64-channel label-free fluorescence detection and single-particle counting device.

A 64-channel detection system for laser-induced fluorescence (LIF) detection at the cell level is established and applied to single event counting. Generally, fluorescence detection at the cellular level requires a dyeing label to enhance the scattered light intensity for the photodetector. However, the dyeing labels, such as fluorophores, probes, and dyes, complicate sample preparation and increase cytotoxicity in the process.

Olive oil classification with Laser-induced fluorescence (LIF) spectra using 1-dimensional convolutional neural network and dual convolution structure model.

Laser-induced fluorescence (LIF) spectroscopy is widely used for the analysis and classification of olive oil. This paper proposes the classification of LIF data using a specific 1-dimensional convolutional neural network (1D-CNN) model, which does not require pre-processing steps such as normalisation or denoising and can be flexibly applied to massive data. However, by adding a dual convolution structure (Dual-conv) to the model, the features of the 1-dimensional spectra are more scattered within one convolution-pooling process; thus, the classification effects are improved.