We have demonstrated a Yb-doped fiber laser (YDFL) based on a multifunctional acousto-optic tunable filter (AOTF) with flexible wavelength generation capability. The number of channels, as well as their diffracted wavelengths and corresponding peak transmittances of the AOTF, can be widely tuned by changing the composite drive signal from a homemade arbitrary wave generation (AWG) board enabling single-/multi-wavelength lasing with different central wavelengths and relative intensities. The maximal wavelength tuning range and minimal resolved wavelength spacing are ∼ 80 n m and ∼ 1.5 n m , respectively, with 3 dB bandwidth less than 0.15 nm for each laser line, showing great potential for further nonlinear frequency conversion. To the best of our knowledge, this is the first demonstration of flexible wavelength generation from a multifunctional AOTF-based YDFL directly driven by an AWG board.

In this study, we introduce a new spectroscopy analysis instrument, along with applied research based on the near-infrared spectroscopy (NIRS) of the major components of milk. Firstly, we analyzed and compared the characteristics of existing near-infrared spectrometers. Then, according to the major component spectra of milk, the spectral range, spectral resolution, and other parameters of the analysis instrument were determined, followed by the construction of a spectroscopy-analysis instrument based on acousto-optic tunable filters (AOTFs). Secondly, on the basis of application requirements, we obtained spectral information from a variety of test samples. Finally, qualitative and quantitative testing of the major components of the milk samples was carried out via typical analysis methods and a mathematical model of NIRS. Thus, this study provides a technical reference for the development of spectroscopy instruments and their applied research.

Near-infrared spectroscopy has been widely applied in various fields such as food analysis and agricultural testing. However, the conventional method of scanning the full spectrum of the sample and then invoking the model to analyze and predict results has a large amount of collected data, redundant information, slow acquisition speed, and high model complexity. This paper proposes a feature wavelength selection approach based on acousto-optical tunable filter (AOTF) spectroscopy and automatic machine learning (AutoML). Based on the programmable selection of sub nm center wavelengths achieved by the AOTF, it is capable of rapid acquisition of combinations of feature wavelengths of samples selected using AutoML algorithms, enabling the rapid output of target substance detection results in the field. The experimental setup was designed and application validation experiments were carried out to verify that the method could significantly reduce the number of NIR sampling points, increase the sampling speed, and improve the accuracy and predictability of NIR data models while simplifying the modelling process and broadening the application scenarios.