A piezoelectric peptide-hpDNA based gas sensor array has been used for the detection of terpenes coming from Cannabis sativa samples. The array consisted in 11 sensors, 6 having pentapeptides and 5 having hairpin DNA as binding elements. The volatile composition of 28 Cannabis sativa samples, assessed by GC[sbnd]MS analysis, allowed their classification into 2 groups having as monoterpenes and sesquiterpenes in different amounts. The response of the gas sensor array to the same samples demonstrated that both type of sensors are sensitive to the terpenes and contribute to classification. A satisfactory classification (79 % of correctly identified samples) was found using a PLS-DA approach. Using the same dataset and a simple ANN approach the headspace analytical profile of the two different groups was predicted with an average prediction error ≤1 %.
Piezoelectric peptide-hpDNA based electronic nose for the detection of terpenes; evaluation of the aroma profile in different Cannabis sativa L. (hemp) samples / Gaggiotti, Sara; Sarapalmieri, ; Dellapelle, Flavio; Sergi, M.; Cichelli, Angelo; Mascini, Marcello; Compagnone, Dario. - In: SENSORS AND ACTUATORS. B, CHEMICAL. - ISSN 0925-4005. - 308:(2020), pp. 1-14. [10.1016/j.snb.2020.127697]
Piezoelectric peptide-hpDNA based electronic nose for the detection of terpenes; evaluation of the aroma profile in different Cannabis sativa L. (hemp) samples
M. Sergi;Dario Compagnone
2020
Abstract
A piezoelectric peptide-hpDNA based gas sensor array has been used for the detection of terpenes coming from Cannabis sativa samples. The array consisted in 11 sensors, 6 having pentapeptides and 5 having hairpin DNA as binding elements. The volatile composition of 28 Cannabis sativa samples, assessed by GC[sbnd]MS analysis, allowed their classification into 2 groups having as monoterpenes and sesquiterpenes in different amounts. The response of the gas sensor array to the same samples demonstrated that both type of sensors are sensitive to the terpenes and contribute to classification. A satisfactory classification (79 % of correctly identified samples) was found using a PLS-DA approach. Using the same dataset and a simple ANN approach the headspace analytical profile of the two different groups was predicted with an average prediction error ≤1 %.File | Dimensione | Formato | |
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