A method of detecting in a biological sample at least two microorganisms belonging to two different taxa from a predetermined set { y j} of a number of K different reference taxa y j, each reference taxon y j being represented by a predetermined intensity vector P j of a space R p obtained by submitting at least one reference biological sample comprising a microorganism exhibiting the reference taxon to a measurement technique generating a multidimensional digital signal representative of the reference sample and by determining said reference vector according to said multidimensional digital signal, where p is greater than 1, the method comprising: ▪ acquiring a multidimensional digital signal of the biological sample with the measurement technology; ▪ determining an intensity vector x of R p according to the acquired multidimensional digital signal; ▪ constructing a set { γ̂ l} of candidate models γ̂ l = ( γ̂, γ̂ 0) l modeling intensity vector x according to relation: x ^ l = ∑ j = 1 K γ ^ j P j a + γ ^ 0 I p in which expression: ∘ x̂ l is a vector of R p reconstructing intensity vector x with model γ̂ l; ∘ γ̂ 0 is a real scalar and I p is the unit vector of; ∘ ∀ j ∈ [[1, K]], γ̂ j is the j th component of a vector γ̂ of R + K; ∘ ∀ j ∈ [[1, K]], P j a = ∑ i = 1 K a ij P i; and ∘ ∀( i,j) ∈ [[1, K]] 2, a ij is a predetermined coefficient; ▪ selecting a candidate model γ̂ sel from set { γ̂ l} of candidate models γ̂ l, solution of a problem according to relation: γ ^ sel = argmin γ ^ l ∈ γ ^ l C v γ ^ l + C c γ ^ l in which expression: ∘ C v ( γ̂ l) is a criterion quantifying a reconstruction error between the intensity vector of biological sample x and reconstruction x̂ l of intensity vector x by a candidate model γ̂ l; and ∘ C c ( γ̂ l) is a criterion quantifying the complexity of a candidate model γ̂ l; ▪ and determining the presence in the biological sample of at least two microorganisms belonging to different taxa of predetermined set { y j} of taxa when at least two components γ̂ j of vector γ̂ of the selected candidate model γ̂ sel are greater than a strictly positive predetermined threshold value. ▪ acquiring a multidimensional digital signal of the biological sample with the measurement technology; ▪ determining an intensity vector x of R p according to the acquired multidimensional digital signal; ▪ constructing a set { γ̂ l} of candidate models γ̂ l = ( γ̂, γ̂ 0) l modeling intensity vector x according to relation: x ^ l = ∑ j = 1 K γ ^ j P j a + γ ^ 0 I p in which expression: ∘ x̂ l is a vector of R p reconstructing intensity vector x with model γ̂ l; ∘ γ̂ 0 is a real scalar and I p is the unit vector of; ∘ ∀ j ∈ [[1, K]], γ̂ j is the j th component of a vector γ̂ of R + K; ∘ ∀ j ∈ [[1, K]], P j a = ∑ i = 1 K a ij P i; and ∘ ∀( i,j) ∈ [[1, K]] 2, a ij is a predetermined coefficient; ∘ x̂ l is a vector of R p reconstructing intensity vector x with model γ̂ l; ∘ γ̂ 0 is a real scalar and I p is the unit vector of; ∘ ∀ j ∈ [[1, K]], γ̂ j is the j th component of a vector γ̂ of R + K; ∘ ∀ j ∈ [[1, K]], P j a = ∑ i = 1 K a ij P i; and ∘ ∀( i,j) ∈ [[1, K]] 2, a ij is a predetermined coefficient; ▪ selecting a candidate model γ̂ sel from set { γ̂ l} of candidate models γ̂ l, solution of a problem according to relation: γ ^ sel = argmin γ ^ l ∈ γ ^ l C v γ ^ l + C c γ ^ l in which expression: ∘ C v ( γ̂ l) is a criterion quantifying a reconstruction error between the intensity vector of biological sample x and reconstruction x̂ l of intensity vector x by a candidate model γ̂ l; and ∘ C c ( γ̂ l) is a criterion quantifying the complexity of a candidate model γ̂ l; ∘ C v ( γ̂ l) is a criterion quantifying a reconstruction error between the intensity vector of biological sample x and reconstruction x̂ l of intensity vector x by a candidate model γ̂ l; and ∘ C c ( γ̂ l) is a criterion quantifying the complexity of a candidate model γ̂ l; ▪ and determining the presence in the biological sample of at least two microorganisms belonging to different taxa of predetermined set { y j} of taxa when at least two components γ̂ j of vector γ̂ of the selected candidate model γ̂ sel are greater than a strictly positive predetermined threshold value.