Automation of fizzy extraction enabled by inexpensive open-source modules

Abstract

The implementation of most instrumental analysis methods requires a considerable amount of human effort at every step, including sample preparation, detection, and data processing. Automated analytical workflows decrease the amount of required work. However, commercial automated platforms are mainly available for well-established sample processing methods. In contrast, newly developed prototypes of analytical instruments are often operated manually, what limits their performance and decreases the chance of their adoption by the broader community. Open-source electronic modules facilitate the prototyping of complex analytical instruments and enable the incorporation of automated functions at the early stage of technique development. Here, we exemplify this advantage of open-source electronics while prototyping an automated analytical device. Fizzy extraction takes advantage of the effervescence phenomenon to extract semi-volatile solutes from the liquid to the gas phase. The entire fizzy extraction process has been automated by using three Arduino-related microcontrollers. The functions of the developed autonomous fizzy extraction device include triggering the analysis by a smartphone app, control of carrier gas pressure in the headspace of the sample chamber, displaying experimental conditions on an LCD screen, acquiring mass spectrometry data in real time, filtering electronic noise, integrating peaks, calculating the analyte concentration in the extracted sample, printing the analysis report, storing the acquired data in non-volatile memory, monitoring the condition of the motor by counting the number of extraction cycles, and cleaning the elements exposed to the sample (to minimize carryover). The performance of this automated system has been evaluated using standards and real samples.

Keywords: Analytical chemistry.

Fig. 1

Simplified scheme for the fizzy extraction system, including a block diagram of the electric control unit. Acronyms: TS – temperature sensor; PS – pressure sensor; RTC – real time clock (N.B. date and time are used to generate file names); CS – motor current sensor.

Fig. 2

Electronic schematic of the control unit used to operate the fizzy extraction system. The resistances of R1 and R2 are 470 Ω; R3 and R5, 4.7 kΩ; and R4, 1.5 kΩ.

Fig. 3

Photograph of the assembled automated fizzy extraction system including the electronic control unit.

Fig. 4

Workflow for the fizzy extraction procedure.

Fig. 5

The effect of a median filter on signal quality. (A) Unfiltered signal recorded by Arduino. (B) The same signal after applying median filter. (C) The corresponding dataset recorded by the original software of the mass spectrometer. Sample: 5.5 × 10⁻⁵ M 1,8-cineole in 5% ethanol/water solution. The SRM data were recorded at m/z 155→81.

Fig. 6

Different ways of interacting with the user: (A) LCD screen; (B) report printed by the thermal printer; (C) screen of the text editor displaying ASCII file saved by the extraction system on the microSD card; (D) smartphone screen with the interface for triggering the fizzy extraction.

Fig. 7

Calibration plots for four compounds. (A) limonene; (B) 1,8-cineole; (C) myrcene; (D) citronellal. For calibration curve equations, see Table 2.

Fig. 8

The correlation of peak area computed by the Arduino script and the instrument software. The solid line is the result of linear regression, and it is described by the equation A_I = (0.89 ± 0.01)A_A + (1.03 × 10⁴ ± 1.79 × 10⁴).

Fig. 9

Typical results obtained with the automated fizzy extraction system: (A) Limonene in 100× diluted orange juice in 5% ethanol/water solution (m/z 137→81); (B-D) 1,8-cineole in 10× diluted saliva (in 5% EtOH water solution) obtained from a volunteer who had previously rinsed their mouth with mouthwash (m/z 155→81). The saliva specimens were obtained (B) 1 min, (C) 5 min, and (D) 10 min after rinsing.