Method for detecting nanoparticles while dispersed in a liquid dispersion sample, said method using an electronic data processor for classifying the sample as having, or not having, nanoparticles present, the method comprising the use of the electronic data processor for pre-training a machine learning classifier with a plurality of nanoparticle liquid dispersion specimens, while dispersed, the method comprising the steps of: emitting a laser modulated by a modulation frequency onto each specimen; capturing a temporal signal from laser light backscattered by each specimen for a plurality of temporal periods of a predetermined duration for each specimen; calculating specimen discrete cosine transformation or Wavelet transform coefficients from the captured signal for each of the temporal periods; using the calculated coefficients to pre-train the machine learning classifier; wherein the method further comprises the steps of: using a laser emitter having a focusing optical system coupled to the emitter to emit a laser modulated by a modulation frequency onto the sample; using a light receiver to capture a signal from laser light backscattered by the sample for a plurality of temporal periods of a predetermined duration; calculating sample discrete cosine transformation or Wavelet transform coefficients from the captured signal for each of the temporal periods; using the pre-trained machine learning classifier to classify the calculated sample coefficients as having, or not having, nanoparticles present; wherein the nanoparticles have a particle size, in any particle direction, below 1 μm; in particular wherein the nanoparticles show distinctive time-dependent fluctuations in scattering intensity. the method comprising the use of the electronic data processor for pre-training a machine learning classifier with a plurality of nanoparticle liquid dispersion specimens, while dispersed, the method comprising the steps of: emitting a laser modulated by a modulation frequency onto each specimen; capturing a temporal signal from laser light backscattered by each specimen for a plurality of temporal periods of a predetermined duration for each specimen; calculating specimen discrete cosine transformation or Wavelet transform coefficients from the captured signal for each of the temporal periods; using the calculated coefficients to pre-train the machine learning classifier; emitting a laser modulated by a modulation frequency onto each specimen; capturing a temporal signal from laser light backscattered by each specimen for a plurality of temporal periods of a predetermined duration for each specimen; calculating specimen discrete cosine transformation or Wavelet transform coefficients from the captured signal for each of the temporal periods; using the calculated coefficients to pre-train the machine learning classifier; wherein the method further comprises the steps of: using a laser emitter having a focusing optical system coupled to the emitter to emit a laser modulated by a modulation frequency onto the sample; using a light receiver to capture a signal from laser light backscattered by the sample for a plurality of temporal periods of a predetermined duration; calculating sample discrete cosine transformation or Wavelet transform coefficients from the captured signal for each of the temporal periods; using the pre-trained machine learning classifier to classify the calculated sample coefficients as having, or not having, nanoparticles present; wherein the nanoparticles have a particle size, in any particle direction, below 1 μm; in particular wherein the nanoparticles show distinctive time-dependent fluctuations in scattering intensity. using a laser emitter having a focusing optical system coupled to the emitter to emit a laser modulated by a modulation frequency onto the sample; using a light receiver to capture a signal from laser light backscattered by the sample for a plurality of temporal periods of a predetermined duration; calculating sample discrete cosine transformation or Wavelet transform coefficients from the captured signal for each of the temporal periods; using the pre-trained machine learning classifier to classify the calculated sample coefficients as having, or not having, nanoparticles present; wherein the nanoparticles have a particle size, in any particle direction, below 1 μm; in particular wherein the nanoparticles show distinctive time-dependent fluctuations in scattering intensity.