like samples printed from the pastes at ambient temperature and the printed samples post-processed by oven baking at 150 °C. Printing pastes were characterized by rheological measurements and the baked samples by X-ray micro tomography, texture measurements and sensory analysis. All formulations showed good printability and shape stability after printing. During baking, the milk powder-based samples expanded to a level that caused a total collapse of the printed multiple-layer samples. Shape retention during baking was greatly improved by adding rye flour to the milk formulation. Sensory evaluation revealed that the volume, glossiness, sweetness and saltiness of the baked samples increased with an increasing level of milk powder in the printing paste. A mixture of milk powder and rye flour shows great potential as a formulation for healthy snack products produced by extrusion-based 3D printing., Competing Interests: The authors declare no conflict of interest.