Determining Moisture and Solids in Dairy Products

Conventionally, wet chemistry techniques have been used for testing moisture and fat in foodstuff samples. However, these techniques are time-consuming and labor-intensive, and often mandate hazardous solvents and skilled technicians. Although a number of rapid techniques — such as FT-NIR, FT-IR, NIR, and TD-NMR — have been introduced, none of these have been universally accepted since there is often a need for wide-ranging calibration development as well as maintenance.

The ORACLE™ is a rapid time-domain NMR (TD-NMR) instrument encompassing advanced technology that enables oil/fat in foodstuffs to be directly determined. In contrast to other rapid techniques, the ORACLE has the ability to completely isolate fat detection in complex matrices, thereby avoiding the need for calibration development. The ORACLE can be paired with a SMART 6™ moisture/solids analyzer to enable rapid testing of moisture/solids and even fat.

A collection of 11 samples were acquired and analyzed for demonstrating the potential of the ORACLE and SMART 6 to reliably and accurately determine the moisture and fat content in dairy samples. The samples were selected such that they represented a wide variety of matrix types and relative component concentrations.

Key System Benefits

• Better repeatability when compared to reference methods
• Bulk measurement (not sensitive to granularity and color)
• Rapid (can be performed within 5 minutes for fat and moisture)
• Direct technique, eliminating the need for calibration

Experiment

To perform each analysis, the samples were pre-dried on the SMART 6 (ca. 3–4 minutes) and subsequently prepped for analysis in the ORACLE (Figure 1). When the samples are inserted into the ORACLE magnet, they are rapidly conditioned (30 seconds) with the help of QuikPrep™ before the NMR analysis (35 seconds). The sizes of the samples ranged from 2 to 3 g. Analyses on each sample were performed in triplicate for the SMART 6–ORACLE analyses and in duplicate for the reference analyses (AOAC-approved methods).

Figure 1. SMART 6–ORACLE moisture and fat systems

Note: It is possible to perform high-throughput analyses by using batch automation with the help of high-capacity heater blocks (100 positions) and an optional robot.

Results and Discussion

Table 1 illustrates the accuracy of results obtained with the help of SMART 6 and ORACLE. Here, the average reference results have been compared with the average of the SMART 6 and ORACLE results. The average difference was in the range of 0.00%–0.09% for fat and 0.01%–0.13% for moisture/solids.

Table 1. Accuracy of the SMART 6–ORACLE for moisture/solids and fat in various dairy matrices

Table 2 illustrates the repeatability, where the standard deviations were in the range of 0.01%–0.12% for fat and 0.01%–0.09% for moisture/solids.

Table 2. Repeatability of the SMART 6–ORACLE for moisture/solids and fat in various dairy matrices

Conclusion

These results show that the SMART 6–ORACLE has the ability to reliably establish the fat and moisture/solids content in dairy samples with an accuracy that is in close correlation with that of the reference methods. Furthermore, it offers intrinsic repeatability advantages over the reference methods, which are prone to be erroneous due to a high dependence on a wide array of experimental factors, such as temperature, solvent composition, extraction time, and so on.

This information has been sourced, reviewed and adapted from materials provided by CEM Corporation.

For more information on this source, please visit CEM Corporation.