The macadamia shell is 5 times harder than a hazelnut shell and has physical properties similar to aluminium. It has a Vickers hardness of 35. When you eventually manage to break into a macadamia nut, a further challenge awaits. Together with almonds, apricots and lima beans, the Macadamia is cyanogenic. This means that it actively synthesizes and accumulates cyanide-containing compounds. If their tissues are damaged, these plants share an ability to release hydrogen cyanide (HCN), which means that they can pose a health risk if inappropriately processed.
Although we are most familiar with the consumption of macadamia nuts as a snack, the whole plant—including its flowers, leaves and husks—has commercial applications in food and beverages (for its decadent flavor profile), as well as in pharmaceuticals and cosmetics where its plethora of bioactive constituents offer health benefits.
However, cracking open Macadamia’s potential is a difficult problem. The presence of cyanogenic glycosides, such as dhurrin, can result in enzymatically catalysed cyanogenesis to produce HCN. Consumption of as little as 0.5-2.5 mg cyanide/kg of body weight causes serious, acute toxicity effects in humans.
Which is where SIFT-MS comes into the picture!
Researchers at The Ohio State University have sought to overcome the problems faced by Macadamia producers by seeking optimum conditions, such as process temperature, heating time and pH, that allow for maximum HCN removal. Earlier work used time-intensive and technically challenging assays that made use of High Performance Liquid Chromatography (HPLC).
For the first time, Selected Ion Flow Tube-Mass Spectrometry (SIFT-MS) has been used to directly measure the real-time headspace HCN concentration above Macadamia samples, under different conditions. This made the optimization of the process conditions easier and much quicker to do, as changes can be implemented and outcomes measured immediately (rather than waiting some time for results to emerge from an HPLC). The ease with which experiments can be repeated makes for more robust recommendations.
Analysis of the sample headspace using SIFT-MS, has shown that cyanogenesis is primarily pH-driven with temperature having a lesser effect, which allowed for optimized conditions to be found. The low SIFT-MS limit of detection (LOD) for headspace HCN enabled accurate quantitation across the Macadamia samples, shown in Figure 1.
These SIFT-MS results provide the first published data on HCN concentration in Macadamia flowers and husks. This is yet another great example of SIFT-MS producing faster analytical results with deeper insights.
Figure 1 – HCN concentration directly analysed by SIFT-MS in the headspace of Macadamia flowers, leaves, nuts, and three varieties of husks at optimised process conditions.