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Hs 100

Manufactured by Alpha MOS
Sourced in France
About the product

The HS 100 is a headspace autosampler designed for the analysis of volatile organic compounds (VOCs) in various sample matrices. It automates the headspace sampling process, improving efficiency and reproducibility of analyses.

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12 protocols using «hs 100»

1

Volatile Compound Analysis of Plant Leaves Using E-Nose

2024
Volatile constituents were analysed using an electronic nose (E-nose) following the method described by Rottiers et al. [80 (link)] with some modifications. The experiment was performed using a Heracles II electronic nose (Alpha MOS, Toulouse, France) equipped with an HS-100 autosampler, a sensor array unit, and a non-polar column (MXT5: 5% diphenyl, 95% methylpolysiloxane, 10 m length and 180 lm diameter). An amount of 0.5 g of fresh leaves was placed in 20 mL screw vials with polytetrafluoroethylene-silicone septa and sealed with a magnetic cap. The vials were then incubated in a shaker oven at 50 °C and shaken at 500 rpm for 20 min. After that, 1000 µL of the headspace was sampled using a syringe and injected into the gas chromatograph with two flame ionisation detectors. At the start of the process, the temperature was raised from 50 °C to 250 °C at a rate of 3 °C/s and held for 21 s. The total separation time was 100 s. The apparatus was calibrated using a solution of alkanes, ranging from n-hexane to n-hexadecane. The Kovats retention indices and volatile compounds were identified using AroChembase V6 software, version 6.0-4.3.0 (Alpha MOS, Toulouse, France) based on the retention times of n-alkanes. Each sample was measured three times. Data acquisition and subsequent analysis were performed using Alphasoft 14.2 and AroChembase (Alpha MOS, Toulouse, France) software. Quantitative analysis was based on the percentage of each peak’s area (relative area) in relation to all the peak areas of a given sample. The percentage composition of volatile compounds was calculated based on this information. Thymol and carvacrol, which are the major volatile monoterpenoid phenols, were identified using the retention time of the analytical standards (Thymol, CRM40188, Sigma Aldrich, USA and Carvacrol, 42632, Sigma Aldrich, St. Louis, MO, USA).
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2

Fecal Volatile Organic Compound Analysis

2024
VOC content was analyzed in the fecal samples. Six infant feces samples per group were analyzed, as indicated in the samples previously used in the sequencing. Samples were analyzed in triplicate. VOCs were analyzed using a flash gas chromatography (GC) electronic nose (FGC- E-Nose) Heracles II with autosampler HS100 (AlphaMOS®, Toulouse, France). The Heracles II was equipped with two columns working in parallel mode: a non-polar column (DB-5: 5% phenyl- 95% dimethyl-polysiloxane and DB-1701: 14% cyanopropylphenyl- 86% dimethyl-polysiloxane). The GC injector was maintained at 200°C. The samples were subjected to a temperature program to separate the VOCs at 50°C for 30 sec, increasing at a constant speed of 10°C/ sec until 280°C. The carrier gas was hydrogen at a continuous 1 mL/min flow. Separate species were detected by electronic nose software using multivariable statistical analysis (Alpha Soft® by Alpha MOS). The fecal samples were weighed with an aliquot of every single sample (approximately 200 mg) and placed in 20 mL hermetically sealed vials sealed magnetically with a plug without any treatment or extraction solvent. Samples were incubated in the autosampler for 900 sec at 40°C with constant stirring at 500 rpm, and then 1 mL of the sample was taken by the headspace in the electronic nose. A single chromatogram was created after superposing the chromatograms obtained in each column. This strategy helped reduce identification errors made by the Kovats index with the C6-C16 standard [18 (link)].
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3

Headspace Analysis of Hydrolysate Samples

2022
The headspace of samples was investigated with an electronic nose system combined with HS100 auto-sampler together with α Soft software for data processing (Alpha M.O.S.—model FOX 4000, Toulouse, France). An amount of 0.5 g of hydrolysate was weight in a 10 mL vial, hermetically sealed with a PTFE/silicone septum and incubated for 900 s at 70 °C under agitation (250 rpm) to allow the volatilization of compounds into the headspace. Synthetic air and nitrogen were used as carrier gas with a flow of 150 mL/min. The volume of injection of the SPH headspace into the measuring chamber of the electronic nose was 2500 µL, with an acquisition time of 120 s. All samples were run in triplicate and the individual signals recorded were used for statistical analysis.
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4

Headspace Analysis of Aroma Profiles

2019
The aroma pattern was analyzed according to Utama et al [18 (link)] with slight modifications. Ground sample (2 g) was placed in a glass vial and sealed with aluminum cap equipped with PTFE/rubber septa. The gas generated in headspace was extracted by using auto sampler (HS100, Alpha MOS, Toulouse, France) after incubation at 60°C with agitation speed of 500 rpm. The 2.5 mL of gas extracted by syringe (65°C) was injected (carrier gas, flow rate, air, 150 mL/min) into the chamber of electronic nose equipped with six metal oxide sensors. The data were processed using principal component analysis (PCA, Alpha soft version 8.01 software, Alpha MOS, France).
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5

Odor Profiling of Maca Powders

2019
Maca powders (1.5 g) of each sample were placed in a 20-mL headspace bottle, and each sample had triplicate. The odor was detected by an electronic nose (FOX 4000, Alpha MOS, Toulouse, France) coupled with an automatic sampler (HS-100) and a high purity air generator (AG 2301). The electronic nose equipped with 17 detectors (Table S3) is sensitive to strong oxidizing gases, toxic gases, combustible gases, flammable gases, aromatic compounds, and organic compounds. The parameters were set to meet standards that the maximum response values are within 0.25–0.85 and the detectors with maximum values more than 0.95 and minimum values less than 0.05 are as few as possible. To verify the optimized parameters, one Maca sample of each color variety was selected for odor analysis. And the instrument stability was tested by randomly selected 30 Maca samples of six cities. By using the optimized electronic nose parameters, odor characteristics of all Maca samples were detected by 17 sensors (Table S3), and used to construct the BP neuron network. The multi-layer feed-forward algorithm (Fig. S2) was used to identify and classify the Maca origin according to the odor fingerprints detected by electronic nose. The BP neural network was constructed by using the pattern recognition toolbox of MATLAB R2015a.
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